Kyle Takayama dissertation.pdf

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DIETARY QUALITY, REPORTING ACCURACY, AND TEMPORAL

EATING PATTERNS AMONG LOW-INCOME, HISPANIC MOTHERS

by

Kyle Takayama

A thesis submitted to the Faculty of the University of Delaware in partialfulfillment of the requirements for the degree of Master of Science in Human Nutrition

Spring 2014

2014 Kyle M. Takayama

All Rights Reserved


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DIETARY QUALITY, REPORTING ACCURACY, AND TEMPORAL

EATING PATTERNS AMONG LOW-INCOME, HISPANIC WOMEN

by

Kyle Takayama

Approved: __________________________________________________________Jillian C. Trabulsi, Ph.D., R.D.Professor in charge of thesis on behalf of the Advisory Committee

Approved: __________________________________________________________

P. Michael Peterson, Ed.D.Chair of the Department of Behavioral Health and Nutrition

Approved: __________________________________________________________Kathleen S. Matt, Ph.D.Dean of the College of Health Sciences

Approved: __________________________________________________________James G. Richards, Ph.D.Vice Provost for Graduate and Professional Education


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ACKNOWLEDGMENTS

I would like to acknowledge my advisor, Dr. Jillian Trabulsi, for introducing

me to the world of research, and whose wisdom and patience I have only begun to

appreciate. I must also acknowledge Dr. Nancy Cotugna, who has been there for me

every step of the way as an undergraduate student, young professional, and graduate

student. I would also like to thank Dr. Mia Papas for serving on my thesis committee

and for affording me the opportunity to work on Project Vida Sana, as well as Dr.

Gregory Dominick and the rest of the research team at La Comunidad Hispana.

Finally, I would like to thank my parents, Kim and Anne, for being the gold standard

of hard work and integrity, and my sister, Kelly, for showing me that theres more

than one way to peel a banana.


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TABLE OF CONTENTS

LIST OF TABLES ........................................................................................................ viABSTRACT ................................................................................................................. vii

Chapter

1 INTRODUCTION .............................................................................................. 1

2 REVIEW OF THE LITERATURE .................................................................... 3

2.1

Diet and Nutrient Intake ............................................................................ 3

2.2

Temporal Eating Patterns .......................................................................... 6

2.3 Reporting Accuracy ................................................................................. 11

3 SPECIFIC AIMS .............................................................................................. 17

3.1 Statement of the Problem ........................................................................ 173.2 Specific Aims .......................................................................................... 17

4 METHODS ....................................................................................................... 19

4.1 Study Design ........................................................................................... 19

4.2

Measurement Tools ................................................................................. 20

4.3 Anthropometrics ...................................................................................... 204.4 Diet Analysis ........................................................................................... 204.5 Statistical Methods .................................................................................. 21

4.5.1 Diet and Nutrient Intake ................................................................. 214.5.2 Reporting Accuracy ..................................................................... 224.5.3

Temporal eating patterns ............................................................. 23

5 RESULTS ......................................................................................................... 24

5.1 Subjects .................................................................................................... 24

5.1.1 Demographics .............................................................................. 245.1.2 Anthropometrics .......................................................................... 25

5.2 Nutrient Intake and Diet Quality ............................................................ 25


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5.2.1 Energy/macronutrient intake ....................................................... 255.2.2 Micronutrient intake .................................................................... 265.2.3 Healthy Eating Index ................................................................... 26

5.3 Temporal distribution of energy and macronutrient intake ..................... 27

6 DISCUSSION ................................................................................................... 29

TABLES ....................................................................................................................... 35REFERENCES ............................................................................................................. 50Appendix ...................................................................................................................... 54

IRB LETTERS ................................................................................................. 55


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LIST OF TABLES

Table 1: List ofmeasures included in Project Vida Sana.....36

Table 2: Scoring standards for the 2010 Healthy Eating Index....38

Table 3: Demographic and anthropometric characteristics of (all subjects).........39

Table 4: Comparison of subject characteristics by reporting accuracy.....40

Table 5: Total daily macronutrient intake of (all subjects).......41

Table 6: Total daily macronutrient intake by reporting accuracy.42

Table 7: Total daily micronutrient intake of (all subjects)............43

Table 8: Healthy Eating Index scores (all subjects)..........45

Table 9: Temporal distribution of energy and macronutrient intake (all subjects)...46

Table 10. Comparison of temporal distribution of energy intake by BMI category(all subjects)........47

Table 11: Comparison of energy and intake and macronutrient distribution fromProject Vida Sana and nationally representative data.48

Table 12: Comparison of daily micronutrient intake among Mexican-Americanwomen ........49


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ABSTRACT

Background:According to the 2010 United States (US) Census, the Hispanic

population now accounts for one of every six people living in the US, and this

proportion is expected to reach one in four by the year 2050. As the Hispanic

population continues to increase, so does its impact on the overall health status of the

US as a whole. Epidemiologic studies have revealed inter-ethnic disparities in health

outcomes experienced by the Hispanic population such as increased prevalence of

obesity and diabetes. A detailed analysis of health and nutrition behaviors such as

dietary quality, temporal eating patterns, and reporting accuracy may lead to a greater

understanding of these disparities, and provide a foundation for the development of

strategies for the prevention and management of health outcomes associated with these

disparities.

Aims:The primary aim of this study is to describe the nutrient intake and diet quality

among Hispanic women of child bearing age. The secondary aim is to assess the

temporal distribution of food intake. These aims will be conducted with consideration

for the accuracy of reported energy intake within the population.

Methods:As part of an ongoing, longitudinal study of Hispanic mothers and their

children, anthropometric data, demographic, health behavior, physical activity and


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dietary data (one 24-hour dietary recall) has been collected. Participant data collection

has been facilitated by bilingual research assistants. Descriptive statistics will be used

to summarize nutrient intake and Healthy Eating Index scores will be used to assess

diet quality. Temporal eating patterns will be summarized using descriptive and

inferential statistics. The Goldberg method will be used to determine the accuracy of

reported energy intake.

Results:Comparison of micronutrient intake to the Dietary Reference Intakes (DRI)

for women between the ages of 18-50 suggests the Hispanic women in this population

may have suboptimal intake of vitamin D and E. Further, the mean Healthy Eating

Index-2010 (HEI-2010) score in this population (47.1 12.2) indicates poor adherence

to federal dietary guidelines. A one-way ANOVA to assess percent daily energy and

percent daily macronutrient intake showed a significant difference in kcal (p < 0.01),

carbohydrate (p < 0.01), protein (p = 0.02), and fat (p < 0.01) across three time

intervals. No significant difference in percent energy intake in the morning (p = 0.92),

afternoon (p = 0.88), and evening (p = 0.65) was observed across body mass index

(BMI) categories. Similarly, no significant difference in percent energy intake at T1 (p

= 0.47), T2 (p = 0.78), and T3 (p = 0.80) with respect to reporting accuracy was

observed.

Conclusion:Nutrition education should focus on improving overall adherence to

federal dietary guidelines, with an emphasis on increasing the intake of whole grains,

oils, and foods low in sodium. More evidence is needed to determine the influence of

meal size during the various time intervals on overall energy and macronutrient intake.


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Chapter 1

INTRODUCTION

According to the 2010 US Census, the Hispanic population now accounts for

one of every six people living in the US and this proportion is expected to exceed one

in four by the year 2050.1As one of the fastest growing ethnic subgroups in the

country, the impact on the overall health status of the country as a whole has become a

topic of interest in public health. Consequently, an understanding of health outcomes

experienced by Hispanics has become an important objective of public health

research, practice, and advocacy.

Researchers have identified disparities in health outcomes across

socioeconomic and ethnic subpopulations of the US.2To date, epidemiologic studies

have shown that Hispanics display higher rates of poverty, food insecurity, and low

socioeconomic status, but also lower participation rates in preventive health care.3

Particularly alarming is the disproportionate prevalence of obesity and diabetes

observed over the last 25 years among the Hispanic population. Data from the 2009-

2010 National Health and Nutrition Examination Survey (NHANES) showed a 40.7%

and 44.3% prevalence in obesity among Hispanic and Mexican-American women

respectively, whereas the prevalence among Caucasian, non-Hispanic women was

33.4%.4However, the cross-sectional nature of these studies has prevented researchers

from suggesting causal links for these health disparities. A detailed analysis of health

and nutrition behaviors such as dietary quality, temporal eating patterns, and reporting

accuracy may lead to a greater understanding of these disparities and provide a


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foundation for the development of strategies for the prevention and management of

health outcomes associated with these discrepancies.

Dietary quality, often described as an individuals compliance to federal

dietary guidelines, is an important factor which may lead to changes in health

outcomes among individuals of differing race and ethnicity.2Large, population-based

investigations have shown that nutrient intake and diet quality are associated with a

variety of risk factors and health outcomes including total serum cholesterol, obesity,

blood pressure, dental caries, diabetes, and cardiovascular disease.5Consequently,

differences in food selection and nutrient intake may lead to alterations in diet quality

and temporal eating patterns. These factors, with consideration for the accuracy of diet

assessment, may have important implications for morbidity and mortality among

ethnic sub-populations.


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Chapter 2

REVIEW OF THE LITERATURE

2.1 Diet and Nutrient Intake

Population based, cross-sectional studies have revealed both inter- and intra-

ethnic variations in food selection and nutrient intake. Differences in nutrient intake

within the Hispanic population, related to age, acculturation, nativity, length of US

residency, and ethnic subgroup have been observed in several studies.3,6-11

With respect to overall diet, analysis of NHANES data collected from 1999-

2002 have suggested a lower mean scores for the Healthy Eating Index among

Mexican Americans compared with non-Hispanic whites older than 60 years of age,

indicating a poorer quality of diet among these older population groups.12Data from

the 1982-1984 HHANES, 1988-1994 NHANES, and 1999-2006 NHANES surveys (n

= 3,935, n = 4,641, n = 4,048 respectively) have been used to investigate trends in

both nutrient intake and chronic health conditions among Mexican-American adults. A

significant increase in carbohydrates and total energy intake, and a significant

decrease in the intake saturated fat, protein, and percent energy from fat were observed

over a 25-year period.5There was also a significant increase in the prevalence of

obesity and diabetes in this population over the same time period.

Changes in health outcomes related to nativity have also been observed.

Mexican-Americans born in the US displayed an increased risk of high blood pressure

and obesity compared to those born outside of the US.5Nativity does indeed account


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for some of the differences in nutrient intake among the Hispanic population.

Investigation of the 2003-2006 NHANES data compared food selection between

Mexican-American and non-Hispanic whites.6The sample included foreign-born

Mexican Americans (n = 539), US-born Mexican Americans (n = 536), and US-born

non-Hispanic whites (n = 2,530). Dietary patterns were assessed via principle

component analysis (PCA) using the subjects food frequency questionnaire. The

results showed an inverse relationship between dietary quality and duration of US

residence among Mexican-Americans. US-born subjects reported greater tendency

towards a western diet favoring red meat, processed meats, desserts, pasta, and fried

potatoes, compared to foreign-born subjects who displayed greater tendency for a

tomato/tortilla diet consisting of more tomatoes, tortillas, and beans. Foreign-born

Mexican-Americans reported higher consumption of fiber, fruits, and vegetables, and

less fiber, whole grains, and total fat compared to US-born Mexican Americans,

although this comparison did not reach statistical significance.6

Acculturation, defined as the adoption of characteristics displayed by the

dominant culture, also appears to play a role in dietary intake. For instance, less

acculturated Hispanics display healthier eating patterns characterized by greater

consumption of fruits, rice, and beans and lower consumption of sugar and sugar-

sweetened beverages.10Also, compared to their US-born counterparts, foreign-born

Hispanics report higher consumption of fruits, vegetables, fruit/vegetable juice, high

fiber/low fat breads, but also a lower intake of snacks, desserts, soda, fruit drinks, and

fast food.11It stands to reason that differences in food preference can lead to changes


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in both macro and micronutrient intake. A large, cross-sectional study using data from

the 1982-1984 Hispanic Health and Nutrition Examination Survey (HHANES)

quantitatively assessed 24-hour recalls collected from first and second-generation

Mexican Americans (n = 475 and 898 respectively) compared to non-Hispanic whites

(n = 2,326) participating in the 1976-1980 NHANES survey. A significantly higher

intake of cholesterol was observed among first and second generation of Mexican-

American women compared to non-Hispanic women. Unexpectedly, first generation

Mexican-American women with lower socioeconomic status consumed significantly

more protein, carbohydrates, cholesterol, vitamins A and C, folic acid, and calcium

compared to second generation Mexican-American or white non-Hispanic women.7

This same data set was analyzed to determine if nationality affects dietary intake

(Mexican-American, Cuban-American, Puerto Rican, and non-Hispanic). Puerto

Ricans were found to consume a higher percentage energy from carbohydrates, and

Cuban-Americans consumed a higher percentage of protein compared to the other

ethnic subgroups (p < .01).8A similar study of Hispanic (n = 711) and non-Hispanic

(n = 226) women over the age of 60. Subjects were stratified women by country of

origin: Puerto Rican, Dominican, and other Hispanic.Nutrient intake was assessed

via 24-hour dietary recall. After adjusting for age, Puerto Rican and Dominican

women were found to consume significantly less total energy and simple sugars but

reported significantly higher intake of polyunsaturated fats compared to non-Hispanic

white women (p < .001). All Hispanic groups consumed more complex carbohydrates,

but less monounsaturated and saturated fats than non-Hispanic white women (p


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.001). Changes in diet related to length of US residency were also observed. As length

of US residency increased, dietary macronutrient composition among Hispanic

subjects trended towards a composition reflective of non-Hispanic participants.9

Healthy eating patterns may also be related to income. Kirkpatrick and

colleagues2analyzed NHANES data collected between 2001 and 2004 to determine

the rate of compliance to the 2005 Dietary Guidelines for Americansamong adults

from varying income levels and ethnic subgroups. As income level increased, so did

the proportion of adults meeting the minimum recommendations for fruit, whole fruits,

total vegetables, dark green vegetables, other vegetables, whole grains, meat and

beans, milk, and oils. Compared to non-Hispanic white and non-Hispanic black adults,

Mexican-American adults were more likely to meet the recommendations for dry

beans and peas and total grains, but less likely to meet the recommendations for dark

green vegetables, starchy vegetables, and oils.

Altogether these data show that differences in food selection and nutrient

intake have been observed among subjects of varying age, gender, and ethnicity. The

current investigation provides a unique opportunity to describe nutrient consumption

of a more specific subset of an ethnic population; low-income Hispanic females of

child-bearing age.

2.2 Temporal Eating Patterns

Body weight is a function of energy balance. In adults, positive energy

balance, an energy intake that exceeds energy expenditure, can result in weight gain


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over time. The temporal distribution of eating events may also play a role in body

weight. Analysis of eating patterns in free-living humans indicates that energy and

macronutrient intake varies at different times of the day.13Temporal eating patterns

have been assessed using dietary data from 24-hour recall or food record assessment

methods; these diet assessment methods are used by researchers to describe the time of

day and nutrient composition of eating events. This component of eating behavior and

its role in body mass index has been explored in several studies to date.

In one of the earliest studies of temporal eating patterns, de Castro and

colleagues14evaluated the 7-day dietary records of 867 adults (375 males, 492

females) to capture dietary intake. The absolute and mean energy throughout the day

was categorized into five different 4-hour intervals (6:00AM to 9:59AM, 10:00AM to

1:59PM, 2:00PM to 5:59PM, 6:00PM to 9:59PM, 10:00PM to 1:59AM). Each interval

includes a period of peak consumption bound by periods of low energy and nutrient

intake as observed in a prior study.13A significant difference in energy intake among

each of the five time intervals was observed (p < .001). The proportion of energy

intake in the morning was negatively correlated with total daily energy intake (r =

-.13, p < .01); in other words, an increased consumption of energy in the morning was

associated with lower total daily energy intake. The proportion of energy intake in the

evening was positively correlated with total energy intake (r = .14, p < .01); a larger

evening meal was associated with greater total daily energy intake. The results also

revealed an increase in meal size and meal frequency during afternoon and evening

intervals. This finding was consistent among both male and female subjects, and in


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food records collected from both week and weekend days. After further investigation

of the same data set, temporal correlations in energy intake were consistent among

macronutrients as well. Total daily intake of carbohydrates, fats and proteins was

negatively correlated with the intake of these nutrients within the morning time

interval.15

Forslund and colleagues16examined circadian eating among one group of

obese women (BMI > 33.5) and one group of randomly selected women living the

southwest region of Sweden (2002). This study utilized a meal pattern questionnaire to

classify eating events as traditionalif eating occurred in any of the following time

intervals: 6:00AM to 9:59AM (breakfast), 12:00PM to 1:59PM (lunch), and 4:00PM

to 7:59PM (dinner). Eating events outside of these time intervals were considered

non-traditional.Compared to the randomly selected reference group, the obese

group consumed more meals during non-traditional meal times (p < .001) in the

afternoon (p < .01) and in the evening (p < .01), but a smaller proportion of total meals

in the morning (p < .01).

Temporal eating patterns of children have also been explored. A large, cross-

sectional study by Eng and colleagues17investigated changes in body mass index

(BMI) related to proportional energy intake during various time intervals in US

children. Subjects were children, ages 2-18 (n = 11,072), who were enrolled in the

NHANES survey between 1999 and 2002. Dietary data were collected from a 24-hour

recall using a multiple-pass approach. Overweight children (BMI = 25.0 - 29.9),

consumed 47% of their total energy intake between the hours of 4:00PM and


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12:00AM, and significantly more calories (23.0 kcal) than normal weight children

(BMI < 25.0) between the hours of 4:00PM and 8:00PM (p < .0001). Obese children

(BMI > 29.9) consumed 49% of their total energy intake between 4:00PM and

12:00AM, and significantly more calories (27.4 kcals) between the hours 4:00PM and

8:00PM compared to their normal weight counterparts (p < .0003).17

Temporal eating patterns may play a role in weight loss as well. Keim and

colleagues conducted a longitudinal investigation of weight loss among female

subjects consuming a isocaloric diet, with one group consuming a large meal in the

morning and the other group consuming a large meal in the evening.18Participants (n

= 10) werebetween 20 and 40 years old, healthy, premenopausal with normal

menstrual cycles, andbody fat 30%. Subjects lived exclusively in a metabolic suite

during the study period. Using a randomized, cross-over study design, subjects were

assigned to the two treatment groups for six weeks each. Total energy requirements

were calculated for each participant using the Harris-Benedict equation and the daily

energy intake was adjusted to lead to gradual weight loss. Group A consumed a meal

containing 35% of their total calories at breakfast (8:00 to 8:30AM), and Group B

consumed a meal containing 35% of their total calories at dinner (10:00 to 10:30PM).

The results showed a significant decrease in overall weight (p < .01) and fat-free mass

(p < .001) among subjects in Group A, whereas subjects in Group B experienced a

greater reduction in body fat percentage (p < .05).

The prevalence of obesity related to various other aspects of eating patterns has

also been explored. In addition to the temporal distribution of meals, meal frequency,


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breakfast participation, and frequency of meals eaten away from home have also been

examined. In one study, subjects (n = 499) between 20 and 70 years old completed

three, 24-hour recalls over the course of a 1-year study period.19Subjects height and

weight were measured on-site, and BMI was calculated each visit. The authors

observed no significant correlation between obesity and the distribution of meals

throughout the day (measured by calculating the average interval between time out of

bed and first eating, average interval between time of last episode of eating and time in

bed, and average time of the largest episode of eating from waking up). However,

eating fewer than three meals per day and regular breakfast skipping were both

associated with an increased risk of obesity. Also, subjects reported significantly

higher daily caloric intake on days when breakfast was skipped.

The studies by de Castro and colleagues13-15attribute differences in the

temporal distribution of energy intake to a decrease in both the satiation and the satiety

value of food relative to time of day, with foods eaten towards the end of the day

being less satiating. The authors hypothesize that a decrease in the satiation value of

food leads to both increased meal size and increased meal frequency as the day

progresses. Epidemiologic investigations of breakfast consumption among children

and adolescents, reviewed extensively by Alexy and colleagues20reinforce this theory;

individuals who regularly skip breakfast display an increased risk of overweight and

obesity. Although the the relationship between body weight and breakfast

consumption has been well studied, the strength and direction of this association is

still unclear.21


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While it is recognized that the cross-sectional nature of studies regarding

temporal eating prevents researchers from establishing a causal pathway for this

relationship, these investigations have consistently revealed a negative correlation

between energy intake in the morning and total energy intake. Since energy intake in

the morning is related to total energy intake, it may also be associated with an

individuals risk of overweight and obesity. Although significant associations between

meal patterns, total energy intake, and body mass index have been observed, little is

known about the strength of this relationship across ethnic groups. The increased

prevalence of overweight and obesity observed among Hispanics is justification for

the current study to learn more about temporal eating patterns displayed by this

population.

2.3 Reporting Accuracy

An important component in diet and health outcome research is an accurate

description of habitual dietary intake. For most studies, assessing the validity of

reported energy intake is an essential component of dietary assessment. Misreported

energy intake can lead to under- or over-estimation of nutrient intake and incorrect

conclusions regarding diet and disease relationships.22Disparities in reported energy

intake versus actual energy consumption may be attributed to inadequate or

incomplete record keeping, conscious misreporting, reporting bias, or lack of

skill/training.23Individuals who report a dietary intake level below what is considered

biologically plausible, given their physiologic status and physical activity level, are


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known as under-reporters.24An extensive review by Poslusna and colleagues

concluded that under-reporting of energy intake is prevalent in the majority of dietary

studies. However, the prevalence varies based on the methods employed to assess

diet.25The prevalence of under-reporting ranged from 21.5-67%, 11.9-44%, and 14.3-

38.5% in studies using 24 hour recall, FFQ, and weighed food records respectively.

Statistically significant predictors of under-reporting were also identified by these

studies. Age, BMI, total energy intake, percent energy from fat, and variability in

number of meals per day were positively correlated with under-reporting whereas

socioeconomic status and level of education were negatively correlated with under-

reporting. Under-reporting also appears to be more prevalent among smokers and

subjects with a history of dieting.25These reviews also found that underreporting is

more common that over-reporting.

One of the largest studies to examine reporting accuracy was conducted by

Tooze and colleagues26using data collected by the National Cancer Institute from

1999-2000. This investigation, called the Observing Protein and Energy Nutrition

(OPEN) study, classified participants (n = 484) as under-reporters, accurate reporters,

or over-reporters based on reported energy intake from both a food frequency

questionnaire (FFQ) and 24 hour recall (24HR) methods. Analysis of reporting

accuracy using the doubly labeled water method, a biomarker of total energy

expenditure (TEE), revealed the magnitude and prevalence of under-reporting among

subjects. When subjects reported energy intake was compared to the median TEE,

men underreported by 11% on 24HRs and by 30% on FFQ. The magnitude of under-


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reporting among women was 17% and 24% of the 24HR and FFQ respectively.

Classification of individual subjects showed that the prevalence of under-reporting

was 22% for women and 21% for men when comparing subjects 24HRs to TEE.

Analysis of subjects FFQ revealed a prevalence of 49% and 50% among women and

men respectively. Furthermore, 13% of female subjects and 14% of males subjects

under-reported on both 24HR and FFQ.

The study by Tooze and colleagues26also identified characteristics associated

with underreporting. Analysis of subjectsFFQs showed that fear of negative

evaluation, weight-loss history, and percent energy from fat were predictive of

underreporting among women (R = .09), and in men the best predictors of

underreporting were BMI, relative activity level, and eating frequency (R = .10).

Analysis of subjects 24HR revealed that social desirability, fear of negative

evaluation, BMI, percent energy from fat, usual activity, and variability in number of

meals per day were the best predictors of under-reporting in women (R = .22),

whereas social desirability, dietary restrain, body mass index, eating frequency,

dieting history, and education were the best predictors among men (R = .25).

However, the prevalence of underreporting with respect to ethnicity was not addressed

in this study.

The gold-standard for validation of reported energy intake is the doubly-

labeled water technique. However, this method is expensive and requires the skill of a

trained technician. A more cost-effective approach is to validate reported energy

intake using the Goldberg method, which uses an empirically derived equation to


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assess reporting accuracy. In this equation, subjects total energy expenditure (TEE) is

calculated by multiplying basal metabolic rate (BMR) by physical activity level

(PAL). To calculate BMR, an individuals weightand age are entered into the

Schofield equation. PAL is set to a constant value of 1.55, which represents the

activity level of a sedentary individual as defined by the FAO/WHO/UNU.27To

validate subjects reported energy intake (rEI), a 95% confidence interval about the

log of the ratio rEI:TEE is calculated. Subjects whose rEI falls within the confidence

interval are classified as acceptable reporters and those that report above or below the

confidence interval are classified as over- and under-reporters, respectively.28

Research conducted by Tooze and colleagues28evaluated the accuracy of the Goldberg

method to classify subjects (n = 484) as either acceptable or unacceptable reporters

using data from the OPEN study; this study identified acceptable reporters using the

Goldberg equation, and compared the results to the gold standard, energy expenditure

as measured by doubly labeled water. The results showed that the sensitivity of

Goldberg method for determining reporting accuracy on an FFQ was 92.6% for men

and 92.1% for women, with a specificity of 87.6% for both. The positive predictive

value was 88% and the negative predictive value was 92% for both men and women.

In comparing the Goldberg method to subjects 24HR, the sensitivity was 45.1% for

men and 54.3% for women, with a specificity of 98.9% and 95.5% respectively. The

positive predictive value for men and women was 92% with a negative predictive

value of 86% for men and 88% for women.28The results of this analysis suggest that

the Goldberg cut-off is highly accurate in identifying subjects who under-report


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energy intake, and can be used in studies to determine reporting accuracy if utilizing

the doubly labeled water method is not possible.

To date, few studies have investigated reporting accuracy among Hispanic

populations. Olendzki and colleagues29described the prevalence of under-reporting

among low-income, low-literacy, Caribbean Hispanics (n = 215) enrolled in a

prevention program for type 2 diabetes. The sample was predominantly female (76%)

with a mean age of 51.5 years old (SD = 11.11). Dietary data were collected from

three unannounced 24-hour recalls performed by bilingual dietitians using a multiple-

pass technique, and energy expenditure was measured by basal metabolic rate. The

prevalence of under-reporting among subjects was not reported, however, the analysis

revealed that subjects under-reported energy intake by an average of 254 kcals per

day. Participants with higher BMI reported lower energy intake compared to their

basal metabolic rate (p < .001). Subjects who were unemployed, physically inactive,

or had siblings diagnosed with diabetes were more likely to under-report, although

these differences did not reach statistical significance.29

A similar study was conducted by Bothwell and colleagues24using random

sample of both Mexican and Mexican-American women (n = 357) between the ages of

21 and 67. This study analyzed the prevalence of under-reporting using four variations

of the Goldberg method. Method 1 used a PAL cutoff value of 1.51, the value

representing sedentary behavior and was adjusted to the full sample size. Method 2

was adjusted for the full sample size and PAL cutoff values of 1.51, 1.58, and 1.76 to

account for physical activity levels of low, moderate, and high physical activity levels


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respectively. Methods 3 and 4 used a conservative sample size (n=1), with Method 3

employing a constant PAL cutoff of 0.81 for sedentary behavior, and Method 4

adjusting for participant physical activity level using 0.81, 0.85, and 0.95 as cutoff

values for low, moderate, and high physical activity levels respectively. The

percentage of participants classified as under-reporting was 72.2% (Method 1), 81.3%

(Method 2), 11.9% (Method3), and 20.5% (Method 4).Clearly the PAL selected

greatly affects the results of the Goldberg method, and likely Methods 3 and 4 selected

PAL levels that were too low. Nonetheless, the results of this study also showed a

significant association between underreporting and both overweight and obesity (p elementary school 37 (57)

Marital Status

Married 46 (71)Not married 19 (29)

Employment

Unemployed, not looking 23 (35)

Unemployed, looking 16 (25)

Working part time 11 (17)

Working full time 15 (23)


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Table 4. Comparison of subject characteristics by reporting accuracy (all subjects)

Under-reporters

(n = 29)

Accuratereporters

(n = 36)

Teststatistic* p-value

Mean (sd) Mean (sd)

Age (years) 31.5 (4.7) 33.6 (5.4) 1.6242 0.1094

Height (cm) 155.9 (5.9) 155.9 (5.3) 0.0112 0.9911

Weight (kg) 70.5 (11.0) 67.4 (10.7) -1.1505 0.2545

Body mass index(kg/m2) 29.0 (4.0) 27.7 (4.2) -1.2488 0.2129

Number of children 2.6 (0.8) 2.8 (1.5) 0.9338 0.3545

Energy (kcals) 965 (151) 1788 (407) 11.2225 < .0001

BMI Category N (%) N (%) 1.1260 0.5695

Normal weight (18.5 - 24.9) 5 (17) 10 (28)Overweight (25.0 - 29.9) 15 (52) 15 (42)

Obese (30.0) 9 (31) 11 (31)

Marital Status 0.0680 0.7936

Married 21 (72) 25 (69)

Not married 25 (28) 11 (31)

Employment 0.5850 0.8998

Unemployed, not looking 10 (34) 13 (36)

Unemployed, looking 7 (24) 9 (25)

Working part time 6 (21) 5 (14)

Working full time 6 (21) 9 (25)

Own a car? 0.7580 0.3839

Yes 25 (86) 28 (78)

No 4 (14) 8 (22)

Education 0.5770 0.4474

Some/Completed elementary 14 (48) 14 (39)

> elementary 15 (52) 22 (61)

Monthly income 0.4800 0.4883

< $2,000 17 (59) 18 (50)

$2,000 18 (41) 18 (50)* Pearson chi-square for categorical and t-test for continuous variables


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Table 5: Total daily macronutrient intake (all subjects)

Nutrient Mean (SD) Median Q1 Q3 AMDR

Energy (kcals) 1421 (519) 1306 1008 1743 --

Discretionary kcals 387 (226) 364 208 501 --

Carbohydrates (g) 262 (140) 229 157 330 --% kcal from CHO 52.1 (11.6) 51.5 45.3 58.8 45-65%

Dietary fiber (g) 13.9 (7.5) 12.9 7.7 18.4 --

Sugar (g) 77.6 (36.2) 70.3 51.6 94.2 --

Protein (g)* 97.0 (60.7) 79.9 48.8 135.5 --

% kcal from PTN 18.8 (9.0) 17.9 14.4 21.8 10-35%

Total fat (g) 72.6 (51.8) 59.1 35.3 95.3 --

% kcal from FAT 30.2 (9.0) 31.1 23.4 37.3 20-35%

Saturated (g) 17.2 (10.4) 15.2 9.3 21.2 --

% kcal from SFAT 8.1 (3.7) 7.2 4.9 11 --

Cholesterol (mg) 233 (177) 187 100 338 --AMDR: Acceptable Macronutrient Distribution Range-- no data available


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Table 6: Total daily macronutrient intake by reporting accuracy

Under-reporters (n = 29) Accurate reporters (n = 36)

Nutrient Mean (SD) Median Q1 Q3 Mean (SD) Median Q1 Q3TestStatistic

p-value

Energy (kcals/d)a 965 (151) 1000 842 1069 1788 (407) 1718 1490 2061 12.90 < 0.01

Discretionary kcalsa 247 (113) 241 161 337 499 (232) 447 365 700 4.72 < 0.01

% kcal from discretionary 25.1 (10.2) 24.3 19.0 32.3 27.5 (11.2) 29.3 20.7 33.0 0.92 0.36

Carbohydrates (g/d)a 133 (37) 128 106 163 221 (63) 212 177 270 4.46 < 0.01

% kcal from CHO 55.0 (11.5) 54.6 47.7 63.3 49.8 (11.3) 50.9 44.0 55.5 -1.81 0.08

Dietary fiber (g/d)a 9.8 (4.8) 8.7 6.6 13.8 17.2 (7.7) 16.5 12.6 21.1 4.53 < 0.01

Dietary fiber (g/1000kcal)a 10.3 (5.0) 9.8 6.3 12.9 9.6 (3.8) 8.9 7.0 11.6 -0.39 0.70

Sugar (g/d)a 64.0 (23.8) 64.6 43.4 76.6 88.7 (40.8) 82.2 64.0 111.3 2.64 0.01

Sugar (g/1000kcal)a 65.6 (19.2) 57.0 51.6 78.3 50.2 (24.4) 44.8 35.2 62.8 -3.57 < 0.01

Protein (g)a 45.5 (13.4) 43.4 35.3 53.8 83.2 (38.0) 79.7 58.7 97.8 3.28 < 0.01

% kcal from PTN 19.2 (6.2) 18.1 14.1 22.1 18.4 (6.0) 17.9 14.6 21.3 -0.49 0.63

Total fat (g/d)a 29.3 (11.9) 29.6 17.6 36.6 65.6 (23.2) 63.6 47.4 81.1 5.95 < 0.01

% kcal from FAT 27.1 (9.6) 26.3 19.4 36.0 32.8 (7.8) 32.2 28.7 39.3 2.55 0.01

Saturated (g/d)a 10.3 (4.4) 10.4 7.2 13.6 22.7 (10.5) 20.6 15.0 28.4 6.35 < 0.01

% kcal from SFAT 7.7 (4.0) 7.0 4.4 11.2 8.4 (3.4) 7.9 5.3 10.9 6.35 < 0.01

Cholesterol (mg/d)a 161 (109) 151 77 206 291 (200) 260 132 385 3.01 < 0.01

Cholesterol (mg/1000kcal)a 171 (125) 146 82 219 162 (109) 147 78 202 -0.33 0.74

a Values were log transformed prior to t-test


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Table 7: Total daily micronutrient intake (all subjects)

Nutrient Mean (SD) Median Q1 Q3 %EAR % RDA/AI < EAR (%) > UL (%)

Vitamins

Thiamin (mg/d) 1.4 (0.7) 1.3 0.9 1.9 156 127 23 --

Thiamin (mg/1000kcal) 1.0 (0.5) 0.9 0.7 1.2 -- -- -- --

Riboflavin (mg/d) 1.7 (0.7) 1.6 1.1 2.2 189 155 12 --

Riboflavin (mg/1000kcal) 1.3 (0.6) 1.1 1.0 1.6 -- -- -- --

Niacin (mg/d) 19 (8) 18 13.0 25 173 136 12 3Niacin (mg/1000kcal) 14.4 (4.8) 13.6 9.2 16.5 -- -- -- --

Vitamin B6 (mg/d) 1.9 (0.9) 1.8 1.20 2.5 173 146 23 0

Vitamin B6 (mg/1000kcal) 1.5 (0.8) 1.2 0.9 1.7 -- -- -- --

Folate (mcg/d) 388 (215) 354 241 512 121 97 59 3

Folate (mcg/1000kcal) 293 (180) 254 159 362 -- -- -- --

Vitamin A (RAE/d) 550 (321) 511 335 697 110 79 49 0

Vitamin A (RAE/1000kcal) 410 (246) 360 254 517 -- -- -- --

Vitamin B12 (mcg/d) 5.3 (3.6) 4.9 2.5 7.5 265 221 17 --

Vitamin B12 (mcg/1000kcal) 4.0 (2.8) 2.9 1.9 5.6 -- -- -- --

Vitamin C (mg/d) 88 (60) 76 41 122 147 117 40 0

Vitamin C (mg/1000kcal) 68 (56) 52 35 84 -- -- -- --

Vitamin D (mcg/d) 4.2 (2.9) 3.9 1.6 6.6 42 28 99 0Vitamin D (mcg/1000kcal) 3.1 (2.1) 2.8 1.2 4.6 -- -- -- --

Vitamin E (mg/d) 5.5 (4.1) 4.8 2.9 6.1 46 37 95 0

Vitamin E (mg/1000kcal) 4.0 (3.2) 3.3 2.6 4.2 -- -- -- --


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: continuedTable 7: Total daily micronutrient intake (all subjects)

Nutrient Mean (SD) Median Q1 Q3 %EAR % RDA/AI < EAR (%) > UL (%)

Minerals

Calcium (mg/d) 744 (362) 723 481 938 93 74 62 0

Calcium (mg/1000kcal) 546 (263) 497 332 689 -- -- -- --

Copper (mg/dl) 933 (415) 901 632 1151 133 104 31 0

Copper (mg/1000kcal) 667 (193) 662 504 794 -- -- -- --

Iron (mg/d) 14.5 (7.4) 13.3 8.2 18.8 179 81 23 0Iron (mg/1000kcal) 10.7 (5.5) 8.8 7.1 12.1 -- -- -- --

Phosphorus (mg/d) 1036 (441) 946 733 1293 179 148 11 0

Phosphorus (mg/1000kcal) 735 (173) 728 624 814 -- -- -- --

Sodium (mg/d) 2483 (1243) 2177 1525 3077 -- 165 -- 46

Sodium (mg/1000kcal) 1754 (609) 1626 1354 2048 -- -- -- --

Zinc (mg/d) 10.8 (6.6) 9.8 5.8 14.3 159 135 35 0

Zinc (mg/1000kcal) 7.7 (4.2) 6.6 4.8 9.3 -- -- -- --

-- no data available


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Table 8. Healthy Eating Index scores (all subjects)

Category (maximum points) Mean (SD) Median Q1 Q3

n (%) meeting

standard

Adequacy

Total Fruit (5) 3.5 (2.0) 5.0 1.7 5.0 37 (57)

Whole Fruit (5) 3.2 (2.3) 5.0 0.0 5.0 32 (49)

Total Vegetables (5) 3.5 (1.7) 3.7 2.4 5.0 25 (38)

Greens and Beans (5) 2.0 (2.3) 0.3 0.0 5.0 18 (28)

Whole Grains (10) 2.4 (3.2) 3.7 2.4 5.0 4 (6)

Dairy (10) 6.0 (3.5) 6.8 3.0 10.0 18 (28)

Total Protein Foods (5) 3.7 (1.8) 5.0 2.2 5.0 33 (51)

Seafood and Plant Proteins (5) 1.2 (1.9) 0.1 0.0 1.9 9 (14)

Fatty Acids (10) 3.8 (3.3) 3.4 0.7 6.0 6 (9)

Moderation

Refined Grains (10) 4.1 (3.6) 3.7 0.6 6.8 10 (15)

Sodium (10) 4.1 (3.6) 3.3 0.0 6.9 6 (9)

Empty Calories (10) 9.6 (6.8) 8.4 4.4 17.3 15 (23)

Total Score (100) 47.1 (12.2) 46.1 39.4 52.4 13.6 - 79.5*

*Range


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Table 9: Temporal distribution of energy and macronutrient intake (all subjects)

a Values were log transformed prior to ANOVA; data presented as absolute valuesbcd Dissimilar superscripts indicate significant difference between time frames

Nutrient T1 (4:00AM - 10:29AM) T2 (10:30AM - 4:59PM) T3 (5:00PM - 3:59AM)

Mean (SD) Median Q1 Q3 Mean (SD) Median Q1 Q3 Mean (SD) Median Q1 Q3

Absolute daily intakea

Energy (kcals)

543

(423)

bc

443 231 708 832 (573)

c

652 370 1137 690 (588)

c

565.0 259 922.0Carbohydrate(g) 79.2 (59.6) 65.8 36.9 109

100.4(72.9) 85.4 44.8 151.7 81.8 (60.1) 69.9 41.9 125.6

Protein (g)

22.7(26.1)b 16.7 5.9 24.7 39.5 (29.9)c 31.9 15.2 58.7 34.9 (37.0)c 19.7 8.6 52.2

Fat (g)

16.1(17.6)b 10.4 4.7 18.5 30.9 (27.1)c 20.7 12.7 44.5 25.6 (29.1)c 16.8 5.1 32.7

Percent daily intake

Energy

26.0(14.5)b 20.9 14.9 39.9 41.8 (21.2)c 37.4 29.6 54.3

32.1(18.3)b 32.0 29.1 48.6

Carbohydrate

30.3(16.7)b 28.8 17.7 40.5 38.0 (21.4)c 35.6 24.1 47.4

31.7(17.1)bc 32.1 21.6 45.7

Protein

22.0(16.5)b 18.1 11.1 28.6 43.4 (26.1)c 39.4 19.4 63.0

34.5(25.5)d 36.5 12.1 55.3

Fat22.2(18.2)b 17.4 8.9 33.0 45.3 (25.3)c 42.9 28.6 59.7

32.5(22.1)d 30.2 13.4 50.3


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Table 10. Comparison of temporal distribution of energy intake by BMI category (all subjects)

T1 (4:00AM - 10:29AM) T2 (10:30AM - 4:59PM) T3 (5:00PM - 3:59AM)

Absolute daily intakea

Normal weight 535 (387) 804 (560) 786 (713)

Overweight 493 (351) 834 (592) 654 (545)

Obese 624 (541) 851 (581) 672 (573)

F-Test statistic (p-value) 0.5757 (p = 0.5653) 0.0292 (p = 0.9713) 0.2579 (p = 0.7735)

Percent daily intake

Normal weight 25.2 (13.8) 40.3 (23.9) 34.5 (20.4)

Overweight 25.8 (13.4) 41.3 (18.6) 33.0 (17.7)

Obese 27.1 (17.1) 43.8 (23.6) 29.1 (18.1)

F-Test statistic (p-value) 0.0863 (p = 0.9174) 0.1296 (p = 0.8787) 0.4268 (p = 0.6545)a Values were log transformed prior to ANOVA


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Table 11. Comparison of energy intake and macronutrient distribution from Project

Vida Sana and nationally representative data

HHANES

(1982-1984)

NHANES

(1988-1994)

NHANES

(1999-2006)

Project Vida

Sana (2012-

2013)

Energy (kcals) 1552 (14.69) 1744 (22.44) 1827 (23.61) 2065 (140.80)

Carbohydrates (%) 47.5 (0.42) 51.7 (0.31) 52 (0.38) 52.1 (11.60)

Total fat (%) 35.7 (0.34) 32.6 (0.26) 32.7 (0.27) 30.2 (1.12)Saturated fat (%) 12.7 (0.14) 10.8 (0.12) 10.7 (0.13) 8.1 (0.46)

Protein (%) 17.2 (0.10) 16.1 (0.09) 15.8 (0.14) 18.8 (0.75)

Table adapted from Fryar and colleagues (2012)

Values reported as mean (SE)


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Table adopted from Gregory-Mercado and colleagues (2007)Values reported as mean (SE)--no data available

Table 12: Comparison of daily micronutrient intake among Mexican-American women

WISEWOMEN (1998-2000) Project Vida Sana (2012-2013)

Vitamins

Vitamin A (RAE) 1191.4 (0.02) 550 (39.77)

Thiamin (mg) 1.25 (0.01) 1.4 (0.08)

Riboflavin (mg) 1.44 (0.01) 1.7 (0.09)

Niacin (mg) 16.8 (0.01) 19 (1.05)

Vitamin B6 (mg) 1.66 (0.01) 1.9 (0.11)Folate (mcg) 269 (0.01) 388 (26.64)

Vitamin B12 (mcg) 2.06 (0.00) 5.3 (0.44)

Vitamin C (mg) 130.2 (0.01) 88 (7.46)

Vitamin D (mcg) -- 4.2 (0.36)

Vitamin E (mg) 6.64 (0.02) 5.5 (0.50)

Minerals

Calcium (mg) 649.4 (0.01) 744 (44.90)

Copper (mg) 1.14 (0.01) 0.93 (0.42)

Iron (mg) 12.6 (0.01) 14.5 (0.91)

Magnesium (mg)* 239.8 (0.01) 223 (11.32)

Phosphorus (mg) 930.7 (0.01) 1036 (54.69)

Sodium (mg) 2572.2 (16.5) 2483 (154.1)

Zinc (mg) 9.26 (0.01) 10.8 (0.82)


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Appendix


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IRB LETTERS


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Documents

Kyle Takayama dissertation.pdf