Current Research in Motor Control IV · Janusz Iskra, Jaros ... Karina Nowak, Grzegorz Sobota, Bogdan Bacik, Grzegorz Hajduk,Damian Kusz.....116 FUNCTIONAL MOVEMENT PATTERNS AND LIMITATIONS

Academy of Physical Education in Katowice

Current Research in Motor Control IV

From Theory to Implementation

Editors: Grzegorz Juras, Kajetan Słomka

Katowice 2012

KOMITET WYDAWNICZY

prof. dr hab. Mirosław Ponczek, (przewodniczący), prof. dr hab. n. med. Andrzej Małecki, prof. dr hab. Sławomir Mazur,

prof. dr hab. Jan Ślężyński, prof. dr hab. Janusz Iskra, dr hab. Władysław Mynarski prof. nadzw., dr hab. Rajmund Tomik prof. nadzw.,

dr hab. Cezary Kucio prof. nadzw., dr hab. Rafał Gnat prof. nadzw., dr Piotr Halemba, dr Jacek Polechoński

Recenzent: prof. dr hab. Wiesław Osiński

ISBN 978-83-64036-01-9

Copyright©2012 by AWF Katowice

Projekt okładki: Kajetan Słomka

Skład tekstu:

BiuroTEXT Bartłomiej Szade www.biurotext.pl Wydawnictwo Akademii Wychowania Fizycznego im. Jerzego Kukuczki w Katowicach

Nakład 150 egz.

Dystrybutor Śląska Księgarnia Kultury Fizycznej, ul. Mikołowska 72A, 40-065 Katowice, e-mail: [email protected], tel. +48 32 2075195 +48 606785430

From Theory to Implementation 3

Contents

Preface ................................................................................................................. 6 REDUCED DUAL TASK INTERFERENCE IN MULTIPLE REPEATED DUAL-TASK TESTS: AUTOMATIZATION OR TASK INTEGRATION?8 Manfred Agethen, Daniel Krause ..................................................................... 8 FUNCTIONAL LIMIT OF STABILITY IN RELATION TO STANDING ON INCLINED SURFACE Kristina Buckova, Zuzana Halicka, Jana Lobotkova, Frantisek Hlavacka 15 THE RELIABILITY OF FORCE PRODUCTION ERROR IN HEALTHY INDIVIDUALS Mariusz Furmanek, Kajetan Słomka, and Grzegorz Juras ......................... 20 FINE MOTOR CONTROL AND INDICATORS OF SCHOOL READINESS 6-YEAR-OLD CHILDREN Bogusława Gierat ............................................................................................. 30 HOW VISUAL BIOFEEDBACK MAGNIFICATION AFFECTS UPRIGHT STANCE Zuzana Halická, Jana Lobotková, Kristína Bučková, František Hlavačka 35 TEACHING METHODS IN HURDLE RACES AND RESULTS IN SPECIAL FIELD AND LABORATORY TESTS Janusz Iskra, Jarosław Gasilewski, Jolanta Hyjek, Rafał Zając, Marzena Paruzel-Dyja..................................................................................... 41 BUILDING SYSTEM OF FORECASTING RESULTS JUMP HEIGHT ON NEURO-FUZZY NETWORK CASCADE Ilya Y. Krivetskiy, Grigoriy I. Popov ............................................................. 49 THE INFLUENCE OF PERTURBATION ON THE LIMITS OF STABILITY Patrycja Kołacz, Rafał Zając, Krzysztof Szydło, Kajetan Słomka, Grzegorz Juras ................................................................................................. 56 COMPARISON OF MECHANICAL PARAMETERS OF THE VERTICAL JUMP WITH VARYING LOAD MUSCLES IN BASKETBALL PLAYERS Henryk Król...................................................................................................... 63

4 Current Research in Motor Control IV THE COMPARATIVE ANALYSIS OF THE STANDING BACKWARD PIKED SOMERSAULT (CASE STUDY) Henryk Król, Małgorzata Klyszcz - Morciniec, Grzegorz Sobota ............... 73 THE INFLUENCE OF PILATES EXERCISES ON POSTURAL STABILITY OF YOUNG AND OLDER WOMEN - COMPARISON OF THE EFFECTS OF SHORT-TERM TRAINING Lidia Kuba, Artur Fredyk, Izabela Zając-Gawlak, Joanna Kantyka ......... 82 CHANGES IN FINE MOTOR BEHAVIOUR WITH AGE (BASED ON VISUO-PROPRIOCEPTIVE AND PROPRIOCEPTIVE ONLY FEEDBACKS) Liudmila Liutsko, Ruben Muiños and Josep Maria Tous-Ral..................... 90 STEP INITIATION: CHARACTERISTICS FROM ACCELEROMETRY AND CAMERA MOTION CAPTURE SYSTEM Jana Lobotkova, Zuzana Halicka, Kristina Buckova, Frantisek Hlavacka 97 PRACTICE-RELATED ADAPTATION TO MOTOR OUTPUT WITH ADDITIVE LOW-LEVEL NOISE Guo Mei-Chun, Hwang Ing-Shiou ................................................................ 104 THE OWN MODIFICATION OF EXERCISE BY MEHRSHEED SINAKI AND NORDIC WALKING FOR SECONDARY PREVENTION IN OSTEOPOROSIS Agnieszka Nawrat-Szołtysik, Józef Opara, Cezary Kucio.......................... 110 EMG SIGNAL ANALYSIS THE MVC TEST BEFORE AND AFTER FUNCTIONAL TESTING IN PATIENTS WITH GONARTHROSIS Karina Nowak, Grzegorz Sobota, Bogdan Bacik , Grzegorz Hajduk, Damian Kusz ................................................................... 116 FUNCTIONAL MOVEMENT PATTERNS AND LIMITATIONS VS. PHYSICAL FITNESS PREPARATION OF 18 YEAR OLD FOOTBALLERS Marzena Paruzel – Dyja, Leszek Dyja, Janusz Iskra, Jarosław Gasilewski ....................................................................................... 122 SPORTS RESULTS IN WEIGHTLIFTING AND THEIR DETERMINANTS Anna Pilis, Krzysztof Mizera, Cezary Michalski, Jakub Jelonek, Łukasz Grela, Karol Pilis .............................................................................. 129

From Theory to Implementation 5 THE USE OF SELECTED LINEAR MODELS IN PREDICTING THE RESULTS OF 400-METRE HURDLES RACES Krzysztof Przednowek, Janusz Iskra, Stanisław Cieszkowski ................... 138 TRANSLATIONAL ABILITIES OF HAND MANIPULATION IN TYPICALLY DEVELOPING SOUTH INDIAN CHILDREN D. Sangkari, Ramkumar Govindarajalu...................................................... 145 FIFTEEN MINUTE TREATMENT WITH LOW FREQUENCY, HIGH INTENSITY TRANSCUTANEOUS ELECTRICAL NERVE SIMULATION (TENS) INCREASES MAXIMUM FINGER FORCE PRODUCTION Karol Sohit and Shim Jae Kun ..................................................................... 152 THE INFLUENCE OF THE ADDITIONAL TASK ON POSTURAL STABILITY Krzysztof Szydło, Kajetan Słomka, Rafał Zając, Patrycja Kołacz, Grzegorz Juras ............................................................................................... 158 CHANGES IN THE LEVEL OF STABILITY WHILE STANDING ON THE BALANCE PLATFORM ON A RIGID AND COMPLIANT SURFACE Dariusz Tchórzewski, Janusz Jaworski........................................................ 165 FITNESS ASSESSMENT IN ICE HOCKEY Milan Turek, Marek Kokinda, Róbert Kandráč HOCKEY FITNESS RELATIVE TO AGE CATEGORIES Milan Turek, Marek Kokinda, Róbert Kandráč......................................... 179 SPECIFICITY OF LEARNING IN STABILOMETER BALANCE TASKS WITH AND WITHOUT VISION Martin Wünnemann ...................................................................................... 186 THE RELIABILITY OF JUMPING TEST AS A TOOL FOR EVALUATION OF MOVEMENT RHYTHM Rafał Zając, Krzysztof Szydło, Patrycja Kołacz, Kajetan Słomka, Grzegorz Juras ............................................................................................... 191 IMPACT OF EXERCISE INTENSITY ON INNER PLEXIFORM LAYER OF THE RETINA Zwierko Teresa, Lubiński W., Czepita D., Lesiakowski P., Krzepota J.... 196

6 Current Research in Motor Control IV

Preface

I believe that tradition is what matters the most. To belive is one thing, to have a chance to create a tradition is valuable. We have created our own tradition by organizing conferences on motor control in Poland. The last edition was organized from 27 to 29 September 2012 in Wisła in Beskidas Mountains. Professor Joachim Raczek, who is one of the founders of the Motor Control conference cycle, during our first meeting in 2000, said that the processes of motor control are crucial in the area of human motor activ‐ity, especially in motor rehabilitation, physical education and sport training. The meaning of this sentence justifies the aim of organizing our conferences. In my opinion, it is important to keep in mind interdiscipli‐nary character of the motor control field but also to consider all possible data analysis and theoretical frameworks which are essential for better understanding of human motor behavior. Both, results of interesting experiments and sophisticated analysis were presented and discussed during this yearʹs conference. All partici‐pants emphasized exceptional, actual and intelligible lectures. Again, it was only possible thanks to our great Speakers! Invited lectures given by Anatol Feldman, Mark Latash, Mindy Levin, Anna Jaskólska, Slobodan Jaric, Klaus Blischke, Alexander Aruin, Nor‐mand Teasdale, Evangelos A. Christou, Marcos Duarte and Jan Celichowski were remarkable. It is worth to mention the Workshop on Motor Control that was organized just after the last edition of our conference. Here, I would like to address my special thanks to Professor Mark Latash, who was a great Lecturer. Thank You for a solid dose of knowledge, inspiration and spreading Bernstein’s ghost among us! The strong will to study and investigate process of motor control is not the only incentive to organize Motor Control conferences in Poland, the second important reason is to facilitate the interaction of scientists from our part of Europe with researchers from all over the World. It is our mission to give young scientists the chance to meet, listen to and to

From Theory to Implementation 7

discuss with World renowned Invited Speakers. It works! The presence of a large number of young scientist not only from Poland but also from Slovakia, Czech Republic, Hungary, Germany and other countries was noticeable. I would like to warmly thank all Participants for their input to our conference! Keeping the tradition of the Motor Control series, involves the publica‐tion of the 4th edition of ”Current research in motor control” which is now in your hands. This collection of reviewed papers is focused on different subfields that compromise the area of motor control. Papers written by professionals from rehabilitation, sport training and clinical field were included in this volume. All of them (and a few more) were presented and discussed in late September 2012 in Wisła. Some of the papers writ‐ten by Invited Speakers were published in Journal of Human Kinetics. And finally, traditionally just before or after the Olympic Games, the next Motor Control will be organized in 2016! You are welcome to participate for the first or fifth time! Tradition must go on!

Grzegorz Juras

8 Manfred Agethen and Daniel Krause

REDUCED DUAL TASK INTERFERENCE IN MULTIPLE REPEATED DUAL‐TASK TESTS: AUTOMATIZATION OR TASK INTEGRATION?

Manfred Agethen, Daniel Krause1

Introduction

The majority of motor skills are presumably controlled with a high de‐gree of automaticity as expertise increases. Since motor control happens unconsciously by large at this level, attention can be directed to other im‐portant aspects like for example the position of the opponent in sport games. Similar situations can be found in everyday life as well. Humans usually do not think about how to move the lower extremities when they are walking. And what is more, they can focus on other things like traffic, traffic lights or shop windows while walking. The availability of more free attentional resources over the course of practice is caused by the process of automatization (Adams 1971; Fitts, Posner 1967; Gentile 1972; Shiffrin, Schneider 1977; Logan 1988; Keele et al. 2003). A gradual shift of motor representation from declarative to non‐declarative memory structures leads to more automatic motor control (Blischke 2000) which goes hand in hand with shifts of neuronal activity (Doyon et al. 2009). As a measure for automaticity many studies used cognitive secondary‐tasks (Blischke 2000; Ruthruff, Van Selst, Johnston, Remington 2006). During simultaneous conduction of a motor and a cog‐nitive task, there should be no or reduced interferences when performing a motor task with a high degree of automaticity. This is where a synchro‐nous strain using a cognitive secondary task, which needs attention, can expose in how far the primary task still needs conscious control. There‐

1 - University of Paderborn, Paderborn, Germany

Reduced Dual Task Interference in Multiple Repeated Dual-Task Tests: … 9

fore, interferences on the working memory level can be assumed, when‐ever there are influences on performance compared to single‐task as‐signments (Abernethy 2001). When defining a secondary task and the consumed resources accord‐ing to Baddeley’s working memory model (2002), we expect higher inter‐ference, if the tasks consume the same working memory resources. And what is more, the Parallel Neural Network Model (Hikosaka, Nakamura, Sakai, Nakahara 1999) implies that control of movement sequences in early learning stages is based on an attention dependent visual‐spatial code, while less attention dependent motor code gets more important in later stages. In order to receive valid answers to the question if and to what extent the primary task still needs attentional resources, the secon‐dary task should include similar requirements for the working memory. Following these ideas we assume that a visual‐spatial secondary task seems especially feasible for spatial movement sequences, because it causes a high interference on resource levels for non‐automatized move‐ments by straining the visual‐spatial‐sketchpad (Baddeley 2002). One important methodological problem of testing automaticity by dual‐task tests derives from findings that are indicative of other explana‐tions for the reduction of dual‐task costs apart from the expected auto‐matization of motor control. Some studies show an effective task integra‐tion of the two tasks (effective switching of attention between the tasks) instead of an automatization of the primary task to reduce dual task costs (Blischke, Wagner, Zehren, Brückner 2010). Extensive practice of a task pair can lead to different effects. On the one hand it can promote auto‐matization of individual tasks, which is characterized by reduced atten‐tional demands in motor control and on the other hand it can teach par‐ticipants to efficiently integrate processing of a specific task pair (Ruthruff et al. 2006). The reduction of dual task costs caused by automatization of the pri‐mary task is not related to the dual‐task context. Therefore, also in dual task contexts, which have not been practiced before (primary task + trans‐fer‐secondary task) reduced DTC should be apparent. Several repetitions of the test could also lead to integrated processing of the task caused by frequent performance of the dual‐task context. As a conclusion, another


transfer task should be conducted to reliably exclude task integration processes as the explanatory approach. If dual‐task tests are multiple‐re‐peated, as it is the case in many experimental studies (e.g. Poldrack et al. 2005), the resulting reduction of dual‐task costs (DTC) can be caused by different mechanisms; automatization or task integration. The present study provides insides to the validity of multiple repeated dual‐task‐tests to monitor task‐automatization. The transfer‐test‐design should differen‐tiate between efficiently integrated performances of a task pair as a con‐sequence of multiple repeated dual‐task tests and automatization of an individual task as a consequence of extensive motor skill practice be‐tween the tests.

Methods

10 subjects (age: 26.9 [SD = 3.8] years; 4 female and 6 male) gave a written informed consent. They practiced an elbow‐extension‐flexion sequence with three movement reversals at 80°, 20° and 70° measured from a defined starting position of an arm lever device with a potenti‐ometer. They should perform the task as precise and as fast as possible. The 460 practice trials were distributed over 6 sessions (see table 1). To support motor skill automatization processes the participants practiced with an attention distracting secondary task (spatial‐working‐ memory‐updating task) which was not used in the dual‐task tests (for the effects of dual‐task practice on automatization see Blischke 2000).

Table 1

Tests and practice blocks of the 7 experimental sessions

Feedback for movement reversals and time was provided after every second trial. In pretests, at the beginning of each session and in one re‐


tention test, the movement task and a visual‐spatial 2‐back task (onset interval: 1000ms; stimulus duration: 500ms) were tested under single‐ and dual‐task conditions (6 trials each). The tests were ordered beginning with 3 single‐task trials of the cognitive task, followed by 3 trails single‐task trials of the motor task, 6 dual‐task trials, additional 3 single‐task tri‐als of the motor task and additional 3 single‐task trials of the cognitive task. Priority was instructed for the 2‐back task (multiple‐repeated). For testing context‐unspecific DTC reduction, a transfer dual‐task test (single‐repeated: pretest and retention) with a visual‐spatial Sternberg‐task (on‐set interval: 900ms; stimulus duration: 450ms) was conducted. The re‐sponses of the cognitive tasks (2‐back events and Sternberg targets) were collected via the space button of a standard PC‐keyboard.

Results

The results indicate a context‐specific reduction of DTC. The 2 (test: pretest; retention) x 2 (cognitive load: single‐task; dual‐task) ANOVA

Fig. 1 Means and standard deviations for single‐ (ST) and dual‐task (DT) performance in pretest and retention for the multiple‐repeated 2‐back test (left) and the single‐

repeated Sternberg test (right)

shows a significant interaction effect for the multiple‐repeated 2‐back test, F(1, 9) = 13.85; p = .010; eta² = .61. The errors for the 2‐back test de‐


creases from pretest to retention in the dual‐task conditions, t(9) = 4.74; p= 001; d = 1.53, while this is not the case for the single‐task condition, t(9) = 1.43; p = .186; d = 0.45 (Fig. 1 on the left). The respective ANOVA for the single‐repeated Sternberg‐test shows no significant interaction, F(1,9) < 0.01; p > .999; eta² < .01 (Fig. 1 on the right). Analyzing the absolute error, neither the ANOVA for the n‐back test, F(1, 9) = 1.41; p > .530; eta² = .14, nor the ANOVA for the Sternberg test, F(1, 9) = 2.41; p = .310; eta² = .21, show significant interactions test x cogni‐tive load.

Discussion

The results show distinct DTC reduction for the multiple‐repeated 2‐back test but not for the single‐repeated Sternberg test. This indicates a context specific DTC reduction. Automatization as a consequence of ex‐tensive motor skill practice allows participants to perform tasks without limited central resources and thus the reduction of DTC should be con‐text independent and should occur in the single repeated Sternberg‐test as well. Contrary to this prediction, we observed no DTC reduction for the Sternberg‐test. Alternatively to task automatization, the multiple repetition of the dual‐task context in the repeated 2‐back test might allow an efficient integration of the two tasks. For instance, repeated dual‐task trials might allow participants to re‐organize two tasks into a single su‐per‐task or optimized task switching processes, thus eliminating resource competition (Ruthruff et al. 2006). According to this task integration hy‐pothesis, integration requires dual‐task practice, which could be given in the 36 repetitions of the specific dual‐task context (arm movement and 2‐back task). Therefore reduced dual task interference in multiple‐repeated dual‐task tests should be considered with caution if automaticity is the con‐struct of interest. While statistical data for the group seem to prove task integration in‐stead of automatization, looking at individuals offers a more heterogene‐ous picture. The results of 3 participants seem to indicate that some indi‐viduals did not adopt a task‐integration strategy. Furthermore they show


a DTC reduction in both tests (2‐back + Sternberg). This might be an indi‐cation for automatization for these particular individuals. Compliance to the prioritization of the motor task seemed to be low, as can be assumed from the behavioral data. Maybe movement intrinsic error feedback directed attention to the movement.

References

Abernethy, B. Attention. In R. N. Singer (Ed.), Handbook of Sport Psychology. Second Edition New York: Wiley & Sons, 2001. Pp. 53‐85.

Abernethy, B. Dual Task methodology and motor skill research. Some Applications and Methodological Constraints. Journal of Human Movement Science, 14, 101‐132, 1988.

Adams, A.J. A closed‐loop theory of motor learning. Journal of Motor Behavior, 3, 111‐150, 1971.

Baddeley A.D. Is working memory still working? American Psychologist, 56, 851‐64, 2002.

Blischke, K. Two procedures one mechanism? Recent findings on the automation of voluntary movements. Journal of Human Kinetics, 4, 3‐16, 2000.

Blischke, K., Wagner, F., Zehren, B., Brückner, S. Dual‐task practice of temporally structured movement sequences augments integrated task processing but not automatization. Journal of Human Kinetics, 25, 5‐15, 2010.

Doyon, J., Bellec, P., Amsel, R., Penhune, V., Monchi, O., Carrier, J., et al. Contributions of the basal ganglia and functionally related brain structures to motor learning. Behavioral Brain Research, 199, 61‐75, 2009.

Fitts, P.M., Posner, M.I. Human Performance. Belmont: Brooks/Cole, 1967

Gentile, A.M. A working model of skill acquisition with application to teaching. Quest, 17, 3‐23, 1972.

Hikosaka, O., Nakahara, H., Rand, M.K., Sakai, K., Lu, X., Nakamura, K., Miyachi, S., Doya, K. Parallel neural networks for learning sequential procedures. Trends in Neurosciences, 22, 464‐471, 1999.

Keele, S.W., Ivry, R., Mayr, U., Hazeltine, E., Heuer, H. The cognitive and neural architecture of sequence representation. Psychological Review, 110, 316‐339, 2003.

14 Manfred Agethen and Daniel Krause Poldrack, R. A., Sabb, F. W., Foerde, K., Tom, S.M., Asarnow, R. F., Bookheimer,

S. Y., Knowlton, B. J. The neural correlates of motor skill automaticity. Journal of Neurophysiology, 25, 5356‐5364, 2005.

Ruthruff, E., Van Selst, M., Johnston J. C., Remington, R. How does practice reduce dual‐task interference: integration, automatization, or just stage‐shortening? Psychological Research, 70, 125‐142, 2006.

Schneider, W., Shiffrin, R. M. Controlled and automatic human information processing: I. Detection, search and attention. Psychological Review, 84, 1‐66, 1977.

Functional limit of stability in relation to standing on inclined surface 15

FUNCTIONAL LIMIT OF STABILITY IN RELATION TO STANDING ON INCLINED SURFACE

Kristina Buckova, Zuzana Halicka, Jana Lobotkova, Frantisek Hlavacka1

Introduction

The ability to move the center of gravity voluntarily and keep balance is fundamental for performing mobility tasks such as reaching for objects, transitioning from a seated to standing position or walking. The maxi‐mum displacement of the center of body mass to external postural per‐turbations that can be controlled with or without a fall or a step is de‐fined as limit of stability (Horak et al., 2005). To investigate limits of sta‐bility (LOS) in absence of external perturbation, the maximum voluntary inclined posture can be evaluated (Schieppati et al., 1994). It is known that limitations in LOS is related to risk for fall or instability during pos‐tural activities and gait. The aim of this study was to investigate the effect of different support surface slope angle on limit of stability in forward direction.

Methods

In the study participated 8 young volunteers (2 male, mean age 26.1 ± 0.9 years, mean height 171.1 ± 1.2 cm, mean weight 62.7 ± 3.1 kg) free of any neurological or musculoskeletal disorders. All subjects gave in‐

1 - Slovak Academy of Sciences, Laboratory of Motor Control, Institute of Normal and

Pathological Physiology, Bratislava, Slovakia

16 Kristina Buckova et al.

formed consent prior to participation and the local Science Ethical Com‐mittee approved the experimental protocol. The subjects stood on support surface with variable slope angle which was placed on force platform equipped with automatic weight correction and with direct output of center of pressure (CoP) signal. CoP data in anterior‐posterior direction were sampled at 100Hz and recorded on PC. Three dual‐axis accelerometers (ADXL203) were placed on the fifth lumbar (L5), the fourth thoracic (Th4) vertebra and on half way of ischial tuberosity and popliteal crease on the posterior aspect of the right thigh (RT). Sensors measured both dynamic and static acceleration with a full‐scale range of ±1.7 g. The acceleration output was low‐pass filtered with cut‐off frequency of 5 Hz and was calibrated in stationary conditions as inclinometer for ± 30 degrees range of body tilt. Participants were instructed to maintain an upright standing position, with arms comfortable crossed on the chest and with feet parallel at their comfortable stance width. Initial stance position was consistent from trial to trial. After hearing sound signal (second sec after trial start) subjects were asked to lean as far as they could at their comfortable speed without lifting heels of flexing their hips (using ankle strategy) and persist in this position till to trial end. The slope angle of support surface was at first in horizontal position with 0° (S0), afterwards in 10° (S1) and 20° (S2) slope angle position. The measurements in these 3 conditions were done with eyes open and eyes closed. Each trial lasted 10s and was repeated 3 times. We evaluated the mean displacement of CoP position and the mean of angle of body seg‐ment tilts in forward direction measured by accelerometers during inter‐val 6‐10s of the each trial. Data were evaluated with MATLAB programs. Analysis of variance with 2‐way repeated measures was used as a statis‐tic method. Post hoc pairwise comparisons with LSD adjustment were performed.

Results

Maximal voluntary body tilt during stance on inclined support sur‐face was related to slope angle of surface. With increasing angle of sur‐


face inclination, the displacement of the CoP during maximal body tilt was decreased (Fig.1). Similar time courses were found during body tilts with eyes closed, with decreased values of maximal CoP excursion (Fig.2A). Statistical analysis showed that CoP displacement during body tilt with eyes open on horizontal support surface in comparison to first plat‐form inclination (S0‐S1) level was significant (p<0.05). Also comparison to second platform inclination (S0‐S2) level was significant (p<0.01). The difference between S1 and S2 was not significant (Fig. 2A). Data from in‐clinometer placed at RT showed (Fig.2B) that the stabilized leaning of this segment was smaller in situations with increased surface slope (p<0.01). Data from L5 inclinometer similarly showed (Fig. 2C) significant effect of support surface angle slope on body lean (p<0.01). No significant results were between horizontal support surface and S1 or S2 data from Th4 in‐clinometer (Fig.2D). We also found out a significant influence of vision on amplitude in CoP and RT (p˂0.01) and L5 (p˂0.05). From Th4 data a significant effect of vision was not improved. Interaction between vision and support surface slope angle was also not occurred.

S0 platform horizontal 0°S1 platform slope angle10°

S2 platform slope angle 20°

0

5

10

0 7654321 8

sound signal

[cm] 4s

9

CoPforward

time [s]10

Fig. 1 The grouped averages of CoP forward displacements in 3 angle slope levels (S0, S1, S2) of support surface in condition with eyes open. Estimated values represented an averages of stabilized forward leaning (last 4 s of the trial).

18 Kristina Buckova et al.

0

2

4

6

8

10

12

14

S0 S1 S2

EO

EC

[cm]**

**

CoP

0

2

4

6

8

10

RT

[ o]

**

*

0

2

4

6

8

10

L5[ o]

**

0

2

4

6

8

10

12

Th4[ o]

*

**

*

*

**

**

A

D

B

CS0 S1 S2

S0 S1 S2 S0 S1 S2

**

Fig. 2

Functional limit of stability presented by CoP displacement and three inclinometers outputs (mean values ± S.E.M.). ** p<0.01, * p<0.05.

Black line‐eyes open, gray line‐eyes closed.

Discussion

Support surfaces inclined from horizontal plane represent a common challenge in daily postural activities of human. An example should be also women’s walking in high heels shoes. In this work we focused on effect of inclined support surface on magnitude of voluntary body tilt, by which is characterized functional limit of stability. Our results showed that functional limit of stability in forward direction is reduced with in‐creasing of support surface angle slope.


If the body is modelled as an inverted pendulum, CoP adjustments can provide insight into how the CNS controlling COM movement (Winter et al., 1990). One of the goals of CNS is to control movement of the COM within the base of support. If we consider an increase of sup‐port surface slope angle as progressive increases in postural threat, it may induce tighter control of posture to decrease the possibility of the COM falling outside of the base support. Thus, the CoM can be regulated within a smaller boundary by reducing the amplitude of CoP displace‐ment. (Adkin et al., 2000). Whole body tilt measured by CoP displacement and body segment tilts measured by inclinometers at L5 and RT levels showed the similar and significant results related to angle of inclined support surface (Fig. 2A,B,C). Inclination of upper trunk at Th4 level were not significant. Be‐cause our subjects were instructed to tilt forward without flex‐ion/extension of knee or hip using only ankle strategy, likely in condition S1 they used combined ankle and hip strategy with flexed trunk. From these results we can conclude that balance stability is reduced during standing and walking on inclined support surface.

References

Adkin A. L., Frank J. S., Carpenter M. G., Peysar G. W. Postural control is scaled to level of postural threat. Gait and Posture. 12: 87‐93, 2000.

Horak F.B., Dimitrova D., Nutt J. Direction‐specific postural instability in subjects with Parkinson´s disease. Exp. Neurol. 193: 378‐395, 2005

Schieppati M., Hugon M., Grasso M., Nardone A, Galante M. The limits of equilibrium in young and elderly normal subjects and in Parkinsonians. Electroencephalog. Clin. Neurophysiol. 93: 286‐298, 1994

Winter D. A., Patla A. E., Frank J.S. Assesment of balance control in humans. Med. Prog. Technol. 16: 31‐51, 1990

Acknowledgments

This work was supported by VEGA grants No. 2/0186/10 and 1/0070/11.

20 Mariusz Furmanek et al.

THE RELIABILITY OF FORCE PRODUCTION ERROR IN HEALTHY INDIVIDUALS

Mariusz Furmanek, Kajetan Słomka, and Grzegorz Juras1

Introduction

Proprioception is described as “afferent information arising from in‐ternal peripheral areas of the body that contribute to postural control, joint stability, and several conscious sensations” (Riemann, Lephart 2002). Therefore, human conscious proprioceptive sense (PS) can be con‐sidered as a subset of somatosensory system. Joint position sense, kines‐thesia and sense of force (resistance or heaviness) all comprise PS. There are three major testing procedures for PS: (1) reproduction of pas‐sive/active positioning – commonly called as JPS (Fridén et al. 2001, Grob et al. 2002, Juul‐Kristensen et al. 2008), (2) threshold to detection of pas‐sive motion ‐ TTDPM (Barrack et al. 1989, Ageberg et al. 2007, Boerboom et al. 2008, and (3) force production sense ‐ FP (Dover, Powers 2003). The last one is studied the least, despite that it provides crucial information regarding proprioception system. The force production tests involve ability of subjects to differentiate between levels of muscle force (Raczek et al. 2002, Pincivero et al. 2000, Docherty, Arnold 2008, Lauzière et al. 2012). In order to assess FP a refer‐ence force is used; it is usually defined as a percentage (e.g. 10, 20, 25, 30, 40, 50, 75, 80, and 90%) of a maximal isometric voluntary contraction (MIVC), which is determined first. In the FP test an attempt is made to replicate the required level of the reference force. Testing can include several movement tasks such as; bilateral force matching task (Carson et

1 - The Jerzy Kukuczka Academy of Physical Education, Department of Human Motor

Behaviour, Katowice, Poland

The reliability of force production error in healthy individuals 21

al. 2002), unilateral contraction (Pincivero et al. 2000), or a global move‐ment task (Furmanek et al. 2009). Using a visual analogue scale (e.g. VAS, CR‐10), a force perception is tested by finding the relationship between the force perceived (from the scale) and the force produced (from a dy‐namometer) (Pincivero et al. 2000, Lauzière et al. 2012). Results of previous investigations showed that the absolute error of force production at a knee joint range from 11.9% to 16.3% depending on the task (Lauzière et al. 2012). The same authors also found that simulta‐neous contraction grip muscles of the hand improved the accuracy of force production. Healthy young and elderly individuals had the same capacity to judge the muscular force of their knee extensors. An interest‐ing finding by Docherty and Arnold (2008) is that the error of FP in ankle testing performed at 10, 20, 30% of MIVC, was significantly (p< 0.05) greater in functionally unstable ankles (3.7 ±2.2N) compared to uninjured ankles (2.8 ±1.1N). This study showed that the FP is an appropriate measure of proprioception. Dover and Powers (2003) examined the reli‐ability of force sense reproduction in the shoulder joint. They obtained ICC values of 0.981 for internal and 0.978 for external rotation at 90% range of motion on two consecutive days. The error of FP ranged from 8.3 to 10.5 Nm. The procedure of the FP error estimation for a knee joint with the use of Biodex apparatus as presented in this work is not reported in the open literature. The goal of this study is twofold; (a) estimate the reliability of force pro‐duction error and (b) compare errors made by healthy female versus male subjects when testing their dominant and non‐dominant leg in per‐ception of force produced at the knee joint.

Methods

Participants Fifteen healthy, untrained, male and female subjects were examined. Characteristics are (mean ±SD); male subjects group (n=7), age 20.7 ±1.2 years, height 1.84 ±0.6 m, weight 80 ±12.8 kg, percentage body fat 11.8 ±3.1%, BMI 23.2 ±2.4 kg/m2, and female subjects (n=8), age 20.8 ±1.1 years,


height 1.66 ±0.5 m, weight 55 ±7.1 kg, percentage body fat 21.6 ±6.4%, BMI 20.1 ±2.2 kg/m2. Neurological and/or orthopedic conditions disquali‐fied the potential participants. None of the participants had a history of knee injury. All subjects gave their written consent to participate in the study. The Institutional Research Ethics Committee approved the study.

Apparatus Biodex System 4 Pro dynamometer (Biodex Medical System, Inc., Shirley, New York, USA) was used to measure the force production. Reliability and validity of isometric torque measurements of the Biodex were inves‐tigated by Taylor et al. (1991) and Drouin et al. (2004). Biodex was cali‐brated before each session following the instructions manual. The dyna‐mometer software produced the average peak torque (PT) over a period of 5 sec for each trial in Newton‐meters [Nm]. Experiments were con‐ducted at standard conditions.

Procedure Before experimental trials, participants warmed up for 5 min. Each participant performed one practice trial to become familiar with the Bio‐dex dynamometer. All received detailed instructions about the number and order of repetitions. Subjects were in shorts, barefoot, and blind‐folded for the test and sat on the dynamometer chair with 75 degrees of hip flexion. They were secured with straps across the chest, pelvis and tight of tested limb. Subjects gripped the straps across the chest to stabi‐lize their bodies during the test procedure. A bilateral assessment of knee extensor strength was performed to identify the dominant and non‐dominant legs. The choice of tested limb was randomized. The axis of rotation of the knee joint was visually aligned as accurately as possible with the axis of rotation of the dynamometer. The tibia pad was secured to the shank of the leg, 2‐3 cm above the line between medial and lateral malleolus. Gravity correction was obtained by measuring the torque ex‐erted on the dynamometer with the knee in relaxed state in full exten‐sion. Value of isometric torque was automatically adjusted for gravity by the Biodex software program. Starting test position was 45° of knee flex‐ion. The complete force production test consisted of twelve trials per‐


formed by each subject. First two trials were conducted in order to obtain the MIVC of knee extensors. Then the remaining ten trials were executed with a target to use 50% of MIVC. The isometric contraction and relaxa‐tion times lasted 5 and 30 sec respectively for the two MIVC trials, and 5 and 15 sec for the remaining ten trials. Subjects received feedback about their force‐matching performance only after first 50%MIVC trial.

Data processing Half of the mean of the two MIVC peak torque values was established as a reference peak torque (RPT) for the ten 50% target trials. Then the average of ten 50% target of MIVC (TMEAN50%) peak torque values was calculated. Finally, the force production error (FPE) was calculated ac‐cording to Equation 1.

Eq. 1. The above procedure was repeated for each subject and results were used in a subsequent repeated‐measure analysis of variance (ANOVA) in STATISTICA package (version 10) to evaluate gender differences and dominant limb impact on the FPE. A p‐value of lower then 0.05 was adopted as statistically significant. The reliability of FP was estimated by the use of intraclass correlation co‐efficient (ICC), as described by Shrout and Fleiss (1979):

Eq. 2. where MSB, MSR, and MSE are the mean square of the 2‐way ANOVA, n is the number of subjects, and k is the number of trials. In addition, the reli‐ability coefficient (R) was calculated by averaging k‐trials and the num‐ber of trials (k) to obtain a target reliability coefficient R (Tab. 1). ICC values range between 0 (no reliability) and 1 (perfect reliability), and is interpreted as follows: <0.40 ‐ poor, 0.40‐0.75 ‐ moderate and >0.75 ‐ ex‐cellent (Fleiss 1986).


Results

The ICC results for the singular trial (ICC2,1) and estimation of the re‐liability coefficient R by averaging k-trials (ICC2,k) for MIVC and FPE are presented in Table 1.

Table 1 ICC values for 1 and k trials, number of trials (k) to be averaged in order to

obtain an ICC≥ 0.90 of analyzed variables, MIVC – maximal isometric voluntary contraction, FP – force production

Variable ICC2,1 ICC2,k k for ICC ≥ 0.90

MIVC [Nm] 0.892 0.943 2

FP [Nm] 0.806 0.954 5

FP [Nm] 0.806 0.976 10 Depending on the variable, a different number of trials should be per‐formed to achieve the required reliability. For the maximal isometric vol‐untary contraction test (MIVC) only two trials are needed to obtain the ICC over 0.90. Whereas, for the force production error test (using 50% of MIVC) five trails are enough to achieve the desired reliability ICC= 0.95 (Tab. 1).

Fig.1 Mean and SD for absolute percentage force production error for M ‐male

and F ‐ female subjects.

Fig.2 Mean and SD for absolute percentage force production error for DLK‐

dominant limb knee and N‐DLK ‐ non‐dominant.


The 2‐way repeated‐measures analysis of variance (ANOVA) showed no significant differences between the genders F(1, 13)= 0.458, p= 0.510 (Fig. 1), and no significant differences between the knees of dominant and non‐dominant limb F(1, 13)= 0.012, p= 0.913 (Fig. 2). The average absolute force production error was 17.1 ±6.5% and showed no dependence on gender or dominant/non‐dominant site.

Discusion

There are several procedures to carry out experiments and calculate force production error. For instance Dower and Powers (2003) requested from their subjects to achieve the target force, then to maintain it for 3 sec, while concentrating on how much force was being exerted. After 3 sec of relaxation and removing the visual feedback the subjects were in‐structed to reproduce the force. Thus, the test was called as a force‐re‐production rather than force production. Their FPE ranged from 8.3 to 10.5 Nm and are lower that the FPE reported in the present study. This difference can be attributed to the procedural easiness of the task their work and the use of different joints. Lauzière et al. (2012) used a similar procedure as in this study; how‐ever they performed only two trails for both MIVC and 50% of MIVC. They also calculated the mean of the peak torque in different way. The PT signal was averaged over a period of 1sec just before the beginning of contraction release. The absolute error was defined differently; as a dif‐ference between the % of force scored on visual analogue scale and that produced on dynamometer. This could explain why they obtained lower FPE values, 14.9% (young group) and 15.8% (elderly group) when com‐pared to the averaged FPE 17.1 ±6.5% reported here. It is interesting to note that the majority (60%) of our subjects underestimated the force sense, with the mean raw force production error ‐2.3 ±13.5%. Lauzière’s team (2012) study showed that the force produced was 12.5% lower than the force perceived (from the analogue scale). This in part could explain the high FPE obtained in the present work. However, it does require more comprehensive investigation. Another factor when considering the FP error differences is a knee angle value used in different investigations.


Pincivero et al. (2000) assessed MIVC at 60° of knee flexion, Lauzière et al. (2012) at 75°, while in this study an angle of 45° was adopted. This value was selected because, as suggested by Barrack et al. (1989), the ab‐sence of proprioceptive afferent input from the anterior cruciate ligament at lower knee flexion angles. Furthermore, testing positioning, equipment calibration, and gravity correction are all important factors when esti‐mating reliability. For instance, Pincivero et al. (2000) obtained ICC=0.98, but for three MIVC and visual feedback and verbal encouragement were administered. As initially postulated, no differences in FPE between gender and dominant limb knee were found in this study. In a study by Boerboom et al. (2008) it was demonstrated that the dominant leg had no influence on the threshold detection. However, a small statistically significant differ‐ence between men and women in threshold detection was noted. Control group in Barrack’s et al. study (1989) produced identical threshold values between left‐right knees (mean variation being less than 2%, p=0.63). Other investigations (Barrett et al. 1991, Fremerey et al. 2000) showed that there were no significant differences between women and men and be‐tween the dominant and non‐dominant sides while testing JPS. There were no gender differences in perceived exertion, across all levels (from 10‐90% MIVC) of exercise intensity in knee extensors (Pincivero et al. 2000) and knee flexors (Pincivero et al. 2003). Despite the fact that males have larger diameter of muscle fibers compared to female (Kanehisa et al. 1996), proprioceptive mechanoreceptors located within the muscu‐lotendinosus tissue (Golgi tendon organs) and the muscle spindles lo‐cated in the muscle tissue responsible for conveying information regard‐ing muscle tension, length and rate of changes in length, are the same in both genders. Hence the functioning of knee extensors is independent of gender and dominant side. A measure of force production might be a better indicator of proprioception because of an increase in afferent in‐formation (muscle spindles) as compared with others method evaluating proprioception. We conclude that regardless of the procedure adopted it is very im‐portant that both the device used in the measurement and the methods of collecting data are reliable. This study revealed that proposed test and


the results analysis procedures of force production error test are highly reliable measures of proprioception. Using BIODEX dynamometer to as‐sess MIVC mean of two trials provided a reliable score, while to achieve a reliable force production error only five repetitions is needed. Further investigation is needed to learn how force sense is affected by injuries as results form force production tests could be a vital concern in rehabilita‐tion/training programme.

References

Ageberg E., Flenhagen J., Ljung J. Test‐retest reliability of knee kinesthesia in healthy adults. BMC Musculoskelet Disord. 2007, 8:57.

Barrack RL., Skinner HB., Buckley SL. Proprioception in the anterior cruciate deficient knee. Am J Sports Med. 1989, 17(1): 1‐6.

Barrett DS., Cobb AG., Bentley G. Joint proprioception in normal, osteoarthritic and replaced knees. J Bone Joint Surg Br. 1991, 73(1): 53‐56.

Boerboom AL., Huizinga MR., Kaan WA., Stewart RE., Hof AL., Bulstra SK., Diercks RL. Validation of a method to measure the proprioception of the knee. Gait Posture. 2008, 28(4): 610‐614.

Carson RG., Riek S., Shahbazpour N. Central and peripheral mediation of human force sensation following eccentric or concentric contractions. J Physiol. 2002, 539: 913‐925.

Docherty CL., Arnold BL. Force sense deficits in functionally unstable ankles. J Ortho. Res 2008, 26(11): 1489‐1493.

Dover G., Powers ME. Reliability of Joint Position Sense and Force‐Reproduction Measures during Internal and External Rotation of the Shoulder. J Athl Training. 2003, 38(4): 304‐310.

Drouin JM., Valovich‐mcLeod TC., Shultz SJ., Gansneder BM., Perrin DH. Reliability and validity of the Biodex system 3 pro isokinetic dynamometer velocity, torque and position measurements. Eur J Appl Physiol. 2004, 91(1): 22‐29.

Fleiss RL. The design and analysis of clinical experiments. New York: John Wiley and Sons. 1986.

Fremerey RW., Lobenhoffer P., Zeichen J., Skutek M., Bosch U., Tscherne H. Proprioception after rehabilitation and reconstruction in knees with


deficiency of the anterior cruciate ligament: a prospective, longitudinal study. J Bone Joint Surg Br. 2000, 82(6): 801‐806.

Fridén T., Roberts D., Ageberg E., Waldén M., Zätterström R. Review of knee proprioception and the relation to extremity function after an anterior cruciate ligament rupture. J Orthop Sports Phys Ther. 2001, 31(10): 567‐576.

Furmanek M., Słomka K., Juras G. Force platform evaluation of kinesthetic movement differentiation ability. Current research in motor control III. From theories to clinical application. The J. Kukuczka AWF Katowice. 2009, 299‐306.

Grob KR., Kuster MS., Higgins SA., Lloyd DG., Yata H. Lack of correlation between different measurements of proprioception in the knee. J Bone Joint Surg Br 84(4): 614‐618.

Juul‐Kristensen B., Lund H., Hansen K., Christensen H., Danneskiold‐Samsøe B., Bliddal H. Test‐retest reliability of joint position and kinesthetic sense in the elbow of healthy subjects. Physiother Theory Pract. 2008, 24(1): 65‐72.

Kanehisa H., Okuyama H., Ikegawa S., Fukunaga T. Sex difference in force generation capacity during repeated maximal knee extensions. Eur J Appl Physiol Occup Physiol. 1996, 73(6): 557‐562.

Lauzière S., Dubois B., Brière A., Nadeau S. Magnitude of force perception errors during static contractions of the knee extensors in healthy young and elderly individuals. Atten Percept Psychophys. 2012, 74(1): 216‐224.

Pincivero DM, Campy RM, Coelho AJ. Knee flexor torque and perceived exertion: a gender and reliability analysis. Med Sci Sports Exerc. 2003, 35(10): 1720‐1726.

Pincivero DM., Coelho AJ., Erikson WH. Perceived exertion during isometric quadriceps contraction. A comparison between men and women. J Sports Med Phys Fitness. 2000, (4): 319‐326.

Raczek J., Mynarski W., Ljach W. Kształtowanie i diagnozowanie koordynacyjnych zdolnosci motorycznych. AWF Katowice. 2002.

Riemann BL., Lephart SM. The sensorimotor system, part I: The Physiologic Basis of Functional Joint Stability. J Athl Training. 2002, 37(1): 71‐79.

Shrout PE., Fleiss JL. Intraclass correlation: uses in assessing rater reliability. Psychological Bulletin. 1979(86): 420–428.


Taylor NA., Sanders RH., Howick EI., Stanley SN. Static and dynamic assessment of the Biodex dynamometer. Eur J Appl Physiol Occup Physiol. 1991, 62(3): 180‐188.

Acknowledgement

This study was supported by statutory funds from Academy of Physical Education in Katowice.

30 Bogusława Gierat

FINE MOTOR CONTROL AND INDICATORS OF SCHOOL READINESS 6‐YEAR‐OLD

CHILDREN

Bogusława Gierat1

Introduction

The question of when a child is ready to start school is a difficult one and school readiness has been a controversial issue for years. The concept of school readiness refers to the child’s attainment of a certain set of skills needed to learn, work and function successfully in school and has tradi‐tionally focused on cognitive and behavioral characteristics, which pre‐dict later achievements (La Paro, Pianta 2000; Wilgocka‐Okoń 2003; Vi‐taro at al. 2005). Many requisite skills for first formal learning of reading, writing and arithmetic falls in the realm of fine motor control, a process that involves coordination of movements of the muscular, skeletal and neurological systems to produce precise movements. Aspects, that are considered to be critical indicators of pre‐school chil‐dren’s degree of school readiness, evolve in tandem with the develop‐ment of movement coordination. A child’s falling behind on one essential skill may have a negative influence on other related skills and gives in‐formation to teachers and parents regarding delays in the child’s devel‐opment. Preventive intervention treatment of children, who show a lower level of cognitive skills, may also require an improvement of motor coordination. Of course, all of this points to the importance and the need not only of the motor development of preschool children, but also of the diagnosis in this area.

1 - The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland

Fine motor control and indicators of school readiness 6-year-old children 31

Most pre‐schools have some form of school readiness assessments. These are usually done in the childʹs final year of pre‐school and inform parents and educators of whether a child will cope effectively in first grade. These assessments are far from perfect and do not necessarily take account of all of the important predictors associated with school readi‐ness. Currently, many experts, mainly clinicians, emphasize, that move‐ment coordination is a critical building block for a child’s maturity to primary school and should never be overlooked. (Case‐Smith 1998; Carlton, Winsler 1999; Kurdek, Sinclair 2000). The purpose of this study was to explore the relationship between fixed and generally assessed school readiness indicators and movement coordination of preschool children, before their leaving kindergarten. Developing a better understanding of how the distinct key elements of school readiness relate to each other prior to formal school entry may by helpful for teachers, kindergarten professionals and parents to devise more comprehensive strategies in helping children become ready for school.

Methods

Twenty‐nine children from three randomly selected public kindergar‐ten classes were recruited for the study. The children’s age was from 6,2 to 7,4. School readiness of preschool children was evaluated toward the end of kindergarten by using the Education Diagnosis Tool for 6‐7–year‐old children (Tryzno 2006). The EDT relies on the teachersʹ perceptions of the childrenʹs skills in 4 ar‐eas:

- skills of visual analysis and synthesis (7 tests) - hearing ‐ language skills (6 tests) - graphomotor skills (4 tests) - preparation for mathematics (6 tests).

After the scores for the subtests are determined, they are added to‐gether to get a total score. The total score was matched to one of the five levels of school readiness.


Aspects of fine motor control were diagnosed using the battery of 4 motor tests: hand coordination, simple and choice reaction time tests (Vi‐ena Computer Test System) and pursuit space orientation test (Computer System “Motoryk”).

Results

To determine the relationship between motor coordination and spe‐cific domains of school readiness of preschool children, Pearson’s simple correlations between the two sets of variables have been calculated first. A correlation matrix is presented in Table 1. The obtained results show, that correlation coefficients are statistically significant in most cases. The observed relationships indicate, that a higher level of the development of orientation, hand coordination, simple and choice reaction time was associated with better scores of school readiness. The coefficients of the correlation demonstrated, that the total score of EDT is significantly related to the results of performance on each of the tested coordination functions.

Table 1

Correlation coefficients indicators of school readiness and aspects of movement coordination

Simple Reaktion Test

Choise Reaktion Test

Two Hand Test

Pursuit Test

Visual Analysis and Synthesis

‐0,33 ‐0,47** ‐0,40* ‐0,43*

Hearing –Language Skills

‐0,35 ‐0,46** ‐0,33 ‐0,31

Graphomotor Skills

‐0,46** ‐0,36* ‐0,24 ‐0,24

Preparation for Mathematics

‐0,30 ‐0,31 ‐0,48** ‐0,41*

Total score EDT ‐0,39* ‐0,48** ‐0,43* ‐0,43* *p ≤ 0.05; ** p ≤ 0.01

Fine motor control and indicators of school readiness 6-year-old children 33

The results of the canonical correlation analysis showed, that the general coordination function aggregate correlates moderately and positively with the development of skills and abilities in preschool age, which de‐fine the readiness for beginning school (Tab. 2).

Table 2

Canonical coefficient for the complex coordination and cognitive variables Canonical R Chi^2 df p .638 12,561 4 .013

Discussion

Despite the shown relationship between motor skills and success or failure at the start of primary school in the clinical literature, fine motor control is generally neglected in the conceptualization and testing of school readiness (Geuze et al. 2001; Sortor, Kulp 2003; Missiuna et al. 2007). The authors of studies, which show an association of motor diffi‐culties with traditionally assessed indicators of school readiness, suggest to give more attention to movement coordination exercising in preschool age, as well as putting the children in a situation, where they will solve different motor problems. In such a way small children will develop their motor and cognitive components of motor behavior, what will contribute to the childrenʹs readiness for school and later to the entire schooling. Children with better coordination abilities can better adapt to different problem situations, activities and tasks at the beginning of and during their schooling. The first step in understanding fine motor control as an indicator of school readiness might be to evaluate the correlation of movement coor‐dination with cognitive characteristics, which have been traditionally used to estimate school readiness, prior to formal school entry. The sta‐tistically significant and positive correlation between a set of aspects of coordination and a set of variables of school readiness determined in this study indicate, that the diagnosis of coordination can be used to develop testing procedures used to determine the school readiness of preschool children.


References

Carlton M. P., Winsler A. School readiness: The need for a paradigm shift. School Psychology Review, 28: 338–352, 1999.

Case‐Smith J. Fine motor and functional performance outcomes in preschool children. American Journal of Occupational Therapy, 50: 52–61,1998.

Geuze R. H., Jongmans M. J., Schoemaker M. M., Smits‐Engelsman B.C.M. Clinical and research diagnostic criteria for developmental coordination disorder: A review and discussion. Human Movement Science, 20: 7‐47, 2001.

Kurdek L. A., Sinclair R. J. Psychological, family, and peer predictors of academic outcomes in first‐ through fifth‐grade children. Journal of Educational Psychology, 92: 449–457, 2000.

La Paro K. M., Pianta R. C. Predicting children’s competence in the early school years: A meta‐analytic review. Review of Educational Research, 70: 443‐484, 2000.

Missiuna C., Moll S., King S., Law M. A trajectory of troubles: Parentsʹ impressions of the impact of developmental coordination disorder. Physical and Occupational Therapy in Pediatrics, 27, 81 – 101, 2007.

Sortor J. M., Kulp M. T. Are the results of the Beery‐Buktenica Developmental Test of Visual‐Motor Integration and its subtests related to achievement test scores? Optometry and Vision Science, 80: 758‐763, 2003.

Tryzno E. Diagnoza edukacyjna dzieci 6‐, 7‐letnich rozpoczynających naukę : wersja po badaniach pilotażowych. Gdańsk : Harmonia, 2006.

Vitaro F., Brendgen M., Larose S., Tremblay R. E. Kindergarten disruptive behaviors, protective factors, and educational achievement by early adulthood. Journal of Educational Psychology, 97: 617‐629, 2005.

Wilgocka‐Okoń B.‐ Gotowość szkolna dzieci sześcioletnich. Warszawa : Żak, 2003.

How visual biofeedback magnification affects upright stance 35

HOW VISUAL BIOFEEDBACK MAGNIFICATION AFFECTS UPRIGHT STANCE

Zuzana Halická, Jana Lobotková, Kristína Bučková, František Hlavačka1

Introduction

To maintain balance effectively, inputs from visual, proprioceptive and vestibular systems are needed. The purpose of biofeedback systems is to provide additional sensory information about body sway. Biofeed‐back is known to improve postural control and reduce postural sway. Visual biofeedback (VBF) consists of supplying individuals with addi‐tional artificial visual information about body motion to supplement the natural visual information and improve human balance (Giansanti et al 2009). Previous studies showed that postural sway can be reduced when VBF of the centre of pressure (CoP) position is provided. Reduction of postural sway was significant when real‐time, unmagnified plot of CoP position was presented compared to a condition in which vision was only fixed on a stacionary target (Gatchev et al 1981). Further reduction should occur, when VBF is magnified. The purpose of the study was to assess the effects of CoP based VBF on balance control during stance on different support surface and to de‐termine the optimal magnification of the CoP position displayed on monitor.

1 - Slovak Academy of Sciences, Laboratory of Motor Control, Institute of Normal and

Pathological Physiology, Bratislava, Slovakia

36 Zuzanna Halicka et al.

Methods

Twenty healthy young adults (9 men and 11 women; mean age 26,5 years, mean BMI 21) participated in the study. They declared neither neurological, orthopaedic, nor balance impairments. They gave their in‐formed consent in agreement with the Declaration of Helsinki. The study was approved by the local Ethics Committee. CoP displacements in the anterior‐posterior (AP) and medio‐lateral (ML) direction in upright stance were measured by custom made force platform with 3 force transducers inbuilt, equipped with automatic weight correction, and with direct output of CoP sampled at 100 Hz. Obtained data were directly recorded on MacPC and analyzed with MATLAB program. The balance was assessed during quite stance in 10 conditions: stance with eyes open on firm / foam surface (control condi‐tions) and stance on firm / foam surface with additional VBF presented at 4 magnifications (Gain 1, Gain 2, Gain 5, Gain 10). VBF was presented as moving red point on monitor screen controlled by CoP positions from a force platform. We evaluated 6 parameters of CoP: amplitude and velocity in anterior‐posterior (Ay, Vy) and medial‐lateral (Ax, Vx) directions, root mean square (RMS) and line integral (LI). The participants stood on the platform barefoot with heels together and feet displayed at angle of about 30º. During control conditions they were given an instruction to fix the gaze on the black point placed in a white scene in front of them, to sway as little as possible and breathe normally. In conditions with VBF they were instructed to minimize the extent of red point movements around the centre of the cross displayed on the monitor. The monitor (38 x 31 cm) was placed in front of the sub‐ject in a distance of 1 m. Participants had time to practice before each ses‐sion. The shift of CoP with value 1, 2, 5 or 10 cm in real was equal to 1, 2, 5 or 10 cm shift of the red point on the monitor. Each trial lasted 50 s. A 2 (surface: firm / foam) x 5 (magnification: eyes open / Gain 1 / Gain 2 / Gain 5 / Gain 10) repeated measures ANOVA was performed on each evaluated variable and body segment separately with a 0.05 level of sig‐nificance. Greenhouse‐Geisser adjustments were performed in cases where the assumption of sphericity was violated. Post hoc pairwise com‐


parisons with Bonferroni adjustment were performed on each level of surface to further explore differences between VBF situations comparing to control situations.

Results

Providing of VBF led to reduction of postural sway during the stance on both types of support surface. ANOVA revealed significant effect of gain in parameters Ax, Ay and RMS. A significant effect of surface was found in all measured parameters. Also a significant interaction between surface and gain was observed in all parameters except Vy (Tab. 1).

Table 1

F coefficients from 2‐way repeated measures ANOVA of CoP parameters: Ax, Ay, RMS, Vx, Vy, LI. Evaluated ANOVA factors were: surface, gain and

interaction of surface and gain ANOVA surface gain surface*gain

Ax 75,019*** 43,588*** 25,722***

Ay 95,708*** 61,435*** 7,178**

RMS 95,157*** 77,171*** 16,550***

Vx 104,404*** 3,075 4,714*

Vy 98,225*** 1,268 2,524

LI 117,782*** 2,687 5,435** *p<0.05, **p<0.01, ***p<0.001

Pairwise comparisons with Bonferroni adjustment performed on each type of surface showed decrease of parameters Ax, Ay and RMS during all VBF conditions except Ax on firm surface (Fig. 1A). Decrease of these parameters was also observed in VBF situations with greater magnifica‐tions comparing to lower magnifications. The most significant changes were observed in parameter Ay. Significant decrease of Ay values on both surface types was observed in higher magnifications (5x, 10x) com‐paring to 1x magnification (Fig. 1B). Similar tendency was seen in pa‐rameter RMS (Fig. 1C). RMS represents the overall stability of upright stance independently from the direction of postural sway therefore it was used to determine the effectiveness of VBF on different support surface


types (firm and foam). Normalized values of parameter RMS showed greater decrease of RMS values during the stance on foam surface (Fig. 1D). Velocity of body sway and LI did not change significantly.

Fig. 1

The grouped averages of CoP parameters: Ax (A), Ay (B), RMS (C) and normalized RMS (D), during the stance with eyes open on firm (EO) and foam (FEO) surface and during situations with VBF magnified 1x (Gain 1), 2x (Gain 2), 5x (Gain 5) and 10x (Gain 10). VBF situations on firm surface (black) are compared to control situation EO. VBF situations on foam surface (grey) are compared to control situation FEO. VBF situations are also compared to each other. The averaged data are presented as mean values ± SEM. Post hoc

differences are marked: *p<0.05, **p<0.01, ***p<0.001

Discussion

Our results suggest that any magnification in range 1x ‐ 10x provides meaningful additional information and is helpful for postural stabiliza‐tion in young healthy population. Litvinenková and Hlavačka (1973)


concluded that the optimal magnification for the control of quite standing balance is between 2x and 4x. Rougier et al. (2004) presented visual feed‐back at magnifications ranging from 2x to 20x and found out that control of corrective processes involved in quite stance increases with increasing magnification of visual feedback. Cawsey et al. (2009) proved that pos‐tural control of quite stance continued to change with increases in magni‐fication of VBF beyond those previously examined. Our results are in agreement with the previous studies. We also observed that the greater magnification led to greater reduction of body sway amplitudes and RMS. According to our results we would determine magnification 5x as the optimal magnification of CoP position. Also magnification 10x showed the same improvement of balance control but many participants marked this magnification as the least pleasant. Amplitudes of body sway and RMS seem to be the most appropriate parameters to test the ef‐fectiveness of additional visual biofeedback. Standing on foam surface led to a greater decrease of these parame‐ters. This finding is also in agreement with previous study of Cawsey et al. (2009) who found out that when balance was made more difficult by standing on foam, posturographic measures reached plateau at higher magnifications than when standing on a firm surface. This suggests a greater reliance on vision to maintain balance in situation when the pro‐prioceptive information from feet is altered. Velocity and line integral of CoP did not show any increase which would suggest the voluntary activation in postural control. We assume that young participants were able to significantly reduce the amplitude in both directions while their velocity showed no increase. The subjects probably used postural strategy with increased stiffness of body sway (Abrahamová and Hlavačka 2008) or other type of strategy (f.e: arousal of attention) is involved in the process of postural stabilization.

References

Abrahámová D., Hlavačka F. Age‐related changes of human balance during quiet stance. Physiol Res. 57: 957‐64, 2008

Cawsey R.P., Chua R., Carpenter M.G., Sanderson D.J. To what extent can increasing the magnification of visual feedback of the centre of pressure


position change the control of quiet standing balance? Gait Posture. 29: 280‐284, 2009

Gatchev G., Draganova N., Dunev S. Role of the visual feedback in postural control. Agressologie.22A:59‐62. 1981

Giansanti D., Dozza M., Chiari L., Maccioni G., Cappello A. Energetic assessment of trunk postural modifications induced by a wearable audio‐biofeedback system. Medical Engineering & Physics. 31(1): 48‐54. 2009

Litvinenková V., Hlavačka F. The visual feed‐back gain influence upon the regulation of the upright posture in man. Agressologie. 14(Spec NO C):95‐9, 1973

Rougier P., Farenc I., Berger L. Modifying the gain of the visual feedback affects undisturbed upright stance control. Clin Biomech (Bristol, Avon). 19(8):858‐67, 2004

Acknowledgments

This work was supported by VEGA grants No. 2/0186/10 and 1/0070/11.

Teaching methods in hurdle races and results in special field and laboratory tests 41

TEACHING METHODS IN HURDLE RACES AND RESULTS IN SPECIAL FIELD

AND LABORATORY TESTS

Janusz Iskra1, Jarosław Gasilewski2, Jolanta Hyjek3, Rafał Zając3, Marzena Paruzel‐Dyja1

Introduction

Hurdling is a technically difficult track and field event. The specific way of clearing the hurdle consists in attacking it with one leg (the lead leg), and then clearing it with the other leg (the trail leg). In a 400 m hurdle race, a longer distance between hurdles and low po‐sition of hurdles do not require the same number of strides (although this situation often occurs), so the hurdle is often attacked with both legs (left and right). The choice of the lead leg is influenced by many factors, i.a. somatic build, the level of fitness and coordination abilities, functional asymmetry of lower limbs and the teaching method (Iskra 1999). A very important role in hurdling is played by technical (rhythmic) preparation. The so‐called “running rhythm” in hurdling events is not clearly defined. In many publications the so‐called “hurdling rhythm” is equated with the number of strides taken while covering the distance between hurdles. Following this way of reasoning Sedlacek and Matousek (1985) intro‐duced the notion of “rhythmic unit”, describing it as the way of covering the distance between hurdles in a given time with a given number of strides, whereas Letzelter et al. (1995) explicitly defined “hurdle rhythm”

1 - The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland 2 - State School of Higher Vocational in Racibórz, Poland 3 - Doctoral Studies, APE Katowice, Poland

42 Janusz Iskra et al.

as the stride rhythm. Iskra (1999) described “hurdling rhythm” as an op‐timal (as far as the number of strides and the running pace are con‐cerned) way of covering the distance in the shortest time possible, taking into consideration the level of motor and technical preparation as well as somatic build. High level of a sense of rhythm can be a sign identifying a good hurdler. The lack of clear definitions of the so‐called “hurdling rhythm” and the so‐called “stride pattern” results in the fact that this is‐sue is more frequently judged in terms of horse races than hurdle races (Deuel, Lawrence 1987, Williams, Norris 2007).

Material and methods

The research was carried out in 2011 as a pedagogical experiment which was conducted with the use of the parallel groups technique. 50 students of Physical Education of the State Higher Vocational School in Racibórz participated in our study. They were divided into three groups – A, B and C (Tab. 1). The experiment consisted in carrying out a pre‐liminary measurement in the form of a laboratory test: “OptoJump” and typical hurdle run tests (the details are presented in table 2). Next, a 5‐week process of teaching the hurdling technique was conducted in the experimental groups. The details of the teaching are presented in table 1. The main purpose of this study was to estimate the influence of differ‐ent methods of teaching hurdle runs on the choice of a lead leg in the groups of physical education students. The following research questions were formulated: 1. Does a 5‐week period of teaching the rudiments of hurdling influ‐

ence the choice of the lead leg? 2. Do the rhythmic movement skills assessed by means of laboratory

tests differ in the research groups? 3. Do the laboratory tests assessing rhythmic abilities can be regarded

as diagnostic while estimating the choice of the lead leg? Rhythmic ability test with the use of “OptoJump” (produced by Mi‐crogate from Bolonia) was used in the research. It enables to estimate the rhythmic abilities for cyclic movements by defining the accuracy with which the imposed rhythm was identified and performed during testing.


Table 1

Research plan

Groups (sex) Number of subjects

Measurement Experimental factor

A. Experimental group (F + M)

16 04‐06.2011

5‐week programme of teaching hurdling with the right leg as the leading one

B. Experimental group (F + M)

15 04‐06.2011

5‐week programme of teaching hurdling with both right and left leg as the leading one

C. Control group 19 04‐06.2011 No experimental factor applied

Table 2

Hurdling conditions in the research groups

Form of a run Number of hurdles

Height of

hurdles Distance between hurdles

60 m run 5 K 76,2 M 83,8

Approach: 13 m, inter‐hurdle spacing: 6,50 m for women and 7,50 m for men.

150 m run (standard hurdle spacing)

9 K 76,2 M 83,8

Hurdles spaced according to the scheme: approach 13 m, inter‐hurdle spacing 15 m, the distance from the last hurdle to the finishing line 17 m.

150 m (non‐standard hurdle spacing)

9 K 76,2 M 83,8

Hurdles spaced according to the scheme: approach 13 m, inter‐hurdle spacing 10‐15‐20‐15‐10‐15‐20‐15, the distance from the last hurdle to the finishing line 17 m.

OptoJump is an optical measurement system consisting of a transmitting and receiving bar. Each of these contains 100 leds working with 100Hz frequency. The leds on the transmitting bar communicate continuously


with those on the receiving bar. The system detects any interruptions in communication between the bars and calculates their duration. In order to calculate the percentage value of attacking the hurdle with the left or right leg the laterality index (LI) was used (Williams and Nor‐ris 2007):

LI = (RL‐LL) / (RL+LL) x 100, LI – laterality index RL – the number of hurdles cleared with the right leg as the

leading one LL – the number of hurdles cleared with the left leg as the

leading one The results of the research were presented by means of descriptive

statistics ( x , SD). The differences between the groups were assessed us‐ing the analysis of variance (ANOVA).

Results

In all research groups the differences in test results and laterality indices before the experiment were statistically not significant. Only in case of the total number of strides taken in a 150 m hurdle race with regular spacing of the hurdles the differences between all three groups were statistically significant with p≤0,05. The detailed data are presented in table 3. After the experiment statistically significant differences in the results (p≤0,05) were observed in case of the laterality index calculated for a 150 m hurdle race with irregular spacing of the hurdles. High correlations were also observed in case of average contact time in the unassisted phase. It shows that the level of the “so‐called” sense of rhythm and the ability to perform the imposed rhythm increased in all three groups. The details are presented in Table 4.


Table 3

The significance of changes in chosen parameters before the experiment (ANOVA)

Parameter Group A1

Group A2

Group A3

F p

60 m hurdle race– result (s) 12,46±1,50 12,74±1,42 12,55±0,97 0,15 0,87 Total number of strides (60 m hurdle race)

17,71±4,70 20,18±1,72 16,15±4,26 3,19 0,05

Laterality index (60m hurdle race)

22,86±89,39 52,73±68,86 60,00±74,83 0,85 0,43

150 m hurdle race – regular inter‐hurdle spacing (s)

30,04±5,71 29,72±4,01 30,31±3,93 0,47 0,95

Total number of strides (150 m hurdle race with regular inter‐ hurdle spacing )

78,93±14,41 76,09±10,51 87,46±9,02 3,18 0,05*


38,10±84,59 5,05±91,13 62,39±69,91 1,49 0,24

150 m hurdle race – irregular inter‐hurdle spacing. (s)

30,67±5,40 30,32±4,18 30,91±3,99 0,05 0,95

Total number of strides (150 m hurdle race, irregular inter‐hurdle spacing.)

85,14±13,30 80,91±11,07 90,23±8,50 2,08 0,14

Laterality index (150m hurdle race) 44,44±73,32 ‐9,09±78,54 35,04±68,77 1,80 0,18

T Cont. (s) – assisted phase 0,86±0,05 0,88±0,03 0,87±0,06 0,54 0,59 T Flight (s) – assisted phase 0,14±0,04 0,13±0,03 0,13±0,04 0,82 0,45 Height (cm) – assisted phase 2,70±1,49 2,06±0,92 2,26±1,24 0,88 0,43 Pace (step/s) – assisted phase 1,00±0,03 1,00±0,01 1,01±0,04 0,29 0,75 T Cont. (s) – unassisted phase 0,82±0,04 0,86±0,05 0,84±0,07 1,36 0,27 T Flight (s) – unassisted phase 0,15±0,05 0,13±0,03 0,14±0,03 1,45 0,25 Height (cm) – unassisted phase 3,21±1,98 2,19±1,07 2,44±1,10 1,67 0,20 Pace (step/s) – unassisted phase 1,03±0,05 1,02±0,03 1,03±0,07 0,18 0,84 * ‐ p≤0,05 Group A1 – followed the programme of teaching hurdling with the right leg leading Group A2 – followed the programme of teaching hurdling with both legs leading Group A3 – followed the teaching programme without the elements of the hurdling technique T Cont – average contact time, T Flight – average flight time, Height – average height of the jump, Pace – average rhythm,


Table 4

The significance of changes in chosen parameters after the experiment (ANOVA) Parameter Group

A1 Group A2

Group A3

F p

60 m hurdle race – result (s) 12,14±1,53 12,44±1,14 12,56±1,02 0,41 0,67 Total number of strides (60 m hurdle race) 17,14±3,98 19,27±1,62 16,08±4,33 2,39 0,11

Laterality index (60 hurdle race) 85,71±53,45 9,09±104,45 63,08±75,65 3,03 0,06 150 m hurdle race – regular inter‐hurdle spacing (s)

28,81±4,66 30,15±4,95 30,13±3,70 0,39 0,68

Total number of strides (150 m hurdle race)

78,43±13,64 83,45±11,42 86,08±12,20 1,29 0,29


76,19±61,92 13,13±88,03 65,81±67,05 2,61 0,09

150 m hurdle race – irregular inter‐hurdle spacing. (s) 29,62±4,95 30,65±5,11 31,36±3,80 0,48 0,62

Total number of strides (150 m hurdle race)

82,29±13,43 87,45±11,48 90,69±7,16 1,99 0,15


85,71±36,57 19,19±85,65 48,72±65,03 3,45 0,04*

T Cont. (s) – assisted phase 0,87±0,05 0,89±0,03 0,87±0,06 0,62 0,54 T Flight (s) – assisted phase 0,14±0,04 0,11±0,02 0,13±0,04 2,74 0,08 Height (cm) – assisted phase 2,75±1,48 1,53±0,59 2,22±1,38 2,90 0,07 Pace (step/s) – assisted phase 0,99±0,03 1,01±0,02 1,02±0,08 0,80 0,46 T Cont. (s) – unassisted phase 0,82±0,04 0,88±0,04 0,88±0,09 3,84 0,03* T Flight (s) – unassisted phase 0,16±0,05 0,12±0,03 0,13±0,04 2,92 0,07 Height (cm) – unassisted phase 3,28±2,00 1,82±0,81 2,24±1,40 3,07 0,06 Pace (step/s) – unassisted phase 1,03±0,05 1,00±0,03 1,01±0,11 0,33 0,72 * ‐ p≤0,05 Legend – as in Table 3

Discussion

The study has an innovative and original character because of the use of OptoJump method for analyzing the hurdle runs, therefore it is diffi‐cult to find similar publications. In 2001 Glazier and Irwin conducted a research on the validity of stride length estimates produced by Opto‐Jump, however the results suggested insufficient validity in this meas‐urement variable from a motor control perspective.


The process of teaching the hurdling technique should consist of three stages: physical and mental preparation for hurdle training, learning a competition technique and contest preparation. As far as coordination abilities are concerned a very important role in developing and improv‐ing the technique (way) of hurdle clearing and running between hurdles is played by rhythmic abilities, kinesthetic diversification and also spatial imagination (Iskra 2001). The research revealed statistically significant differences in the results of all groups in 150 m hurdle race with regular inter‐hurdle spacing only before the experiment (Tab. 3). This allows us to form a conclusion that none of the methods of teaching the hurdling technique significantly in‐fluenced the choice of the lead leg and the number of strides at the dis‐tance with regular inter‐hurdle spacing. It suggests that hurdlers, know‐ing the distance between the hurdles, reacted spontaneously, with the in‐born level of rhythmic abilities and spatial intelligence. After the experi‐ment statistically significant differences in the results were observed in the laterality index of the 150 m hurdle race with irregular hurdle spacing (Tab. 4). It clearly proves that the level of chosen coordination abilities (movement rhythmization, spatial intelligence) increased. It can be con‐cluded that the hurdler who doesn’t know the distance to the next hurdle has to react more consciously, using the store of special flexibility exer‐cises which also includes the level of chosen coordination abilities. The subjects didn’t have to react spontaneously with their inborn ability po‐tentials but randomly chose the leading leg without concentrating on a fitter or more dominant one, because the difference in the level of fitness of both limbs reduced. This fact is also confirmed by statistically significant differences in the average contact time in the unassisted phase which prove that after the teaching process the subjects did better in per‐forming the memorized stride rhythm. In the group which was trained to hurdle with both right and left leg leading after a 5‐week period of teaching the students jumped slightly lower and adjusted the rhythm better, especially in the unassisted phase (Tab. 4). Moreover, the height of the jump can be significant for keeping the rhythm (the students jumped lower and adapted better to the memo‐rized rhythm which lengthened the ground contact time).


Conclusions

1. A 5‐week process of teaching the rudiments of hurdling signifi‐cantly influences the choice of the lead leg in case of the distance with irregular inter‐hurdle spacing.

2. The rhythmic movement skills assessed by means of laboratory tests do not differ in three research groups. The method of hurdle runs’ teaching partly influences the increase of rhythmic abilities.

3. Diagnostic usefulness of laboratory rhythmisation tests for estimat‐ing the choice of the lead leg requires further research.

References

Deuel N.R., Lawrence L. M. 1987. Laterality in the gallop gait of horses. Journal of Biomechanics 20(6): 645‐649.

Glazier P. Irwin G. 2001. Validity of stride length estimates obtained from optojump. 19 International Symposium on Biomechanics in Sports, University of San Francisco, pp. 98‐101.

Hay L. Schoebel P. 1990. Spatio‐temporal invariants in hurdle racing patterns. Human Movement Science 1: 37‐54.

Iskra J. 1999. Bieg na 400 m przez płotki. Przygotowanie sprawnościowe i techniczne na różnych poziomach zaawansowania. WSP Rzeszów.

Iskra J. 2001. Morfologiczne i funkcjonalne uwarunkowania rezultatów w biegach przez płotki. Akademia Wychowania Fizycznego, Katowice.

Letzelter H., Letzelter M., Honda Y., Steiman W. (1995). Schrittgestaltung im 400‐m‐Hürdenlauf der Jugend. Leichtathletik, 10:41‐47; 11: 49‐51; 12: 52‐58.

Mehlich R. 2004. Poczucie rytmu zawodników uprawiających bieg przez płotki. Praca doktorska. Akademia Wychowania Fizycznego, Katowice.

Sedlacek J., Matousek R. 1985. Analŷza zmiĕn rychlosti v bĕhu na 400 m prekážek. Atletika 10, 16‐18.

Waśkiewicz Z., Juras G., Raczek J. 1999. The computer supplemented diagnosis of rhythm. Journal of Human Kinetics 2 67‐78.

Williams D.E., Norris B.J. 2007 Laterality in stride pattern preferences in racehorses. Animal Behavior 74, 941‐950.

Building system of forecasting results jump height on neuro-fuzzy network … 49

BUILDING SYSTEM OF FORECASTING RESULTS JUMP HEIGHT ON NEURO‐FUZZY

NETWORK CASCADE

Ilya Y. Krivetskiy, Grigoriy I. Popov1

Introduction

High jump has a fairly complicated biomechanical structure, which is difficult to model using the system of equations due to high degree of freedom in motion of different parts of the body which varies signifi‐cantly in its kinematic characteristics during the process of a jump. There is an opportunity to overcome these difficulties owing to modern com‐puter technologies, in particular, neural system formalization. Previously, we attempted to create two models of a high jump, pro‐jecting both the success of clearing the bar and doing so on a specific mark [2]. The first system is based on discriminant analysis, and the sec‐ond is done using cascaded fuzzy neural network; N.S. Bezrukov et al [1] theoretically proved the possibility of employing this network for mod‐eling various prediction systems. We found out that system based on fuzzy neural network has less forecast error rather than discriminant model, which is important for working out the jump technique. More‐over, it has a number of advantages: user can educate the system himself as the number of experiments grows; software interface allows not only forecasting, but modeling the conditions for the successful jump by changing input data. Therefore, we can assume that neural network sys‐tems are promising in technical track and field athletics, in particular, high jumping.

1 - The Russian State University of Physical Education, Sport, Youth and Tourism,

Department of Natural Sciences, Moscow, Russia.

50 Ilya Y. Krivetskiy and Grigoriy I. Popov

A number of biomechanical indicators, allowing the highest forecast accuracy for the success of a jump on a set height, were selected for building a neural network as a result of multiple discriminant analysis. At the same time, for the real‐life training practice it is recommended for the model to include those parameters that can be changed along the way to achieve rational biomechanical correlations ensuring the success of a high jump. The aim of our study was to construct a biomechanical model of a high jump based on cascaded fuzzy neural network to optimize the training process of high jumpers.

Methods

An athlete – master of sports – took part in the research. Videocyclo‐graphy was the main instrument for collecting data; it recorded kine‐matic motions of high jumper as he performed competitive exercises. Two high‐speed video cameras (210 frames per second and 50 frames per second) angled at 45º to each other were used. Reflective markers were placed on the ankle, knee, hip, shoulder and elbow of the subject. 92 high jumps were recorded. Processing and calculation of the reel were done with video analysis software Dartfish (Switzerland) by 48 ki‐nematic characteristics: time, angle, speed and line.

Results

In order to select those biomechanical parameters which can be influ‐enced by the means of training we chose those characteristics that influ‐ence the success of a jump for each model in the system. We analyzed samples of kinematic parameters in successful and failed attempts, and then, we defined parameters significantly different in the following sam‐ples using Student criterion: 1. Successful and failed attempts (actual height, recorded at the level

of general center mass passing the highest lifting point, below the bar);

2. Successful and failed attempts (actual height is either above or equal to the bar).


Then the 10 parameters that can be subject to correction with methodic instructions or training means were selected out of the received kine‐matic parameters (Tab. 1, 2). Systems for forecasting the success of a high jump were modeled us‐ing cascaded fuzzy neural network in Medical Toolbox package [1]. These systems have the same structure, showed on the Fig. Input data are parameters determined above. Forecast system consists of two units: pre‐processing unit and neural fuzzy output unit. Pre‐processing unit is an exponential function for each input signal and serves to ensure the equivalence of transformed data in range and distribution. Pre‐processing unit ensures that transformed data is in the same range (‐1, 1) with an equal distribution.

Table 1

Kinematic characteristics of the subject influencing the success of a jump on a given height

Subject 1 Competitive result (bar height) (m) 2 Distance between the take‐off point to the bar projection (m) 3 Body angle to the vertical line at the take‐off (degrees) 4 Body angle to the vertical line at the amortization stage at the penultimate

stride (degrees) 5 Average length of the last three strides (m) 6 Average tempo of the last three strides (steps/second) 7 Coefficient of tempo mobilization 8 Hip angle at the take‐off point (degrees) 9 Angle at the placement for take‐off (degrees) 10 Hip joint angle in the take‐off amortization stage (degrees) Neural fuzzy output unit (Fig.1) is a convergent tree‐like structure consisting of four layers with nodes (first layer has 5 nodes, the second – 2 nodes, the third and the fourth each have 1 node). Each layer has hybrid network with ANFIS (Adaptive‐Network‐Based Fuzzy Inference System) architecture. It was suggested by Jang in early 1990s and is of the hybrid fuzzy neural networks – neural network of direct distribution of the special signal [3].


Table 2

Kinematic characteristics of the subject influencing the success of clearing the bar at the actual height of a jump

Subject 1 Actual height of a jump (m) 2 Distance between the take‐off point to the bar projection (m) 3 Body angle to the vertical line at the take‐off (degrees) 4 Body angle to the vertical line in the amortization stage at the penultimate

stride (degrees) 5 Velocity of the last stride (m/s) 6 Duration of the support phase for the last stride prior to the take‐off (s) 7 Length of the last stride prior to the take‐off (m) 8 Average length of the last three strides (m) 9 Average tempo of the last three strides (steps/second) 10 Tempo mobilization coefficient

Fig. 1

Structure of the forecasting system of a success of a jump based on cascaded fuzzy neural networks


Neural fuzzy output unit (Fig.) is a convergent tree‐like structure con‐sisting of four layers with nodes (first layer has 5 nodes, the second – 2 nodes, the third and the fourth each have 1 node). Each layer has hybrid network with ANFIS (Adaptive‐Network‐Based Fuzzy Inference System) architecture. It was suggested by Jang in early 1990s and is of the hybrid fuzzy neural networks – neural network of direct distribution of the spe‐cial signal [3]. Hybrid networks have single‐type structure and are distinguished by the coefficients which are determined during the education of network in the software. Performance of each sub‐system is estimated at the output of fuzzy neural output unit. For the first one it is an athlete will clear the bar is below 0, otherwise – won’t be able to do that. The error in this system for the training data was 0% and for the verification data – 7.7%. For the sec‐ond one it is an athlete will jump higher than the bar level and clear it if less than 0, otherwise – will be able to jump higher than the bar, but will knock it down. The error in this system for the training data is 9.1% and for the verification data – 16.7%.

Discussion

As demonstrated by the research [2], success of a jump depends on a number of kinematic characteristics, and they depend on each particular athlete. Core contradiction in the motion education lies in the fact that daily practice of using training methods is based on forming the inner content of motions by trainee trying to imitate some ideal external forms of the exercise showed to him by a coach as an example [5]. In the meantime, it is obvious, that those external motions themselves are derived from changes in the inner content, in particular, coordination between muscle groups (intermuscular coordination) of an athlete in any given exercise. Deep‐rooted practice of training by imitating and the virtual absence of any methodical means to control the correct inner content of motions hindered the comprehension of the said contradiction. Inability of the trainee to master the complicated move right away forced


coaches to use such training plans that simplified and broke down the move into several elements. The approach used in our research is aimed at combining the coordi‐nating components of the movement structure during a jump with exter‐nal parameters of this motor activity, controlled by the biomechanical analysis of a jump and pedagogical observations available to a trainer. Certainly, only a limited number of parameters can be analyzed by the developed neural model due to its inherent limit on the used indicators. But even this model if provided with some parameters can predict other indicators which would allow improving the result of a particular athlete. These estimations will lay down the foundations for the training process aimed at achieving a particular result.

Conclusion

At present, comparing the model and actual parameters achieved in a jump ensures the optimum selection of training methods. At the same time, the static and average nature of model characteristics prevents from taking individual features of an athlete into account from the point of view of biomechanics of a jump. The interactive system for forecasting jump success which we devel‐oped is based on the analysis of biomechanical characteristics of a high jump. It allows creating individual models for high‐class athletes. Using this instrument, which takes into account individual biomechanical fea‐tures of the jumping style of an athlete, we can analyze all the stages of a jump in detail and improve the technique by pointed correction of par‐ticular motions of body parts. It will lead to achieving a perfect combina‐tion of kinematic parameters which would insure the highest result. The important feature of the developed system with hybrid networks with ANFIS architecture is a possibility of reeducating it as a number of experiments grows. An opportunity for mutual improvement of a system and a user is what distinguishes it from a set of modern model features used in theory and practice of training. Software interface allows chang‐ing input system data and therefore model a result of a jump for different combinations of its biomechanical parameters.


References

Bezrukov N.S., Eremin E.L., Ermakova E.V., Kolosov V.P., Perelman Y.M. Automated system Medical Toolbox for diagnosing bronchial asthma using rheoencephalography results. Informatics and Control Systems. 1 (11): 73‐80, 2006.

Jang J.‐S. R. ANFIS: Adaptive‐Network‐Based Fuzzy Inference System. IEEE Trans. Systems & Cybernetics. 23: 665‐685, 1993.

Krivetsky I.Y., Popov G.I., Bezrukov N.S. Modeling the success of motions in high jumps. Informatics and Control Systems. 2 (28): 126‐132, 2011.

56 Patrycja Kołacz et al.

THE INFLUENCE OF PERTURBATION ON THE LIMITS OF STABILITY

Patrycja Kołacz, Rafał Zając, Krzysztof Szydło, Kajetan Słomka, Grzegorz Juras1

Introduction

Postural stability is a very complex phenomena that is beyond our conscious processes. Fear of falling or anxiety concerning stability loss can be reflected in modification of body posture during quiet stand‐ing (Adkin et al. 2000, 2001, Carpenter et al 2001, Brown et al. 2006). It is well known that fear of falling may lead to decrease of the center of pres‐sure range (COP) or increase in the center of pressure velocity. It is due to increased ankle joint stiffness and increased agonist and antagonist mus‐cles work (Adkin, 2002). In order to manipulate psychological factor which is fear of falling, usually heights is used (Adkin et al. 2002, 2003, 2009, Brown et al. 2005, Carpenter et al. 2001, Davis et al. 2008, Min et al. 2011). According to Brown et al. (1999) fear of falling is based on per‐ceived risk of injury. They also reported that the significantly higher in‐stability can be observed when people stand 81 cm above the ground level compared to 19 cm. Usually similar studies are limited to quiet standing. Therefore the aim of this work was to define the influence of height on postural stability with the use of Limits of Stability Test (Juras et al. 2008). In order to increase fear of falling visual information was limited.



The influence of perturbation on the limits of stability 57

Methods

The study was conducted on 15 healthy, male, PE students. They were at the age of 21 ± 1,2 years, their average body weight and height were 75 ± 5,1 kg, and 181 ± 6,5 cm respectively (mean±SD). Sub‐jects who were professional sportsmen, or reported any injuries, or fear of heights were excluded from the experiment. The study was approved by the Institutional Ethics Committee. COP displacements were recorded with the use of force platform (AMTI Accugait) with the sampling frequency set to 50 Hz. The force platform was placed on the ground level (LO), and at the height of 90 cm above the ground level (HI). The platform was placed on the stable wooden platform with the support surface size of 120x 120 cm and height of 90 cm. For security reasons gymnastic mats were placed in front of the platform. In order to check the influence of height on the extent of postural sta‐bility in the sagittal plane the limits of stability (LOS) test was used (maximum forward lean). Subjects were asked to use ankle joint strategy to perform the forward lean. The test was conducted barefoot. Partici‐pants stood straight with feet hip‐width apart, with arms along their sides and gaze directed straight ahead. Each trial lasted 30 seconds. All trials started with 10 seconds of quiet standing, after which an acoustic signal was triggered to initiate the forward leaning phase. Subjects exe‐cuted the leaning movement at their own pace until they reached their maximal range. A maximal leaning position achieved by the subjects was maintained until the end of the trial, 15 seconds on average. If partici‐pants lost balance they were asked to repeat the trial (Juras et al. 2008). Each measurement was repeated two times and these results were aver‐aged for the further analysis. The experiment consisted of eight trial conditions sequenced in the following order: forward LOS test at a height of 90 cm above the ground with eyes open and next with eyes closed, backward LOS test at a height of 90 cm above the ground with eyes open and next with eyes closed. The same procedure was repeated at the ground level.


Fig. 1

Experimental conditions (LOS test forward)

Fig. 2

COP displacement during LOS test forward in AP The following variables were taken to further analysis: the linear re‐gression coefficient (B), the average value of the center of pressure tra‐jectory (S), COP range (R), time to move the COP (T) and the maximal value of COP A/P trajectory with an appropriate index according to the phase (MAX) Next we conducted one‐dimensional tests for repeated measurements and multivariate tests for repeated measurements.


Results

Forward Limits Of Stability Test There was no significant effect height and vision in forward Limits Of Stability test. There was a significant main effect for regression coeffi‐cients in the 1st phase, 10s of quiet standing [F(3,39) = 2.96, p= 0.043]. There were no significant main effects for regression coefficients 3rd phase, maintenance of maximal forward leaning position [F(3,39)= 0.18, p= 0.901]. There were no significant differences between the heights and no significance between the eyes closed and open. No significant differences were observed for the average value of COP path in the 1st phase [F(3,39)= 0.32, p= 0.811]. There was no significant dif‐ference between the heights and no significant effect between the eyes open and closed. There was no significant main effect for range in 1st phase [F(3,39)= 0.24, p=0.61] and 3rd phase [F(3,39)= 0.78, p= 0.423]. In both phases was not sig‐nificance between the heights LO and HI and between eyes open and closed. There was no significant main effect for maximal value of the COP A/P trajectory within all three phases [F(3,39)= 2.01, p= 0.128] (Fig. 3). There was no significant main effect between with eyes open at the height LO and open eyes on height HI (8.79 cm). It was the same with eyes closed on height LO and eyes closed on height HI.

Limits of stability test in backward lean There was no significant effect for analyzed variables on Limits Of Sta‐bility test in backward between the heights and vision and no vision. There was not a significant main effect for regression coefficients in 1st phase [F(3,39)=0.90, p= 0.449] . There was a significant main effect for re‐gression coefficients in the 3rd phase [F(3,39)= 4.08, p=0,013]. A significant effect was between eyes open at the height LO and eyes closed on height Hi. There were no significant differences between the rest of the sentence conditions.


0123456789

10

O_LO O_HI C_LO C_HI

Max

imum

incl

inat

ion

[cm

]

Fig. 3 Average values of maximum inclination forward. O‐ eyes open, C‐ eyes closed,

LO‐ surface height 0cm, HI‐ height 90 cm above ground level.

0

12

3

4

56

7

89

10

O_LO O_HI C_LO C_HI

Max

imum

incl

inat

ion

[cm

]

Fig. 4 Average values of maximum inclination back O‐ eyes open, C‐ eyes closed,

LO‐ surfach height 0cm, HI‐ height 90 cm above ground level. The next step was analyzed S1 where was not significant main effect [F(3,11)= 2.31, p= 0.131]. There was no significance between the heights and no significance between the eyes open and closed. There was a significant main effect for range in 1st phase [F(3,11)= 3.82, p= 0.043]. A significant effect was between eyes open on height LO and eyes closed at the same height. In other cases, there was no difference.


There was no significant main effect for range in 3rd phase [F(3,39)= 1.29, p=0,289]. Last variable analyzed was the maximal value of the COP A/P trajec‐tory with an according to the phase. There was no significant main effect [F(3,11)= 0.54, p= 0,665], as well as between eyes open at height LO and open eyes on height HI. The same situation was in the case of eyes closed on height LO and eyes closed on height HI.

Discussion

The aim of this study was to determine the effect of the human pos‐tural stability. In these studies, there was no significant effect height on postural stability in contrast to the studies conducted by Adkin et al (2002), Brown et al (2005), Carpenter et al (2001), Davis et al (2008). When threatened, participants adopted a tighter control of posture, character‐ized by smaller amplitude and higher frequency of postural sway during quiet standing and reduced displacement and velocity of the COP. It is due to increased ankle joint stiffness and increased agonist and antago‐nist muscles work (Adkin, 2002). Increased ankle joint stiffness would lower the value of the parameter maximum inclination but maximum in‐clination was no significant effect. On this basis, it can be assumed that there was no fear of falling and height of 90 cm did not affect the postural stability. Perhaps this is due to the selection of the research group.

Referents

Adkin A.L., Frank J.S., Carpenter M.G., Peysar GW. Fear of falling modifies anticipatory postural control. Exp Brain Res, 2002. 143: 160–170.

Adkin A.L., Frank J.S., Jog M.S. Fear of Falling and Postural Control in Parkinson’s Disease, Movement Disorders, 2003. 18: 5.

Adkin A.L., Frank J.S., Carpenter M.G., Peysar G.W. Postural control is scaled to level of postural threat. Gait & Posture, 2009. 12: 87–93.

Brown L.A., Melody A.P., Doan J.B. The effects of anxiety on the regulation of upright standing among younger and older adults. Gait & Posture, 2006. 24:397–405.

Carpenter M.G., Frank J.S, Silcher C.P., Peysar G.W. The influence of postural threat on the control of upright stance. Exp Brain Res, 2001. 138(2) 210–8.

62 Patrycja Kołacz et al. Davis R., Campbell A., Adkin A.L., Carpenter M.G. The relationship between fear of

falling and human postural control, Gait & Posture, 2009. 275–279.

Juras G., Słomka K., Fredyk A., Sobota G., Bacik B. Evaluation of the Limits of Stability (LOS) Balance Test, Journal of Human Kinetics, 2008. 19 39‐52

Min S., Kim J., Parnianpour M. The effects of safety handrails and the heights of scaffolds on the subjective and objective evaluation of postural stability and cardiovascular stress in novice and expert construction workers, Applied Ergonomics, 2011. 1‐8.

Comparison of mechanical parameters of the vertical jump with varying load... 63

COMPARISON OF MECHANICAL PARAMETERS OF THE VERTICAL JUMP

WITH VARYING LOAD MUSCLES IN BASKETBALL PLAYERS

Henryk Król1

Introduction

Mechanical characteristics of skeletal muscle have a major effect on force and speed of muscle actions, whereby the central nervous system has a preferred muscle action strategy to maximize performance in most fast movements. This strategy is most beneficial in high‐effort events, but it is also usually selected in sub‐maximal effort movements (Knudson, 2007). Most movements unconsciously begin with a stretch‐shortening cycle (SSC): a counter movement away from the intended direction of motion, which is decelerated with eccentric muscle action; and this is immediately followed by concentric action in the direction of interest. “This bounce out of an eccentric results in potentiation (increase) of force in the following concentric action if there is minimal delay between the two muscle actions” (Bober 1995; Elliott et al., 1999; Knudson, 2007 and Wilson et al., 1991). During muscular movements, the periodic shorten‐ing‐stretch cycle concludes when the muscles undergo concentric short‐ening followed by eccentric elongation, as the muscle torque decreases below the resistance torque (Rassier, 1999). “The performance benefit of the SSC coordination over purely concen‐tric actions is usually between 10 and 20%, but can be even higher, and the biomechanical origin of these functional benefits is still unclear” 1 - The Jerzy Kukuczka Academy of Physical Education, Biomechanics Laboratory,

Katowice, Poland

64 Henryk Król et al.

(Knudson, 2007). Many biomechanical variables have been examined to study the mechanism of the SSC, and the benefits of the SSC are depend‐ent on when these variables are calculated (Bird and Hudson, 1998), and the resistance moved (Cronin et al., 2001). Beneficial mechanisms of the SSC coordination effects are of considerable interest to biomechanics. There are four potential sources of the greater muscle force in the con‐centric phase of the SSC: contractile potentiation, reflex potentiation, storage and reutilization of elastic energy, and the time available for force development (Knudson, 2007; Komi, 1986). Independently of the sources of greater muscle force during the con‐centric phase of the SSC, sport practitioners are mainly interested in how to better exploit this phenomenon in sports performance. In sport prac‐tice, the exercises based on muscle stretch‐shortening cycle are named plyometric exercises or, simply, plyometrics. These exercises stimulate changes in the neuromuscular system, increasing muscle group abilities to a faster and stronger response to small and rapid changes in muscle length (Kielak and Pac‐Pomarnacki, 2002). Plyometric training will likely increase the athlete’s ability to tolerate higher eccentric muscle forces and increase the potentiation of initial concentric forces (Komi, 1986; Trzas‐koma and Trzaskoma, 2001). “Plyometrics are most beneficial for athletes in high‐speed and power activites” (Knudson, 2007). One of the classical examples of such exercises is the vertical depth (drop) jump. There has been considerable amount of biomechanical re‐search concerning lower‐body drop jumping plyometrics (Bober et al., 2006; Komi and Bosco, 1978; Zatsiorsky and Kraemer, 2006). During the depth jump (in the landing phase), a high energy load is exerted on knee joint straightening and ankle joint flexing muscles. Athletes who stereo‐typically utilize jump movements show increased muscle activity (exci‐tation), which resulted from adaptation (mainly at the spinal cord level) of the central nervous system (CNS) to sport‐specific activities (Sale, 1992). As confirmed by Komi (by Sale, 1992) in his study on volleyball players, the CNS adaptation during sport‐specific exercise extended to a full range of loads (various heights of drop jump). Komi’s research, however, showed that, in comparison to the control group (untrained), the volleyball players group achieved better drop jump results at only the


0.60 m height. In Trzaskoma’s (2001) opinion, this height approximates the height of an average elevation of the center of gravity during the ver‐tical jump, which was registered in high performance volleyball players. Analogically results of female gymnasts, in comparison to the control group, were significantly better in the full range of extending loads. The greatest differences were observed in the largest drop jump heights (Bober et al. 2007). The aim of this study was to determine if the greater load in the expan‐sion phase (eccentric muscle action) in the jump affects the size of the mechanical parameters of the jump.

Material and methods

Thirteen young male basketball players participated in this investiga‐tion. Their mean age was 17.0 ± 0.63 years, height 189.8 ± 6.1 cm, and weight 82.4 ± 6.8 kg. All subjects (athletes of the Sport High School) gave their written consent to participate in the study. All subjects were tested under the same time and temperature condi‐tions in our laboratory. The warm‐up included 10 minutes of cycling on an Excalibure Sport ergometer (LODE, USA) at a constant cadence of 70 rotations per minute (RPM) with a work load of 1.5 W/kg, followed by a 10‐minute stretching program. The warm‐up were followed by 3 counter movement jumps on a force plate. Afterwards, subjects were instructed about how to carry out the jumps: ʺjump as high and as fast as possibleʺ. Then, tests were performed that consisted of five single “maximal” standing vertical jumps1 (counter movement jump – CMJ). The task for the basketball players2 was to touch an over head bar (five single vertical jumps ‐ special counter movement jump – SCMJ). The subjects also per‐formed five single drop jumps from an elevation of 0.40 m (DJ). Intervals

1 A modified version of the test, without the use of arm swings, was adopted (Komi and

Bosco, 1978). 2 According to earlier studies (Król, 2001), it is known that full engagement of subjects is

achieved only when a specific task is presented to them. Athletes who practiced for 6 weeks with the bar improved their jump height about 4 cm, and those who practiced without the bar - only about 2 cm.


between consecutive jumps were set on the basis of prior testing experi‐ence. The interval between single jumps and the series of jumps varied from 45 to 60 s. Ground reaction forces were registered using the KI‐STLER 9182C force platform. MVJ software was used for signal process‐ing (Król, 1999) and enabling calculations of kinematic (height of the jump ‐ h) and kinetic parameters (mean power ‐ Pmean and peak power ‐ Ppeak during the take‐off phase) of the subject’s jumping movements (On‐line system).

Statistical analysis

Statistical analysis were carried out using software Statistica v. 9.0 (Statsoft, USA). Descriptive statistics (mean, and standard deviation) for all measured mechanical parameters were calculated. The normality dis‐tribution of data was checked with the Shapiro‐Wilk test. Because data distribution was not normal, the Wilcoxon Matched Pairs Ranks test was used.

Results and discussion

The height of CMJ describes the motor potential, and more precisely, ac‐cording to Trzaskoma (2001), the jumping potential. According to Trzas‐koma’s criteria (2001), results of selected competitors, presented in table 1, are relatively poor.

Table 1

The average height of jump (h), the average mean power (Pmean) and the average peak power (Ppeak) in three kinds of vertical jump: counter movement jump (CMJ), special vertical jump (touching a bar with the head) (SCMJ) and drop

jump (DJ) of basketball players Parameter h [m] Ppeak [W] Pmean [W]

CMJ Mean; SD

0.394 ± 0.058 2136.6 ± 345.1 1277.0 ± 262.7

SCMJ Mean; SD

0.401 ± 0.047 2241.2 ± 359.8 1389.5 ± 254.6

DJ Mean; SD

0.397 ± 0.052 2349.1 ± 779.7 1471.6 ± 542.2


However, it needs to be stressed that subjects performed the modified jump version, with their arms behind their back. It is acknowledged that due to the lack of arms swing, jumps can be lower of about 5 cm on aver‐age. In similar conditions, elite athletes from 6 different individual and team sports obtained even lower results (Tab. 2; Kollias et al., 2004) in comparison to our results. In comparison to ski jump competitors (report for AZS‐AWF Katowice club; unpublished data; Tab. 2), these findings are significantly smaller, as we had expected. On the other hand, in com‐parison to first league soccer players, the results of basketball players are comparable (Tab. 2; Król, 2008). Interestingly, in most of the examined cases, slightly higher values of jump heights were achieved when sub‐jects performed the drop jump (DJ; Tab. 1). It is even move visible when analyzing mean power (Pmean) and peak power (Ppeak). In order to verify these differences in CMJ, SCMJ and DJ, Wilcoxon’s Matched Pairs Test was conducted. Results are presented in table 3. Results show that the height of the jump (h), the mean power (Pmean) and the peak power (Ppeak), are not statistically significant higher in DJ in comparison to CMJ. Results enclosed in Table 3 do not prove fine adap‐tation of the nervous system of basketball players to muscle extension and workload, as a result of the drop jump from the height of 40 cm, as we had expected. This regularity has been found, while for acrobats. Pre‐sumably, this height is closest to that which acrobats experience in land‐ing, after performing flic‐flacs or roundoffs. Trzaskoma (2001) provided a similar example with volleyball players, however, their optimal height is, as mentioned earlier, 0.60 m, or according to Komi and Bosco (1978), 0.66 m. The figure presented by Trzaskoma (2001; p. 127), shows jump values of acrobats in drop jumps from different heights. Here, one can observe the highest values of 0.40 m height drop jump. Our results confirm results presented by Bober et al. (2001), which concern more skilled basketball players. They were jumping from heights ranging from 0.15 m to 0.76 m, and achieved worse results in average in DJ than in CMJ.




Table 6

he results of Wilcoxon’s Matched Pairs Test for the height of the jump (h), the mean power (Pmean) and the peak power (Ppeak), in three variances of the vertical jump: counter movement jump (CMJ), special vertical jump (touching a bar

with the head; SCMJ) and drop jump (DJ) of basketball players Parameter Pair of

variables Number of cases

T p

h CMJ ; DJ 13 29 0.2489 h SCMJ ; DJ 13 39 0.6496 Pmean CMJ ; DJ 13 38 0.6002 Pmean SCMJ ; DJ 13 44 0.9165 Ppeak CMJ ; DJ 13 27 0.1961 Ppeak SCMJ ; DJ 13 42 0.8068

T – value of Wilcoxon’s test for group of N<25, bold font refers to a sta‐tistically significant result (p<0.05).

Conclusion

Based on the results obtained from our study, we drew the following conclusions:

- Young basketball players of our study, obtained mediocre jump heights. It is probably not the most important factor for success in this sport discipline.

- Drop jumps did not proved to be a more effective way to increase power output and jumping performance than other vertical plyometric exercises such as the counter movement jump in bas‐ketball players.

References

Bird M., Hudson J. (1998) Measurement of elastic‐like behavior in the power squat. J. Sci. Med. Sport, 1:89‐99.

Bober T. (1995) Działanie mięśni w cyklu rozciągnięcie‐skurcz a skuteczność techniki sportowej. Sport Wyczynowy, 1‐2:

Bober T. et al. (2006) Biomechanical criteria for specifying the load applied in plyometric training. Res. Yearbook Stud. Phys Educ. Sport, 12:227‐231.


Bober T., Rutkowska‐Kucharska A., Pietraszewski B. (2007) Ćwiczenia plyometryczne – charakterystyka biomechaniczna, wskaźniki, zastosowanie. Sport Wyczynowy, 7‐9:5‐23.

Bober T., Rutkowska‐Kucharska A., Szpala A. (2001) Hard vs. soft landing in depth jump. Acta Bioengin. Biomech., 4:suppl. 1:595.

Cronin J.B., Mcnair P.J., Marshall R.N. (2001) Magnitude and decay of stretch‐induced enhancement of power output. Eur. J. Appl. Physiol, 84:575‐581.

Elliott B.C., Baxter K.G., Besier T.F. (1999) Internal rotation of the upper‐arm segment during a stretch‐shortening cycle movement. J. Appl. Biomechanics, 5: 381‐395.

Kielak D., Pac‐Pomarnacki A. (2002) Ćwiczenia plyometryczne – ich istota i znaczenie (I). Sport Wyczynowy, 11‐12:13‐24.

Knudson D. (2007) Fundamentals of biomechanics. Chico, Springer.

Kollias I., Panoutsakopoulos V., Papaiakovou G. (2004) Comparing jumping ability among athletes of various sports: vertical drop jumping from 60 centimeters. J Strength Cond Res, 2004 18: 546‐550.

Komi P.V. (1986) The stretch‐shortening cycle and human power output. In: Jones N.L., McCartnay N., McComas A.J. (Eds.), Human muscle power (pp.27‐39). Champaign, IL: Human Kinetics.

Komi P.V., Bosco C. (1978) Utilization of stored elastic energy in leg extensor muscles by men end women. Med. Sci. Sport., 10(4):261‐265.

Król H. (1999) The influence of external factors on result of a motor performance, (In Polish). Acta Bioengin. Biomech. 1:suppl.1:253‐256.

Król H. (2008) Speed strength abilities of football players with different field specialization. In: Juras G., Słomka K. (eds.), Current Research in Motor Control III. From Theories to clinical applications (pp. 191‐196), AWF, Katowice.

McClymont D. (2003) Use of the reactive strength index (RSI) as a plyometric monitoring tool. 5thWorld Congress of Science in Football, Lisabon.

Rassier D.E., Macintoh B.R., Herzog W. (1999) Length dependence of active force production in skeletal muscle. J. Appl. Physiol, 86:1445‐1457.

Sale D.G. (1992) Neural adaptation to strength training. In: Komi P.V. (ed), Strength and power in sport (pp. 249‐265), Oxford, Blackwell Scientific Publications.

72 Henryk Król et al. Trzaskoma Z., Trzaskoma Ł. (2001) Kompleksowe zwiększanie siły mięśniowej

sportowców. COS, Biblioteka Trenera, Warszawa. Wilson G.J., Elliott B.C., Wood G.A. (1991) The effect on performance of imposing

a delay during a stretch‐shortening cycle movement. Med. Sci. Sport. Exer, 23:364‐370.

Zatsiorsky V.M., Kraemer W.J. (2006) Practice and Science of Strength Training. Champaign, IL: Human Kinetics.

The comparative analysis of the standing backward piked somersault (case study) 73

THE COMPARATIVE ANALYSIS OF THE STANDING BACKWARD PIKED

SOMERSAULT (CASE STUDY)

Henryk Król, Małgorzata Klyszcz ‐ Morciniec, Grzegorz Sobota1

Introduction

The structure of the acyclic movement generally consists of the initial, main and final phase. As far as the specific acyclic movements such as jumping, throwing and kicking are concerned, the names of the phases are differentiated. Taking into account for example tumbling (backward somersault) particular phases are as follows counter‐movement, take‐off, flight and landing. The counter‐movement is a special case of the initial phase whose aim is to create optimal conditions for the implementation of the main phase. It is achieved by the pre‐stretching of the muscles of both limbs (gastrocnemius, quadriceps and gluteus) and a trunk (erector spinae pars lumborum). It increases the elastic energy of the muscles, which is called, in biomechanics, the stretch‐shortening cycle (Cronin I wsp. 2001). Both the take‐off and the flight are the main phases since the main task is then performed. The main task is connected with the body rotation around the free axis. The purpose of the take‐off is to provide the projection velocity needed to lift the body and the angular momentum required to perform a rotary motion. The flight includes grouping (dur‐ing the ascent of the body) and come out (during the descent). The effec‐tiveness of this phase is determined by the skillful use of the principle of the conservation of the angular momentum (Hochmuth, 1981; Bober, 2003). The aim of the landing is to break the momentum and the angular



momentum of the body. Moreover, its purpose is to protect the joints of lower limbs from damage and to restore the standing position. Although each phase of the movement is important, special attention in the evaluation of the jumping technique needs to be paid to the take‐off phase. In the presented literature the kinematics of the standing backward somersault is determined by the method of film (Łukin, 1966; Parlak 1973). Dworak (1996) and Łukjan and Parlak (2005) also used the force platform. Bioelectric activity of the two muscles during the back‐ward somersault was studied by Łojek et al. (1999). EMG of three mus‐cles was used to obtain the characteristics of the muscle activation of the gymnasts during the take‐off. It was implemented by McNeal et al. (2007). There is no information about kinematics, kinetics and bioelectrical activity of the muscles during the performance of the standing backward somersault. A similar measurement system using the SMART‐E system and a force platform allowed for achieving the aim of our research, which was connected with the comparative analysis of the standing backward piked somersault performed twice every half a year.

Methods

The research is a pilot study to a broader issue which is to investigate the mechanism of the take‐off in tumbling. The research was conducted twice (December 2010, June 2011) on a woman gymnast (body mass 52kg and body height 161cm, championship class in artistic gymnastics). She also had a thirteen‐year‐training experience. She was informed of the nature of the research in advance and prior to the research she provided a written consent to participate. After a general and then a special warm up the research subject made two attempts to perform a standing back‐ward piked somersault. The rest periods between the jumps lasted about 3 min. Using the SMART‐E measuring system (BTS, Italy) a multi‐dimen‐sional registration of the motion was made. The system includes six in‐frared cameras with a frequency of 120Hz, modules for wireless meas‐urement of the bioelectrical activity of the muscle called EMG Pocket and


the AMTI force platform (USA). The smart sofware (Smart Capture, Smart Tracker i Smart Analyzer) enables the spatial modeling (3D) and the calculation of mechanical parameters. Large spatial accuracy was achieved by attaching the test passive markers to the body of the research subject. The markers were placed in specific locations on both sides of the body allowing to determine the overall center of the mass (COM) of the body. After the calibration of the system the accuracy of the distance between two markers in 3D was 0,4mm. Before the performance of the exercises the skin of the player was specially prepared in the place where the mounting surface electrodes were to be located in the places of the motor activation of the muscles (according to the direction of fibers) were supposed to monitor the level of involvement of certain muscles (tibialis anterior, gastrocnemius (captur mediale), rectus femoris, biceps femoris, gluteus maximus, rectus abdominis, deltoideus (pars anterior), erector spinae. All of the electrodes remained in the same place until the end of the second at‐tempt. EMG signals were recorded using Pocket EMG system with a sampling rate of 1kHz. Then they were filtered through the medium of a Butterworth 20‐500Hz bandpass filter – the pass. In order to obtain an integrated electromyographic record (IEMG) at fixed intervals of 0,1s and separately for each muscle MATLAB computer software was imple‐mented. A vertical component of a ground reaction force was recorded and then its size within characteristic moments of time was determined. All of the measurements were synchronized in time by using the CUP. This approach allowed to analyze the changes in movement within the kinematic and kinetic parameters. It also gave the analysis of the level of the muscle activity. The use of this modern measurement system has helped in the overall description of the techniques used in the backward piked somersault.

Results and discussion

Tables 1, 2, 3, and 4 and Figures 1 and 2 present the obtained results. It allowed to compare the way the backward piked somersault is per‐formed in two different studies. Taking into account the aim of the take‐off somersault, it is to give both the exit (project) velocity necessary to lift the


body (volume displacement conditioning the body in flight) and to obtain the an‐gular momentum needed to perform a rotary motion. These mechanical prop‐erties were mostly analyzed. In the first study the vertical displacement of the body during the piked somersault was about 0.01m lower in comparison with the second study. However, in the second study the position that was adopted by the gymnast after landing was 0.03m more shallow (Tab. 1).

Table 1

Temporary positions and vertical displacements of the gymnast’s body [m] in the first and second study

Location Vertical displacement Position

Study 1 Study 2 Study 1 Study 2 Starting Squat in the end of the counter‐movement The highest position in flight Squad in landing phase

0.99

0.78

1.19

0.52

0.97

0.82

1.24

0.55

‐0.21

0.41

‐0.67

‐0.15

0.42

‐0.69

‐ (‐) downwards movement of the body In the first study smaller displacement of the body (0.41m) in flight during the piked somersault is reflected in the duration of this phase (Tab. 2). This is due to a smaller angular momentum in this attempt (Tab. 3). In the second study in slightly more upright body position at the end of the take‐off phase (see the value of the moment of inertia) greater an‐gular momentum resulted in higher temporary velocity at that moment. Horizontal displacements center of mass (COM) in the sagittal plane throughout the course of both studied somersaults were similar. In the second somersault temporal reduction of the counter‐movement and take‐off phase by 0.20s and 0.01s respectively was observed. What is more, an increase in the flight phase (about 0.03s) was also presented.


Table 2

The duration of particular phases (s) Phase Study 1 Study 2

Counter‐movement Take‐off Flight

0.93 0.40 0.52

0.73 0.39 0.55

Table 3

The angular momentum and its components in the end of the take‐off phase

Study Angular momentum (kgm2/s)

Vertical velocity (rad/s)

Moment of inertia (kgm2)

1 49.1 4.90 10.0 2 55.0 5.17 10.6

Table 4

Vertical reaction force in the characteristic periods of time (Fig. 1B and 2B) Vertical reaction force (N) Study

R1 R2 R3 R4 R5 R6 1 378 665 460 807 248 1609 2 391 666 393 774 188 1688 Looking at the characteristics of the vertical reaction force with refer‐ence to two somersaults (Fig. 1B and 2B and Tab.4) only slight differences within the selected moments of time were shown (R1‐R6 parameters). However, the vertical reaction force does not directly affect the displace‐ment of the body in the flight phase of the gymnast. As it is known from the impulse‐momentum relationship (V=Ft/m) time influences the projection velocity and thus the size of the displacement of the body (h = V2/2g). The mass of the gymnast must be constant.


Fig. 1 Temporal characteristics of the standing backward piked somersault in the first study: A) vertical displacement COM of the body (Y COM); B) vertical ground reaction (Ry); body weight (Q); C) integrated EMG TIB – tibialis anterior, GA – gastrocnemius (caput mediale), REC – rectus femoris, BFE – biceps

femoris, ABD – rectus abdominis, GLM – gluteus maximus, DEL – deltoideus (pars anterior), ERS – erector spinae. Phases of the movement: Phase 1 – conter‐movement, Phase 2 – take‐off; Phase 3 – flight. Characteristic values of the

ground reaction force; R1‐R6.


Fig. 2 Temporal characteristics of the standing backward piked somersault in the

second study.The explanations of the symbols are found in Fig.1 Bearing in mind all of the characteristics of R(t), from the beginning of the counter‐movement to the completion of the the take‐off phase (Fig. 1B and 2B), the impulse of the take‐off phase in the second study is probably slightly higher than in the first study. The size of the obtained ground re‐action force is a response to the forces developed by the individual groups of the muscles acting on the bone. Although they were not deter‐


mined in the conducted research, an indirect (very rough) way of as‐sessing the contribution of individual muscle groups in the overall reac‐tion force may be a degree of a muscle involvement, established on the basis of the bioelectrical activity. Fig. 1C, and 2C present integrated elec‐tromyograms (IEMG) of the investigated muscles. Two of them, i.e. mus‐culus gastrocnemius and musculus deltoideus (pars anterior) are character‐ized by a clearly high activity. It mostly applies to two moments of time, i.e. the end of the take‐off phase and the beginning of the landing phase. If we were to take into consideration the remaining muscles, our atten‐tion should be focused on the larger activity of musculus rectus abdominis at the beginning of the flight phase in the piked somersault.

Summary

Using a thorough methodology while examining the standing piked backward somersault allows to present its external and internal structure of the movement (Król and Mynarski, 2005). Both functional relation‐ships between kinematic and kinetic parameters of the movement and their relatively large dependence on the activity of the participating mus‐cles facilitates the analysis of performing the exercise. Comparing the parameters and the temporal characteristics of the piked somersault presented by the gymnast in the first and second study, the differences were rather small. The backward piked somersault in the second study, in comparison with the first one, was characterized by the higher height of the flight phase and greater angular momentum devel‐oped by the end of the take‐off phase. The rhythm of the analyzed exer‐cise was varied. It was the result of shortening the duration of the counter‐movement and take‐off phases and increasing the flight phase. On the basis of the recorded EMG signals the highest activity was ob‐served for two muscles: the gastrocnemius muscle and the deltoideus (pars anterior) muscle. It is mostly connected with two moments of time: the end of the take‐off phase and the beginning of the landing phase. In ad‐dition, the rectus abdominis muscle showed greater activity in the early phase of the flight.


Judging from the results of the pilot study one is not able to predict similar temporal characteristics which could apply to different players. In this article we wanted to present similarities and differences between the parameters and temporal characteristics of only one gymnast performing the same exercise twice. It was done by means of a modern and compre‐hensive research methodology.

References

Bober T. Technika sportowa – ujęcie biomechaniczne. W: Zagadnienia sportu – technika ruchu. C. Urbanik (red.) AWF, Warszawa, 5‐18, 2003.

Cronin J.B., McNair P.J., Marshall R.N. Magnitude and decay of stretch‐induced enhancement of power output. European Journal Applied Physiology, 84, 575‐581, 2001.

Dworak L., Mączyński J., Wojtkowiak T. Zależność przeciążeń dynamicznych od rodzaju podłoża na przykładzie elementu akrobatycznego salto w tył z miejsca. Wychowanie Fizyczne i Sport, 4, 33‐40, 1996.

Hochmuth G. Biomechanik sportlicher Bewegungen. Sportverlag, Berlin, 1981.

Król H., Mynarski W. (2005) Cechy ruchu – charakterystyka i możliwości parametryzacji. AWF, Studia nad motorycznością ludzką nr 7, Katowice.

Łojek M., Kosmol A., Kuder A., Obrębski D. Elektromiograficzna analiza salta w tył wykonywanego na podłożu o różnej elastyczności. In: Gimnastyka – taniec w teorii oraz praktyce wychowania fizycznego i sportu. Z. Szot, D. Fostiak, M. Lipowski (red.) Materiały z Międzynarodowej Konferencji Naukowo‐Metodycznej Gdańsk 20‐21 listopada 1997, 139‐142, 1998.

Łukin M. Salto nazad z miesta. Teoria i Praktika Fiziczeskoj Kultury, 12, 1966.

Łukjan M., Parlak J. Porównanie struktury odbicia w wyskoku dosiężnym i w przewrocie lotnym w tył z miejsca (studium przypadku). Amtropomotoryka, 32, 51‐58, 2005.

McNeal J.R., Sands W.A., Shultz B.B. Muscle activation characteristics of tumbling take‐offs. Sport Biomechanics, 6, 375‐390, 2007.

Parlak J. Ocena skuteczności fazy odbicia w skokach akrobatycznych z miejsca i z rozbiegu. Praca doktorska, AWF, Warszawa, 1973.

82 Lidia Kuba et al.

THE INFLUENCE OF PILATES EXERCISES ON POSTURAL STABILITY OF YOUNG AND OLDER WOMEN ‐ COMPARISON

OF THE EFFECTS OF SHORT‐TERM TRAINING

Lidia Kuba, Artur Fredyk, Izabela Zając‐Gawlak, Joanna Kantyka1

The subject of the rate of injuries which are caused by falls induced by imbalances of the elderly has been undertaken repeatedly. These are many separate factors which cause the decline in the balance of the eld‐erly: reduced sensitivity of skin receptors, poor eyesight and vestibular dysfunction, slowed down reaction times and the aggravation of other co‐ordination skills (Skeleton 2001). Balance disorders are the major symptom of postural instability. Especially the dynamic postural stability decrease recorded before and after the age of 85. This concerns mainly physically inactive persons (Rockwood et al. 2004). Especially for women, studies showed an increase in the level of instability with aging when compared to men. It is necessary to minimize this problem by increasing the preventive action (Simey 1999). The paramount importance of physi‐cal activity for the development of postural stability in postmenopausal women must be emphasized here. Starting physical activity at the age of 60 years gives similar results for the people who have always been active, whereas much better than for those who have been inactive for their life‐time (Perin et al. 1999). An effective strategy counteracting the age related negative postural stability changes is needed which would allow to iden‐tify the cause and to take action already at the young age (Choy et al. 2003). The optimum seems to be applying appropriate training programs


The influence of pilates exercises on postural stability of young and older... 83

and the systematic participation in appropriate physical activities from youth to old age. The object of this study is the evaluation of the influence of short–term Pilates training on the postural stability of young and older women as well as verification of the fraction of women in whom an improvement or a deterioration was observed in terms of the analyzed parameters. The effects of training designed to improve postural stability in both groups of women has also been compared.

Methods

Fifty women, including twenty‐two at the age of 20.5±0.5 and twenty‐eight at the age of 59±5 have taken part in this experiment. The experi‐mental group consisted of healthy subjects only. All the applied methods, the plan and scope of the research have been accepted by the IRB board of the Academy of Physical Education in Katowice. Both the young and the older women participated in a three‐month Pilates training program. The women exercised twice a week for one hour for three months. Both groups used the same exercises, however, the program was assumed to adapt to the level of difficulty of the exer‐cises to the individual abilities of its participants. We used the AMTI (typ BP6009000) force platform to evaluate the subject’s postural stability during quiet standing (closed base). Measure‐ments included the assessment of postural stability parameters: total path length of the foot to the ground pressure (lenght_COP ‐ center of pres‐sure), the average speed of COP displacements (AVG_velosity) and stan‐dard deviations pressure force COP displacement in the sagittal plane (COP_X) and coronal (COP_Y). Measurements were made twice: the first stage involved the measurement of starting parameters and a second evaluation of the same parameters after three months of practicing. Prior to all calculations the standard descriptive statistics were per‐formed. The influence of Pilates training on the registered parameters was analyzed by means of relative differences between the obtained em‐pirical data before and after the training. The level of significance was set at p < .05. The fraction of women who showed either improvement or


deterioration in their postural stability parameters was estimated on the basis of independence chi‐squared tests. An analysis of variance with in‐teractions was used to compare the effects of training for both groups.

Results

The obtained results which constituted the selected parameters of the postural stability of women have been analyzed. In order to verify the correctness of conclusions in terms their significance on the basis of the confidence interval the skewness and kurtosis values for the differences have been estimated which have confirmed that the analyzed variables show normal distributions. As far as the COP path length and the speed of COP displacements are concerned no statistically important influence of the applied exercise program on the postural stability of the examined women has been found. The results of the analyses for these variables are identical as both these variables are related to each other by the linear function AvgVel=lenght/(time of measurement). Still, the parameters of the standard deviation of the COP foot pressure force on the ground in the analyzed planes (sagittal and coronal) look different in the groups of young and, respectively, older women. In the case of the young women both variables have improved, however, the statistically important dif‐ference was recorded in terms of the standard deviation in the coronal plane. These variables look different with the older women. Both tested parameters have declined in a statistically important way (Tab. 1). The percentage of improvement and deterioration of the postural sta‐bility of the tested women has been estimated by the chi‐squared test. The results were different for both these groups. More cases of worsening of COP path length and the average speed of COP displacement pa‐rameters have been recorded for the older women whereas for the young ones these values stayed at the same level. However, in terms of the standard deviation of the COP foot pressure force on the ground in the sagittal and coronal planes the results have improved slightly for the majority of these women and have worsened for few of them, still, this deterioration was so remarkable that it had counterweighed the change thus making it negative. These values are different for the young women,



slightly more than half of them had a deterioration and the rest of them an improvement (Tab. 2). Following to the variance analysis including interaction no statisti‐cally significant difference of the effects of short term Pilates training on the postural stability improvement for both test groups of women (p>0.05) has been recorded. The level of the postural stability of the test subjects has been verified. It was higher in a statistically significant man‐ner in the group of young women than in the older group in both tests together (p<0.022).

Discussion

The applied 3 month Pilates training turned out to be insufficient as a stimulus for improvement of the analyzed parameters of postural stability both in the group of young and older women. No improvement of motor skills has been recorded in similar tests on the influence of Pilates exercises on the balance of elderly women (Bird 2011, Kloubec 2010). Also, in other short term training programs where various recreation classes were used the balance rate has not improved, however, the researched parameters have dropped for the inactive control group (Buchner et al. 1997, Lord et al. 1995). Therefore, it can be assumed that the balance maintained by the older women at the same level by means of the applied motor exercises constitutes a positive result. Most proba‐bly, adding some appropriate exercises or counterbalance elements to the applied program will help to get the expected effects. According to Nitz et al. (2004) a specific training based on the strategy of balance develop‐ment with the use of special training equipment brings better effects than the traditional classes. The analysis of the fraction of women who sustained either improve‐ment or deterioration of the researched parameters of their postural sta‐bility allowed to look more closely into the changes which were taking place in the tested groups. By 46% of women the results have improved and by 54% they have declined which means that the training had brought better effects for the postural stability of the older women who sustained improvement. In the same way, these values have dropped


slightly by the older women who sustained aggravation. The effects of the training were different in the case of young women whose 50% sus‐tained improvement and the other 50% have declined. These differences might have depended on the individual abilities of the exercising persons and that they have resulted from the difficulties of learning all parts of the training which are the condition of training effectiveness. The most important parts include the following skills: abdominal deep muscles contraction, movement improving correct breathing technique, body sta‐bilization, assuming the correct starting positions and the precise per‐formance of exercises as well as the coordination of all these elements while training. However, the effects of the training arouse interest in terms of the standard deviation parameters of COP pressure in the analyzed planes. 71% improvement and 29% deterioration was recorded for the older women in the sagittal plane, alike in the coronal plane, where 68% got improvement and 32% deterioration. It means that the regular participa‐tion in Pilates classes has caused a significant differentiation of the ana‐lyzed deviations in both planes for a small number of older women. This phenomenon looked different by the young women where 45% have ob‐tained better and 55% worse results, though, these values differed much less than by the older women. Such variance of deviations differing both groups from each other depends most probably on the individual abili‐ties of each single person to perform the training program. Especially by the older women some single cases of remarkable aggravation were re‐corded which, in consequence, have negatively affected the summarized values of the tested parameters. The last stage of this research involved the comparison of the effects of short term Pilates training for young and older women and the verifi‐cation of their postural stability level for both tested groups. No statisti‐cally important difference of training effects has been noted in both groups. The training turned out to be insufficient both for the young and the older women. The applied training program was, most certainly, too short, which has not allowed its participants to obtain the expected ef‐fects. It should have been extended, additionally, by some specific bal‐ance exercises designed to improve the postural stability. The summa‐


rized level of the postural stability of the younger women was statisti‐cally noticeably higher than of the older women (p<0.022). The studies performed by other scientists in similar age groups have also confirmed higher instability of the elderly (Hasselkus, Shambes 1975). To conclude, this study showed that 3 month Pilates training is not suffi‐cient in terms of stimulating the increase of the postural stability of the tested groups. The fraction of the women who sustained either improve‐ment or deterioration of postural stability appears different for any single parameter in both tested groups. No statistically significant difference was recorded in terms of the effects of short term Pilates training for the postural stability in both tested groups of women (p>0.05).

References

Bird M.L., Hill K.D., Fell J.W. A randomized controlled study investigating static and dynamic balance in older adults after training with Pilates. Arch Phys Med Rehabil. 2012; 93 (1):43‐9.

Buchner D.M, Cress ME, de Lateur BJ, Esselman P.C., Margherita A.J., Price R., Wagner E.H. The effect of strength and endurance training on gait, balance, fall risk, and health services use in community‐living older adults. J Gerontol Med Sci. 1997; 52A: M218–M224.

Choy N.L., Brauer S., Nitz J. Changes in Postural Stability in Women Aged 20 to 80 Years. J Gerontol A Biol Sci Med Sci 2003; 58 (6): M525‐M530.

Hasselkus B.R., Shambes G.M. Aging and Postural Sway in Women. J Gerontol. 1975; 30: 661 – 667.

Kloubec JA. J Strength Cond Res. Pilates for improvement of muscle endurance, flexibility, balance, and posture. J Strength Cond Res. 2010; 24(3):661‐7.

Lord S.R., Ward J.A., Williams P., Strudwick M. The effect of a 12‐month exercise trial on balance, strength, and falls in older women: a randomized controlled trial. J Am Geriatr Soc. 1995; 43 (11):1198‐206.

Nitz J.C., Choy N.L. The efficacy of a specific balance‐strategy training programme for preventing falls among older people: a pilot randomised controlled trial. Age Ageing, 2004; 33:52 – 58.

Perrin P.P., Gauchard G.C., Petrrot C., Jeandel C. Effects of Physical and sporting activities on balance control in eldery people. Br. J. Sports Med. 1999; 33:121‐6.


Rockwood K., Howlett S.E., MacKnight C., Beattie B.L., Bergman H., Hébert R., Hogan D.B., Wolfson C, McDowell I. Prevalence, Attributes, and Outcomes of Fitness and Frailty in Community‐Dwelling Older Adults: Report From the Canadian Study of Health and Aging J Gerontol A Biol Sci Med Sci. 2004; 59: 1310 – 1317.

Simey P., Pennington B. Physical activity and prevention and menagement of falls and accidence among Older People: Guidelines for Practice. London: Health Educatin Authority, 1999.

Skeleton D.A. Effects of physical activity on postural stability. Age and ageing. 2001; 30‐5: 33‐39.

90 Liudmila Liutsko et al.

CHANGES IN FINE MOTOR BEHAVIOUR WITH AGE (BASED ON VISUO‐

PROPRIOCEPTIVE AND PROPRIOCEPTIVE ONLY FEEDBACKS)

Liudmila Liutsko1, Ruben Muiños1 and Josep Maria Tous‐Ral2

Introduction

Given that human life expectancy is increasing and the percentage of elderly people is on the rise, the question of how to maintain health among the elderly is becoming ever more crucial. Preventive measures with integrative approach are required involving different areas of re‐search. One of these areas concerns postural control and proprioception, where studies have examined ways of preventing age‐related sensori‐motor deficits. Changes in vision and balance: a progressive worsening is usually ob‐served in vision after age 50 and in balance after age 65 (Sturnieksa, Georgea and Lord 2008). Proprioception also decreases with age and this is closely related to the loss of muscle and joint strength. Muscle strength has been found to peak until the fifth or sixth decade, but shows a 50% decrease by age 80 (Cole, Rotella and Harner 1999; Sturnieksa, Georgea and Lord 2008). Research in this context has suggested that just as brain exercises are needed to maintain cognitive performance as one gets older, it is also important to engage in daily physical activity (Ribeiro and Oliveira 2007) and eat a diet that is rich in antioxidants or essential fatty

1 - Laboratory Mira y López, Department of Personality, Assessment and Treatments,

Faculty of Psychology, University of Barcelona, Spain 2 - Neuroscience Institute, Faculty of Psychology, University of Barcelona, Barcelona,

Spain

Changes in fine motor behaviour with age (based on visuo-proprioceptive and... 91

acids (Willis, Shukitt‐Hale and Joseph 2009) in order to preserve one’s proprioceptive function. As far as lateralization is concerned, some researchers have shown de‐creased hand asymmetry in motor tasks with aging (Przybyla, Haaland, Bagesteiro and Saiburg 2011), while others have reported faster work of the right hemisphere (left hand), which did not change much with age on non‐verbal and visual tasks (Stern, Oster and Newport 1980). Research using fMRI has also identified an age‐related shift from automatic to more cognitively controlled movements as subjects get older (Heuninckx, Wenderoch, Debaere, Peters and Swinnen 2005). In light of these findings, our aim here was to examine whether age has an influence on proprioceptive and visuo‐proprioceptive function in relation to fine motor performance. Specifically, this was a cross‐sectional study in which subjects of different ages were required to trace over a model line. We measured precision of line length and task speed in the frontal and transverse directions and for both hands (in order to examine asymmetry in motor lateralization).

Methods

Participants The sample comprised subjects from the general population with nor‐mal or corrected‐to‐normal vision (N=196, age=33±21 years, range: 12‐95, men: 75%). Those individuals who had any been forced to change their hand dominance at school were excluded from the study. All subjects took part voluntarily, were informed about the aims of the research and gave their consent prior to inclusion in the study.

Software and Intstruments The validated (Muiños 2008) computerized test (Tous and Viadé 2002; Tous, Viadé and Muiños 2007) was based on the original manual version proposed by Mira (1958) as a method of myokinetic psychodiagnosis (MKP). It comprised a tactile screen (LGE, resolution of 1280x1024, opti‐mal frequency of 60 Hz) in conjunction with a sensory stylus (for hand drawings), both of which were connected to a laptop computer (Pentium


IV) on which was installed specially designed software for data coding and analysis (Tous 2008), and a piece of cardboard (or opaque screen) to hide the active arm and prevent the subject from receiving movement feedback.

Procedure Instructions to participants: “You have to trace the model line as much precise as you can, without inter‐rupting or lifting the hand”. The task was repeated for both hands and transversal and frontal movement types. Participants made 3 trials (complete movements) in PV test condition, followed by 10 trials in P sensory conditions. The line lengths of the last movements of each sensory condition were compared; as well total time (ms) spent and each sensory condition of the task.

Statistical Analysis Paired correlations (r) and differences (t) for repeated measures were calculated with use of SPSS.18 to see how the motoralization asymmetry between hands was changed with age, as well as existence of P/PV de‐pendence in the performance. Regressions analyses were performed to see how the precision and velocity of task were changes through life.

Results

Fine motor behaviour, in the form of the precision of line lengths (tracing over 40 mm model lines) and task speed, was measured in the frontal and transverse directions for both hands and under two test con‐ditions: proprioceptive information only (P) and proprioceptive + visual information (PV). Age was shown to have an influence on the precision and speed of fine motor performance under different test conditions, with the best performance being achieved at middle age and with a quadratic polynomial function providing the best fit for most of the variables. Inflection points (i.e. the critical age at which the graphical analysis showed the change in performance) ranged from 31 to 48 years old for the best fitting regressions in relation to precision, and from 36 to 40 in relation to time (Tab. 1, 2).




As regards asymmetry of motor lateralization, the results showed that left‐hand performance was significantly faster for some variables and some age groups; however, the precision was worse when the differences between L and R hands were statistically significant.

Discussion and conclusions

The age‐related relationship between fine motor precision and task velocity based on either proprioception only or visuo‐proprioceptive feedback had a non‐linear dependence. This relationship was fitted to a quadratic polynomial as a general approximation (improving their values up to middle age, when both precision and velocity reached their best performances) and followed by a posterior worsening due to aging processes. Proprioception was the first to deteriorate and had higher variance in performance, especially in older subjects. Practical implications: proprioception training to maintain it at good level will allow that quality of life (physical and psychological) to be prolonged with aging.

References

Cole KJ, Rotella DL, Harner, JG (1999) Mechanisms for age‐related changes of fingertip forces during precision gripping and lifting in adults. The Journal of Neuroscience, 19(8): 3238‐3247

Goble D, Coxona JP, Wenderotha N, Impea, AV, Swinnena, SP (2009) Proprioceptive sensibility in the elderly: Degeneration, functional consequences and plastic‐adaptive processes. Neuroscience & Behavioral Reviews, 33(3): 271‐278

Heuninckx S, Wenderoch N, Debaere F, Peters R, Swinnen S (2005) Neural basis of aging: the penetration cognition into action control. The Journal of Neuroscience, 25(29): 6787‐6796. doi: 10.1523/JNEUROSCI.1263‐05.2005

Mira E (1958) Myokinetic psychodiagnosis. (M. K. P.) New York: Logos

Muiños R (2008) El psicodiagnóstico miokinético: desarrollo, descripción y análisis factorial confirmatorio [thesis in Spanish, abstract in English], Barcelona: UB

96 Liudmila Liutsko et al. Tous JM, Viadé A (2002) Advances in MKP‐R. Psicologia em Revista, 8(12): 95‐

110. [In Spanish, English summary].

Tous JM, Viadé A, Muiños R (2007) Validez estructural de los lineogramas del psicodiagnóstico miokinético, revisado y digitalizado (PMK‐RD). Psicothema, 19(2): 350‐356

Tous, J.M. (2008) Diagnostico Propioceptivo del Temperamento y el Carácter DP‐TC. Barcelona: Lab. Mira y López. Department of Personality, Assessment and Psychological Treatments, University of Barcelona.

Przybyla A, Haaland K, Bagesteiro, Saiburg R (2011) Motor asymmetry reduction in older adults. Neuroscience Letters, 489(2): 99‐104

Ribeiro F, Oliveira J (2007) Aging effects on joint proprioception: the role of physical activity in proprioception preservation. Eur Rev Aging Phys Act: 4:71‐76; doi 10.1007/s1556‐007‐0026‐x.

Sturnieksa DL, Georgea RSt, Lord SR (2008) Balance disorders in the elderly. Clinical Neurophysiology, 38(6): 467‐478

Willis LM, Shukitt‐Hale B, Joseph JA (2009) Modulation of cognition and behaviour in aged animals: role for antioxidant‐ and essential fatty acid‐rich plant foods. Am J Clin Nutr, 89(5): 1602S‐1606S

Step initation:Characteristics from accelerometry and camera motion … 97

STEP INITIATION: CHARACTERISTICS FROM ACCELEROMETRY AND CAMERA

MOTION CAPTURE SYSTEM

Jana Lobotkova, Zuzana Halicka, Kristina Buckova, Frantisek Hlavacka1

Introduction

Step initiation is a complex motor task that entails the transition from a quiet standing posture to dynamic equilibrium that allows forward body progression (Rocchi et al., 2006). Important aspect of the beginning of gait is the anticipatory postural adjustments (APAs) reflecting the body´s ability to predict the postural disturbances occurring with for‐ward movement (Crenna, Frigo, 1991). Disturbances during gait initia‐tion (GI) occur often in elderly and may cause a decrease in balance con‐trol ability during walking. There are many studies, which evaluate GI by different types of temporal‐spatial parameters. The purpose of this pilot study was to determine the most sensitive kinetic and kinematic pa‐rameters of GI in young healthy subjects when taking short, normal and long steps.

Methods

Ten healthy young adults (4 men and 6 women; mean age 27.3±1.2 years) participated in the study. None of the subjects reported orthope‐dic, cardiovascular, or neurological diseases or dysfunctions, they had any history of falls, or any pain at the time of testing. All subjects gave

1 - Laboratory of Motor Control, Institute of Normal and Pathological Physiology, Slovak

Academy of Sciences, Bratislava, Slovakia

98 Jana Lobotkowa et al.

written informed consent prior to participation and the Local Science Ethical Committee approved the experimental protocol. Foot reactions were measured at the initial stance using custom made 0.45 x 0.45 m force platform equipped with automatic weight correction, and quantified by displacement of center of foot pressure (CoP) in the anterior‐posterior (AP) and medial‐lateral (ML) direction. Accelerations of upper and lower trunk were measured by two inertial MTx sensors (Xsens Technologies, B.V., Netherlands) with 3‐D accelerometers (± 1.7g range) mounted on the anterior trunk at the level of sternum (AccSter) and the posterior trunk at the level of the fifth lumbar vertebra (AccL5). Data from the force platform and accelerometers were acquired at 100 Hz. Spatial and temporal kinematic gait initiation parameters were automatically recorded by motion capture system (BTS Smart DX, Italy) equipped with 6 infrared cameras and sampling frequency of 100 Hz. Twelve spherical, retro‐reflective markers (1.5 cm in diameter) were bi‐laterally placed on anatomically well‐defined points: acromion, antero‐superior iliac spine, knees, heels, lateral malleoli and the fifth metatarsal head, two retro‐reflective markers were also placed identically with ac‐celerometers on sternum and L5. Subjects stood on a force plate, a walkway was expanded from the force plate by additional 1.3 m wide and 3 m long wooden platform, with force plate embedded. Subjects were instructed to initiate gait from a standing posture after an auditory cue and take 5‐6 steps in a forward direction, starting with the right leg. In each trial, they walked at their self‐selected, shorter and longer steps, respectively. Initial stance position was consistent from trial‐to‐trial by tracing foot outlines on the force plate. Five trials of step initiation in each step length condition were acquired and averaged. In each trial, the first step was evaluated and analyzed with MATLAB program. In the process of step initiation, we differentiated between two phases – preparatory (APAs) and execution phase (EXE). In both phases, pos‐tural sway was evaluated from CoP displacements after applying 10‐Hz cut‐off filter. The sternum and L5 acceleration output was also low‐pass filtered with cut‐off frequency of 3 Hz as well as trunk angle calculated from markers placed on upper and lower trunk.


0 50 100 150-10

-5

5

10[cm]

0

0 50 100 150-4

-2

0

2

4

0 50 100 150-4

-2

2

4

[ms]

[ms]

[ms]

[m.s-2]

[m.s-2]

0 50 100 150-10

-5

0

5

10[cm]

[ms]

0 50 100 150-4

-2

0

2

4

0 50 100 150-4

-2

0

2

4

[ms]

[ms]

[m.s-2]

[m.s-2]

ACoP

BAcc L5

CAcc Ster

AP direction ML directionforward

backward

step side

stance side

APAs EXE EXEAPAs

HO TO HC

0

HCTOHO

Fig. 1 Excursions of CoP (A), lower (B) and upper (C) trunk accelerations during

APAs and execution phase (EXE) of GI in representative subject: short step trial (dashed), normal (grey) and long (black) step trials. Left panel – anterior‐

posterior, right panel – medial‐lateral directions. HO, TO, HC: heel off, toe‐off, heel contact of stepping leg in long step trial.

The following characteristics were compared from the CoP displacements and trunk accelerations: (1) APAs AP amplitudes, that are peak of CoP excursion, peak AccL5 and AccSter trunk accelerations from the baseline in AP direction and (2) APAs ML amplitudes that are peak of CoP excur‐sion, peaks of upper and lower trunk accelerations in ML direction. The same parameters we evaluated also during the execution phase of GI,


especially in two events, toe‐off (TO) and heel contact (HC) of stepping leg: (3) EXE AP amplitudes, that are peak of CoP excursion, peak AccL5 and AccSter trunk accelerations in AP direction in toe‐off as well as in heel contact and (4) EXE ML amplitudes, that are peak of CoP excursion, peak AccL5 and AccSter trunk accelerations in ML direction in both, toe‐off and heel contact events. Trunk angle and trajectories of markers placed on sternum and L5 in sagittal and lateral plane were also evalu‐ated. Data were statistically analyzed using Student´s t‐test, p<0.05 was considered significant. Figure 1 shows example of CoP and acceleration signals (sternum, L5) collected from a representative subject both in AP and ML directions, together with the extracted features.

Results

Gait initiation involves the preparation (APAs) and execution of the first step. Significant differences in APAs while taking short, normal and long steps were showed in parameter CoP displacement and acceleration of upper trunk in both AP and ML directions (Fig.2 A). The sternum ac‐celeration showed lateral‐backward excursion in correspondence to the lateral‐backward displacement of CoP. Backward CoP shift in APAs was 3.67 cm, 5.04 cm, 5.77 cm during the short, normal and long step trials, respectively. Lateral CoP shift in APAs toward the swing leg was 3.33 cm, 3.79 cm, 3.94 cm during the short, normal and long step trials, re‐spectively (Fig.2 A‐a). Backward acceleration of sternum in APAs was 0.27 ms‐2, 0.40 ms‐2, 0.61 ms‐2 and lateral sternum acceleration toward the swing leg was 0.51 ms‐2, 0.56 ms‐2, 0.67 ms‐2 during the short, normal and long step trials, respectively (Fig.2 A‐b). In acceleration of lower trunk, APAs appeared only in two subjects in all conditions in both AP and ML directions. In trunk angle and trajectories of sternum and L5 markers, APAs were detected in none of the trials of subjects. In execution phase of the first step, we focused on CoP displacement and sternum and L5 accelerations in two different events – toe‐off and heel contact of the stepping leg. Significant differences between short, normal and long step trials were found in AP CoP displacements (Fig.2 B‐a), AP and ML acceleration of sternum (Fig.2 B‐b) and AP acceleration


of lower trunk (Fig.2 B‐c) during the toe‐off. At the end of execution phase, in heel contact, acceleration of sternum (Fig.2 C‐b) and L5 (Fig.2 C‐c) in both AP and ML directions was significantly different. No signifi‐cant differences were found in CoP displacement at the time of heel con‐tact. Changes in trunk angle and trajectories of sternum and L5 markers during execution phase were not significantly different comparing short, normal and long step trials.

0

0,5

0,7

0,1

0

1

3

5

7

0

2

4

6

8

10

0,3

0

2

4

6

8

10

0

0,05

0,15

0,25

0,35

0

1

2

3

0

0,2

0,4

0,6

0,8

1

0

0,5

1,5

2,5

3,5

AAPAs

CoP [cm] Acc Ster Acc L5

BEXE-TO

CEXE-HC

[m.s -2] [m.s -2]

AP ML AP ML MLAP

a b

ca

a

b

b c

* * ** * * **

* ***

* ***

* * ** * **

* * ** * **

* **

* ***

* * ** * * ** *

* * **

**

* * ** * * * *

* ** **

Fig. 2 Grouped averages of GI parameters: CoP displacement, acceleration of sternum and L5 during the APAs (A), execution phase in toe‐off (B) and heel contact (C) in AP and ML directions. Three different step length conditions are compared:

short (light grey), normal (grey) and long (black) steps. The averaged data are presented as mean values.

Significant differences are marked: *p<0.05, **p<0.01, ***p<0.001.


Discussion

Our results suggest that CoP displacement in AP direction is more sensitive for recording changes during GI while taking steps with differ‐ent length, more significant in APAs. Other studies are oriented mostly in both, AP and ML directions, especially in patients (Henriksson et al., 2011), but some of them consider as more significant ML direction (Roc‐chi et al., 2006). Little is known about determining of GI parameters by accelerometers. Mancini et al. (2009) evaluated APAs in Parkinson pa‐tients using Acc L5, but our results indicate that more suitable for de‐tecting changes during GI in both APAs and execution phase is recording of upper trunk acceleration. In our pilot study, APAs were detected only in two from ten subjects using accelerometer on L5, in contrary accelera‐tion of sternum revealed significant changes in all subjects during APAs and also execution phase in both, AP and ML directions. More significant differences during execution phase showed accelerations of upper and also lower trunk in both directions in heel contact of stepping leg, than in toe‐off event. Data from motion capture system are often used for deter‐mining gait initiation events like TO, HC or temporal parameters. We also focused on trunk tilt in sagittal and lateral planes during GI and on trajectories of sternum and L5 markers. In spite of promising changes in trunk angle, these were not significantly different in relation to different step length conditions.

References

Crenna P., Frigo C. A motor programme for the initiation of forward‐oriented movements in humans. J Physiol. 437: 635‐53,1991

Henriksson M., Henriksson J., Bergenius J. Gait initiation characteristics in elderly patients with unilateral vestibular impairment. Gait & Posture. 33:661‐667, 2011

Mancini M., Zampieri C., Carlson‐Kuhta P., Chiari L., Horak FB. Anticipatory postural adjustments prior to step initiation are hypometric in untreated Parkinson´s disease: an accelerometry based aproach. European Journal of Neurology. 16:1028‐1034, 2009


Rocchi L., Chiari L., Mancini M., Carlson‐Kuhta P., Gross A., Horak FB. Step initiation in Parkinson´s disease: Influence of initial stance condition. Neuroscience Letters. 406: 128‐132, 2006

Acknowledgments

This work was supported by VEGA grant No. 2/0186/10.

104 Guo Mei-Chun and Hwang Ing-Shiou

PRACTICE‐RELATED ADAPTATION TO MOTOR OUTPUT WITH ADDITIVE

LOW‐LEVEL NOISE

Guo Mei‐Chun, Hwang Ing‐Shiou1

Introduction

Movement variability is an inherent feature for all stages of motor learning. Attainment of motor success is a dynamic process for acquiring movement solutions and minimization of movement variability (Fetters, 2010). Increase in the degree of noise relative to motor command during motor learning could impedes information detection, adding to uncer‐tainty and learning difficulty for increase in cognitive and perceptual loads (Newell, Slifkin, 2000). However, many recent work on stochastic resonance show that noise at very low level may facilitate task perform‐ance. Through an adequate low‐level mechanical noise, that may cause small or random receptor potential fluctuations, added to a normally subthreshold, and makes the stimulus being detected (Magalhaes, Kohn, 2011; McDonnell, Albert, 2009). It is of theoretical interests to explore the adaptive roles of unperceivable noise on motor learning, if the feedback system contains information irreverent to task goals. This experiment contrasted practice benefits between force‐matching guided by visual feedback with and without artificial noise. It was hypothesized unper‐ceivable noise was detrimental to motor skill advancement.

1 - Institute of Allied Health Science, National Cheng Kung University, Tainan, Taiwan

Practise related adaptation to motor output with additive low level noise 105

Methods

Twenty‐four healthy volunteers participated in this study and were randomized to two groups with equal subjects, the control and interfer‐ence groups. Upon hearing an auditory cue, the subject soon responded to conduct a force‐matching by coupling the force impulse generated by thumb‐finger precision to the trajectory of a visual target (Fig. 1(a), Fig. 2). The subjects in the control group (n=12) were provided with visual feedback of actual force response, whereas visual feedback of force impulse for subjects in the interference group (n=12) was artificially added with low‐level of noises (signal‐to‐noise ratio = 50). There were a total of twelve experimental trials with 1‐minute rest period between trials. Each trial allowed the subjects roughly eighteen force‐matching attempts. The first 3 experimental trials were baseline pre‐training trials, followed by 6 practice trials and 3 post‐practice trials (Fig. 1(b)). Figure 2 displays the experimental setups. Analyzed behavior parameters, in‐cluding normalized force error (NFE), variability of matching error, reac‐tion time (RT), time to force peak (Tpk), and force impulse variability were calculated. Two‐way repeated measures ANOVA (2 x 2) were used to compare group effect (control vs. interference) and phase effect (pre‐practice vs. post‐practice) on all behavior measures. Paired‐t test was used as post‐hoc. A significant level of .05 was adopted.

(a) (b)

Fig. 1 (a) ‐ Force‐matching cycle diagram (b)‐ experimental procedure


Fig. 2

Experimental settings

Results

Representative raw data and behavioral parameters are shown in figure 3. Normalized force errors (NFE) showed a significant decrease after practice for both groups (P < .001) without interaction between practice and group effects (F1,22=.101, P= 0.754; F1,22=1.473, P= 0.238) (Fig. 4(a)). Likewise, force‐matching practice led to a significant decrease in vari‐ability of force‐matching error for both the control and interference groups (P < .001) (Fig. 4 (b)). Although the interference group exhibited a shorter Tpk than the control group in the baseline and post‐practice conditions, yet practice did not alter RT (P= .694), and Tpk (P = .619)(Fig. 5(a), (b)). The variability of force impulses was also independent of group (F1,22=.254, P = .619) and practice (F1,22=.325, P = .574) effects.


Fig. 3

Representative raw data and behavioral parameters

Number of blocksPre 4 5 6 7 8 9 Post

Norm

alize

d fo

rce

erro

r (%

)

0

15

20

25

30

35ControlInterference

∗

∗

Number of blocksPre 4 5 6 7 8 9 Post

Varia

bilit

y of

mat

chin

g er

ror

0

4

6


∗

∗

(a) (b)

Fig. 4 The contrasts of (a) normalized force error (NFE) and (b) the variability

of matching error (* < .001).


Pre Post

Rea

ctio

n tim

e (m

s)

0

100

200

300


303.5(18.3)

304.7(17.8)

312.2(19.3)

321.9(15.5)

Pre Post

Tim

e to

pea

k fo

rce

x 10

2 (ms)

0

2

4

6

8

10

12

14 ControlInterference* *

1049.5 (48.2)

1059.3 (56.2)

802.8(69.3)

823.2(69.6)

(a) (b)

Fig. 5 The contrasts of (a) reaction time (RT) and (b) the time to peak force (Tpk)

(* < .001).

Discussion

Practice benefit at the behavioral level is not contextually influenced by motor output with additive low‐level noise. The shorter Tpk indicated a strategic change in generation of force impulse and effective execution to attain force‐matching success. Similar to the control group, the present study suggested that the interference group could overcome uncertainty and improve force‐matching quality through practice, underlying adap‐tive neural process to cope with visuomotor variability during skill ac‐quisition.

References

Fetters L., Perspective on variability in the development of human action. Physical Therapy, 90(12):1860‐1867, 2010.

McDonnell M.D., Abbott D. What is stochastic resonance? Definitions, miscopnceptions, debates and its relevance to biology. PLos Comput Biol, 5(5):e1000348, 2009.


Magalhaes F.H.,Kohn A.F. Vibratory noise to the fingertip enhances balance improvement associated with light touch. Exp Brain Res, 209:139‐151, 2011

Silfkin A.B., Newell K.M. Variability and noise in continuous force production. Journal of Motor Behavior, 32(2): 141‐150, 2000.

Newell K.M., Silfkin A.B. The nature of movement variability. In Motor Behavior and Human Skill: A Multidisciplinary Approach. J.P. Piek, eds. Champaign, IL: Human Kinetics, 1998. Pp. 141‐160.

110 Agnieszka Nawrat-Szołtysik et al.

THE OWN MODIFICATION OF EXERCISE BY MEHRSHEED SINAKI AND NORDIC

WALKING FOR SECONDARY PREVENTION IN OSTEOPOROSIS

Agnieszka Nawrat‐Szołtysik, Józef Opara, Cezary Kucio1

Introduction

Due to lack of effective drugs in osteoporosis it is absolutely required to conduct comprehensive treatment, i.e. medication, proper diet and adequate physical activity. There is convincing evidence that physical activity effectively slows bone loss in postmenopausal women in a dose‐dependent manner. Exercise programs may increase bone mineral den‐sity (Schmitt et al. 2009). In addition, strengthening of the paraspinal muscles may maintain not only good bone mineral density (BMD) but also reduce the risk of vertebral fractures (Pfeifer et al. 2004). The author of one of the first exercise programs for patients with osteoporosis was Mehrsheed Sinaki who recommended following exercises: stretching, strengthening the muscles of the back, abdomen and low back‐rump, and isometric exercises with resistance (Sinaki et al. 2010). Excellent form of physical activity in osteoporosis is a walk, during which the skeleton works due to the universal force of gravity. Its action, associated with variable muscle tone, improves the resorption and bone formation and strengthens the muscles of the spine. Today more and more common form of walking becomes a Nordic Walking, which forces the upper body involvement and activates those muscles which usually are passive during normal walk (Kocur 2006). 1 - The Jerzy Kukuczka Academy of Physical Education, Faculty of Physiotherapy,

Katowice, Poland

The own modification of exercise by mehrsheed sinaki and nordic walking for … 111

The aim of this study was to assess the impact of modified Mehrsheed Sinaki exercises and Nordic Walking on bone density, range of motion of the arms, pain, motor skills, balance and reducing the risk of falling.

Material and Methods

The study included 24 residents of three nursing homes, aged 65‐89 years (av. 77 years). The study involved women who were able to understand the nature of research, in whom the osteoporosis was confirmed by densitometric examination (T‐score ≤ ‐2.5). All were treated pharmacologically with anti‐osteoporosis drugs. The subjects were divided into two groups, 12 persons each. In group 1 (intervention) were women who participated in the annual exercise and Nordic Walking classes, mean age 78 years, mean T‐score ‐3.39. Group 2 (control) did not benefit from any form of motor activity, mean age 80 years, mean T‐score ‐3.58. Main outcome measure: bone densitometry, the assessment of pain using a numerical scale (NRS), the assessment of shoulder joint motion using a goniometer (raising arm by bending forward ‐ Wzp, raising the limb side by visiting ‐ Wzb), Timed ʺUp & Goʺ Test and Functional Reach Test (FR). According to Podsiadło and Richardson`s the Timed Up & Go test allows to assess a pace, changing position from sitting to standing and from standing to sitting position without assistance (Podsiadło, Richardson 1991). FR test measures how far the patient can lean forward with a stable pelvis and alloys while still remaining in contact with the ground. All tests were carried out twice: before and after the year of treatment.

Own modification of Meehrsheed`s Sinaki exercise program The original program of exercises by Sinaki was conducted in 5 starting positions: standing, sitting in a chair, supported kneeling, sitting on the floor and lying on the ground (Sinaki et al. 2010). To be available and safe for elderly women, we performed only in the sitting position on a chair. The program consists of a warm‐up, stretching exercises, exercises to strengthen abdominal and back muscles, isometric exercises, drills and exercises with resistance relaxants. Classes were held for a year, twice a week for 45 minutes each session.


Nordic Walking Nordic Walking Classes were conducted on the level ground, throughout two times a week (depending on weather conditions). The training session consisted of 5‐10 min. warm‐up of stretching and breathing exercises, 30 min. main part ‐ time (increased every three months for 5 min.), fade phase. Statistical analysis was performed based on the elements of descriptive statistics: arithmetic mean, standard deviation. In order to compare the obtained results we used Studentʹs t‐test, indicating significance at p<0.05.

Results

Our study showed an improvement or a tendency to improvement in the group 1 (intervention) in all parameters. Statistically significant results (p <0.05) were observed only in the Timed ʺUp & Goʺ test, FR, NRS scale, the Wzp. The execution time of ʺUp & Goʺ test in the intervention group was average 20.63 seconds before the study and has been shortened to the average of 17.58 seconds (p = 0.004). In the control group, the time needed to perform the Timed Up & Go test before study was an average of 28.85 seconds and after a year has been extended to 29.99 sec. The results of the Functional Reach test improved in the intervention group in 83.33% patients and only in 16.67% patients in the control group. Average distance stretching before treatment in group 1 was 22.67 cm and 20.25 cm in group 2, and after a year was an average 24.83 cm in group 1 (p = 0.03) and 18.42 cm in group 2. In 58% of patients in group 1 the reduction of pain has been observed on average by 2.5 percentage points in NRS scale (p = 0.04) while in group 2 the reduction of pain was observed in 41.67% of patients by an average of 1.4 points NRS. Improvements in mobility of shoulder in the intervention group was an average of 2.5 degrees (Wzp) (p = 0.02) and 1.25 degree (Wzb). In the control group decreased mobility of an average of 5 degrees (Wzp) and 0.84 degree (Wzb) has been observed. As for bone density: T‐score has been grown in both groups: an average of 0.27 in the intervention group, while in the control group 0.19.


Discussion

Several studies have shown that the introducing physical exercise in patients with osteoporosis increased the therapeutic effect compared to a group of patients in which there is no physical activity (Roch Radziszewski et al. 2004). The analyzed results showed walking to be ef‐fective on both BMD of the spine 1.31 [95%CI (‐0.03, 2.65)] and the hip 0.92 [95%CI (0.21, 1.64)]. Aerobic exercise was effective in increasing BMD of the wrist 1.22 [95%CI (0.71, 1.74)] (Bonaiuti et al. 2002). In our study results showed the persistence of T‐scores at the same level with a tendency to improve both in the intervention group (mean improvement 0.27 T‐score) and controls (mean improvement 0.19 T‐score). The researchers speculate that does not get too high an increase in bone density in the test group relatively to the control may be associated with an insufficient supply of calcium in the diet in subjects. The study of Owecki et al. showed a significant statistical improve of bone density only in the group that consumed large amounts of dairy products, including high physical activity (Owecki et al. 2002). In all patients who attended for 12 months for Nordic Walking and participated in a modified exercise program Sinaki statistical significant improvement in Get up & Go test has been observed. 66.67% of patients from group 1 obtained after one year score below 20 seconds. Physical activity in older ages can be recommended to improve mus‐cle strength and balance, to reduce the risk to fall and fractures (Karlsson et al. 2006). Mętel and co‐workers in their study showed an improvement in the Functional Reach test when carried out in women in the 65‐70 years age category, while in the control group it was observed the reduce of average distance to reach in both women and men (Mętel et al. 2010). In our study, similar results were obtained. In the FR test 58.33% of group 1 achieved the result of more than 25 cm, while only one person from Group 2. According to the guidelines given by the Jonsson and co‐workers, the result of FR test 15‐25 cm is twice higher risk of falling (Jonsson et al. 2002). Roghani et al. showed that the two exercise pro‐grams stimulate bone synthesis and decrease bone resorption in post‐menopausal women with osteoporosis, but that exercise while wearing


a weighted vest is better for improving balance (Roghani et al. 2012). Although we didn`t observed high increase in bone density, a number of other beneficial changes under the influence of physical activity in intervention group could be observed, thereby reducing the risk of falling.

Conclusions:

Own modification of Meehrsheed`s Sinaki exercise program together with Nordic Walking improved the range of motion in the joints of the arms, pain, motor skills, balance, and reduced the risk of falling in the intervention group consisted of 12 elderly women suffering from osteoporosis comparing to the control group.

References

Bonaiuti D., Shea B., Iovine R., Negrini S., Robinson V., Kemper H.C., Wells G., Tugwell P, Cranney A. Exercise for preventing and treating osteoporosis in postmenopausal women. Cochrane Database Syst. Rev. 3: CD000333, 2002

Jonsson E., Henriksson M., Hirschfeld H. Does the Functional Reach Test reflect stability limits in elderly people? J. Rehabil. Med. 35: 26‐30, 2002

Karlsson M.K., Nordqvist A., Karlsson C. Physical activity, muscle function, falls and fractures. Food Nutrition Res. 52: 1‐7, 2008

Kocur P., Wilk M. Nordic Walking – a new form of exercise in rehabilitation. Medical Rehabilitation 10(2): 1‐8, 2006

Mętel S., Milert A., Szczygieł A., Drozd A., Kwiatkowska A., Krzemińska M. The influence of 6‐months sensomotoric training on physical performance in the elderly with chronic back pain. Advances in Rehab. 24(3): 51‐65, 2010 (Polish)

Owecki M., Horst‐Sikorska W., Baszko‐Błaszyk D., Sowiński J. Influence of diet and physical activity on the course and therapy of osteoporosis. Pol. Merkur. Lekarski 13(78): 473‐476, 2002 (Polish)

Pfeifer M., Sinaki M., Geusens P., Boonen S., Preisinger E., Minne H.M. for theASBMR Working Group on Musculoskeletal Rehabilitation. Musculoskeletal Rehabilitation in Osteoporosis: A Review. J. Bone Mineral Res. 19(8): 1208–1214, 2004


Podsiadło D., Richardson S. The Timed “Up & Go”: A test of basic functional mobility for elderly persons. J. Am. Geriatr. Soc. 39: 142‐148, 1991

Roch Radziszewski K., Nicpoń K., Marszałek A. Comparative evaluation of the effectiveness of physical and pharmacological treatments for osteoporosis. Fizjoter. Pol. 4(3): 218‐225, 2004 (Polish)

Roghani T., Torkaman G., Movasseghe S., Hedayati M., Goosheh B., Bayat N. Effects of short‐term aerobic exercise with and without external loading on bone metabolism and balance in postmenopausal women with osteoporosis. Rheumatol. Int. Mar 24, 2012 [Epub ahead of print]

Schmitt N.M., Schmitt J., Dören M. The role of physical activity in the prevention of osteoporosis in postmenopausal women ‐ An update. Maturitas 20; 63(1): 34‐38, 2009

Sinaki M., Pfeifer M., Preisinger E., Itoi E., Rizzoli R., Boonen S., Geusens P., Minne H.W. The role of exercise in the treatment of osteoporosis. Curr. Osteoporos Rep. 8(3): 138‐144, 2010

116 Karina Nowak et al.

EMG SIGNAL ANALYSIS THE MVC TEST BEFORE AND AFTER FUNCTIONAL TESTING

IN PATIENTS WITH GONARTHROSIS

Karina Nowak1, Grzegorz Sobota1, Bogdan Bacik1 , Grzegorz Hajduk2, Damian Kusz2

Introduction

Muscle work performed is closely interwoven with the phenomenon of fatigue, the mechanism is still under investigated. To assess the fatigue and musculoskeletal parameters can be used surface electromyography, which is a non‐invasive method involving the registration of bioelectrical activity of muscles (Minns 2005). Under the influence of muscle fatigue changes are visible in the record electromyogram by changing the pa‐rameters of the EMG signal. This process increases the amplitude and shift towards lower frequencies of the EMG signal. The resulting local muscle fatigue is one of the emerging problems in the analysis of bio‐electrical signals that can affect test results (Błaszczyk 2004). The study was therefore an attempt to assess changes in the parameters of the signal spectrum quadriceps femoris muscle bioelectrical, recorded during the so‐called MVC test before and after a series of standardized tests assessing the functional status of the patient. The research questions was:

1 - The Jerzy Kukuczka Academy of Physical Education, Department of Motor Human

Behaviour, Institute Biomechanics, Katowice, Poland 2 - Medical University of Silesia Katowice, Department and Clinic of Orthopaedic and

Traumatology, Katowice, Poland

EMG signal analysis the MVC test before and after functional testing in … 117

1. Are there changes in mean and median frequency bioelectrical signal quadriceps femoris muscle MVC recorded during the test before and after a series of functional tests?

2. Is the observed changes may affect the further analysis and interpre‐tation of the standard bioelectrical signal?

Methods

A prospective study of 22 patients hospitalized in the Independent Public Clinical Hospital, Department of Orthopaedics and Traumatology Clinic Locomotor Medical University of Silesia in Katowice. The research project has been approved by the Bioethics Committee of the Medical University of Silesia (L.dz.KNW/0022/KB1/145/09). Patients with os‐teoarthritis of the knee were enrolled for a total knee surgery. After initial screening for further studies joined 20 patients (Tab. 1). The study was carried out before surgery. For measuring device used MyoTrace400 (Noraxon, USA) recording bioelectric signals of the three heads of quadri‐ceps femoris muscle during MVC performed the test twice, before and af‐ter a series of functional tests.

Table 1

Characteristics of the research group Age [years] Height [m] Weight [kg]

Sex N

X SD X SD X SD Female 13 67.4 6.33 1.61 0.079 85.8 13.20 Male 7 68.8 7.52 1.69 0.092 89.7 15.55 Together 20 67.8 6.54 1.63 0.091 87.0 13.64 X – mean, SD – standard deviation Electrodes provided on the body surface tested in accordance with the general recommendations by SENIAM on the right leg (RT) and left (LT) signals were recorded from the following muscles: Rectus Femoris (RF), Vastus Medialis Obliguss (VMO) and Vastus Lateralis Obliguss (VLO). Pa‐tients performed functional tests, which are utilitarian function, during which the muscle function was studied. The duration of the trial was 3 min. 40 seconds. to 4 min. 50sek. Tests were performed sequentially, ʺsit‐


ting down and getting up from a chairʺ, ʺgoing up and down the stairsʺ, ʺstraightening and bending the kneeʺ and ʺwalking on a treadmillʺ at 1km/h. Number of test samples ranged from one to several repetitions (at least once, max. three times). The difference resulted from the exercise to take the state and the involvement of the patient. Trying maximum voli‐tional isometric contraction (MVC) took about 7‐8 seconds for each test. The subject accepted a sitting position, bending your hips at 90 degrees with the trunk stabilized chair. Then, in the sagittal plane flexural posi‐tion knees to an angle of 65 degrees you tried to overcome the resistance lever rigidly attached to a chair while trying to straighten the knee. For each signal an average maximum voltage bioelectric in 1 second window was taking and performed FFT analysis in whole test period. To evaluate the statistical differences between the first and second study used a pack‐age Statistica (Statsoft, Poland).

Results

Set the parameters of descriptive statistics, and because of the lack of normal distribution was performed nonparametric test statistics for measuring the system dependent ‐ Wilocoxona test.

Table 2

Wilocoxon test results for comparison of the mean frequency bioelectrical signal before and after the functional tests. Bold results significant on the level p<0.05 Pair of variables N T Z p

MeanF1_LVLO & MeanF2_LVLO 20 44,50 2,03 0,042 MedF1_LVMO & MedF2_LVMO 20 33,50 2,66 0,007 MeanF1_LVMO & MeanF2_LVMO 20 34,00 2,65 0,008


Fig. 1

The average value of the mean frequency [Hertz] for selected heads of the quadriceps femoris muscle (VLO – Vastus Lateralis Obliguss, RF ‐ Rectus Femoris, VMO ‐ Vastus Medialis Obliguss) on the right (R) and left (L)

before and after the functional tests

Fig. 2 The value of the median frequency [Hertz] for selected heads of the quadriceps muscle (VLO – Vastus Lateralis Obliguss, RF ‐ Rectus Femoris, VMO ‐ Vastus Medialis Obliguss) on the right (R) and left (L) before and after the functional

tests


Discussion

Understanding the processes involved in the emerging muscle fatigue using surface electromyography in the clinical assessment of patients is important. Provides direct insight into the work the muscle, facilitates the measurement of the activity and allows you to identify weak muscles (Benedetti et al. 2003). Undertaking a number of studies on the analysis and interpretation of bioelectrical signals demonstrates the need for stan‐dardization of test methods MVC. There is no predetermined time dura‐tion as the isometric contraction and its range during the research varies from 3 seconds to 8 seconds. Imposed on the duration of isometric con‐traction often do not take into account the individual characteristics of the respondents, it may be too short or too long. The disadvantage test MVC is not sure that the person actually trying to test 100% of their cur‐rent capabilities to perform isometric tension (Callaghan et al. 2009). Conducted studies show the changes of frequency and amplitude of bio‐electrical signal quadriceps MVC recorded during the test before and af‐ter a series of functional tests as compared to the prior tests, which may be indicative of muscle fatigue. Bioelectric signals from the test MVC is used to normalize the amplitude of electromyography recording, so the difference could be as high as several percent, not because of increased muscle activity, but because of his fatigue and changes in amplitude and frequency characteristics (Nowak et al. 2010).

Conclusions

1. There are changes in the average frequency of the EMG signal measured during the test MVC before and after the functional tests. Changes are observed in two of the three muscles evaluated: Vastus Lateralis Obliguss (VLO) and Vastus Medialis Obliguss (VMO) on the left side.

2. The observed changes may affect the further analysis and interpretation of the standard bioelectrical signal changing its value by as much as ten percent. It is reasonable to control the degree of muscle fatigue especially during long or intensive research effort.


References

Benedetti M.G., Catani F., Bilotta T.W., Marcacci T.W. Mariani E. , Giannini S.: Muscle activation pattern and gait biomechanics after total knee replacement Clinical Biomechanics 18: 871–876, 2003

Błaszczyk J.: Biomechanika kliniczna. Wydawnictwo Lekarskie PZWL Warszawa, 2004. Pp. 171‐191

Callaghan MJ., McCarthy CJ., Oldham JA.: The reliability of surface electromyography to assess quadriceps fatigue during multi joint tasks in healthy and painful knees. J Electromyogr Kinesiol. 19(1): 172‐80, 2009

Minns R.J.: The role of gait analysis in the management of the knee. Knee 12: 157‐162, 2005

Nowak K., Sobota G., Hajduk G., Bacik B., Kusz D.: The effect of fatigue on the value of bioelectrical signal MVC test gonartrozą patients. Current problems of Biomechanics 4: 143‐146, 2010

122 Marzena Paruzel-Dyja et al.

FUNCTIONAL MOVEMENT PATTERNS AND LIMITATIONS VS. PHYSICAL FITNESS

PREPARATION OF 18 YEAR OLD FOOTBALLERS

Marzena Paruzel – Dyja1, Leszek Dyja2, Janusz Iskra1, Jarosław Gasilewski3

Introduction

Motor fitness tests i.e. sprints or endurance tests are an important part of footballer’s motor fitness preparation control. There are many possi‐bilities of testing speed and endurance. The most important factors of choosing adequate methods are reliability and possibility to use them in each period of sport preparation without interference in the training process. Functional Movement Screen is a grading system created a decade ago in the USA by Cook G., to asses functional movement patterns, limi‐tations and asymmetries or even the risk of injuries. The tool is most popular in the USA, recently used also in Poland. The aim of this pilot study was to find correlations between the results in FMS system and other components of motor fitness preparation (i.e. speed of running and endurance test PWC 170), years of sport practice as well as a number of previous injuries in the group of 18‐year old footballers.

Methods 1 - The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland 2 - Polish Football National Team, Poland 3 - State Higher Vocational School in Racibórz, Poland

Functional movement patterns and limitations vs. physical fitness preparation … 123

The research group consisted of Polish footballers from teams playing in the highest youth league (n=48, age 17.59 ± 1.03). Body mass and height measurement were done with standard procedures and devices. To assess the level of motor fitness of the subjects, the authors used several tests. Among them there was Functional Movement Screen, which reliability has been established across multiple studies, examining this 21 point screen (Frohm et al. 2011; Onate et al. 2012; Teyhen et al. 2012). These studies have revealed high reliability between raters who have been trained in the Functional Movement Screen, even with mini‐mal training (4 hours) using videotaped as well as real time scoring methods. In this study there were two independent raters (certified FMS experts) and the screening process was recorded by a camera recorder as well. During this test each subject performed seven exercises (deep squat, hurdle step, shoulders’ mobility, in‐line lunge, active straight leg rise, push up, rotary stability). The score varied from 3 to 0 points, where 3 was a good movement pattern and 0 when pain occurred. The result was a sum of grades gained in seven exercises (maximum 21 points). Another test was a 30 m sprint run from a standing pose, done after a warm‐up. A set of three point photocells was used to measure the time of 5 m, 10 m, 20 m and 30 m. Subjects run the distance of 30 m three times with an approximate 6 minute rest. On the basis of the same tests the ex‐plosive power of lower limbs (or one may call it the ability to perform a speedy start) was calculated as follows: result of 10 m divided into two minus first 5 m. Best trials were used for the analysis. The next test was a PWC 170 test, performed on cycle ergometer of Daum Electronic 8008 TRS and applied to find physical capacity of the footballers. The trial lasted 8 to 12 minutes, depending on the pulse of the subject, plus 7 minutes of rest. The pulse rate of each person was taken by a Polar RS300X training computer, 5 minutes before (sitting), during and 7 minutes after the test. Additionally the subjects were asked to fill in a short survey about their sport experience, number and type of previous injuries, hours per week spent at training sessions as well as recreation activities.


Gathered data were analyzed with the use of basic statistical methods (mean, SD, minimum and maximum values) as well as the analysis of variance (ANOVA) – Statistica program.

Results

General statistics (mean ± SD, min and max values) of the research group are shown in table 1. The age of the footballers varied from 16 to 19 but at the same time the minimal training experience was 3 years and maximal 16 years. They spent usually about 12 hours a week at football training sessions, while some of them claimed it was even 25 hours a week. The average number of points in FMS test was almost 18 (17.7) – the lowest was 15 and the highest score was 20. The FMS exercises were: 1st – deep squat, 2nd ‐ hurdle step, 3rd ‐ in‐line lunge, 4th ‐ shoulders’ mo‐bility, 5th ‐ active leg rise (back lie), 6th ‐ push up and 7th – rotary stability (in “on all fours” position). Best results were achieved in the fifth exer‐cises, what means that generally the young competitors had good flexi‐bility of hamstring muscles. The lowest average result was in the last movement (2.08), but at the same time there wasn’t any one‐point result, what means that there were mostly two point grades. In all seven FMS tests there was no case of a zero grade, what means that no one have felt pain during the exercises. As far as an indirect test of physical capacity PWC 170 is concerned, the average result of the subjects was 3.23 ± 0.45 watt/kg and in PWC 130 it was 1.74 ± 0.34 watt/kg. In speed tests (5 m and 30 m) the footballers achieved accordingly 1.028 ± 0.041 s and 4.216 ± 0.127 s. The analysis of variance was done after dividing the research group into two subgroups (A and B), according to sum of points in FMS results. The results are shown in tab. 2. The most significant correlations were found between the final score in FMS and speed as well as explosive power of lower limbs (p≤0.04; F=4.62 and F=4.64). There was also a significant relationship between the FMS score and physical capacity measured during an indirect test PWC 170 (F=4.26; p≤0.05). The average hours spent at training sessions in


a week time have positively influenced the footballers’ functional move‐ment patterns (p≤ 0.05; F=4.10). Additionally I was noticed that among the subtests of FMS, there were some exercises more significantly influencing the final score in FMS. Among them there were: in‐line lunge (p ≤0.001), shoulders mobility (p ≤ 0.01), hurdle step (p≤ 0.01), push‐up (p ≤ 0.01) and deep squat (p ≤ 0.05; Tab. 2).

Table 1

General characteristics of research group No Variable Unit Mean Min Max SD

1. Age years 17.59 16.00 19.00 1.03 2. Body height cm 177.95 166.00 191.00 6.10 3. Body mass kg 69.43 53.00 85.40 6.99 4. Training experience years 8.14 3.00 16.00 2.84 5. Training h/week hours 12.08 7.5 25.00 3.22 6. Recreation h/week hours 2.20 0 6.00 1.73 7. Injuries no 1.22 0 4 1.02 8. FMS (1) points 2.22 1 3 0.48 9. FMS (2) points 2.61 2 3 0.50 10. FMS (3) points 2.64 1 3 0.55 11. FMS (4) points 2.79 2 3 0.42 12. FMS (5) points 2.90 2 3 0.32 13. FMS (6) points 2.53 1 3 0.65 14. FMS (7) points 2.08 2 3 0.28 15. FMS (sum) points 17.74 15.00 20.00 1.58 16. PWC 170 watt/kg 3.23 2.50 4.28 0.45 17. PWC 130 watt/kg 1.74 0.90 2.53 0.34 18. 5 m s 1.028 0.930 1.115 0.041 19 explosive power s 0.297 0.202 0.376 0.041 20. 30 m s 4.216 3.968 4.446 0.127


Table 2

Results of the analysis of variance (ANOVA) in the group of 18‐year old footballers

No Variable Group A Group B ANOVA F p

1. Age 17.47 17.67 0.87 0.36 2. Body height 178.47 177.41 0.86 0.43 3. Body mass 68.11 71.66 0.23 0.80 4. Training

experience 7.36 8.77 2.41 0.14

5. Training h/week 13.21 11.16 4.10 0.05* 6. Recreation h/week 2.60 1.87 1.54 0.23 7. Injuries 1.06 1.34 0.68 0.42 8. FMS (1) 2.42 2.05 6.35 0.02* 9. FMS (2) 2.89 2.39 12.66 0.002** 10. FMS (3) 2.95 2.39 13.45 0.001*** 11. FMS (4) 3.0 2.62 9.92 0.004** 12. FMS (5) 3.0 2.88 3.79 0.06 13. FMS (6) 2.83 2.29 7.67 0.01** 14. FMS (7) 2.12 2.05 0.62 0.44 15. PWC 170 3.45 3.02 4.26 0.05* 16. PWC 130 1.86 1.62 0.97 0.34 17. 5 m 1.015 1.047 4.62 0.04* 18 explosive power 0.28 0.311 4.64 0.04* 19. 30 m 4.194 4.234 0.97 0.34 * p≤ 0.05, ** p ≤ 0.01, *** p≤ 0.00

Discussion

There were some former studies, that have utilized screening statistics to establish the cut off score of 14 points as being appropriate to identify individuals who are at risk of an injury (Kiesel et al., 2007, O’Connor et al., 2011). Odds ratio in these studies have ranged between 2.3‐8.3 in pro‐fessional football players, basic training soldiers and firefighters in training. Chorba et al. (2010) have also found similar correlations in col‐lege athletes, however the positive likelihood ratio was not significant. Other research (among firefighters) examining the cut score of the FMS


has suggested that failure on the FMS, operationally defined as below 16 points in this study, was strongly associated with an injury in the previ‐ous year (Peate et al., 2007). On the contrary, Schneiders et al. (2011) did not observe any significant difference in FMS scores of active adults in subjects with a prior injury. The subjects of our study were healthy competitors, with on average 1.22 injuries in previous years, however there was no correlation found between the result in FMS and former injuries, like in Schneiders’ studies. Maybe it resulted from the fact that the average result in FMS in our re‐search group proved to be higher in comparison to the US studies. The relations found between PWC 170 test as well as speed test and the results of FMS are quite puzzling. Additionally we found correlation between FMS results and time spent on training sessions per week. We may conclude that probably the competitors attending more (or longer) training sessions, had more possibilities to be generally well prepared. According to Gray Cook (2004) core stability training, balanced body and lack of asymmetries in muscle strength is essential in elite athletes. It enables the athletes to train efficiently, avoid injuries and increase their sport performance. The relationship between the level of functional movement patterns and other aspects of strength and conditioning of young footballers found in this study shows it may be true in this case as well. However, broader study is essential to learn if the results may refer to the whole population.

References

Cook G. 2004. Athletic body in balance – optimal movement skills and conditioning for performance. Human Kinetics, United States of America

Chorba RS, Chorba DJ, Bouillon LE, Overmyer CA, Landis JA. 2010. Use of a functional movement screening tool to determine injury risk in female collegiate athletes. N. Am. J. Sports Phys. Ther. Jun;5(2):47‐54.

Frohm A, Heijne A, Kowalski J, Svensson P, Myklebust G. 2012. A nine‐test screening battery for athletes: a reliability study. Scand. J. Med. Sci. Sports. Jun; 22 (3):306‐15

128 Marzena Paruzel-Dyja et al. Kiesel K, Plisky PJ, Voight M. 2007. Can serious injury in professional football be

predicted by a preseason Functional Movement Screen? N. Am. J. Sports Phys. Ther.; 2 (3): 76‐81.

OʹConnor FG, Deuster PA, Davis J, Pappas CG, Knapik JJ. 2011. Functional movement screening: predicting injuries in officer candidates. Med. Sci. Sports Exerc. Dec;43(12):2224‐30.

Onate JA, Dewey T, Kollock RO, Thomas KS, Van Lunen BL, DeMaio M, Ringleb SI. 2012. Real‐time intersession and interrater reliability of the functional movement screen. J. Strength Cond. Res. Feb; 26(2):408‐15.

Parchmann CJ, McBride JM. Relationship between functional movement screen and athletic performance. 2011. J. Strength Cond. Res. Dec; 25(12):3378‐8.

Peate WF. Bates G. Lunda K. Francis S. Bellamy K. 2007. Core strength: A new model for injury prediction and prevention. J.Occup. Med. and Tox. 2

Schneiders AG, Davidsson A, Hörman E, Sullivan SJ. 2011. Functional movement screen normative values in a young, active population. Int. J. Sports Phys. Ther. 6 (2):75‐82.

Teyhen DS, Shaffer SW, Lorenson CL, Halfpap JP, Donofry DF, Walker MJ, Dugan JL, Childs JD. 2012. The Functional Movement Screen: a reliability study. J. Orthop. Sports Phys. Ther. Jun;42(6):530‐40

Sports results in weightlifting and their determinants 129

SPORTS RESULTS IN WEIGHTLIFTING AND THEIR DETERMINANTS

Anna Pilis, Krzysztof Mizera, Cezary Michalski, Jakub Jelonek, Łukasz Grela, Karol Pilis1

Introduction

A great deal of emphasis in sport science is placed on the effect of body size on its physiological function. It has been demonstrated that the relationships between body size (independent variable) and physiologi‐cal variables, including weightlifting sports results (dependent variables) were intensively studied. None of the models proposed by Lietzke (5), Vorobjev (8) and Croucher et al. (2) for world records achieved in indi‐vidual weight classes was adopted. Sinclair (7) was the first researcher to isolate log‐linear allometric equation as a special model that described exercise abilities of weight‐lifters with respect to their body mass. He determined men’s world re‐cords in each weight class as a function of body mass. However, the sports results were not linearly adjusted to body mass, whereas the equation of the second degree that described this relationship caused a disturbance in the allometric model. Sinclair avoided the homogeneity of body structure and composition in the heaviest weight class by using the method of least squares for calculations (7). Moreover, Sinclair devel‐oped a table of coefficients which allow for comparison of exercise abilities of weightlifters across weight categories. Hester et al. (4) argued that the model proposed by Sinclair is best adjusted to men as it points to the lowest coefficient of variation, although it gives some preference to athletes with body mass >120 kg. Hence, Hester et al. (4) concluded that

1 - University of Czestochowa, Institute of Physical Educations and Tourism, Poland

130 Anna Pilis et al.

the ultimate formula of exercise abilities of weightlifters depending on body composition and mass was not developed yet. The goal of the present study is to provide answer to the question of how the results in snatch, clean and jerk and both lifts described by Sin‐clair score, absolute values (kg) and the values relative to body mass (kg/kg) and lean body mass (kg/kgLBM) change depending on body height, mass and composition in weightlifting athletes.

Material and Methods

The study analyzed 10 non‐obese athletes (age – 23,2 ± 2,86 years; height – 174,5 ± 7,78 cm; weight – 93,05 ± 15,87 kg, BMI – 30,79 ± 3,15 kg/m2; fat content – 13,87 ± 3,15 %, mean sports results in both lifts – 368,10 ± 45,26 kg) who were members of the national Polish weightlifting team whose sports results obtained in snatch, clean and jerk and both lifts as well as the corresponding Sinclair score were recorded during the Na‐tional Polish Championships. Before the competition, the age and body height of the subjects was recorded and some somatic measurements were made by means of a device body composition analyzer ‐ Tanita TGF 300 (Japan). The device was used for determination of: body mass, BMI index, fat mass expressed in percentage and kg, water content and lean body mass (LBM) expressed in kg. These data were used for calculation of water content in the body and LBM expressed in percentage and the weight/height ratio. After calculating the arithmetic means and standard deviations for individual variables between sports results (snatch, clean and jerk and both lifts) expressed in absolute terms (kg) and relative terms with respect to body mass (kg/kg) and LBM (kg/kgLBM) and the above somatic variables, the authors calculated Pearson’s linear correla‐tion coefficients. The statistical significance was set at p<0.05.

Results

Linear correlation coefficients contained in table 1 exhibit a statisti‐cally significant positive relationship between the results achieved in snatch, clean and jerk and both lifts expressed in absolute terms and the




following somatic variables: body mass, body height, fat content, water content (kg), BMI and weight/height ratio. Furthermore, the percentage values of water content and LBM exhibit statistically significant negative correlations with the results obtained for snatch, clean and jerk and both lifts. The magnitude of results expressed in kg, kg/kg, kg/kgLBM and Sinclair score units in both lifts correlated positively with BMI and weight/height ratio, as well as negatively with percentage body water content (p<0.05) – table 1, 2, 3. Analysis of table 2 reveals that the results of snatch, clean and jerk and both lifts expressed relative to body mass correlated significantly and negatively with such somatic variables as body mass, body height, fat content (kg), water content (kg), lean body mass (kg) and body weight/height ratio. Analysis of table 3 revealed only significant negative correlations of results in snatch, clean and jerk and both lifts expressed as relative to LBM, with body water content expressed in kg and with body weight/height ratio. As results from the data contained in Table 4, Sin‐clair score significantly correlates with absolute results in snatch, clean and jerk and both lifts, whereas it did not exhibit significant correlations with sports results expressed relative to body mass and LBM.

Discussion

Analysis of the data above revealed that body mass and mass‐related parameters (body height, percentage fat expressed in percentage and kg, LBM and water content expressed in kg, BMI and weight/height ratio correlate positively and linearly with absolute results in snatch, clean and jerk and both lifts and negatively with LBM and water content expressed in percentage terms. Part of these relationships was discussed in previous study by Pilis et al. (6). This linear relationship of men’s world record to‐tals in weightlifting with respect to body mass was proposed by Vorobjev (8). This model was justified in times when the heaviest weight class in‐cluded only the athletes with body mass over 90 kg, since the body mass in all weight classes was more homogenous. Vorobjev’s reasoning was consistent with previous log‐linear model of world record relationships


in weightlifting triple event (replaced later with the double event) and body mass in individual weight classes (including the heaviest > 90 kg), which was proposed by Lietzke (5). Introduction of additional weight classes in the following years, i.e. 52, 100, 100 and > 110 kg disturbed the homogeneity of body composition, whereas log‐log straight linear for the relationship discussed showed lower slope which amounted to 0.58 for both events, which was regarded to be a manifestation of the disappear‐ance of linearity in terms of the discussed relationship. It is in these heavier weight classes where the mass of non‐twitch tissues starts to rise rapidly, resulting in a loss of the linear relationship, also with respect to other mass‐related parameters. Nowadays, there are three weight classes with body mass over 90 kg i.e. 94, 105 and > 105 kg, which causes that the linear model of the dependency of the results in snatch, clean and jerk and both lifts on body mass can be unfit. It is estimated that the threshold of body mass at which body composition homogeneity starts to disap‐pear is 83 kg in men (3). Although 4 out of 10 athletes in our study had body mass < 90 kg whereas 3 of them were < 83 kg, the Pearson’s coeffi‐cients of linear correlation of body mass and its derivatives with the re‐sults in snatch, clean and jerk and both lifts remained at the level of p<0.001 (Tab. 1). It is likely that the lower sport skill level presented by athletes in this study compared to world record holders allowed for maintaining linear relationships. Another cause of these linear relation‐ships of sport skill results and body mass with its derivatives can be that the homogeneity threshold for body composition in weightlifters today has been shifted towards higher values of body mass. The shift in homo‐geneity of body composition in the group studied can be explained by e.g. low content of fat tissue, which was on average 13.9 ± 3.2 %. Apart from the determination of the threshold of homogeneity of body mass of different men’s weight classes below 83 kg, Ford et al. (3) also deter‐mined an optimum threshold body height at which the best sports results can be obtained (183 cm), which was exceeded by only 2 athletes in the group studied. Both somatic determinants of sports results i.e. body mass and weight correlated with the results in snatch, clean and jerk and both lifts: body mass at the level of p<0.001, whereas body height for snatch and both lifts at p<0.01 and for jerk at p<0.05. If both somatic variables


were combined into one index, i.e. body weight/height, it was observed that it exhibited the strongest correlation of all somatic variables with ab‐solute sports results (p<0,001) and Sinclair score for both lifts (p<0.05). It results that the group of weightlifters presented in this study was homo‐geneous in terms of body composition. Sinclair score calculated in the present study showed high correlation coefficients with results of snatch, clean and jerk and both lifts (p<0.001) (Tab. 4), although these points, compared to world results in weightlift‐ing remains curvilinear (1, 7). A distinct flattening of this relationship for the years 2009‐2012 for men is observed over the body weight of 105 kg, whereas mean body mass of weightlifters in the present study fluctuated around 93 kg. Obtaining these high coefficients of linear correlation of sports results with body mass in the present study suggests a homogene‐ity of body composition in the weightlifters studied. The Sinclair score, which describes the sports results, did not correlate with the most of so‐matic variables, which suggests that, when developing Sinclair’s coeffi‐cients, variables other than mass were also considered (7). In somatic variables, the only significant correlation coefficients for Sinclair score that expressed the results in clean and jerk and both lifts were positive for BMI and negative for percentage body water. Furthermore, as mentioned above, the weight/height ratio correlated positively with Sinclair’s index calculated for clean and jerk. However, when sports results are expressed in relative units to body mass or LBM, their correlations with body mass and other somatic vari‐ables and their derivatives are negative, less significant or insignificant (Tab. 2 and 3), which was partially demonstrated before (6). The relative results in weightlifting, particularly in respect to body mass, are easy to be calculated and therefore used in practice by many coaches. However, they contain wrong indications with respect to heavier weight categories where homogeneity of body composition starts to disappear. Therefore, Sinclair score was used in order to provide opportunities for comparison of sports results obtained in different weight categories, which consid‐erably compensates for the lack of homogeneity of body composition of the heaviest athletes (over 105 kg), which is not compensated when using the relative results in snatch, clean and jerk and both lifts expressed in


kg/kg units. This means that comparison of sport skill level across differ‐ent weight categories by relative sports results calculated with respect to body mass with exclusion of the heaviest athletes is possible. The signifi‐cant correlation coefficients present in our study relative to body mass with somatic variables, with mean body mass of 93.05 ± 15.87 kg and con‐siderable standard deviation suggests that the threshold of homogeneity of body composition might reach the heavier weight classes, including the category of 105 kg. It should be expected that body composition above this threshold will not be homogeneous and using these sports re‐sults for intergroup comparison will be incorrect. It seems that it is not only the elevated value of fatty tissue that im‐pacts on changes in homogeneity of body composition in the group of weightlifters studied. This also results from the fact that when calculating the relationships of sports results expressed as relative to LBM with so‐matic variables, these correlations disappeared in the most of cases, whereas significant relationships with these sports results were main‐tained for body water content expressed in kg and weight/height ratio. Therefore, it should be expected that apart from fatty tissue, other non‐twitch tissues might cause changes in homogeneity of body composition, with particular focus on heavier weight categories. The usefulness of ex‐pressing sports results in relative terms (i.e. kg/kg LBM) should be viewed as little significant for evaluation of training adaptations in weightlifter’s body. When using relative sports results, the Sinclair score does not play a significant role in evaluation of sport skill level in study participants since the absolute sports results described by this score did not correlate with relative achievements and the most of somatic variables.

Conclusions

1. The highest predictive value for the evaluation of sport skill level in weightlifters, expressed by linear correlation, is observed in somatic variables when the results in snatch, clean and jerk and both lifts are expressed in absolute values. Lower predictive value can be found if these values are expressed as relative to body mass and to LBM.


2. When evaluating the sport skill level in weightlifting, body weight/height ratio exhibits the highest effect among all the somatic variables, regardless whether sports results are expressed in absolute terms or relative terms with respect to body mass or LBM.

3. Sinclair score provides a good description of sport skill level ex‐pressed in absolute results in snatch, clean and jerk and both lifts, even in the case of calculating linear correlations.

4. The relative results in weightlifting expressed as relative to body mass and LBM do not show linear correlations with absolute results in snatch, clean and jerk and both lifts and the corresponding Sinclair score, although using them for comparison of sport skill level of the athletes from different weight categories is justified if body composi‐tion homogeneity is maintained.

References

Batterham A.M., George K.P. Allometric modeling does not determine a dimensionless power function ratio for maximal muscular function. J. Appl. Physiol. 83(6): 2158‐2166, 1997.

Croucher J.S. An analysis of world weightlifting records. Res. Q. Exerc. Sport 55: 285‐288, 1984.

Ford L.E., Detterline A.J., Ho K.K., Cao W. Gender‐ and height‐related limits of muscle strength in world weightlifting champions. J. Appl. Physiol. 89: 1061‐1064, 2000.

Hester D., Hunter G., Shuleva K., Kekes‐Sabo T. Review and evaluation of relative strength handicapping models. National Strength Conditioning Assoc. J. 12: 54‐57, 1990.

Lietzke M.H. Relation between weight‐lifting totals and body weight. Science 124: 486‐487, 1956.

Pilis W., Langfort J., Zarzeczny R., Zając A., Wojtyna J. Morphological and physiological characteristic of top weight lifters. Biology of Sport, 7(2): 113‐126, 1990.

Sinclair R.G. Normalizing the performances of athletes in Olympic weightlifting. Can. J. Appl. Sport Sci. 10: 94‐98, 1985.

Vorobjev A.W. Tjazelaja Atletika. Moscow: Fizkul’tura I Sport, 1981.

138 Krzysztof Przednowek et al.

THE USE OF SELECTED LINEAR MODELS IN PREDICTING THE RESULTS OF 400‐METRE HURDLES RACES

Krzysztof Przednowek1, Janusz Iskra2, Stanisław Cieszkowski1

Intrudaction

The use of linear multiple regression in predicting sports results was, inter alia, described in a study (Maszczyk, Zając, Ryguła 2011) in which models predicting the result of a javelin throw were presented. The model was constructed in such a way that it could be used to assist in the choice and selection of prospective javelin throwers. On the basis of a selected set of input variables, the distance of a javelin throw was pre‐dicted. Predicting sports results by using linear regression was also pre‐sented in another study (Przednowek, Wiktorowicz 2011). A ridge re‐gression model was used which predicted the results of a race walking competition following a set of direct pre‐race preparation (DPP) training. At the model input stage basic somatic features (height and weight) were given, as well as training loads on selected days of DPP, while the output model was the predicted result over a distance of 5 km. The use of such solutions supports and sometimes optimizes the selection of training loads. The following study presents the application of selected linear models realizing the task of predicting the results in 400‐metres hurdles races. Each of the selected models was described in terms of the error which it generates during the prediction of results. The main aim of this work is to verify the effectiveness of using selected forms of multiple lin‐ear regression to predict the results of 400 m hurdles races.

1 - University of Rzeszow, Department of Physical Education, Poland 2 - The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland

The use of selected linear models in predicting the results of 400-metre… 139

Material

The models were constructed using training data from 400 m hurdles athletes. The annual training cycle was divided into three equal periods: the general preparation period (from October to January), the special preparation period (from February to May) and the pre‐competition pe‐riod (from June to September). The analysis involved 22 groups of train‐ing means (Iskra 2001). Additionally, the results of six performance tests done at every stage of a yearly cycle and somatic feature of the competi‐tors (Body Mass Index) were taken into account. There were 96 patterns in total. The description of the variables used to construct the models is shown in table 1. It is worth mentioning that competitors were highly trained. Taking into account the difficulty in conducting a test at 400 m hurdles in each of the analyzed stages of training as a valid outcome the result achieved at the distance of 500 m was used. In numerous scientific studies (Iskra 2001) it was demonstrated that results obtained from 500 m sprint are strongly correlated to results achieved over 400 m hurdles races.

Method

The linear multiple regression model was used to predict the results of all the 500 m races. Apart from the classic model calculated by the method of the smallest squares, shrinkage regression was implemented. Shrinkage models include inter alia: ridge regression (Hoerl, Kennard 1970) and Lasso regression (Tibshirani 1996). Characteristically these models use a weighting system during calculating which is a type of penalty linked to the size of the regression coefficients. Shrinkage regres‐sion is used to select variables in models with numerous input sets. As all the compared models had different characteristics, uniform quality crite‐ria were applied to all the calculated regressions. Given this, there is al‐ways the possibility of the models compared and marked generating very small error. To qualify this, a cross‐test cross validation test was used. This is a qualitative evaluation method, in which data is divided into two subsets: teaching and testing (validation) subsets. In the study


Table 1

Description of the variables used to construct the models Variable Description Variable Description y Expected 500 m sprint (s) x17 Aerobic endurance (m) x1 Age (years) x18 Strength endurance i (m) x2 Body mass index x19 Strength endurance ii (n)

x3 30 m sprint (s) x20 General strength of lower limbs (kg)

x4 Five‐jump‐test (m) x21 Directed strength of lower limbs (kg)

x5 Overhead shot put throw (m) x22

Specific strength of lower limbs (kg)

x6 Squat (kg) x23 Trunk strength (amount) x7 Olympic clean (kg) x24 Upper body strength (kg)

x8 Current 500 m sprint (s) x25 Explosive strength of lower limbs (amount)

x9 Period OPO* x26 Explosive strength of upper limbs (amount)

x10 Period OPS* x27 Technical exercises – walking pace (min)

x11 Maximal speed (m) x28 Technical exercises running pace (min)

x12 Technical speed (m) x29 Runs over 1‐3 hurdles (amount)

x13 Technical and speed exercises (m)

x30 Runs over 4‐7 hurdles(amount)

x14 Speed endurance (m) x31 Runs over 8‐12 hurdles (amount)

x15 Specific hurdle endurance (m)

x32 Hurdle runs in varied rhythm (amount)

x16 Pace runs (m) *‐in accordance with the rule of introducing a qualitative variable of a “training type” with the value of general preparation period, specific preparation period and competitive period (pre‐race preparation period) was replaced with two variables x9 and x10 holding the value of 1 or 0. “leave‐one‐out” cross‐validation was used, which was based on separat‐ing teaching subsets from the data set and where it meant a number of all models (trainings). Each subset was constructed by eliminating one pat‐tern from the data set, which then became an element of the testing set (Koronacki, Ćwik 2005). The measure of error is the mean square error:


= 96 (patterns), – real value, – generated value,

Results

Classic multiple regression model First, a classic model of multiple regression was calculated by the least squares method. The model underwent cross‐validation in order to cal‐culate the generated error. The error value ( ) was ranked at 1.34 s2. The model took into account all variables as all the coefficients differed from 0. Table 2 presents the coefficients of the ordinary least squares re‐gression.

Ridge regression model In ridge regression a parameter λ is selected, which provides an addi‐tional penalty connected to the size of the coefficients of regression. In the case where λ = 0, then the ridge model is simplified to classic multiple regression. The study marked the correlation between the cross‐valida‐tion error of the parameter λ changing within the range from 0 to 40 with 1 steps (Fig. 1). According to calculations the smallest error generated for the model in which the parameter equals λ = 22. The cross‐validation error for the optimal ridge regression equals 0.71 s2. Analysis fig. 1. it is also noticed that in the initial phase of model optimization, together with the growth of the penalty parameter the prediction error suddenly de‐creases. Table 3 shows the values of the coefficients of the optimal ridge model and similarly to the previous case all the coefficients are different from 0.

Lasso regression model Another model in the shrinkage regression family is Lasso regression. While calculating this regression a parameter L1 is chosen having values from 0 to 1. Lasso regression with L1 = 1 parameter is simplified to classic multiple regression. A period of correlation was marked between the values L1 and the prediction error (fig. 1). The set of coefficients shows


(Tab. 4) that variables x2, x4, x6, x7, x10 ,x11, x13, x14, x16 ,x18, x20, x21, x22, x23, x28, x31, x32 are considered in the task of predicting the results over the 500 m distance (coefficient equal 0). The error generated by the optimal Lasso model (L1 = 0.5) reached the level of 0.52 s2 which makes it the best model to achieve the task of predicting outcomes over 500 m.

Table 2

The values of coefficients of classic multiple regression model Intercept x1 x2 x3 x4 x5 x6 x7 21.34 ‐0.19 0.069 2.57 0.47 ‐0.33 4.62*10‐3 3.24*10‐3 x8 x9 x10 x11 x12 x13 x14 x15 0.61 0.74 0.43 ‐1.28*10‐4 1.26*10‐4 9.07*10‐5 2.09*10‐6 ‐1.15*10‐4 x16 x17 x18 x19 x20 x21 x22 x23 ‐3.82*10‐5 1.82*10‐6 9.43*10‐6 ‐5.07*10‐5 ‐3.15*10‐6 2.89*10‐6 1.17*10‐6 ‐6.77*10‐6 x24 x25 x26 x27 x28 x29 x30 x31 ‐3.82*10‐5 ‐1.09*10‐3 ‐7.68*10‐4 1.62*10‐3 ‐6.14*10‐4 ‐2.87*10‐3 ‐1.98*10‐3 2.47*10‐3 x32 3.16*10‐4

Table 3

The values of coefficients of ridge regression model (λ = 22) Intercept x1 x2 x3 x4 x5 x6 x7 35.25 ‐0.17 ‐0.073 3.07 0.12 ‐0.17 7.63*10‐4 ‐1.37*10‐3 x8 x9 x10 x11 x12 x13 x14 x15 0.41 0.47 0.30 1.22*10‐5 1.48*10‐4 9.08*10‐5 5.52*10‐7 ‐9.06*10‐5 x16 x17 x18 x19 x20 x21 x22 x23 ‐3.12*10‐6 1.11*10‐6 ‐9.51*10‐6 ‐5.25*10‐5 ‐1.59*10‐6 ‐2.11*10‐6 4.40*10‐6 ‐1.76*10‐6 x24 x25 x26 x27 x28 x29 x30 x31 ‐2.63*10‐5 ‐1.06*10‐3 ‐9.42*10‐4 2.42*10‐3 1.31*10‐4 ‐3.16*10‐3 ‐3.01*10‐3 1.40*10‐5 x32 6.69*10‐4

Table 4

The values of coefficients of the optimal Lasso model (L1=0,5) Intercept x1 x3 x5 x8 x9 x12 x15 18.41 ‐0.11 1.28 ‐0.09 0.71 0.33 6.46*10‐5 ‐6.3*10‐5 x17 x19 x24 x25 x26 x27 x29 x30 4.51*10‐7 ‐2.77*10‐5 ‐3.82*10‐6 9.49**10‐4 7.46*10‐4 1.4*10‐3 6.96*10‐4 1.31*10‐3


0,00,20,40,60,81,01,21,41,61,82,0

0 4 8 12 16 20 24 28 32 36 40

Ridge regression

0,00,51,01,52,02,53,03,54,04,55,0

0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1L1

Lasso regression

Fig. 1

The graph representing an error in relation to λ (ridge) and L1 (Lasso) parameters

Discussion

The study presents an innovative approach towards supporting the selection of training loads. The models presented may serve to predict the result achieved by the competitor after completing the suggested plan of training. The outcome of the studies shows that Lasso shrinkage re‐gression (with a parameter of L1 = 0.5) is the best linear model for pre‐dicting the results over 500 m (which is strongly correlated to 400 m hur‐dles races). The model generates a prediction error of = 0.52 s2, which after reducing it into units of seconds gives an error of around 0.72 seconds. The classic multiple regression model with the value = 1.34 s2 generates the highest prediction error and gives a much worse re‐sult than the other models. The authors plan to extend their research in future to verify the validity of using not only linear but also nonlinear models to predict the results in sporting races.

References

Hoerl A.E., Kennard R.W.: Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, vol. 12, no. 1, 55–67, 1970.

Iskra J.: Morfologiczne i funkcjonalne uwarunkowania rezultatów w biegach przez płotki. AWF Katowice 2001.

144 Krzysztof Przednowek et al. Koronacki J., Ćwik J.: Statystyczne systemy uczące się. Wydawnictwo Naukowo‐

Techniczne, Warszawa 2005.

Maszczyk A., Zając A., Ryguła I.: A neural Network model approach to athlete selection. Sport Eng, no. 13, 83–93, 2011.

Przednowek K., Wiktorowicz K.: Neuronowy system optymalizacji wyniku sportowego zawodników uprawiających chód sportowy. Metody Informatyki stosowanej. no. 4 (29), 189–200, Szczecin 2011.

Tibshirani R.: Regression Shrinkage and Selection via the Lasso. Journal of Royal Statistical Society. Vol. 58, no. 1, 267–288, 1996.

Translational abilities of hand manipulation in typically developing south… 145

TRANSLATIONAL ABILITIES OF HAND MANIPULATION IN TYPICALLY DEVELOPING

SOUTH INDIAN CHILDREN

D. Sangkari1, Ramkumar Govindarajalu2

Study Purpose

The aim of the study is to establish the mean average scores of transla‐tional abilities of hand manipulation for typically developing children of age group 3 to 8 years. The rationale behind the study is to know the contextual interference in the specific region of south India by consider‐ing the theoretical background of dynamical systems theory of motor control that plays in various age groups with classification of genders. The performance of typically developing children will be considered as reference scores and this will acts as a quick screening component to de‐termine the quality of dexterity skills required in daily activities. Based on the aims & objectives the null hypotheses were considered before conducting the study. They are as follows,

1. There is no significant difference found in the age and gender performance in translational abilities of south Indian children of Chennai region.

Related Literatures pertinent to translational abilities

Translation Skills: This is one of the components of hand manipulation. It is a linear movement of the object from the palm to fingers and fin‐gers to the palm. Since In‐hand manipulation skills develop during the ages of three to six, facilitation of these skills is a critical part of preschool 1 - SRM University, Chennai, India 2 - Hamad Medical Corporation, Qatar

146 Sangkari D. and Ramkumar M.G

children. It is important to offer many experiences to children that allow them to develop different in‐hand manipulation skills. Through these ex‐periences, children will eventually be able to grasp a pencil, crayon, etc., to scribble and write letters (Exner, 1989; 1992). Two studies were given importance that addresses on the translation abilities of children towards the performance over age and gender vari‐ables. Humphry R, Jewell. K, Rosenberger. RC, (1995): This study examines the age‐related performance of in‐hand manipulation. Children between 2 years and 7 years of age, of total 184 children were considered. They were observed during the in‐hand manipulation activities of rotation, finger to palm translation and palm to finger translation. This study demonstrated that the frequency of two types of in hand manipulation which increases with age & gender that illustrates the uneven nature of development of different types of in hand manipulation skills. Pehoski. C, Henderson A, (1997): This study explains the development of translation skills in young children. 154 right‐handed children between the ages of 0 to 3 years and 6 to11 years of age & 13 adults were also considered. Participants were asked to pick 2 to 3 pegs and stored them in their dominant hand and move the pegs out and placing again in the pegboard. Result of this study inferred that the girls are favored when compare to boys and also age was found to be a significant factor in both the gender while handled number of pegs. Significant findings are how the children solved the problem in moving the peg in and out of the palm when compared with the adults.

Methodology

The study was conducted on 300 children with typical development after screening. It consisted of equal number of boys and girls (n = 150). The children were included based on the following screening criteria. They are 1) 3 – 8 year old, 2) No physical and motor deficits, 3) No vision and hearing impairment, 4) No illness lasting for more than 3 months, 5) No illness at the time of the test. The following materials used in this study are Coins, Stopwatch, Table and Chair, Plastic money‐pot and


Plastic plate. Five different denominations of coins were used. They are 25 paise, 50 paise, 1 Rupee, 2 Rupees and 5 Rupees. Coins were selected as measuring tool as it essentially requires the translational abilities to manipulate as well as it is one of the most commonly used material in daily life. The children were selected from a classroom randomly. They were seated on the chair and asked to perform the test in their own writ‐ing desk that was provided in their schools. The children were positioned with hip in flexion up to 90˚ and feet placed on the floor. The desk height was adjusted according to the child, in order to accommodate their elbow on it at their chest level. The scoring system is based on the time taken to perform and the number of errors made by the children while picking up and transferring the coins from the plate to the money pot. Dropping the coins outside the money pot or plate was considered as an error. The scoring system was considered by time taken during the performance of the finger to palm translation of coins from the plate and palm to finger translation of coins into the money pot. The data was collected from 4 dif‐ferent schools using convenient sampling method. From each school 75 children were taken into the study. Prior permission was obtained from the school principal and chairman through proper channel.

Data Analysis

One way ANOVA was used to analyze the difference in the score across the various age groups at p<0.05.Unpaired’ test was used to find the comparison of performance between two genders (Boys and Girls) at p<0.05.

Discussion

Table 1 below represents that comparison of time taken to perform finger to palm transfer of coins between boys and girls. It is found that the girl’s performance is better than boys. There is significant difference between boys and girls in finger to palm translation. Therefore the estab‐lished null hypothesis which states no difference between boys and girls in this context is rejected. The reason behind the performance of girls


may be because of the following literature evidence as stated by Dr. Bruce Perry (2006), Houston, Neurologist. The statements are as follows:

• The area of the brain involvement in the language and fine motor skills mature about six years earlier in girls than in boys.

• Girl’s corpus callosum 25 % larger than boys. This enables more cross talks between hemispheres because of the greater cross talks.

• More cortical areas devoted to verbal functioning and hence girls are performing better than boys in complexities of reading and writing tasks.

• Girl’s stronger neural connectors and a larger hippocampus pro‐vide greater use of sensory memory details in speaking and writing

Table 2 and 3 below represents that comparison of error occurred dur‐ing the performance of finger to palm transfer and palm to finger transfer of coins in both the genders. It shows that there is significant difference in occurrence of error between boys and girls. The occurrence of error is lesser in girls than boys. Hence the null hypotheses which state no differ‐ence in error occurrence between boys and girls are rejected. This may be due to the reason that girls have stronger neural connectors, which cre‐ates better listening skills than boys. The above statement is supported by findings of Dr. Bruce Perry (2006), Houston, Neurologist.

Normative Table for FINGER TO PALM Translation Table 1

Normative table for Translation in both genders Time (Seconds) Errors (Number)

Boys Girls Boys Girls Age Group Mean SD Mean SD Mean SD Mean SD

3 – 4 24.1 7.75 21.5 7.10 2.62 1.33 1.10 1.47 4 – 5 17.6 7.33 15.6 5.69 1.67 0.99 1.89 1.36 5 – 6 18.4 5.91 17.1 4.95 2.80 1.82 8.24 5.23 6 – 7 19.6 6.53 17.4 6.10 2.11 1.54 1.46 0.66 7 – 8 15.9 4.66 13.8 4.56 1.33 0.62 1.20 0.42


Table 2

Normative table for Errors in Translation among both genders Time (Seconds) Errors (Number)

Boys Girls Boys Girls Age Group

Mean SD Mean SD Mean SD Mean SD 3 – 4 18.7 6.93 15.9 5.12 1.45 0.82 0.73 1.39 4 – 5 15.1 5.26 13.3 5.6 1.75 0.71 1.67 0.58 5 – 6 17.0 6.09 15.1 4.86 1.73 0.79 7.95 4.93 6 – 7 17.4 6.17 14.7 5.68 1.80 1.22 1.38 0.52 7 – 8 14.6 4.10 13.9 4.11 1.50 0.71 1.33 0.52

Table 3

Normative table for palm to finger Translation in both the genders Time (Seconds) Errors (Number)

Boys Girls Boys Girls Age Group

Mean SD Mean SD Mean SD Mean SD 3 – 4 14.1 7.45 14.2 7.63 1.30 0.48 2.00 1.31 4 – 5 11.8 4.19 10.0 3.99 1.25 0.50 1.33 0.58 5 – 6 13.5 4.80 12.5 5.16 1.14 0.38 7.00 3.35 6 – 7 11.8 4.37 12.0 3.44 1.00 0.00 1.31 0.63 7 – 8 14.4 6.81 11.8 3.24 2.00 1.7 1.00 0.00

Table 3 below represents that comparison of time taken to perform palm to finger transfer of coins between boys and girls. This table shows that there is no significant difference between boys and girls. The null hypothesis is accepted. Palm to finger translation is difficult than finger to palm translation is due to the influence of the development of wrist and elbow stability as the proximal control for finger actions. Also, Hen‐derson (1995) stated that palm to finger translation is more difficult for the child, so the child first learn finger to palm translation. This is because more amount of motor control is essential to guide the movement to‐wards gravity in a refined manner (Eccentric control). Other findings relevant to age represents there is significant improve‐ment of performance pertinent to increase of age. Hence the established null hypothesis regarding to age is rejected with acceptance of alternate


hypothesis. In children from 2 to 6 years of age development of fine mo‐tor skills by learning and manipulating small objects is supplemented by the growth in the intrinsic muscles. Thus, as age increases, the voluntary control in intrinsic muscles of hand are also increases in importance to muscular properties which leading to the development of in‐hand ma‐nipulation skills. This supporting rationale is found in www.iowa/ gov/educate

Conclusion

This cross sectional study of 300 typically developing children gives the mean scores for 3 to 8 years children performance in translatory abilities of hand manipulation. Further the study investigates the quality of performance between boys and girls. Results clearly depicts that there is a significant difference in the performance of translational task be‐tween the gender and age variables. This normative data would serve as a reference criteria and it can be used as a screening component for es‐tablishing baseline measures and also to measure the outcome after any treatment protocols for the children with manipulatory deficits.

Study Limitations:

1. Due to the time constraints, the study was conducted in a sample of children which is too small to the proportion of population in Chennai region. So, it cannot be generalized to the entire population.

2. Researchers used a common object viz., coin as a standard object of manipulation. But many standardized dexterity tests involve ma‐nipulation of many common objects like paper clips, bolts, coins, etc. Therefore the ability to manipulate the coins may not be gener‐alized to the ability to manipulate other common objects.

Recommendations

1. This is a preliminary study done to establish the normative data for the translational skills in children using coins and it may serves as quick


screening tool with reference scores. Follow up studies are required to test the reliability and validity of the tool created in this study.

2. Translation is one among the three in‐hand manipulation skills for which the normative data has been established in this study. Further studies can be done to determine the normative data for other in‐hand manipulation skills (shifting and rotation).

3. In this study, the normative data of translation skills for normal children has been determined. The sensitivity of the tool to discriminate the children with and without dexterity problem can be studied in the future.

4. Due to time constraints, the study was conducted only 3 – 8 years old children. It can be further extended to other higher age groups, until the rate and accuracy of children performance reaches plateau.

Major References

The zone of proximal development in in hand manipulation skills of non dysfunctional 3 – and 4 – year – old children”. Oct; 44 (10); 884 – 91, 1989, American occupational therapy Journal.

Humphry R, Jewell K, Rosenberger RC. “Development of In – hand manipulation and relationship with activities”. Sep; 49(8): 763 – 71, American occupational therapy Journal

Pehoski C, Henderson A, Tickle – Degnen L.“ In hand manipulation in young children translation movements”. Oct; 51(9): 719 – 28, 1997 American occupational therapy Journal

152 Karol Sohit and Jae Kun Shim

FIFTEEN MINUTE TREATMENT WITH LOW FREQUENCY, HIGH INTENSITY

TRANSCUTANEOUS ELECTRICAL NERVE SIMULATION (TENS) INCREASES MAXIMUM

FINGER FORCE PRODUCTION

Karol Sohit1, Shim Jae Kun2

Introduction

Cutaneous feedback is one of the primary sensory modalities for suc‐cessful completion of day‐to‐day dexterous manipulation tasks (Johnson 2001) and loss of cutaneous feedback from the fingers has been reported to produce lower magnitudes of maximum voluntary force (MVF) production (Shim, Karol et al. 2012). Lower MVF by the fingers is known to be a predictor of poor general health conditions (Sayer, Syddall et al. 2006). While it is well established that loss of cutaneous feedback results in a decrease in maximal force production (Shim, Karol et al. 2012), it is not yet known if the maximum motor output could be enhanced by increas‐ing the cutaneous feedback from the fingers. In recent years, transcutane‐ous electrical nerve stimulation (TENS) has emerged as an important treatment to enhance the cutaneous sensation in patients with sensory deficits (Celnik, Hummel et al. 2007). Although the exact neurophysi‐ological mechanism of this phenomenon is not yet known, it has been suggested that that TENS could enhance the somatosensory input by selectively stimulating certain afferent fibers, which in turn could modu‐late the motor output (Akyuz, Guven et al. 1995). Further, different pa‐

1 - Harvard University, Boston, USA 2 - University of Maryland, College Park, USA

Fifteen minute treatment with low frequency, high intensity transcutaneous … 153

rameters of TENS are known to have a varied effect on the neuromuscu‐lar system, and frequency of TENS stimulation has been shown to be one of the most important parameters (Walsh, Foster et al. 1995). The purpose of this study is to investigate the effect of low frequency (4Hz) TENS as well as high frequency (110Hz) TENS on MVF production during multi‐digit pressing. It has been hypothesized that TENS treat‐ment would facilitate the motor output by enhancing the cutaneous feed‐back during multi‐digit pressing, thus resulting in greater MVF.

Methods

Ten healthy young volunteers (5 males and 5 females, age: 21.0±2.3 years) participated in the study. Customized equipment consisting of four one‐dimensional force sensors was used to measure the maximum finger forces (Models 208 M182 and 484B, Piezotronics Inc., Depew, NY, USA). The equipment has been used in previous studies by our group (Shim, Karol et al. 2012). C‐shaped aluminum thimbles were fixed at the bottom of each sensor in order to rest the distal ends of the fingers while pressing. The subject’s hand were bent slightly at the metacarpopha‐langeal joint (MCP), proximal interphalangeal joint (PIP) and distal inter‐phalangeal joint (DIP) in order to make a dome shape, when the fingers rested on the testing equipment. The forearm of the subjects rested on a frame, and the wrist and forearm were constrained using Velcro straps. During the experiment subjects pressed down on the C‐shaped thimbles. Five different finger combinations (four single‐finger and one four‐fin‐ger combination) for the MVF task were presented to the subjects. One trial was performed for each finger combination. The five combinations were presented to the subjects in a randomized order, with an interval of 3 minutes between consecutive pressing conditions. This process was re‐peated for three different experimental conditions of 4Hz, 110Hz and no TENS treatment. TENS was delivered via a portable unit (Elpha 3000 II, Danmeter A/S, biphasic, pulse width 200μs) with circular, self‐adhesive electrodes on the finger pads of index, middle, ring and little finger. The intensity was self‐selected by the subjects and set to the maximum toler‐able level. The electrodes were removed before conducting the MVF tri‐


als. Data were collected on three different days at the same time of the day. The order of treatment conditions was randomly assigned to the subjects.

Results

The MVF values for the single finger tasks increased significantly compared to the baseline condition for the index, middle and little fingers after 4Hz TENS treatment. Specifically, the MVF values increased 28% for the index finger (from 41.6±6.3N to 53.2±7.2N), 30% for the middle finger (from 39.9±5.2N to 49.2±6.3N) and 25% for the little finger (from 24.6±4.2N to 31.3±3.9N). No significant differences were observed for the ring finger task. Further, MVF values for the 110Hz condition were simi‐lar to the baseline condition during the individual finger tasks. These results were supported by ANOVA (Index: F2,18 = 13.5; p<.01; Middle: F‐2,18=5.83; p<.05; Little: F2,18= 6.05; p<.05). For the four finger pressing task, MVF values were 30% greater than the baseline for 4Hz condition and 15% greater than the baseline for the 110Hz condition. MVF increased from 77.0±9.2N in the baseline condition to 89.0±11.6N in the 110Hz con‐dition and 101.0±12.8N in the 4Hz condition. The results were supported by ANOVA (F2,18 = 8.94; p<.01).

Discussion

Consistent with our hypothesis, MVF increased significantly with TENS treatment in comparison to the baseline condition. Specifically, 4Hz condition produced greater MVF as compared to the baseline condi‐tion and the 110Hz condition for index, middle and little fingers. No sig‐nificant differences were observed for MVF in the ring finger among the three experimental conditions, although the data exhibited a similar trend of higher MVF values with 4Hz frequency. However, no changes in MVF were observed in individual finger tasks after 110Hz TENS treat‐ment. In addition, 4Hz condition produced greater MVF for all the four fingers pressing together, followed by 110Hz and baseline condition re‐spectively. 110Hz TENS treatment also produced greater MVF values than the baseline conditions for all the four fingers pressing together.


The experimental setup for this study afforded to stimulate the finger pads and had a minimal effect on the intrinsic or extrinsic muscles of the hand directly. Thus, changes in motor output due to neuromuscular stimulation, as observed in some previous studies (Dickstein and Kafri 2008), could therefore be ruled out in this study and the findings could be attributed to changes in somatosensory inputs alone. Cutaneous feedback is known to be one of the major sensory modalities affecting the MVF production by the fingers of the hand (Collins, Knight et al. 1999; Shim, Karol et al. 2012). Although the exact neurophysiological mechanism of changes in cutaneous sensation with TENS is not yet known, it has been shown that TENS treatment could temporarily increase the cutaneous sensitivity (Urasaki, Wada et al. 1998). TENS is thought to gate the soma‐tosensory input at the peripheral level, through large afferent fibers and centrally through the cuneatus nucleus (Nardone and Schieppati 1989; Luft, Manto et al. 2005). There are four classes of low‐threshold mechano‐receptors in human glabrous skin innervated by four classes of periph‐eral afferent nerve fibers. The neurons in the primary motor cortex have receptive fields in the periphery that receive inputs from the primary somatosensory cortex (Johansson 1998). It is possible that low frequency TENS increased the cutaneous feedback, therefore changing the inputs to the primary somatosensory cortex, which in turn facilitate the inputs to the motor cortex, thus resulting in an increased motor output. These re‐sults are consistent with our recent study where MVF values decreased with the removal of cutaneous feedback (Shim, Karol et al. 2012). Low frequency somatosensory stimulation of peripheral nerves has been known to increase the motor evoked potential as well as cortico‐motor excitability in previous studies (Nardone and Schieppati 1989; Mima, Oga et al. 2004; Mima, Oga et al. 2004). Results from this study suggest that the increase in motor output could depend on TENS fre‐quency as well as the digits involved in the task. Increase in MVF after short term TENS treatment is consistent with results from such studies. The significant increase in MVF with short term TENS is encouraging and should be followed up with more elaborate studies to establish the most optimal combination of TENS parameters to obtain maximum mo‐tor output.


In conclusion, this study suggests an important technique to enhance the MVF production capacity by employing low frequency, high intensity TENS.

References

Akyuz, G., Z. Guven, et al. (1995). ʺThe effect of conventional transcutaneous electrical nerve stimulation on somatosensory evoked potentials.ʺ Electromyogr Clin Neurophysiol 35(6): 371‐376.

Celnik, P., F. Hummel, et al. (2007). ʺSomatosensory stimulation enhances the effects of training functional hand tasks in patients with chronic stroke.ʺ Arch Phys Med Rehabil 88(11): 1369‐1376.

Chesterton, L. S., N. E. Foster, et al. (2003). ʺEffects of TENS frequency, intensity and stimulation site parameter manipulation on pressure pain thresholds in healthy human subjects.ʺ Pain 106(1‐2): 73‐80.

Collins, D. F., B. Knight, et al. (1999). ʺContact‐evoked changes in EMG activity during human grasp.ʺ J Neurophysiol 81(5): 2215‐2225.

Dickstein, R. and M. Kafri (2008). ʺEffects of antecedent TENS on EMG activity of the finger flexor muscles and on grip force.ʺ Somatosensory & Motor Research 25(2): 139‐146.

Johansson, R. S. (1998). ʺSensory input and control of grip.ʺ Novartis Found Symp 218: 45‐59; discussion 59‐63.

Johnson, K. O. (2001). ʺThe roles and functions of cutaneous mechanoreceptors.ʺ Curr Opin Neurobiol 11(4): 455‐461.

Kellis, E. and V. Baltzopoulos (1996). ʺResistive eccentric exercise: effects of visual feedback on maximum moment of knee extensors and flexors.ʺ J Orthop Sports Phys Ther 23(2): 120‐124.

Luft, A. R., M. U. Manto, et al. (2005). ʺModulation of motor cortex excitability by sustained peripheral stimulation: the interaction between the motor cortex and the cerebellum.ʺ Cerebellum 4(2): 90‐96.

Manjarrez, E., J. G. Rojas‐Piloni, et al. (2002). ʺInternal stochastic resonance in the coherence between spinal and cortical neuronal ensembles in the cat.ʺ Neurosci Lett 326(2): 93‐96.

Mima, T., T. Oga, et al. (2004). ʺShort‐term high‐frequency transcutaneous electrical nerve stimulation decreases human motor cortex excitability.ʺ Neurosci Lett 355(1‐2): 85‐88.


Moss, F., L. M. Ward, et al. (2004). ʺStochastic resonance and sensory information processing: a tutorial and review of application.ʺ Clin Neurophysiol 115(2): 267‐281.

Nardone, A. and M. Schieppati (1989). ʺInfluences of transcutaneous electrical stimulation of cutaneous and mixed nerves on subcortical and cortical somatosensory evoked potentials.ʺ Electroencephalogr Clin Neurophysiol 74(1): 24‐35.

Oldfield, R. C. (1971). ʺThe assessment and analysis of handedness: the Edinburgh inventory.ʺ Neuropsychologia 9(1): 97‐113.

Richardson, K. A., T. T. Imhoff, et al. (1998). ʺUsing electrical noise to enhance the ability of humans to detect subthreshold mechanical cutaneous stimuli.ʺ Chaos 8(3): 599‐603.

Sahaly, R., H. Vandewalle, et al. (2001). ʺMaximal voluntary force and rate of force development in humans‐‐importance of instruction.ʺ Eur J Appl Physiol 85(3‐4): 345‐350.

Sayer, A. A., H. E. Syddall, et al. (2006). ʺIs grip strength associated with health‐related quality of life? Findings from the Hertfordshire Cohort Study.ʺ Age Ageing 35(4): 409‐415.

Shim, J. K., S. Karol, et al. (2012). ʺTactile feedback plays a critical role in maximum finger force production.ʺ J Biomech 45(3): 415‐420.

Slobounov, S., J. Johnston, et al. (2002). ʺMotor‐related cortical potentials accompanying enslaving effect in single versus combination of fingers force production tasks.ʺ Clin Neurophysiol 113(9): 1444‐1453.

Tinazzi, M., S. Zarattini, et al. (2005). ʺLong‐lasting modulation of human motor cortex following prolonged transcutaneous electrical nerve stimulation (TENS) of forearm muscles: evidence of reciprocal inhibition and facilitation.ʺ Exp Brain Res 161(4): 457‐464.

Urasaki, E., S. Wada, et al. (1998). ʺEffect of transcutaneous electrical nerve stimulation (TENS) on central nervous system amplification of somatosensory input.ʺ J Neurol 245(3): 143‐148.

Walsh, D. M., N. E. Foster, et al. (1995). ʺTranscutaneous electrical nerve stimulation. Relevance of stimulation parameters to neurophysiological and hypoalgesic effects.ʺ Am J Phys Med Rehabil 74(3): 199‐206.

Yue, G. and K. J. Cole (1992). ʺStrength increases from the motor program: comparison of training with maximal voluntary and imagined muscle contractions.ʺ J Neurophysiol 67(5): 1114‐1123.

158 Krzysztof Szydło et al.

THE INFLUENCE OF THE ADDITIONAL TASK ON POSTURAL STABILITY

Krzysztof Szydło, Kajetan Słomka, Rafał Zając, Patrycja Kołacz, Grzegorz Juras1

Introduction

Human postural stability is determined by a complex processes that take place beyond our consciousness. Maintaining stable posture does not require any effort in most cases and therefore it is usually taken for granted. In a standing position we do not need to think about maintain‐ing a stable body position. It is controlled automatically if a person does not have any impairments. Postural stability can be viewed as a regulatory process. It takes place in the nervous system, and is made possible by processing the signals that reach mainly to the labyrinth, ocular apparatus, proprioceptors and touch receptors. Under physiological conditions these processes allow you to maintain a stable posture (Dijkastra at al., 1994, Kuo at al., 1998). Proprioception plays a key role in maintaining a balanced body position. Every person has to deal with difficulties with body balance which re‐quires high performance of multiple tasks at the same time. In the litera‐ture this term can be found as a dual‐task paradigm (Hsiang‐Ju et al.2001, Moghadam 2011,Donker et al. 2007,). The base idea of this concept is to reduce attention which is essential to perform the main task, by per‐forming additional task (Müller et al., 2007). Doker et al. (2007) concluded that posture equilibrium depends on the amount of attention invested in body control. It implies that the more at‐



The influence of the additional task on postural stability 159

tention we put into maintaining balance, the more stable it will be. How‐ever in some situations, internal concentration can disrupt balance con‐trol. Additional task depending on task difficulty can also affect the sta‐bility (Teasdale, Simoneau 2001). Therefore the effect of additional task is influenced by trial design. The aim of this study was to determine the effects of additional simple and choice reaction time task on the postural stability measured in a simple postural task. Participants task was to point the target displayed in the 3D glasses with their center of pressure viewed in real time on the oculars screen.

Materials and methods

Fifteen healthy young male volunteers participated in this study. Mean age was 25 ± 2.21 years, and average body height and weight were 175 ± 5.53 cm, and 75 ± 4.23 kg respectively. All subjects were informed about the execution of the experiment and signed written consent, ac‐cording to procedures approved by an Institutional Review Board. Ground reaction forces were acquired with the use of force platform (AMTI, model AccuGait, USA), with a PC computer supported by Balance Trainer software, head mounted display (Z800 3Dvisor eMagin, USA), and reaction time measurement device (MCZR/ ATB 1.0) In simple reac‐tion time condition participant responded to 12 stimuli’s, in choice reac‐tion time condition to 18 stimuli’s. In order to create more challenging conditions in some trials soft base of support was used (sponge sized to match the platform dimensions‐ 50x50 cm). During the whole experiment all subjects stood on the force plate and watched the movement of its own center of pressure (COP) in the head mounted display. Their main goal was to point the target marked on the monitor with a dot representing the center of pressures actual location. Targets were located in the 50% of the maximum base of the support area. Only one target was active at the same time, and after reaching it new target appeared. Order of targets appearance was randomized to eliminate learning process. In the first trial reaching the set of targets was the main and only goal for participant. In the second trial subject addi‐tionally responded to the acoustic stimulus by pressing the button held in


his hand (simple reaction condition). The third trial included the same task but there were 2 types of signals (high pitch and low pitch) and par‐ticipant responded with right or left hand. Next three trials were per‐formed in the same order as the first three but on the soft base of support (10 cm thick foam). Each trial lasted 60 seconds. The observed variables from AMTI platform were: ̵ Average Target Hit Time (Avg Hit t) [Sec.] ̵ Average Area Deviation (Avg Area Dev) [Cm.]

Therefore, these variables were further analyzed because they charac‐terize the performed task best. All data were evaluated in the statistica 10 software package. To examine the effects of experimental conditions on the observed variables repeated measures ANOVA was performed with significance levels set at P < 0.05, and post hoc tests with Tukey’s correc‐tions were carried out.

Results

Results show that increasing additional task difficulty resulted in less regular COP path in the research group. Perturbation during performing the main task while simultaneously executing the additional tasks in simple reaction condition significantly reduced whole body reaction time. Means and standard deviations for all variables are presented in table 1. All participants performed the same set of trials in random order. Mean values of TargetHitT, are presented in figure 1.

Table 1

Mean values and standard deviations of observed variables. Variables Non perturbed trial Perturbed trial NR SR CR NR SR CR

TargetHit T 2,961 ±0,740

2,670 ±0,735

2,601 ±0,602

2,668 ±0,489

2,596 ±0,634

2,349 ±0,462

AreaDev 30,941 ±5,894

32,091 ±7,230

36,201 ±11,915

35,741 ±11,686

42,135 ±13,743

29,049 ±8,072


Fig. 1

Average Target Hit Time (Avg Hit t) [sec.] Legend: NR – no reaction, PNR – perturbed no reaction, SR – simple reaction, PSR‐ perturbed simple reaction, CR – complex reaction, PCR‐ perturbed com‐

plex reaction There was a significant main effect of trial condition on average target hit time ((F= 5, 10) = 4.813, p<0.05). Target hit time was significantly shorter in the PSR (M= 2.349 sec., S= 0.634 sec.) compared with the NR condition (M=2.961 sec. S=0.740 sec.). Figure 2 shows the mean values of AreaDev across the trials. There was a significant main effect of trial condition on AreaDev ((F=5, 10) = 2.947, p < 0.05). Area deviation was significantly different in NR (M=30. 941 cm, S=5. 894 cm) than in CR (M=36.201 cm, S= 11.915 cm) condition. Similar results were observed in complex reaction condition (M=36.201 cm, S= 11.915 cm). Reaction times were significantly different than in PSR condition (M=42.135 cm, S=13.743 cm).


Fig. 2

Average Area Deviations (Avg Area Dev) [cm]

Discussion

The purpose of this study was to present the influence of additional task on the primary postural task. Increasing additional task difficulty resulted in less regular COP path in the research group. Perturbation during performing the main task while simultaneously executing the ad‐ditional tasks in simple reaction condition significantly reduced whole body reaction time. This may indicate that the difficulty of the additional task is not always linked to deterioration of postural stability. Results show that the average target hit time was significantly lower in more demanding tasks. Similar results were presented by Doker at al. 2007, and Dault at al. 2004. Results also show that increasing concentra‐tion on additional task can result in faster and more accurate execution of the main task. The average area deviation was increasing with task diffi‐culty. In perturbed quiet standing condition the path of the COP was significantly higher. Pellecchia (2003) also suggest that the increasing dif‐ficulty of dual task causes the increase in COP length. Authors presenting both different concepts and trying to prove its authenticity are mainly inspired by ideas taken from the field of psychology. It is also worth to note that the degree of risk for the postural stability does not matter and


the processes concernig postural tasks are priorities (Muller at al. 2007). In this study results indicate that additional task may improve effective‐ness but only if the additional task is not too difficult. There is also a trade‐off between execution accuracy and speed. The slower the move‐ment the more regular measurement. This information can be used in proprioception training in sports and rehabilitation. The additional task may improve execution of primary task but should be performed slowly and should not require significant attention in task execution.

References

Dault M.C., Frank J.S. Does practice modify the relationship between postural control and the execution of a secondary task in young and older individuals? Gerontology. 50: 157‐164. 2004

Dijkstra T.M., Schoner G., Gielen C.C. Temporal stability of the action‐perception cycle for postural control in a moving visual environment. Exp. Brain Res., 97: 477‐486. 1994.

Donker F., Roerdink M., Greven A.J., Beek P.J. Regularity of center‐of‐pressure trajectories depends on the amount of attention invested in postural control. Exp. Brain Res., 181:1‐11. 2007

Fitzpatrick R., McCloskey D. I. Proprioceptive, visual and vestibular thresholds for the perception of sway during standing in humans. Journal of Physiology, 478.1. 1994

Huang H., Mercer V. Dual‐Task Metodology: Applications in Studies of Cognitive and Motor Performance in Adults and Children. Pediatr Phys Ther. 13; 133‐140. 2001

Kuo A.D., Rosemary A., Speers R., Peterka J., Horak B. Effect of altered sensory conditions on multivariate descriptions of human postural sway. Exp. Brain Res., 122: 185‐195. 1998

Moghadam M., Ashayeri H., Salavati M., Sarafzadeh J., Taghipoor K., D., Saeedi A., Salehi R. Reliability of center of pressure measures of postural stability in healthy older adults: Effects of postural task difficulty and cognitive load. Elsevier, Gait & Posture. 33: 651‐655. 2011

Müller M., Redfern M., Jennings R. Postural prioritization defines the interaction between a reaction time task and postural perturbations. Exp. Brain Res. 183: 447‐456. 2007

164 Krzysztof Szydło et al. Pellecchia G., L., Postural sway increase with attentional demands of concurrent

cognitive task. Gait Posture. 18: 29‐34. 2003

Teasdale N., Simoneau M. Attentional demands for postural control: the effects of aging and sensory reintegration. Gait Posture 2001, 14: 203‐210.

Winter D., A., Human balance and posture control during standing and walking. Gait & Posture. 3: 193‐214.1995

Yeh T., Boulet J., Cluff T., Balasubramaniam R. Contributions of delayed visual feedback and cognitive task load to postural dynamics. Neuroscience Letters 481, 2010, 173‐177.

Changes in the level of stability while standing on the balance platform on... 165

CHANGES IN THE LEVEL OF STABILITY WHILE STANDING ON THE BALANCE

PLATFORM ON A RIGID AND COMPLIANT SURFACE

Dariusz Tchórzewski1, Janusz Jaworski

Introduction

The contribution of individual of individual sensory inputs in pos‐tural control is not equal. It is believed that a particularly important role in the development of postural response proprioceptive inputs play (Pe‐terka 2002; Horak 2006). Fluctuations of the balance platform (seesaw) cause continuous changes in orientation, which is a major difficulty in the use of proprioceptive information about the relative body positions (Ivanenko et al. 1997). While standing on an unstable surface comes to the reorganization of postural control mechanisms and their adaptation to new environmental conditions, resulting in increased body’s sways and a change in postural strategy (Gantchev and Dimitrova 1996). This ability to compensate of the loss or retention of information from one or even two sources of sensory input by reorganizing and re‐establish the impor‐tance of information from all the senses is important to maintain stability (Mergner et al. 2003; Horak 2006). The main aim of this study was to determine to what extent, with the help of visual feedback mechanisms of postural control can compensate the distortion of prioprioceptive information caused by two factors. First factor disturbance with balancing on a rigid surface of the platform, while the second additional place on a platform of compliant surface. An 1 - University School of Physical Education in Cracow, Faculty of Physical Education and

Sport, Poland.

166 Dariusz Tchórzewski and Janusz Jaworski

attempt to answer the questions: (1) as additional disturbance due to soft ground affects the level of postural stability? and (2) if this effect is simi‐lar in the planes of motion?

Methods

Subjects Experiments were performed on 20 young, healthy men (age: 20.71±0.63). The average men’s height was 180,0 cm at the weight – 72,7 kg (BMI 22,4). No one person had problems with stability and had no injury which could influence on the results of the measurements. All participants volunteered for the study.

Equipment The level of dynamic balance was assessed using the Libra wobble‐board by EasyTech. It allows determining the level of the ability to maintain dynamic balance in enforced conditions. The outcome of the measurement on the wobble board is global (final) result, which com‐prises the following parameters: total area – zone between the actual line and the model central line (left/right in the frontal plane; front/back in the sagittal plane); extend area – zone behind the settled level of difficulty; extend time – sum of the times existence beyond the level of difficulty, recovery time – the longest period of existence beyond the given area. The final result is the weighted average (a number from 0 to 100) of 8 mentioned parameters (the value of 100 means the weakest balance, the value of 0 the best one) (Tchórzewski et al. 2007) To obtain the conditions of soft surface Togu Aerostep (rubber made therapeutically device with two separate chambers filled with air, with the possibility of changing the air pressure inside) was put on the plat‐form.

Procedures Subjects were asked to maintain the balance platform in horizontal plane. The measurements were made in two series. During the first one the subjected person stood on rigid surface, during the second on Aerostep. In every session one minute balancing was registered in frontal


and sagittal plane. The measurement was made without shoes, in upright position with parallel feet. A straight line was the movement pattern (Fig.1). Each measurement was preceded by a 30 s warm‐up Tests were carried out using visual feedback. At eye level of tested at 1 m from the edge of the platform was placed 15 ʺmonitor screen, which displayed a graphic image of the course. The test was required to observe the screen during the whole measurement and manipulation of such a platform that he plotted on a computer screen to best reflect the line of the line model (coinciding with it). The scope of the degree of difficulty was visualized on the monitor screen in the form of two located on both sides of the pattern of parallel lines. These parameters were based on earlier studies carried out on the plat‐form Libra (Tchórzewski et al. 2010).

Fig. 1 Example of graphic record of trial’s process; A – frontal plane, B – sagittal plane

Data analyses The data obtained from individual measurements initially developed the basic methods of descriptive statistics. Calculated the arithmetic mean, variance, standard errors of the mean, standard deviation. To con‐firm the significance of differences between the two dependent tests were used matched pairs Wilcoxon test. Calculations were performed with the help of a computer program Statistica 10.0.


Results

The results of measurements of the stability parameters were analyzed separately for the rigid and compliant surfaces including the frontal and sagittal plane range of motion. The resulting arithmetic mean, standard er‐ror and 95% confidence interval average stability index (SI), total area (TA), the extend area (EA) and the extend time (ET) is shown in figure 2. Differences between the parameters of stability in standing on a rigid and compliant surfaces highlight a different impact on the balance of the support in both considered planes of motion. In frontal plane, in most cases indicate either a lack of differentiation between the terms of the base of support or at a better balance of respondents on compliant sur‐face. In the sagittal plane better stability among tested on a rigid surface was showed (Tab.1).

Table 1

Differences between the results of the stability of parameters during the balancing of rigid and compliant surface

frontal plane sagittal plane

parameter difference ( x ±sx)

T p difference

(%) difference ( x ±sx)

T p difference

(%) stability index

‐0,46±0,2 48,0 0,0586 ‐12,7 0,70±0,3 55,0 0,0420 20,0

total area ‐5,94±4,3 70,5 0,1978 ‐6,8 19,68±5,5 33,5 0,0076 24,7 external area

‐1,78±0,5 28,0 0,0040 ‐80,5 0,23±0,2 94,0 0,9679 7,8

external time

‐0,51±0,3 55,5 0,0646 ‐45,5 0,12±0,2 90,5 0,8563 7,3

statistically essential values were distinguished in bold type % difference IS=[(ISCS‐ISRS)/ISRS]*100; % difference TA=[(TACS‐TARS)/TARS]*100; % difference EA=[(EACS‐EARS)/EARS]*100; % difference ET=[(ETCS‐ETRS)/ETRS]*100;

RS–rigid surface; CS–compliant surface; IS–stability index; TA–total area; EA–external area; ET– external time

There were no differences between stability parameters obtained in both planes. The results obtained on compliant surface showed a better stability in the frontal plane (Tab. 2).


RYC poprz


Table 2 Differences between the results of parameter stability in the frontal

and sagittal planes rigid surface compliant surface

parameter difference ( x ±sx)

T p difference

(%) difference ( x ±sx)

T p difference

(%) stability index

‐0,10±0,3 96,5 0,7510 ‐2,8 1,05±0,3 16,5 0,0027 33,6

total area ‐7,68±4,6 69,0 0,1790 ‐8,8 17,94±5,6 38,0 0,0124 22,0 external area

0,70±0,5 91,5 0,6143 31,5 2,70±0,8 22,0 0,0033 626,7

external time

0,46±0,4 75,5 0,2708 41,1 1,09±0,3 32,5 0,0119 177,9

statistically essential values were distinguished in bold type % difference IS=[(ISSP‐ISFP)/ISFP]*100; % difference TA=[(TASP‐TAFP)/TAFP]*100

% difference EA=[(EASP‐EAFP)/EAFP]*100; % difference ET=[(ETSP‐ETFP)/ETFP]*100; SP–sagittal plane; FP–frontal plane; IS–stability index; TA–total area; EA–external area; ET– external time

Conclusion

The use of compliant surface was designed to determine the signifi‐cance of mechanoreceptors interest in maintaining the balance in unsta‐ble ground conditions. Change in surface hardness of the base of support is one of the most widely used techniques in the study, the role of the somatosensory system in postural control (Marin et al, 1999, Patel et al, 2008). This method is based on the belief that the introduction of the de‐formable surface of the base of support to a large extent interferes with somatosensory afferent signals responsible for the maintenance of equi‐librium, and thus increases its dependence on visual information and vestibular (Wu and Chiang, 1996). The usage of compliant surface in this study improved the level of sta‐bility of the subjected in frontal plane, but it was not changed in the sag‐ittal one. Compliant surface on the platform produces smaller corrective movements, which are performed with some delay. The usage of compli‐ant surface could contribute to reduction of corrective movements’ am‐plitude and can influence the improvement of the subjected stability.


References

Gantchev GN, Dimitrova DM. Anticipatory postural adjustments associated with arm movements during balancing on unstable support surface. International Journal of Psychophysiology,1996; 22;117–122.

Horak FB. Postural orientation and equilibrium: what do we need to know about neural control of balance to prevent falls? Age and Ageing, 2006; 35–S2: 7–11.

Ivanenko UP, Talis VL. Effect of support surface stability on the postural vibration reactions in man. Human Physiology, 1995; 21: 116–124.

Marin L, Bardy BG, Baumberger B, Fluckiger M, Stoffregen TA. Interaction between task demands and surface properties in the control of goal–oriented stance. Hum Movement Sci, 1999; 18: 31–47.

Mergner T, Maurer C, Peterka RJ. A multisensory posture control model of human upright stance. Prog Brain Res., 2003; 142: 189–201.

Patel M, Fransson PA, Lush D, Gomez S. The effect of foam surface properties on postural stability assessment while standing. Gait Posture, 2008; 28: 649–656.

Peterka RJ. Sensorimotor integration in human postural control. J Neurophysiol, 2002; 88: 1097–1118.

Tchórzewski D, Jaworski J, Bujas P. Influence of long–lasting balance on unstable surface for changes in balance. Human Movement, 2010; 11(2): 144–152.

Wu G, Chiang JH. The effects of surface compliance on foot pressure in stance. Gait Posture, 1996; 4: 22–129.

Acknowledgments

The authors would like to thank Technomex Company for allowing them to use their equipment to do the research.

172 Milan Turek et al

FITNESS ASSESSMENT IN ICE HOCKEY

Milan Turek, Marek Kokinda, Róbert Kandráč1

Introduction

Exercise testing is irreplaceable with regard to qualified control of the training process providing information on the states and changes in all sports, whose primary aim is to enhance performance. Its interpretation enables coaches to actively engage in the training process and to predict the increase and decline in sports performance. On the basis of the as‐sessment of both the level and changes as a result of sustained training, exercise testing serves to assess the predispositions necessary for the training process itself. The success in both individual and team sports lies in the scientifically justified system of training activity. The selection of appropriate and verified methods under both specific and general condi‐tions has to be performed on the basis of testing, through which the in‐definite character of exercises is eliminated. Athleteʹs training level refers to a specific form of organismʹs adaptation to physical load. The adapta‐tion is the result of genetic dispositions and sustained training. Differen‐tiation between both effects in real conditions is practically impossible without unconventional methods used to uncover ʺweakʺ performance‐determining links, which are used for the analysis and assessment of per‐formance. Bunc (2010) states that testing is based on a model physical load, which must mimic the competition conditions of identical intensity and duration. At the same time, it is necessary to assess the motor abilities of the tested person and apply dynamic load. Both components depend on the motor competence that determines the performance of a particular motor task included in the assessment. The motor competence is charac‐ 1 - University of Presov in Presov, Faculty of Sport, Presov, Slovakia

Fitness assessment in ice hockey 173

terized by two parameters: 1. Skill – technique acquisition related to a motor task; 2. The state of the muscular system. Sports performance in ice hockey is alternate with regard to exercise intensity and falls into the category of the so‐called ʺmultiple sprint sportsʺ (Helešic, 2009). These types of sports performances are character‐ized by alternation of maximal‐intensity short‐duration intervals and rest intervals. The purpose of the study was to design a probability model the applica‐tion of which would refine the assessment of parameters of actual and continuous states in ice hockey players using factor and analytical proce‐dures.

Methods

Tests and test‐related norms in the test battery 1 of the Methods De‐partment of the Slovak Ice Hockey Federation (MD SIHF) include general and specific tests for individual age categories. The test battery serves for the U15, U18 and U20 categories and includes 10 subtests, which measure running and skating speed, lower‐body explosive strength, maximal strength of arm extensors and shoulder joint muscles. The score scale ranges from 1 to 80 points for the general fitness tests and 1 to 90 points for the specific fitness tests (see Table 1). Using the materials of MD SIHF, we devised a test battery 2 for the assessment of general and specific motor fitness. As shown in Table 1, battery 2 includes field and laboratory tests measuring strength, lower‐body flexibility, skating speed and agility. The test of maximal skating speed (Behma et al., 2005) was carried out together with the test of skat‐ing speed around the rink, which is used especially at the exhibitions of the National hockey league or Kontinental hockey league. Single‐leg broad jump test and agility test were performed adhering to the methods de‐scribed by Brooks (Training center). The laboratory measurement of strength was conducted using a jump ergometer. Taking into account previously conducted studies, 4 variables were selected: no. 15, 16, 17 and 18 (see Table 1). The battery included also single‐leg squats, which were evaluated by time needed to perform ten squats and the test of lower‐


body flexibility. The results of the test batteries were processed using cor‐relation and factor analysis. Contrary to the correlation computations, which provide information not only on the degree of correlation between several measured values, the factor and analytical procedures are ori‐ented at the quality of the correlation. The orthogonal rotation of factors into a simple structure was performed using the Varimax method.

Results

The test batteries 1 and 2 represent two relatively independent units with certain consistency in individual test items. Almost every test item is related to another variable, which is most probably a determining factor of skating speed. The level of significance of factor loading was set at < 0.4 (Perič, 2008). Their significance is indicated in bold letters. In test battery 1 of the MD SIHF four factors, which are indicators of conditioning abilities, were extracted. With regard to the conducted analysis, we may conclude partial incompleteness of the battery and the need to complement the battery with test items, which would be more indicative of skating performance. The saturation of five factors within the test battery 2 has demon‐strated hierarchy of individual parameters, which are actually indicative of skating performance. With certain degree of scientific skepticism, this battery may be regarded as a relevant indicator. The factor 1 covers 22 percent of the whole complex area. Its base is formed by abilities related to explosive strength. The loading of the factor 2 of the total variance equaled 20.4 %. This factor can be considered the factor of skating and running speed, speed endurance and agility as it is saturated by variables of jump ergometry and the test of skating speed. Therefore, the factor can be regarded as the energetic factor of skating performance. The loading of the factor 4 of the total variance amounted to 13.8 % and is saturated by variables of agility and single‐leg broad jumps. The factor represents frequency speed and explosive strength. Factor 6 seems to be highly specific being saturated by the variable of skating speed and representing factor of maximal skating velocity. This is


indicative of its specific position in terms of skating speed. Specific vari‐ance (SV – 17.5 %) of the factor model 1 is not directly associated with the content of the test items. It is probably saturated by personal, psychologi‐cal and other variables, which do not make part of motor parameters.

Table 1

Arithmetic means and standard deviations in test batteries 1 and 2 Ice hockey players U18 (N=22) No Variable

x s

Specific motor fitness (Battery 1)

1. 2. 3. 4.

Forward skating 36m (s) Backward skating 36m (s) Forward skating 6x9m (s) Forward skating 6x54m (s)

5.2 6.2 13.5 49.4

0.2 0.2 0.6 1.2

Specific motor fitness (Battery 2)

5. 6.

Test of maximal skating velocity (km/h) Test of skating speed – 1 lap around the rink (s)

32.8 15.3

1.8 0.5

General motor fitness (Battery 1)

7. 8. 9. 10. 11. 12.

60m run (s) 400m run (s) 1500m run (s) 6x9m run (s) Standing broad jump (cm) Bench press (kg)

8.3 65.1 381.9 14.6 230.5 68.7

0.2 2.0 28.7 0.2 7.3 8.4

General motor fitness (Battery 2)

13. 14. 15. 16. 17. 18. 19. 20. 21. 22.

Sit‐and‐reach (cm) Agility ‐ octagon (s) J. ergometer tc time of contact (s) J. ergometer P performance in the active push‐off phase (W/kg) J. ergometer v velocity in the final push‐off phase (m/s) J. ergometer h height of jump (cm) Single‐leg broad jump: left leg (cm) Single‐leg broad jump: right leg (cm) 10 single‐leg squats: left leg (s) 10 single‐leg squats: right leg (s)

16.5 5.0 0.2 39.2 2.4 30.4 192.7 194.3 6.6 6.7

4.9 0.6 0.1 5.4 0.1 3.1 14.4 11.2 0.5 0.6

Legend: No. – order number, J. ergometer – jump ergometer, N – number of players, x – arithmetic mean, s – standard deviation.


Within the factor model 2 three factors were extracted with higher rate of specific variance compared to the model 1. The factor 1, which may be termed the skating‐related factor of lower‐body explosive strength, cov‐ered 29.9 % of the total variance. The factor 2 is saturated by variables sensitively assessing the level of lower‐body strength in terms of domi‐nance and preference. The factor covered 23.5 % of the total variance. Factor 3 represents the factor of skating speed endurance and maximal strength of arm extensors and shoulder joint muscles. Its saturation amounted to 16.1 %. The value of the specific variance of the factor model 2 equaled 31.2 %. Almost one third of the motor area covered indicates that the content validity increased relatively significantly.

Fig. 1

Factor loading in test battery 1 and 2

Fig. 2

Factor loading in models 1 and 2


The factor matrix itself in the numerical form includes all information, but their relevance remains unclear. Therefore, the percentages of indi‐vidual factor loadings need to be illustrated graphically (Turek, 1997).

Discussion

The primary purpose of the conducted exercise testing was to exam‐ine the variables covering the common variance, which may be termed as ʺmotor areaʺ. Factor and analytical procedures with the highest degree of verification enable to clarify these criteria, which are included in the in‐dividual test items. At the same time, they enable to reduce the number of redundant test items, which are possible to identify on the basis of multivariate mathematical statistics. The conduction of comparative analysis of the test batteries resulted in two factor models, one of which exhibited sufficient rate of validity. With high degree of probability, it is possible to conclude incidence of common base of running parameters (factor model 1), which despite different character of loading shared identical base. This contradiction represents certain knowledge paradox indicating that the implementation of these items into the test battery does not sufficiently assess the general fitness in ice hockey players and their number seems to be redundant. This finding was confirmed by the saturation of jump ergometry as an indi‐cator of lower‐body explosive strength. The attempt to analyze the simple structure of the factor model showed that several test items shared an identical criterion. This enables both to refine the test battery content and to reduce their number as is the case of the variable: sit‐and‐reach, which does not fall into the proposed models due to the character of its content. We may assume that cooperation with experts in the field will enhance the quality of diagnostics in the training process.

References

Behma G. D., Wahl J. M., Button C. D., Power E. K., Anderson G. K. Relationship between hockey skating speed and selected performance measures. In: J Strength Condit Res, 19: 326‐331, 2005.

178 Milan Turek et al Bunc V. Diagnostika trénovanosti ve fotbale – možnosti a limity. In: Sborník

referátů z 9. mezinárodní vědecké konference Hry 2010. Plzeň: FPE ZČU, 2010. pp. 15‐25.

Helešic J. Některé aspekty kondiční přípravy hokejistů ve vztahu k rychlosti bruslení. In: ATLETIKA MASTERS – OTROKOVICE. Retrieved October 15, 2010, from http://atletika‐masters.webnode.cz/news/rychlost‐brusleni/

Perič T. K možnostem identifikace struktury sportovní talentovanosti. Praha: Karolinum, 2008.

Turek M. Faktory pohybovej výkonnosti 6 a 10 ročných detí. In: Zborník z Medzinárodnej vedeckej konferencie: Telesný rozvoj a pohybová výkonnosť detí a mládeže. Prešov: VSTVŠ, 1997. pp. 113‐118.

Acknowledgments

This work was supported by VEGA GÚ 1/0088/11

Hockey fitness relative to age categories 179

HOCKEY FITNESS RELATIVE TO AGE CATEGORIES

Milan Turek, Marek Kokinda, Róbert Kandráč1

Introduction

Exercise testing is irreplaceable in terms of training regulation. The testing itself provides information on particular states and their changes, whose primary aim is to enhance performance. Scientifically based inter‐pretation of test results enables coaches to actively regulate training load and predict sport performance from a long‐term perspective. On the basis of the assessment of the level or the changes as a result of sustained training, the testing serves for the assessment of the predispositions for training. The basis of exercise testing is formed by a model physical load, which should mimic the competition conditions in terms of intensity and duration of effort. It is also required to assess motor abilities and to apply dynamic load in tested persons. Both components are dependent on the motor competence underlying the conduction of a particular motor task, which makes part of the assessment. Motor competence is characterized by two parameters: 1. Skill – technique acquisition of a concrete motor task; 2. State of the muscular system. Sports performance in ice hockey, which is considered a multiple sprint sport (Helešic, 2009), is characterized by changing intensity of ef‐fort. Such sports performances include alternation of short‐duration work intervals performed at maximum intensity and rest intervals. Hockey skating is one of the most complex activities requiring long‐term skill learning, which depends on a variety of factors (Pavliš, Perič, 2003). Ac‐

1 - University of Presov in Presov, Faculty of Sport, Presov, Slovakia

180 Milan Turek et al.

cording to Stamm (2001), the most important aspect of preparation in skating sports is the development of skating technique. The purpose of the study was to determine indicators underlying the level of hockey fitness in players of respective age categories using cor‐relational analysis.

Methods

The sample comprised 27 ice hockey players aged 9 to 15 years. Sam‐ple characteristic is shown in Table 1.

Table 1

Sample characteristic BH (cm) BW (kg) Age

(years) n

x s x s 9 4 135 3.9 28.5 4.3 10 5 146 6.3 36.8 6.6 11 3 147 6.4 38.3 3.5 12 7 156 6.8 47.5 8.4 13 4 158.5 2.6 50.5 3.1 14 4 170 7.9 60.2 3.1

Legend: n – number of subjects; x – arithmetic mean; s – standard deviation; BH – body height; BW – body weight.

The research was conducted in cooperation with a specialized hockey center HDC Košice, which is equipped with professional personal and material conditions such as synthetic ice, skatemill and FiTRO Hockey Set test battery for the assessment of hockey‐specific fitness (www.hockeydts.com/sk/fitro‐diagnostika). The FiTRO Hockey set in‐cludes the following specific tests: FiTRO Reaction, FiTRO Jumper, FiTRO Tapping, FiTRODYNE, FiTRO Sway, FiTRO Light Gate and FiTRO Stride Power. Out of these, FiTRO Reaction: 40 trials, FiTRO Tap‐ping: number of repetitions in 6 seconds, FiTRO Jumper: maximum jumping height, FiTRO Sway: static stance on one foot during 10‐second interval with eyes open were administered. To maximize the amount of information about the level of actual hockey fitness including skating, the laboratory testing was comple‐


mented by field‐based tests. The test that enables to monitor skating skill is: 90° squat to seated position on a bench in 60 seconds. The applied di‐agnostic methods should with high degree of probability increase the quality of training in ice hockey players with special emphasis on skat‐ing. The sample values were converted to C score. The data were subse‐quently used to assess individual test parameters using Pearsonʹs prod‐uct moment correlation. The statistical significance for correlations was set at p<.05

Results

The test results were processed using correlational analysis that deter‐mines the internal relationship between individual variables. The pa‐rameters of hockey fitness are shown in table 2. These parameters may be regarded as determining factors of skating skills. With regard to the analysis of complex reaction time, it may be concluded that this parameter is determined primarily by genetic and personality predispositions. Lower‐body explosive power is one of the basic deter‐minants of skating speed. In the investigated age groups the results showed high degree of sample heterogeneity in terms of frequency speed and lower‐body explosive power. Unilaterality of stance of right and left lower limb is irreplaceable in terms of the development of skating skills. With respect to more detailed analysis of basic skating skills, we refer to basic stance, transferring the center of gravity, skating push‐offs, gliding, crossovers and braking. The configuration of individual correlates (see table 3) shows that most extracted parameters of hockey fitness correlate with somatic pa‐rameters. The variable no. 1 – Age is irreplaceable with regard to the sen‐sitive periods in the development of young ice hockey players. In U15 age categories, the lower‐body complex reaction time decreases (fig. 1), which determines the development of lower‐body explosive strength (see table 3). Lower‐body frequency speed is with high degree of probability determined both by age between 9th and 15th year of life and body height, which in this age group may negatively affect the motor skills of young


Table 2

Hockey fitness parameters Unilaterality of stance (mm/s) Initials

Age (years)

LB CRT (ms)

FS (number)

Jump height(cm)

Squats (number)

Right Left

R. B. 9 836.4 59 30.9 98 52.9 44.1 F. B. 9 1081.4 43 23.1 78 30.4 35.2 R. F. 9 1153.8 45 28.8 62 71.9 56.7 M. L 9 1056.6 40 27.7 77 38 44.5 A. P 10 1160 52 26.1 80 31.5 31.8 O. G. 10 1011.4 52 23.3 54 63.5 67.2 D. P. 10 1442.3 61 28.8 58 45.7 79.5 M. Š. 10 1423.8 53 23.4 83 42.1 44 M. P. 10 1096.6 46 28.8 57 55.8 60.1 A.G. 11 1096.6 51 24.2 68 45.2 40.8 B.J. 11 925.1 59 31.3 85 36.8 30.8 M. L. 11 1051.4 55 28.8 64 72.8 42.9 I.Č. 12 668.8 51 28.6 62 43.7 53.8 T. D. 12 891.2 45 25.6 75 52 55.9 R. F. 12 1019.7 49 29.9 73 40.1 49.4 L. G. 12 817 48 26.3 65 42.9 60.9 O. G. 12 780.7 40 35.1 73 47.7 48.9 J. K. 12 1237.1 58 26.6 73 47.7 48.9 R. Z. 12 787.5 55 30.9 85 44.3 48.5 A.M. 13 647.9 58 38.2 76 48.9 47.7 T. J. 13 984.4 49 29.9 73 47.7 48.9 M. K. 13 1199.9 55 35 88 40.3 34.8 T. Š. 13 1121.1 63 30.5 91 45.4 57.3 M. D. 14 809.1 55 23.9 69 74.3 73.7 J. M. 14 730.1 59 27.3 83 51.2 36 M. M. 14 747.5 59 31.9 74 39.4 34.4 Ľ. V. 14 602.1 66 33.8 69 35.8 44.5 Legend: LB CRT – lower‐body complex reacton time; FS – frequency speed


ice hockey players. With regard to the unilaterality of static stance on both dominant and non‐dominant leg, the results showed minimal en‐hancement of strength. Figure 1 illustrates changes in general motor fit‐ness in players aged 9 to 11 and 12 to 14. The analysis showed that the period of approximately 12th year of age appears to be most sensitive to the development of hockey fitness through enhancement of frequency speed and lower‐body explosive strength. For better overview, table 3 shows the inter‐correlation relationships of correlates at p<.05.

Table 3

Values of correlation coefficients of somatic, reaction, frequency and dynamic parameters (n = 27)

Variables: 1. 2. 3. 4. 5. 6. 7. Age Body height 0.87 Body weight 0.87 0.93 LB CRT ‐0.54 ‐0.58 ‐0.57 FS 0.45 0.38 Jump height 0.39 ‐0.40 Squats

Right ‐0.40 Ul. Left ‐0.57

Level of significance: r 0,37. Legend: LB CRT – lower‐body complex reaction time; FS – frequency speed;

Ul. – unilaterality of stance

The unilaterality of stance on both dominant and non‐dominant lower limb did not reveal significant variability. The variability is more evident in the category of 12‐year‐olds, which may lead to more significant de‐velopment of motor abilities especially frequency speed and lower‐body explosive strength.


Fig. 1

Changes in hockey fitness between 9 – 11 and 12 – 14 years of age

Discussion

The purpose of the exercise testing in ice hockey players of various age groups was to determine probable indicators underlying the level of hockey fitness. These would with high degree of probability enable to improve the assessment of skating technique. The partial correlation analysis showed that the use of exercise testing tools in sports prepara‐tion of ice hockey players enables to a great extent to reveal factors de‐termining skating skills. Among the limiting factors of the enhancement of hockey fitness are the sensitive periods and somatic predispositions of players that form the basis for performance prediction in the future. In terms of performance prediction, the assessment of complex reaction time appears to be insufficient as reaction speed is determined not only by the performance level, but also by mental and personality traits.

References

Bunc V. Diagnostika trénovanosti ve fotbale – možnosti a limity. In: Sborník referátů z 9. Mezinárodní vědecké konference Hry 2010. Plzeň: FPE ZČU, 2010. pp. 15‐25.


Helešic J. Některé aspekty kondiční přípravy hokejistů ve vztahu rýchlosti bruslení. In: Atletika masters – Otrokovice [online]. 2009. [cited 2010‐10‐15]. Available at: http://atletika‐masters.webnode.cz/news/rychlost‐brusleni/

Pavliš Z.,Perič, T. Abeceda hokejového bruslení. Praha: Český svaz ledního hokeje, 2003. 89 p.

Stamm L. Laura Stamm’s POWER SKATING. Champaign, IL: Human Kinetics, 2001. 225 p.

Acknowledgments

This work was supported by VEGA GÚ 1/0088/11

186 Martin Wünnemann

SPECIFICITY OF LEARNING IN STABILOMETER BALANCE TASKS

WITH AND WITHOUT VISION

Martin Wünnemann1

Introduction

Proteau (1992) claims that motor learning is specific for the sensory information available during practice. Robertson and Elliott (1996) tested this hypothesis with the task of quickly crossing a balance beam. They showed specific improvements for the task without vision. However, they could not confirm specific learning for the task with vision available due to floor effects. This transfer experiment is designed to test the speci‐ficity hypothesis concerning availability of vision with balance tasks where it is unlikely that the high expertise in the task with vision avail‐able prevents further improvements of performance.

Methods

36 healthy subjects participated in this study, of which 10 were female and 26 were male. The average age was 26.1 years (SD = 5.2 years). Sub‐jects who possibly might show positive transfer from prior experience to the tasks of this experiment were not included. These were subjects who had expertise in sports like skating, surfing and gymnastics, as well as subjects with considerable experience in tasks on a stabilometer or other balance training devices like ankle discs or wobble boards. All subjects gave written informed consent according to the Declaration of Helsinki and the guidelines of the local ethics committee.

1 - Universität Paderborn, Paderborn, Germany

Specificity of Learning in Stabilometer Balance Tasks With and Without Vision 187

Subjects balanced on a stabilometer under two visual conditions, either with vision available (task vision) or wearing opaque glasses (no vision). The stabilometer had two rotary axes ‐ one medial‐lateral rotary axis (ml) allowing movements orthogonal to the sagittal plane and an additional anterior‐posterior rotary axis (ap) allowing movements orthogonal to the coronal plane. The stabilometer was set up close to a wall allowing the subjects to hold on to the wall to take up the starting position. The start‐ing position required the subjects to stand on the horizontal platform with their knees slightly flexed (about 10°), their feet parallel (26 cm apart) and their line of sight directed straight ahead to a mark on the wall. Then, subjects were asked to stand with their hands on their hips while trying to keep the platform leveled for 30 seconds. For each axis, the maximum deviation from the horizontal was limited to ten degrees rotation. The deviation was measured in degrees by potentiometers mounted to the rotary axes of the stabilometer with a sampling rate of 200 Hz. The dependent variables are the root mean square errors (RMSEs) of the platform‐deviation from the horizontal in each axis. All subjects accomplished each task twice during an acquirement phase and once during a pretest that began 15 minutes after the acquire‐ment phase. The pretest was followed by a four week training‐phase. Subjects were evenly assigned to three groups, parallelized by pretest results. Two groups each practiced one of the tasks (group vision, group no vision). One group served as control group and conducted no training session. Each training session consisted of ten one‐minute‐trials with one‐minute‐breaks in between. Five weeks after the pretest with a time lag of five to seven days after the last training‐session, all subjects took part in a posttest that was identical to the pretest but without the previous ac‐quirement phase. Two‐way factorial analyses of variance (ANOVAs) for each RMSE with test (pretest, posttest) as within‐subject factor and group (group vi‐sion, group no vision, control group) as between‐subjects factor were conducted. P‐values were adjusted (padj) according to Benjamini and Ho‐chberg (1995) in order to control the false discovery rate. The alpha level for significance was set at padj < .05. Effect sizes were calculated using partial eta squared values (η2p). The interactions are of particular impor‐


tance because a significant interaction indicates different alterations of performance between the groups. Post‐hoc two‐way ANOVAs test (pre‐test, posttest) x group (two of the groups) were used to explain the inter‐actions. The specificity hypothesis predicts larger improvements of per‐formance for the specific training group to the other groups and no difference between the non‐specific training group and the control group, which is tested for p > .20 without adjusting p‐values.

Results

Means and standard deviations of all groups in both tasks and both axes at pretest and posttest are depicted in figure 1. In the ap‐axis of task vision groups alter performance differently from pretest to posttest, F(2,33) = 4.27, padj = .015, η²p = .21. This interaction can be explained by the larger improvement of group vision compared to each other group, group no vision, F(1,22) = 6.81, padj = .004, η²p = .24, con‐trol group, F(1,22) = 6.74, padj = .005, η²p = .23. Whereas there is no differ‐ence between group no vision and the control group, F(1,22) = 0.18, p = .674, η²p = .01. Regarding RMSE in the ml‐axis, the interaction test x group is also sig‐nificant, F(2,33) = 3.04, padj = .041, η²p = .16. Group vision reduces the RMSE more than the control group, F(1,22) = 5.26, padj = .016, η²p = .19. However, group vision does not improve performance significantly compared to group no vision, F(1,22) = 1.11, padj = .152, η²p = .05. Further‐more the expected absence of different alterations between group no vi‐sion and the control group cannot be confirmed, F(1,22) = 2.96, p = .100, η²p = .12. Contrary to the predictions of the specificity hypothesis there is no sig‐nificant interaction for the ANOVA regarding RMSEs in the ap‐axis of task no vision, F(2,33) = 0.41, padj = .445, η²p = .02. Post‐hoc analyses reveal no difference in alteration from pretest to posttest between the groups, group no vision, group vision, F(1,22) < 0.01, padj = .990, η²p < .01, group no vision, control group, F(1,22) = 0.67, padj = .424, η²p = .03, group vision, control group, F(1,22) = 0.64, p = .432, η²p = .03.

Specificity of Learning in Stabilometer Balance Tasks With and Without Vision 189

The interaction for the RMSEs in the ml‐axis of task no vision, F(2,33) = 9.25, padj = .001, η²p = .36, is significant as predicted. Group no vision re‐duces RMSE more than each other group, group vision, F(1,22) = 11.17, padj = .002, η²p = .34, control group, F(1,22) = 12.85, padj = .001, η²p = .37. Group vision and the control group do not differ, F(1,22) = 1.22, p = .282, η²p = .05 (Fig. 1).

Fig. 1 RMSEs of all groups (different marks and lines) in task vision (left) and task no

vision (right), ap‐axis (filled marks) ml‐axis (non‐filled marks).

Discussion

All in all results are in accordance with the specificity hypothesis. Though, there are two results that do not support the specificity hypothe‐sis completely. First, the expected absence of transfer from task no vision to task vision regarding RMSEs in the ml‐axis could not be verified. Sec‐ondly, task‐specific improvements in group no vision are limited to the ml‐axis. These results indicate that visual information may be more rele‐vant for learning to reduce sway in the ap‐axis than in ml‐axis of this sta‐bilometer. O’Connor and Kuo (2009) showed that in parallel stance centre of pressure variability is more affected by visual perturbations in ap‐di‐rection than by visual perturbations in ml‐direction, whereas in tandem


stance the opposite is true. They explain their results by differences in stability requirements leading to different weighting of visual informa‐tion. Their explanation may be true for the results of this experiment, too. Generally, deviations in the ap‐axis are larger than in the ml‐axis (fig. 1), which may emerge from mechanics of the stabilometer and the standing position on the stabilometer. So, stability requirements seem to be higher in the ap‐axis than in the ml‐axis. If visual information is of minor rele‐vance for balancing on the stabilometer in the ml‐direction, training without vision could reduce sway in that direction even when vision is available. On the other hand, if vision is of high importance for balancing in the ap‐direction, the lack of visual information might impair learning in this direction.

References

Benjamini, Y., Hochberg, Y.. Controlling the False Discovery Rate: a New and Powerful Approach to Multiple Testing. J. Roy. Stat. Soc. B 57: 289‐300, 1995.

O’Connor, S. M., Kuo, A. D. Direction‐dependent Control of Balance during Walking and Standing. J. Neurophys. 102: 1411‐1419, 2009.

Proteau, L. On the Specificity of Learning and the Role of Visual Information for Movement Control. In: Vision and Motor Control. L. Proteau and D. Elliott, eds. Amsterdam: North‐Holland, 1992. Pp 67‐103.

Robertson, S., Elliott, D. Specificity of Learning and Dynamic Balance. Res. Q. Ex. Sport 67: 69‐75, 1996.

The reliability of jumping test as a tool for evaluation of movement rhythm 191

THE RELIABILITY OF JUMPING TEST AS A TOOL FOR EVALUATION OF MOVEMENT RHYTHM

Rafał Zając, Krzysztof Szydło, Patrycja Kołacz, Kajetan Słomka, Grzegorz Juras1

Introduction

Each day every person encounters many difficulties while performing routine activities like maintaining stable body posture during standing, walking and reaching objects but also with maintaining the proper rhythm of these activities and in some cases synchronizing with exter‐nally provided rhythm. Rhythmic motor activity and ability to synchronize with the given tempo, has been widely explored (Repp, 2005). Most of studies concerns finger tapping in various experimental groups like children with devel‐opmental coordination disorder (DCD) (Whitall, et al., 2008), or with at‐tention deficit hyperactivity disorder (ADHD) (Rubia, et al., 2003). Other studies aim to explore the development of rhythmic abilities (Drake, Jones, & Baruch, 2000), synchronisation with auditory signals under vari‐ous experimental conditions (Hui‐Ya Chen 2006) and neural structures corresponding to this ability (Dhamala, et al., 2003). Differences in ex‐perimental design demands from researchers evaluation of reliability of the performed tests (Corriveau et al. 2000, Lafond et al. 2004, Słomka et al. In Press). Evaluation of rhythmic motor ability in most cases requires special ap‐paratus which makes them impossible to perform outside the laboratory.



192 Rafał Zając et al.

Therefore in this study we used a simple test jumping test, which could be performed without any additional devices. The main goal of this study was to find a simple and reliable measure of rhythmic ability with the use of intra class correlation coefficients (ICC).

Methods

Participants Eleven young healthy subjects took part in the experiment. All sub‐jects were informed about the execution of the experiment and signed written consent, according to procedures approved by an Institutional Review Board.

Apparatus Jumping rhythm was measured with the use of Optojump (Microgate ITA). In the further data analysis we analyzed jumping pace expressed in jumps per second, and mean deviation from desired jumping frequency (1Hz). Both variables were calculated for each trial phase separately.

Procedure Subjects were asked to stand in the area between two optojump bars and to perform six trials. All trials were made up of two phases: guided and self‐paced. During guided phase subject performed 5 jumps with the goal to synchronize jumping tempo to the rhythm provided by the met‐ronome. The frequency of the acoustic signals was set to 1 Hz. Then, after five jumps the metronome was turned off and subjectʹs task was to maintain jumping pace in the next 20 jumps. All subjects were allowed to rest as much as they needed between the trials. The reliability of the conducted measurements was estimated with the use of intraclass correlation coefficients (ICCs). Based on ANOVA results this model calculates the reliability of a single measurement (equation 1):

Where MSB, MSR and MSE are mean squares of repeated measures ANOVA, n is the number of trials and k is the number of subjects. Based


on results derived from the first equation, next model predicts the reli‐ability of consecutive trials (equation 2):

Where R is the reliability of single trial and m is the trial number. The target ICC coefficient was set to R= .70. All analyses were performed with the use of Statistica 10 software package.

Results

The Results of the performed analysis indicate that the mean devia‐tion from desired frequency is less sensitive to the number of repetitions and thus seems to be a more reliable measure. The results of the ICC for mean deviation from 1Hz are presented in figure 2. Depending on the test phase different numbers of trials should be performed. Results of jumping pace should be registered in 11 trials for the guided phase to reach R= .70, while in the self‐paced phase 5 repetitions will provide the same level of reliability. At the same amount of repetitions during guided phase jumping pace reach R=.51 For mean deviation from 1Hz reliability of recordings in both phases are almost equally the same and reaches the level of 0.70 after six repeti‐tions. The difference between the two phases of measurement equals only 0.01 and remains at the same level after each of the following trial.

Fig. 1 Number of trials required for mean deviation from 1Hz to reach the desired

reliability coefficient (. 70).

194 Rafał Zając et al.

Discussion

The main goal of this study was to find a simple and reliable measure of rhythmic ability with the use of intra class correlation coefficients (ICC). The Results of the performed analysis indicate that the mean de‐viation from desired frequency is less sensitive to the number of repeti‐tions and thus seems to be a more reliable measure. Rhythmic motor activity and synchronization with auditory and vis‐ual cues are included in many studies concerning finger tapping. Usually tests used in these researches require special apparatus what makes them impossible to perform outside the laboratory. Differences in experimental design demands from researchers evaluation of reliability of the per‐formed tests. Some studies aim to solve this problem by determining the number and duration of trials required to reach a satisfying level of measurement reliability. The main goal of this study was to use a simple test evaluating the rhythmic ability and to find a reliable measure with the use of intra class correlation coefficients (ICC). The results show that test demanding from subject synchronization of jumping pace with the metronome is less reliable than without a given tempo. Perhaps it was due to the low number of jumps during guided phase. Another possible explanation is that subjects were concentrating more on metronome signals than on maintaining given rhythm during the trial. Donker et al. States that the efficiency and automatism of the performed task is related to attention invested in postural control, but also substantiate that in certain situations an increased internal focus may in fact be detrimental to postural control. (Donker et al. 2007). Reliability of mean deviation from the desired frequency of jumps was almost the same for both phases. In this case the low number of jumps in the guided phase did not influence measurement reliability. Considering these two variables the mean deviation from the desired fre‐quency seems to be a more reliable measure in the evaluation of the rhythmic ability.


References

Bortoletto, M., Cook, A., Cunnington, R. Motor timing and the preparation for sequential actions. Brain and Cognition. 75: 196–204, 2011

Corriveau, H., Hebert, R., Prince, F., Raiche, M. Intrasession Reliability of the ‘‘Center of Pressure Minus Center of Mass’’Variable of Postural Control in the Healthy Elderly. Archives of Physical Medicine and Rehabilitation. 81: 45‐48, 2000

Dhamala, M., Pagnoni, G., Wiesenfeld, K., Zink, C. F., Martin, M., Berns, G. S. Neural correlates of the complexity of rhythmic finger tapping. Neuroimage. 20: 918–926, 2003

Donker, S. F., Roerdink, M., Greven, A. J., Beek, P. J. Regularity of center‐of‐pressure trajectories depends on the amount of attention invested in postural control. Experimental Brain Research. 181: 1‐11, 2007

Drake, C., Jones, M. R., & Baruch, C. The development of rhythmic attending in auditory sequences: attunement, referent period, focal attending. Cognition. 77: 251‐288, 2000

Chen H., Wing A.M., Pratt D. The synchronisation of lower limb responses with a variable metronome: The effect of biomechanical constraints on timing. Gait & Posture. 23: 307‐314, 2006

Lafond, D., Corriveau, H., Hebert, R., MPhil, Prince, F. Intrasession Reliability of Center of Pressure Measures of Postural Steadiness in Healthy Elderly People. Archives of Physical Medicine and Rehabilitation. 85: 896‐901, 2004

Repp, B. H. Sensimotor synchronization: A review of the tapping literature. Psychonomic Bulletin & Review. 12: 969‐ 992, 2005

Rubia, K., Noorloos, J., Smith, A., Gunning, B., Sergeant, J. Motor Timing Deficits in Community and Clinical Boys With Hyperactive Behavior: The Effect of Methylphenidate on Motor Timing. Journal of Abnormal Child Psychology. 31: 301–313, 2003

Słomka, K., Juras, G., Sobota, G., & Bacik, B. (In Press). The reliability of a rambling–trembling analysis of center of pressure measures. Gait and Posture.

Whitall, J., Chang, T. Y., Horn, C. L., Jung‐Potter, J., McMenamin, S., Wilms‐Floet, A., Clark, J. E. Auditory‐motor coupling of bilateral finger tapping in children with and without DCD compared to adults. Human Movement Science. 27: 914–931, 2008

196 Teresa Zwierko et al.

IMPACT OF EXERCISE INTENSITY ON INNER PLEXIFORM LAYER OF THE RETINA

Teresa Zwierko1, W. Lubiński2, D.Czepita2, P. Lesiakowski1, J.Krzepota 1

Introduction

Information processing in the visual system begins with the initiation of a retinal signal that is finally transmitted to other visual centers in the brain. Between the input side of the retina, the photoreceptors, and the output side of the retina, the ganglion cells, there are multiple classes of interneurons formed by bipolar and amacrine cells. Retinal signal is modulated by neural circuitry in the inner plexiform layer. Modulation systems are important for the perception of brightness, light intensity and especially for the contrast sensitivity functions. The retina is highly metabolically active, resulting in a high rate of oxygen consumption (Tinjust et al. 2002); therefore, the retinal cells are particularly susceptible to changes in blood flow (Iester et al. 2007) and hypoxia states (Feigl et al. 2008). Physical effort induces both increased blood flow and hypoxic states. Previous studies (Kergoat H, Forcier 1996; Okuno 2006) show that inner retinal layer are very sensitive to hemody‐namic changes induced by exercise. The flash electroretinography (fERG) measures the electrical re‐sponses of various cell types in the retina. The most noticeable compo‐nent of the fERG is the b‐wave. High frequency wavelets, termed oscil‐latory potentials (OPs), frequently appear superimposed on the b‐wave of the fERG. It is generally accepted that they reflect the activation of

1 - University of Szczecin, Department of Physical Culture and Heath Promotion, Poland 2 - Pomeranian Medical University, Szczecin, Department of Ophthalmology, Poland

Impact of exercise intensity on inner plexiform layer of the retina 197

amacrine and/or ganglion cells (Yu, Peachey 2007). Wachtmeister (1998) indicates that the origin of the OPs is related with the involvement of the axon terminals of the bipolar cells, the processes of the amacrine cells and the dendrites of the ganglion cells in the generation of the OPs. Experi‐mental findings show that exercise of moderate intensity (heart rate of 140 beats per minute) extends the culmination of oscillatory potentials in terms of OP4 (Kergoat, Forcier 1996) and the reduction of the sum of the amplitudes of each wave OP (Watanabe et al. 1985). The aim of our study was to investigate this phenomenon further. We analysed the effect of re‐petitive physical exercise with increasing intensity on the retinal bioelec‐tric function by examining the oscillatory potentials of the fERG. Effort intensity was determined by the individual lactate range of each partici‐pant.

Methods

Twelve male volunteers aged 20–23 years (mean=20.8, SD=1.12) par‐ticipated in this study. They were subjected to routine ophthalmological examinations. All participants had monocular Snellen visual acuities of 20/20 or better and no history of systemic or ocular disease. The partici‐pants were informed about the testing protocol, and gave written in‐formed consent to participate. Using a cycloergometer one effort test with incremental intensity was performed by each participant until refusal. The research procedure be‐gan with a 10 min rest in a reclining position, after which time blood was collected from a finger for biochemical determinations. The effort‐test was preceded by a 5‐min warm up at 25 watt (W). The main test com‐menced at 70 W, with 70 revolutions per min (rpm). The effort was con‐tinued with an increasing workload (20 W increments every 3 min) until refusal. During the last 15 sec of each 3‐min effort at a given workload, capillary blood samples were drawn directly into a capillary tube from a fingertip for the enzymatic determination of blood lactate concentration (Dr Lange Cuvette Test LKM 140, Germany) using miniphotometer LP 20 Plus (Dr Lange, Germany). Using a Polar S610 heart‐rate monitor (Polar, Finland), we registered resting heart rate and its change during effort.


Oxygen consumption during effort was estimated using an Oxycon gas analyzer (Jaeger, Germany). An individual lactate threshold was calcu‐lated using a linear regression graph log LA and the log of effort inten‐sity. Based on the results of the effort‐test, an individual workload value (W) for each subject was calculated at three levels; 40% VO2max (below the lactate threshold), 60% VO2max (in the lactate threshold range), and 80% VO2max (above the lactate threshold). The full‐field flash ERG was recorded with an LKC UTAS‐E‐2000 (LKC Technology, Gaithersburg, MD, USA.). The right eye was selected formonocular stimulation. After dilatation of the pupil to 7–8 mm in di‐ameter with topical phenylephrine (10%) and tropicamidum (1%), a Burian–Allen bipolar contact lens electrode was applied to the topically anaesthetized (Alcaine) cornea with a ground electrode placed on the ip‐silateral earlobe. The oscillatory potentials (OPs) were recorded from the dark‐adapted eye, using the 3.0 cd.s.m‐2 flash stimulus. The peak‐to‐trough amplitude and peak implicit times of the four major OPs were analyzed (OP1, OP2, OP3, OP4). The fERG was performed following a protocol established by the ISCEV (International Society of Clinical Electrophysiology of Vision) (Marmor et al. 2009). Representative recording of fERGs is shown in fig. 1.

Procedure The first fERG recording was performed at rest (ERGr). A 5‐min warm‐up on the cycloergometer (25 W) preceded the 10‐min effort at an intensity below lactate threshold (40% VO2max). Directly after the effort, fERG recording was performed (ERG1). The participant then performed a 10‐min effort at the lactate threshold intensity (60% VO2max), followed by the next fERG recording (ERG2). Subsequently, an effort at an inten‐sity above the lactate threshold (80% VO2max) lasting 10 min was per‐formed, after which the fERG recording was made (ERG3). One hour af‐ter the end of the last effort, a final fERG recording was made under re‐covery conditions (control) (ERGc).


Fig. 1 Representative example of the pre‐exercise averaged OP waveforms for one

participant. The character “F” indicates the stimulus flash

Results

The analysis of fERGs during rest, exercise and recovery conditions indicated significant effort induced changes in amplitude of the OP3 (F=3.12, p<0.05) and OP4 (F=12.03, p<0.01). Figure 2 indicates that signifi‐cantly reduction of OP3 and OP4 amplitude values were observed after exercise at 80% VO2max (ERG3). In contrast, the amplitude of OP1 (F=1.97, p>0.05) and OP2 (F=0.82, p>0.05) were not significantly different between pre‐ and post‐ exercise conditions. After exercise, however, the OPs implicit time were not significantly changed in comparison with the rest and control conditions.

Fig. 2 Pre‐ and post‐exercise values of OPs amplitude. Significant reduction in OP3

amplitude (* ‐ p<0.05) and OP4 amplitude (** ‐ p<0.01) after exercise


Discussion

The purpose of this study was to determine whether the inner plexi‐form layer of the retina is altered after exercise by examining the oscilla‐tory potentials of the flash electroretinogram in healthy individuals. With increasing intensity of exercise the gradual reduction in amplitude of OP3 and OP4 was noted. The greatest amplitude decrease (p<0.01) was observed in OP4 after the third effort test (80% VO2max). Similarly, in Kergoat and Forcer’s (1996) study, the function of the neural generators of the OP complex was altered in scotopic conditions (decrease in OP3 amplitude and a delay in OP4 implicit time after exercise). The OPs are known to be strongly dependent on retinal circulation and lower oxygen levels (Wachtmeister 1998). The OPs of the fERG revealed a significant decrease in amplitude during hypoxic exposure (15‐min exposure to a simulated altitude of 5500 m) (Janaky et al. 2007). During intensive physical effort, the work done to contract the muscle for the most part comes from anaerobic energy‐yielding metabolic processes. Reduction of oxygen concentration, in response to cellular demand, leads to bioenergetic hypoxia states (Luk’janowa 1997). Under these conditions, anaerobic glycolysis processes are intensified. Change in plasma hy‐poxanthine concentration is one of the main symptoms of tissue hypoxia as an effect of physical fatigue (Sahlin et al. 1991). This mechanism also exists in the retina (Peachey et al. 1993). It is generally accepted that OP3 and OP4 waves are rod‐generated OPs, while the preceding waves OP1 and OP2 are cone‐generated OPs (King‐Smith et al. 1986). In our study the amplitude of OP1 and OP2 were not significantly different between pre‐ and post‐exercise conditions in contrast to OP3 and OP4. In common with our previous results (Zwierko et al. 2010), these findings may confirm that the rod system is more sen‐sitive than the cone system to the physiological stress induced by dy‐namic exercise. The discrepancy between rod and cone fERG responses after exercise may result from metabolic differences between the photore‐ceptor systems. During scotopic adaptation, oxygen consumption is higher than in photopic adaptation in the whole retina (Wangsa‐Wira‐wan, Linsenmeier 2003). The main explanation of this phenomenon is


that light reduces the high sodium permeability of the photoreceptors. Under these conditions, demand for metabolically driven sodium trans‐port to maintain cellular homeostasis is lower (Linsenmeier 1990). Those specific differences in photoreceptor systems could explain why cones appear more resistant than rods to hypoxia conditions. In summary, we conclude that fERG tests, particularly in scotopic conditions, may be used as neurophysiological indicator in defining the cardiovascular status of the physical performance.

References

Feigl B. , Stewart I. B. , Brown B., Zele A. J. Local neuroretinal function during acute hypoxia in healthy older people. Invest Ophthalmol Vis Sci. 49: 807‐813, 2008

Iester M., Torre P.G., Bricola G., Bagnis A., Calabria G. Retinal blood flow autoregulation after dynamic exercise in healthy young subjects. Ophthalmologica 221:180‐185, 2007

Janaky M., Grosz A., Toth E., Benedek K., Benedek G. Hypobaric hypoxia reduces the amplitude of oscillatory potentials in the human ERG. Doc Ophthalmol.114: 45‐51, 2007

Kergoat H., Forcier P. Correlation of an exercise‐induced increase in systemic circulation with neural retinal function in humans. Doc Ophthalmol. 92:145‐157, 1996

King‐Smith P.E., Loffing D.H., Jones R. Rod and cone ERGs and their oscillatory potentials. Invest Ophthalmol Vis Sci.27: 270‐273,1986.

Linsenmeier R. Electrophysiological consequences of retinal hypoxia. Graefes Arch Clin Exp Ophthalmol. 228:143‐150, 1990

Luk’janowa L.D. Bioenergetic hypoxia: definition, mechanisms, and methods of correction. Biull Eksp Biol Med. 124: 244‐254, 1997

Marmor M.F., Fulton A.B., Holder G.E., Miyake Y., Brigell M., Bach M. ISCEV Standard for full‐field clinical electroretinography (2008 update), Doc Ophthalmol. 118: 69‐77, 2009

Okuno T., Sugiyama T., Kohyama M., Kojima S., Oku H., Ikeda T. Ocular blood flow changes after dynamic exercise in humans. Eye. 20:796‐800, 2006

202 Teresa Zwierko et al. Peachey N.S., Green D.J., Ripps H. Ocular ischemia and the effects of allopurinol

on functional recovery in the retina of the arterially perfused cat eye. Invest Ophthalmol Vis Sci. 34: 58‐65, 1993

Sahlin K., Ekberg K., Cizinsky S. Changes in plasma hypoxanthine and free radical markers during exercise in man. Acta Physiol Scand. 142: 275‐281,1991

Tinjust D., Kergoat H., Lovasik J. Neuroretinal function during mild systemic hypoxia. Aviat Space Environ Med. 73: 1189‐1194, 2002

Wachtmeister L. Oscillatory potentials in the retina: what do they reveal. Prog Retin Eye Res.17: 485‐521, 1998

Wangsa‐Wirawan N., Linsenmeier R. Retinal oxygen: fundamental and clinical aspects. Arch Ophthalmol 121: 547–557, 2003

Watanabe Y., Mizuno Y., Nagaoka R. The influence of the physical effort on the electroretinogram (ERG). J Sports Med Phys Fitness. 25: 747‐752, 1985

Yu M. Z., Peachey N. S. Attenuation of oscillatory potentials in nob2 mice. Doc Ophthalmol. 115: 173‐186, 2007

Zwierko T., Czepita D., Lubiński W. The effect of physical effort on retinal activity in the human eye: rod and cone flicker electroretinogram studies. Graefes Arch Clin Exp Ophthalmol. 248: 659‐666, 2010

Documents

Current Research in Motor Control IV · Janusz Iskra, Jaros ... Karina Nowak, Grzegorz Sobota, Bogdan Bacik, Grzegorz Hajduk,Damian Kusz.....116 FUNCTIONAL MOVEMENT PATTERNS AND LIMITATIONS