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TRACKING OBESITY RELATED BEHAVIORS FROM CHILDHOOD TO
ADOLESCENCE: THE FUN 5 STUDY
A DISSERTATION SUBMITTED TO THE GRADUATE DIVISION OF THE UNIVERSITY
OF HAWAII AT MANOA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR
THE DEGREE OF
DOCTOR OF PHILOSOPHY
IN
EPIDEMIOLOGY
MAY 2014
By
Md Mahabub ul Anwar
Dissertation Committee:
Andrew Grandinetti, Chairperson
Claudio Nigg
Eric Hurwitz
Dongmei Li
Min Liu
Kamal Masaki
Keywords: Obesity, childhood, adolescent, physical activity, fruit and vegetable
consumption, sedentary behavior, theory of planned behavior, body mass index
i
Table of Contents
Table of contents ................................................................................................................... i
List of tables ........................................................................................................................ iii
List of figures ....................................................................................................................... iv
Abbreviations ....................................................................................................................... v
Abstract .............................................................................................................................. vi
Introduction ........................................................................................................................ 1
Specific Aims ........................................................................................................ 3
Framework of three papers ................................................................................................ 3
Paper 1: Tracking physical activity from childhood to adolescence: the Fun 5 study ..................................................................................................................... 7
Introduction ......................................................................................................... 7
Conceptual model: TPB to explain childhood PA ................................................ 9
Methods ............................................................................................................. 12
Results ................................................................................................................ 17
Discussion........................................................................................................... 26
Strengths and limitations of the study .............................................................. 29
Paper 2: Predictors of fruits and vegetables consumption from childhood to adolescence: the Fun 5 study ............................................................................ 30
Introduction ....................................................................................................... 30
Conceptual model: TPB to explain childhood FVC ............................................. 32
Methods ............................................................................................................. 34
Results ................................................................................................................ 39
Discussion........................................................................................................... 47
Strengths and limitations ................................................................................... 50
ii
Paper 3: Predicting adolescents’ obesity related behaviors: the Fun 5 study ................. 52
Introduction ....................................................................................................... 52
Methods ............................................................................................................. 56
Results ................................................................................................................ 61
Discussion........................................................................................................... 69
Conclusion ......................................................................................................................... 72
References ........................................................................................................................ 75
iii
List of tables
Table 1 Descriptive statistics for respondents, predictors, and outcome variables ............................................................................................................. 18
Table 2 Piecewise growth mixer modeling for growth trajectories in PA over time for cohort 1 and cohort 2 .......................................................................... 21
Table 3 Bivariate correlation among TPB constructs to explain childhood PA .............. 22
Table 4 Goodness of fit indices for the models to explain childhood PA by TPB constructs .......................................................................................................... 23
Table 5 Predicting adolescent obesity by childhood PA, adolescent PA, and demographic variables ....................................................................................... 24
Table 6 Descriptive statistics for respondents, predictors, and outcome variables ............................................................................................................. 40
Table 7 Piecewise growth mixer modeling for growth trajectories in FVC over time for cohort 1 and cohort 2 .......................................................................... 43
Table 8 Bivariate correlation among TPB constructs to explain childhood FVC ............ 44
Table 9 Goodness of fit indices for the models to explain childhood FVC by TPB constructs ........................................................................................................... 45
Table 10 Predicting adolescent obesity by childhood FVC, adolescent FVC, and demographic variables ....................................................................................... 46
Table 11 Hypotheses to predict adolescents’ obesity related behaviors ........................ 56
Table 12 Descriptive statistics for respondents, predictors, and outcome variables ............................................................................................................. 62
Table 13 Goodness of fit indices for the models to explain adolescent PA, FVC, and SB by childhood PA, FVC, and SB with all paths.......................................... 64
Table 14 Goodness of fit indices for the revised models to explain adolescent PA, FVC, and SB by childhood PA, FVC, and SB with significant paths only ............. 65
Table 15 Goodness of fit indices for the revised models to explain adolescent PA, FVC, and SB by childhood PA, FVC, and SB combining both cohorts ................ 65
iv
List of figures
Figure 1 Model of the three papers framework ................................................................ 6
Figure 2a Observed PA of 10% randomly selected students from Cohort 1 over time .................................................................................................................... 20
Figure 2b Observed PA of 10% randomly selected students from Cohort 2 over time .................................................................................................................... 20
Figure 3a Standardized path coefficients of TPB constructs with 95% confidence interval explaining PA of cohort 1-BL participants (n=251) ............................... 23
Figure 3b Standardized path coefficients of TPB constructs with 95% confidence interval explaining PA of cohort 2-BL participants (n=247) ............................... 24
Figure 4a Observed FVC of 10% randomly selected students from Cohort 1 over time .................................................................................................................... 42
Figure 4b Observed FVC of 10% randomly selected students from Cohort 2 over time .................................................................................................................... 42
Figure 5a Standardized path coefficients of TPB constructs with 95% confidence interval explaining FVC of cohort 1-BL participants (n=248) ............................. 45
Figure 5b Standardized path coefficients of TPB constructs with 95% confidence interval explaining PA of cohort 2-BL participants (n=243) ............................... 46
Figure 6a Standardized path coefficients of childhood behaviors with 95% confidence interval explaining adolescent behaviors of cohort 1 participants in revised model (n=192), significant paths only ........................... 66
Figure 6b Standardized path coefficients of childhood behaviors with 95% confidence interval explaining adolescent behaviors of cohort 2 participants in revised model (n=182), significant paths only ........................... 67
Figure 6c Standardized path coefficients of childhood behaviors with 95% confidence interval explaining adolescent behaviors of participants for both cohorts in revised model (n=374), significant paths only ......................... 68
v
Abbreviations
BL Baseline
BMI Body Mass Index
CDC Centers for Disease Control and Prevention
CFI Comparative Fit Index
CVD Cardiovascular Disease
FVC Fruit and Vegetable Consumption
MVPA Moderate to Vigorous Physical Activity
NHANES National Health and Nutritional Examination Survey
NHOPI Native Hawaiians and other Pacific Islanders
PA Physical Activity
PBC Perceived Behavioral Control
RMSEA Root Mean Squared Error of Approximation
SB Sedentary Behavior
SCT Social Cognitive Theory
SD Standard Deviation
SRMR Standardized Root Mean Square Residual
T1 One-Year Follow-Up
T2 Five-Year Follow-Up
TPB Theory of Planned Behavior
TV Television
WRMR Weighted Root Mean Square Residual
vi
Abstract
Obesity in the United States has increased dramatically, and has become a public
health crisis. Studies documented that healthy lifestyle, including healthy eating,
increased physical activity (PA), and decreased sedentary behavior (SB), can lower the
risk of becoming obese, as well as developing obesity related diseases. In order to
explore the changing pattern of PA and fruit and vegetable consumption (FVC) from
childhood to adolescence, as well as to investigate how childhood PA, FVC, and SB
influence adolescence PA, FVC, and SB, this dissertation utilized the data of Fun 5 study,
which is a longitudinal cohort study. Two cohorts of data were utilized for this
dissertation, where students were followed up for five years. Using piecewise growth
mixer modeling, and random coefficient model, paper one of this dissertation did not
find any significant pattern of PA change from childhood to adolescent. Structural
equation modeling revealed the applicability of theory of planned behavior (TPB) for
childhood PA. Findings also documented that self confidence is the strongest variable to
influence childhood PA followed by attitudes. Paper one did not find any impact of
childhood and adolescent PA on adolescent obesity. Using piecewise growth mixer
modeling, and random coefficient model, paper two revealed a declining pattern of FVC
from childhood to adolescent. Structural equation modeling revealed the applicability of
TPB for childhood FVC. Findings also documented that self confidence is the strongest
variable to influence childhood FVC followed by subjective norms. Paper two did not
find any impact of childhood and adolescent FVC on adolescent obesity. Using structural
equation modeling, paper three of this dissertation revealed that all three childhood
vii
behaviors (PA, FVC, SB) predicted the same adolescent behaviors, which implies that
these behaviors develop at childhood and continue to adolescence. Findings suggest to
target early childhood for intervention activities regarding increasing PA, FVC, and
decreasing SB, as well as target children’s self confidence, subjective norms, and
attitudes towards PA and FVC. Findings also recommend further studies to explore the
change of obesity related behaviors from childhood to adolescent, as well as the
predictive ability of these behaviors to adolescent obesity.
1
INTRODUCTION
Childhood obesity in the United States has increased dramatically over the last three
decades. The percentage of obese (BMI ≥ 95th percentile) children aged 6-11 years has
risen from 7% in 1980 to 18% in 2010, while the percentage of obese adolescents aged
12-19 years increased from 5% to 18% over the same period (1). Results from the 2009-
2010 National Health and Nutritional Examination Survey (NHANES) estimate that about
one third of children and adolescents aged 2-19 years are overweight or obese (BMI ≥
85th percentile) (1). Excess weight puts children at greater risk for elevated cholesterol,
plasma insulin, and systolic blood pressure (2, 3), which are risk factors for chronic
conditions like cardiovascular disease (CVD) (4) and type 2 diabetes (5, 6). Childhood
obesity also increases the risk for negative psycho-social consequences, such as
discrimination and stigmatization, and may lead to low self-esteem, social alienation,
lack of self-confidence, depression, and other mental health risks (7-10); moreover, it
contributes to health care costs. Wang and colleagues (11) predict that the prevalence
of overweight in children will nearly be double by 2030 contributing to approximately
16-18% of the total US healthcare costs.
This increasing prevalence of childhood obesity may be addressed by focusing on
primary prevention to avoid the burden of disease, and economic consequences.
Furthermore, as childhood obesity is a significant predictor of adult obesity (12-15),
identifying the related risk behaviors for early intervention efforts is important to
prevent adult obesity and its associated complications. Studies reveal that lack of
physical activity (PA) (16-19), poor diet (16, 20), and sedentary behavior (SB) (16-19) are
2
key risk factors for obesity. The combination of unhealthy nutrition practices and
decreased PA behaviors affect energy balance, which is the simplest explanation for
weight gain. Several studies have found declining trends of PA from childhood to
adolescence to adulthood (21-23). On the other hand, research has shown that SB,
especially TV watching, increases with age and remains relatively consistent over time
(24, 25), as well as obesity. Viner and Cole (25) report that each additional hour of
watching TV on weekends at 5 years of age increases the risk of adult obesity by 7%.
Studies further reveal that food behavior and food choices, especially fruit and
vegetable consumption (FVC), are established in childhood or adolescence and may
persist into adulthood (26-28). In addition, studies observe that many adolescents have
developed multiple health risk behaviors related to PA, SB, and FVC, and the number of
these health risks increases from childhood to adolescence (29-30).
Among the US populations, Native Hawaiians and other Pacific Islanders (NHOPI)
are especially prone to obesity and its consequences (31-33). Novotny and colleagues
(34) reveal that almost one in three NHOPI children of 5-8 years old is either obese or
overweight. To decrease the prevalence of preventable diseases, it is essential to
understand how childhood health behaviors influence adolescence. However,
longitudinal research of obesity-related behaviors, with long term following up from
childhood to adolescence, targeting the NHOPI populations, especially Hawaii, is very
limited. Therefore, this study will track obesity-related behaviors in the Hawaii
populations in the transition from childhood to adolescence.
3
Specific Aims
The specific aims of this study are:
1. To explore the changing pattern of PA from childhood to adolescence,
2. To explore the changing pattern of FVC from childhood to adolescence,
3. And to investigate how childhood PA, SB, and FVC behaviors influence
adolescence PA, SB, and FVC behaviors.
FRAMEWORK OF THREE PAPERS
Recent literature documents that regular PA improves reductions in body
adiposity, increases aerobic fitness, and contributes to a range of other health benefits
from decreased blood pressure to increased bone mass in adulthood (16-19). PA also
benefits children physically, mentally, emotionally, and socially (7, 8, 10, 17-19). Studies
reveal that time spent in moderate to vigorous physical activity (MVPA) is positively
associated with a lower childhood BMI (35, 36). Considering the health benefits of PA,
the Centers for Disease Control and Prevention (CDC) recommends at least 60 minutes
of MVPA for children every day (37). However, a significant number of US children do
not follow this recommendation (38, 39). According to Beighle (40), less than half of the
6-11 year old children met the recommendation. Moreover, the percentage of children
meeting recommended PA levels declines with age (21-23). In a pooled analysis of 26
studies, Dumith and colleagues (21) reveal a 7% PA decline per year among adolescents.
4
Conversely, several studies document that SB, especially TV watching, is
associated with multiple risk factors for negative health outcomes, including obesity (24,
25, 41-43). Proctor and colleagues (44) reveal that eating meals in front of the TV,
especially, salty snacks, pizza, and soft drinks may influence energy intake. Despite the
negative health consequences, TV watching appears to increase with age (24, 25).
Healthy diets have health benefits and help reduce obesity (45). The Dietary
Guidelines for Americans recommends a diet rich in FV, whole grains, and fat-free or
low-fat dairy products for individuals of 2 years and older. The guidelines also
recommend limiting the intake of solid fats for children, adolescents, and adults (46).
However, most young people are not following these recommendations (46-48).
Moreover, studies further reveal a decreasing pattern of FVC from childhood to
adolescence to adulthood (26-28).
Several studies document that PA, SB, and FVC are associated with each other
(29, 30, 49) and influence each other over time (49, 50). For example, Driskell and
colleagues (30) document that healthy diet, intention to be active, and PA preferences
cluster with PA. Lippke and colleagues (51) also reveal a strong correlation between
nutrition and PA. These associations of obesity risk behaviors imply that a group of risk
behaviors tend to occur together, and may be tied with common underlying factors;
thus an intervention that addresses these behaviors together could potentially be more
effective in the long run. Considering the interactions, researchers (52) suggest that
multiple behavior change interventions may have a greater impact than single behavior
change interventions.
5
Since PA, SB and FVC behaviors change from childhood to adolescents, it is also
important to identify the age when these behaviors change. Findings of such studies
may be helpful to develop age appropriate interventions. It is also important to find out
how these behaviors in childhood influence adolescent behaviors, as during adolescence,
individuals start to make their own choices and develop a personalized lifestyle.
Therefore, this study provided evidence to answer two questions regarding the
changing pattern of health behaviors from childhood to adolescence:
1. Do PA and FVC change from childhood to adolescence?
2. How do childhood PA, SB, and FVC behaviors influence adolescence PA, SB,
and FVC behaviors?
This first paper answered whether PA changed from childhood to adolescents
among Hawaii populations, and examined what accounts for behavior changes using the
constructs of the theory of planned behavior (TPB). In addition, this paper also explored
the influence of PA on obesity over time. The second paper answered the same question
for FVC. Paper #3 explored how these behaviors (PA, SB, FVC) in childhood influenced
adolescent behaviors over time. Figure 1 (next page) illustrates the model of the three
papers framework.
6
Figure 1 Model of the three papers framework
Paper 1: Tracking physical activity from childhood to adolescence
Paper 2: Predictors of fruits and vegetables consumption from childhood to adolescence
Paper 3: Predicting adolescents’ obesity related risk behaviors
Adolescence Childhood
Single behavior
Predictors PA PA Obesity
Predictors FVC FVC Obesity
Multiple behaviors
PA
SB
FVC
PA
SB
FVC
PA
SB
FVC
7
PAPER ONE: TRACKING PHYSICAL ACTIVITY FROM CHILDHOOD TO
ADOLESCENCE: THE FUN 5 STUDY
Introduction
Recent literature documents that regular physical activity (PA) helps to reduce
body adiposity; increase aerobic fitness; decrease blood pressure; and increase bone
mass in adulthood (16-19). PA also benefits children physically, mentally, emotionally,
and socially (7, 8, 10, 17-19). Studies reveal that time spent in moderate to vigorous
physical activity (MVPA) is positively associated with a lower childhood BMI (35, 36). The
Centers for Disease Control and Prevention (CDC) also recommends that children should
engage in MVPA for at least 60 minutes every day (37); however, a significant number of
US children do not follow this recommendation (38, 39). According to Beighle (40), less
than half of 6-11 year old children participate in MVPA for one hour per day. Moreover,
the percentage of children meeting the recommended PA levels declines as they grow
up (21-23). In a pooled analysis of 26 studies, Dumith and colleagues (21) reveal a 7% PA
decline per year among adolescents; and the decline is more prevalent among females
than males (53). Studies have also found that girls (54) and overweight children (55, 56)
have lower overall MVPA levels than boys and normal weight children respectively.
Since lack of PA is one of the risk factors of childhood obesity (16-19), which
increases the risk of remaining obese in adulthood as well as experiencing other long-
term adverse health consequences (57, 58), children are the primary target for PA
interventions. Interventions are based on the belief that PA changes over time (59). PA
8
tracking studies quantify whether a child will maintain his/her relative rank for activities
within his/her cohort over time (60, 61). Tracking PA levels for periods of stability
provides insights as to when the root determinants and early antecedents of PA
behavior changes occur. The tracking studies can also be used to examine whether
childhood PA values help to predict PA levels in later life (62). Such studies also suggest
how to measure behavior risks, and identify targeted interventions for the “at-risk”
children. Moreover, a high degree of association between childhood PA patterns and PA
in later life might suggest that early measurement and intervention could promote
healthy PA levels in later life. This strategy might have long-term implications for good
health as the causal relationship between PA and cardio vascular disease (CVD)
outcomes has been reported in adults (63, 64). Additionally, tracking studies help us to
understand how PA develops naturally as related to health related phenomena. For
example, knowing that PA remains stable from childhood to adolescence would provide
evidence that the root determinants of PA occur in the early life.
There are several PA tracking studies (21,60-62, 65, 66), but very few have
targeted children of ethnic minority groups (59, 67). In the US, the Native Hawaiian and
other Pacific Islander (NHOPI) populations consist of diverse minority groups and are
especially prone to obesity and its consequences (31-33). Novotny, Oshiro, and Wilkens
reveal that almost one third of 5-8 year old NHOPI children are either obese or
overweight (34). Studies also reveal a high prevalence of health risk behaviors and
obesity/overweight among Hawaiian youth (68). According to the 2008 Hawai‘i Physical
Activity and Nutrition Surveillance Report, 71% of middle school children do not follow
9
the recommended 60 minutes of MVPA every day (68). Moreover, the longitudinal
studies that track the PA level of the Hawai‘i population are very limited.
In order to develop effective PA interventions, a better understanding of PA
behavior trends is necessary (69-71). Analyzing data under a strong theoretical
framework might prove to be an efficient way to identify intervention targets and
increase PA more effectively among children (71-74). However, most studies are not
based on a strong theoretical framework (75), such as the theory of planned behavior
(TPB) or the social cognitive theory (SCT). Consequently, the results from such studies
have shown limited success in increasing PA levels (76). Baranowski, Anderson and
Carmack (71) have reviewed 23 PA intervention studies, and reveal the importance of
using behavioral theory for successful interventions. In addition, there are a few
longitudinal studies considering the TPB, especially targeting the Hawai‘i population.
Therefore, the specific objectives of this study were to:
1. track PA changes from childhood to adolescence among the Hawai‘i
population.
2. apply the TPB constructs to explain childhood PA.
The secondary objective of this study was to examine whether childhood and
adolescent PA can predict adolescent obesity.
Conceptual model: TPB to explain childhood PA
The TPB is frequently used to study a broad range of behaviors (77, 78) with a
particular focus on health (78), including PA (79,80). The theory has been used
10
extensively to predict and influence PA in a variety of studies (81). Several meta-
analyses (80, 82, 83) have shown evidence of the applicability of the TPB in the
explanation of PA intentions and behavior. Plotnikoff and colleagues have found that
TPB explains 34% of variance in PA while SCT explains 24% (82). According to the TPB, a
person’s individual motivational factors predict his/her specific behavior (84). A
behavior is predicted by the intention and the perceived behavioral control (PBC) when
it is not completely volitional. The intention is hypothesized to be determined by
attitudes, subjective norms, and PBC. An attitude represents an individual’s assessment
of their own beliefs regarding the target behavior’s agency in producing effective
outcomes. The subjective norms represent a person’s evaluation of whether significant
others want them to engage in the target behavior and include his/her motivation to
comply with the desires of those significant others. The effects of attitude and
subjective norms on the behavior are mediated by the intention. The PBC represents an
individual’s assessment of their own capacity to affect behavioral engagement.
According to the TPB, children with strong intentions are more likely to engage in
PA than the ones with weaker intentions (84). Subjective norm, individual’s attitudes,
and PBC influence the intentions to engage in PA. Children who have positive attitudes
toward PA are more likely to have strong intentions than those with negative attitudes.
For instance, children who have fun (i.e., the experiential aspect of an attitude) being
physically active are more likely to make plans to be more active. Children who perceive
that significant adults (e.g., physical education teachers) expect them to engage in PA,
and are motivated to obtain their teacher’s approval, are likely to have strong intentions
11
to participate in PA. In other words, children who are aware of their teacher’s desire for
them to be active, and want to please their teachers in that regard try to (i.e., develop
an intention to) be more active. Finally, children who have control over their PA level
under any circumstances are likely to have high intentions to perform PA compared to
those with weaker control (85).
This paper explored the changing pattern of PA, and evaluated the TPB
constructs to explain childhood PA among Hawai‘i populations. This paper also
examined the ability of childhood and adolescent PA to predict adolescent obesity. We
hypothesized that:
Hypothesis 1: Higher childhood PA precedes higher adolescent PA
Hypothesis 2: The TPB constructs explain childhood PA
a. children with favorable attitudes, strong perceptions about subjective
norms, and strong PBC, all regarding PA, will express greater intentions to
engage in PA compared to those reporting less positive cognitions
b. PBC in childhood will have a direct positive influence on childhood PA as
well as be mediated by intention
c. children with greater intentions will do more PA compared to those with
weaker intentions
d. Subjective norm or attitude in childhood will not directly predict
childhood PA but be mediated by intention
12
Hypothesis 3: Childhood and adolescent PA can predict adolescent obesity
a. children who have higher PA levels will have a lower BMI in adolescence
compared to those who have lower PA levels
b. adolescent who have higher PA levels will have a lower BMI in
adolescence compared to those who have lower PA levels
Methods
Design and sample
For this study, we used the data of two cohorts from the Fun 5 study (86,87),
which had adopted a longitudinal cohort design, and included three consecutive surveys
- baseline (BL), one-year follow-up (T1), and five-year follow-up (T2). In cohort 1, 259
students from 4th-6th grades (9-12 years old) were randomly selected in 2006 for the BL
survey. Out of 259 students, 172 participated in T1 (2007), and 116 in T2 (2011). In
cohort 2 248 students participated in the BL survey in 2007, 176 in T1 (2008), and 88 in
T2 (2012). We replicated the findings using two cohorts of students.
Description of Fun 5 study
The Fun 5 study is an evidence-based PA and nutrition promotion program,
which has been implemented in over 160 Hawai‘i state-legislated elementary A+ after-
school programs for ten years. The program has been implemented with the aim of
promoting PA and fruit and vegetable consumption (FVC) among children. The program
trained after-school staff on PA, nutrition, and sustainability. The nutrition component
13
included lessons on benefits of FV, managing portion sizes, and sugar intake. The staff
was also encouraged to be good role models in terms of snacking during A+ time, and to
acknowledge when the children brought healthy snacks. To promote FVC, the program
developed and distributed an activity booklet for students, which included word
searches, cross words, coloring pages, and word/picture matching activities addressing
local culturally appropriate FV. In order to increase the children’s PA, the program
offered after school students a variety of organized, non-competitive, non-gender-
specific, and fun physical activities in which children of all skill levels could participate
and experience success (86, 87).
Data collection procedure
After obtaining a written consent from parents, the BL surveys were
administered in after-school classes by the Fun 5 staff at the beginning of the school
year. It took about 15 minutes for the students to complete the survey. At the end of
the school year, T1 was administered in after-school classes by the Fun 5 staff. For the
T2 survey, the research staff mailed the BL participants at the addresses they provided
on the consent form. The mail included a cover letter, a survey form, and a self-
addressed and stamped envelope. The students who did not return the surveys were
reminded twice through mail, once every two weeks. Data collection was stopped after
three months. Each student received a $10 gift card for returning the follow-up survey.
The study was approved by the University of Hawai‘i Human Studies Program.
14
Measures
Demographics Children reported their age, grade, and gender in the BL survey.
At T2, they also reported their height in feet and inches, and weight in pounds. This
information was converted into centimeters and kilograms; BMI percentile was
calculated using the age- and sex-specific growth curves from the CDC (88). They also
reported their ethnicity at T2.
PA The Fun 5 study adopted the Godin Leisure-Time Exercise Questionnaire to
measure the PA of the children (89, 90). To fill in the questionnaire, children had to
indicate the number of days per week, and the minutes per day in which they engaged
in strenuous, moderate, and mild PA (not including physical education). The minutes per
day were listed as 10-minute increments starting with zero minutes and ending with 60+
minutes. All PA levels were defined and examples were given. The number of days
children engaged in each PA level was multiplied by the number of minutes spent in
each PA level each day, resulting in a separate score of minutes per week for strenuous,
moderate, and mild PA. Strenuous PA minutes per week and moderate PA minutes per
week were added together and then divided by seven to calculate MVPA minutes per
day. Only the children who reported both moderate and strenuous activities were
included in the MVPA calculation. The Godin Leisure-Time Exercise Questionnaire has
documented test-retest reliability (r = 0.81) and adequate validity (r = 0.39) when
compared to kilocalories expended per day in a sample of 5th, 8th, and 11th graders
(91). Children reported their PA at BL, T1, and T2.
15
TPB Constructs A five point Likert type scale (from “disagree a lot” to “agree a
lot”) was used to measure the TPB constructs. The intention, attitude, subjective norms,
and PBC were assessed by asking whether the children agree or disagree with these
statements “I plan to be physically active at least five times a week”; “Physical activity is
fun”; “Most people who are important to me think I should be physically active on a
regular basis”; and “I believe I can be physically active every day” respectively. Children
reported their intention, attitude, subjective norms, and PBC towards PA at BL, and T1.
Statistical Analysis
Descriptive statistics were produced for age, sex, ethnicity, BMI, and predictor
and outcome variables. To examine hypothesis 1, we developed piecewise growth mixer
modeling (92-95) of PA during BL to T1, and T1 to T2, with 2-level hierarchical linear
modeling. We also used a random coefficient model to validate our results. These more
complex analytic methods provide both the average trajectory across a group of
individuals and capture heterogeneity in individual trajectories if it exists.
To examine hypothesis 2, structural equation modeling was used. We repeated
the same model for BL data point of two cohorts. The overall fit of the models were
assessed using a number of goodness of fit indices representing absolute, comparative
and residual aspects of fit, specifically: chi-square index, comparative fit index (CFI), root
mean squared error of approximation (RMSEA), and weighted root mean square
residual (WRMR). The larger the probability associated with the chi-square, the better
the fit of the model to the data (96).The CFI >.95 indicates excellent model fit (97). An
16
RMSEA of <.05 is excellent (96). The cut-off point for WRMR is <.90. Hypothesis 2a. was
evaluated by the significance of standardized path coefficients of attitude, subjective
norms, and PBC on intention; and R2 on intention. Hypothesis 2b. was evaluated by the
significance of standardized path coefficients of PBC on intention and childhood PA.
Hypothesis 2c. was examined by the significance of standardized path coefficient of
intention on childhood PA; and R2 on childhood PA. Hypothesis 2d. was examined by the
significance of standardized path coefficients of attitude, subjective norms on childhood
PA.
A three-step multivariate linear regression was used to examine hypothesis 3.
Covariates were entered into step one, childhood PA variable into step two, and
adolescent PA variable into step three. Based on the available variables of the Fun 5
study data set, age, sex, ethnicity were assessed for statistical significance and were
included in each PA level model as covariates if significant at p < .05. In cohort 1, 259
students participated in BL survey and only 116 students followed up till the end of the
study and in cohort 2, out of 248 students, only 88 students followed up till the end of
the study. Comparisons of BL predictor and outcome variables of students who
continued the study and who lost to follow up were examined by t-test. Two cohorts of
data were analyzed separately using the same analytical process.
17
Results
Descriptive statistics
A summary of individual characteristics is provided in Table 1. Average age of
students of cohort 1 was 14.70 (SD=.87), and cohort 2 was 14.61 (SD=.92), almost half
were girls for both cohorts, most of them were from fourth grade (45.2% for cohort 1,
and 42.6% for cohort 2), the largest ethnic group represented was Asian (68.5% for
cohort 1, and 60.5% for cohort 2), and the mean BMI percentile for age for cohort 1 was
58.42 (SD = 27.28), and cohort 2 was 67.35 (SD=25.08). Overall, students reported high-
level attitude, mid level of subjective norms, intention, and PBC for both cohorts (Table
1).
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Table 1 Descriptive statistics for respondents, predictors, and outcome variables
Characteristics Baseline(BL) One year follow-up(T1) Five year follow-up(T2)
Cohort 1
(2006)
Cohort 2
(2007)
Cohort 1
(2007)
Cohort 2
(2008)
Cohort 1
(2011)
Cohort 2
(2012)
n 259 248 172 176 116 88
Age (mean in
years and SD)
14.70(.87) 14.61(.92)
Sex (in %)
Boys 51.2 52.9
Girls 48.8 47.1
Education (in %)
4th Grade
45.2 42.6
5th Grade
39.0 40.1
6th Grade
15.8 17.4
Ethnicity
Native Hawaiian 15.7 11.1
Asian 68.5 60.5
White 11.1 19.8
Others 4.7 8.6
19
(Table continued)
Predictor and outcome variables Mean(SD)
Attitude 4.23(1.13) 4.45(.97)
Subjective
norms
3.58(1.25) 3.70(1.26)
PBC 3.95(1.18) 3.94(1.10)
Intention 3.79(1.14) 3.81(1.20)
MVPA (in
minutes/day)
42.89(29.12) 38.40(29.13) 44.92(30.78) 40.20(30.95) 47.04(28.98) 45.06(28.68)
BMI
percentile for
age
58.42(27.28) 67.35(25.08)
SD = standard deviation, PBC= perceived behavioral control, MVPA=moderate and vigorous physical
activity
Hypothesis one
Figure 2a, and 2b present findings of observed PA for randomly selected samples
of 10% of students over time from cohort 1 and cohort 2 respectively. Students’
reported PA showed no pattern for change over time. Table 2 presents findings from the
piecewise growth mixer modeling. Mean MVPA for cohort 1 students was 42.66 minutes
per day, which increased 2.59 minutes from time BL to T1, and further increased .08
minutes from time T1 to T2. Mean MVPA for cohort 2 students was 38.21 minutes per
day, which increased 3.13 minutes from time BL to T1, and further increased 2.98
20
minutes from time T1 to T2. However, the increases were not statistically significant for
both cohorts. Results from the random coefficient model revealed similar findings for PA
change over time (table not shown).
Figure 2a Observed PA of 10% randomly selected students from Cohort 1 over time
Figure 2b Observed PA of 10% randomly selected students from Cohort 2 over time
21
Table 2 Piecewise growth mixer modeling for growth trajectories in PA over time for
cohort 1 and cohort 2
Time Coefficient Standard
error t-ratio df p
Cohort 1
Intercept 42.66 1.90 22.44 242 <0.001
BL to T1 2.59 2.69 0.96 242 0.336
T1 to T2 0.08 3.45 0.02 242 0.981
Cohort 2
Intercept 38.21 1.93 19.85 235 <0.001
BL to T1 3.13 2.67 1.17 235 0.243
T1 to T2 2.98 3.54 0.84 235 0.400
Hypothesis two
The TPB model was tested separately for both cohorts. The bivariate correlations
among study variables are provided in table 3. Significant intercorrelations were
observed among TPB constructs, and all the constructs were associated with childhood
PA. The hypothesized model revealed an excellent representation of the data in
practical terms for both cohorts (table 4). Figure 3a, 3b present the path coefficients of
TPB constructs explaining children’s PA and intention at BL of cohort 1 and cohort 2
respectively. Attitude, subjective norms, and PBC explained 28-35% of the variance of
intention to childhood PA, and intention explained 19-25% of the variance of childhood
PA. Path coefficients revealed that attitude, subjective norms, and PBC were positively
related with intention for childhood PA; and PBC, and intention were also positively
22
related to PA. Path coefficients also revealed that subjective norm or attitude did not
directly predict PA, rather were mediated by intention.
Table 3 Bivariate correlation among TPB constructs to explain childhood PA
Cohort 1
Childhood PA Intention Attitude SN PBC
Childhood PA -
Intention .37** -
Attitude .22** .35** -
SN .14* .34** .24** -
PBC .28** .43** .40** .35** -
Cohort 2
Childhood PA Intention Attitude SN PBC
Childhood PA -
Intention .26** -
Attitude .12* .36** -
SN .14* .33** .27** -
PBC .29** .57** .29** .26** -
** = significant at the 0.01 level; * = significant at the 0.05 level; SN = Subjective norms; PBC = Perceived
behavior control
23
Table 4 Goodness of fit indices for the models to explain childhood PA by TPB constructs
Time Chi2 RMSEA
CFI WRMR Value df p Estimate 90%CI Pro<=0.5
Cohort 1
BL 1.58 2 .45 .00 .00-.12 .64 1.0 .35
Cohort 2
BL 1.12 2 .57 .00 .00-.11 .73 1.0 .29
RMSEA= root mean squared error of approximation; CFI= comparative fit index; WRMR= weighted root
mean square residual
Figure 3a Standardized path coefficients of TPB constructs with 95% confidence interval
explaining PA of cohort 1-BL participants (n=251)
PBC=perceived behavioral control
Attitude
PBC
Intention Childhood PA Subjective norms
.21(.10-.31)
.30(.19-.41)
.19(.10-.29)
.22(.01-.42)
.33(.19-.46)
R2 =.28
R2 =.25
24
Figure 3b Standardized path coefficients of TPB constructs with 95% confidence interval
explaining PA of cohort 2-BL participants (n=247)
PBC=perceived behavioral control
Hypothesis three
Results from three step linear regression revealed that controlling for gender
and ethnicity, both childhood PA and adolescent PA were not statistically significant
predictors of adolescent BMI (table 5).
Table 5 Predicting adolescent obesity by childhood PA, adolescent PA, and demographic
variables
Variable B Std. Er. β 95%CI of B R2 ΔR2
Cohort 1
Step 1 .06
Sex -108.31 45.57 -.23 -198.74, -17.89
Age 8.95 26.16 .03 -42.97, 60.87
Attitude
PBC
Intention Childhood PA
.25(.12-.38)
.46(.34-.58)
.17(.07-.27)
.21(.02-.40)
.24(.10-.38)
R2 =.35
R2 =.19
Subjective norms
25
(Table continued)
Step 2 .06 .000
Sex -109.47 47.11 -.24 -202.98, -15.96
Age 9.62 27.06 .04 -44.01, 63.32
Childhood PA -.08 .78 -.01 -1.62, 1.46
Step 3 .09 .033
Sex -118.84 46.77 -.26 -211.68, -26.00
Age 10.75 26.72 .04 -42.28, 63.78
Childhood PA .27 .79 .04 -1.29, 1.84
Adolescent PA -1.54 .82 -.19 -3.16, .08
Cohort 2
Step 1 .04
Sex -46.71 49.19 -.11 -144.67, 51.25
Age -36.50 29.40 -.14 -95.06, 22.06
Step 2 .04 .001
Sex -46.87 49.57 -.11 -145.62, 51.89
Age -36.31 29.75 -.14 -95.57, 22.96
Childhood PA -.06 .87 -.01 -1.80, 1.69
Step 3 .06 .020
Sex -50.66 49.45 -.12 -149.19, 47.87
Age -42.36 30.00 -.17 -102.13, 17.41
Childhood PA .26 .90 .03 -1.55, 2.06
Adolescent PA -1.13 .88 -.15 -2.88, .63
26
Discussion
The specific aims of this study were to investigate PA from childhood to
adolescence, and to evaluate if the TPB constructs to explain childhood PA among
children from Hawai’i. The secondary aim was to examine the ability of childhood and
adolescent PA to predict adolescent obesity. While previous studies examined PA over
time, as well as TPB constructs to explain childhood PA, very few studies have sought to
understand these factors among NHOPI. NHOPI populations consist of diverse minority
groups and are especially prone to obesity and its consequences (31-33). Studies also
reveal a high prevalence of health risk behaviors and obesity/overweight among
Hawaiian youth (68).
Piecewise growth mixer modeling and random coefficient modeling revealed no
pattern for PA change from childhood to adolescence among Hawaiian populations. The
FLAME study (98) supports our findings of no significant pattern for change among
preschoolers. Two other longitudinal studies report no change of PA over time among
young children (99, 100). However, several other studies (21, 101, 102) have revealed a
significant PA change over time among children and adolescents. The results in these
studies, on significant patterns of change are contradictory; furthermore, the direction
of change is also contradictory. Several cross-sectional studies have documented that
PA levels generally decline at rates ranging from 1% per year to over 20% per year (103,
104). In a longitudinal study, Kimm and colleagues (105) recruited 2379 females at 9-10
years of age and followed them for 10 years and found a 35% decline in PA levels.
Dumith and colleagues (21) also noted a 7% PA decline per year among adolescents.
27
Conversely, some studies (62, 106) reported small increases (4–9%) in PA levels among
children from 10 to 15 years of age.
Since the results are contradictory, it is important to compare the studies and
conduct further research with proper methods. While almost all tracking studies have
used Spearman rank correlation or Kappa coefficient for statistical analysis, we have
used more advanced statistical methods, like piecewise growth mixer modeling, and
random coefficient modeling. Random coefficient models with participant as a random
effect were used to analyze the repeated measures at each assessment and data for all
three time points simultaneously. These models take into account the underlying
correlations between repeated observations. Both this study and the FLAME study (98)
have used random coefficient modeling to evaluate PA change pattern, and noted
similar results. Moreover, studies based on self-report measures have reported
contradictory results, while, studies that collected objectively measured data on PA
have reported somewhat more consistent results on PA tracking (99, 107, 108). In
addition, a longitudinal study allows investigators to track individuals, to see how their
PA changes over time. Therefore, a further longitudinal study with objectively measured
PA assessment is recommended to confirm the findings.
This study also investigated the TPB constructs to explain childhood PA among
NHOPI populations. Overall, the model was an excellent fit to the data for both cohorts,
meaning that our results support the TPB as an appropriate theoretical model for
explaining childhood PA behaviors. The model accounted for 28-35% of the variance in
intention, and 19-25% of the variance in PA behavior, which is similar to most TPB-PA
28
studies in children and adolescent populations (109-115). Our findings indicate that
when a child holds a positive attitude toward PA, perceives significant others believe he
or she should participate in PA, and feels that he or she has the requisite ability to
participate, that individual will form a strong intention. Similar findings have been
reported by many studies (109, 111, 115). Strong intentions will then lead to more
activity. For both cohorts, PBC was found to make the largest contribution to predicting
PA intention followed by attitude. Results also showed that PBC is more strongly
associated with intention than with PA, which is also reported in several studies (110-
115).
The findings of this study provide us insights for tailoring future intervention
designs for this population. To achieve higher activity levels among children, future
interventions should implement strategies that reflect our findings on the importance of
attitude and PBC. Findings of this study suggest that PA interventions aimed at children
should attempt to provide information highlighting the benefits of participating in
regular PA (109, 116); for example, making PA fun and enjoyable and developing
strategies to show the importance of being physically active. More importantly,
strategies in developing confidence in participating in PA when tired, busy with
homework, and engaged with other friends in sedentary activities, are required to
enhance the efficacy and control for PA behavior change. Indeed, Blanchard et al. (117)
argue that improving self-efficacy along with access to PA facilities for children may
enhance participation in PA in this target population.
29
Strengths and limitations of the study
The longitudinal design is a major strength of this study, which allowed us to
evaluate changes over time and conclude regarding the predictors of change. In addition,
two different cohorts allowed us to replicate our findings and validate our conclusions.
However, there were limitations that should be acknowledged when interpreting the
results. First, data were collected by self-report in a supervised group setting (BL and T1)
and on mail-return questionnaires (T2), which might have introduced social desirability
and recall bias. However, these methods are commonly used in childhood and
adolescent PA studies. Moreover, PA measures have documented reliability and validity
(91). Second, the ethnic composition of this population might limit the generalizability.
Third, like all longitudinal studies, this study experienced lost to follow-up; however, the
BL comparisons between participants who lost to follow up and who continued the
study showed no statistically significant difference in predictor and outcome variables.
These non significant differences however might not be enough to minimize the
selection bias. Fourth, single indicators were used for the TPB constructs, which might
have potentially limited the content validity of the constructs. However, at least one
other study (109) has successfully used single items to measure subjective norms and
intention. Moreover, our findings are similar to other TPB PA studies. Finally, this study
did not collect data on some possible confounding variables, for example, socio-
economic status, parents’ motivation towards PA, environmental factors, etc.
30
PAPER TWO: PREDICTORS OF FRUITS AND VEGETABLES CONSUMPTION FROM
CHILDHOOD TO ADOLESCENCE: THE FUN 5 STUDY
Introduction
Childhood obesity has dramatically increased and become a growing concern of
the United States over the last three decades (1). Previous studies have documented
that healthy eating behavior both in childhood and adolescence is not only important
for proper physical growth and development, but also for preventing health problems
such as obesity (43, 118-120). Furthermore, a poor diet can increase the risk for lung,
esophageal, stomach, colorectal, and prostate cancers (121). Considering the benefits of
a healthy diet, the Dietary Guidelines for Americans recommend a diet rich in fruit and
vegetable, whole grains, and fat-free and low-fat dairy products for individuals 2 years
and older. The guidelines also recommend limiting the intake of solid fats for children,
adolescents, and adults (46). However, most young people are not following these
recommendations (46-48). The state Indicator Report on Fruits and Vegetables, 2013
reveals that US adolescents consume fruit about 1.0 time and vegetables about 1.3
times per day (122). Moreover, studies further observed a decreasing pattern of the
fruit and vegetable consumption (FVC) with age (27, 28, 123).
During adolescence, individuals start to make their own choices and develop a
personalized lifestyle. Many of such choices are related to the risk factors for obesity
such as unhealthy diet patterns, increased TV watching, and lack of physical activity (PA),
which are generally modifiable. Modifying these behaviors at a young age is much easier
31
than at later ages. A longitudinal study on food choice behavior among adolescents
(124) recommends the initiation of healthy diet interventions during childhood as it
identifies that food habits develop during early life.
The Native Hawaiians and other Pacific Islanders (NHOPI) populations are prone
to obesity and its impacts on their health (31-33). Novotny, Oshiro, and Wilkens find that
almost one in three NHOPI children (5-8 years) is either obese or overweight (34).
Evidence further reveals a high prevalence of health risk behaviors and
obesity/overweight among youth in Hawai‘i (68). The state Indicator Report on Fruits
and Vegetables in 2013 reveals that Hawai’i adolescents consume less FV than the
national average (122). According to the 2008 Hawai‘i Physical Activity and Nutrition
Surveillance Report, 78% of middle school children do not consume the recommended
amount (five daily servings) of FV (68). In order to design effective interventions, it is
important to know the factors that influence FVC behavior. Moreover, information
about the tracking of such behavior at different phases of life would be useful in
planning for interventions. However, there are a lack of longitudinal studies addressing
FVC tracking among the Hawaiian populations.
Moreover, most studies lack a strong theoretical framework (75) such as the
theory of planned behavior (TPB) or the social cognitive theory (SCT). Although a
number of interventions have been implemented, most of them have experienced
limited success (76). Therefore, a better understanding of the theoretical constructs that
explain FVC behavior would be helpful to develop effective FVC interventions (70, 72,
74). However, there are few studies that adopt the TPB using longitudinal data,
32
especially targeting the Hawaii populations. Therefore, the specific objectives of this
study were to:
1. track FVC changes from childhood to adolescence among the Hawai‘i
population.
2. apply the TPB constructs to explain childhood FVC.
The secondary objective of this study was to examine whether childhood and
adolescent FVC can predict adolescent obesity.
Conceptual model: TPB to explain childhood FVC
The TPB is frequently used to study a broad range of behaviors (77, 78) with a
particular focus on health (78), including FVC (125-127). Several studies reveal the
applicability of the theory in predicting FVC among children and adolescent (125-127).
According to the TPB, a person’s individual motivational factors predict the performance
of his/her specific behavior (84). A behavior is predicted by the intention and the
perceived behavioral control (PBC) when it is not completely volitional. The intention is
hypothesized to be determined by attitudes, subjective norms, and PBCs. An attitude
represents an individual’s assessment of their own beliefs regarding the target
behavior’s agency in producing effective outcomes. The subjective norms represent a
person’s evaluation of whether significant others want them to engage in the target
behavior and include his/her motivation to comply with the desires of those significant
others. The effects of attitude and subjective norms on the behavior are mediated by
33
the intention. The PBC represents an individual’s assessment of their own capacity on
behavioral engagement.
According to the TPB, children with strong intentions to consume more FV are
more likely to do so than the ones with weaker intentions (84). Subjective norm,
individual’s attitudes, and PBC influence intentions regarding FVC. Children who have
favorable attitudes toward FVC are more likely to have strong intentions than those with
negative attitudes. For instance, children who have fun (i.e., the experiential aspect of
an attitude) eating FV are more likely to make plans to eat more. Children who perceive
that significant adults (e.g., parents) expect them to eat FV, and are motivated to obtain
the parents’ approval, are likely to have strong intentions to eat more FV. In other
words, children who are aware of their parents’ desires for them to eat more FV, and
want to please their parents in that regard, try to (i.e., develop an intention to) eat more
FV. Finally, children who feel in control of their FVC are likely to report strong intentions
to eat FV compared to children with weaker perceptions of control (125-127).
This paper explored the changing pattern of FVC, and evaluated the TPB
constructs to explain childhood FVC among Hawai‘i populations. This paper also
examined the ability of childhood and adolescent FVC to predict adolescent obesity. We
hypothesized that:
Hypothesis 1: Higher childhood FVC precedes higher adolescent FVC
34
Hypothesis 2: The TPB constructs explain childhood FVC
a. children with favorable attitudes, strong perceptions about subjective
norms, and strong PBC, all regarding FVC, will express greater intentions
to consume FVC compared to those reporting less positive cognitions
b. PBC in childhood will have a direct positive influence on childhood FVC as
well as be mediated by intention
c. children with greater intentions will eat more FV compared to those with
weaker intentions
d. Subjective norm or attitude in childhood will not directly predict
childhood FVC but be mediated by intention
Hypothesis 3: Childhood and adolescent FVC can predict adolescent obesity
a. children who have higher FVC levels will have a lower BMI in adolescence
compared to those who have lower FVC levels
b. adolescent who have higher FVC levels will have a lower BMI in
adolescence compared to those who have lower FVC levels
Methods
Design and sample
For this study, we used the data of two cohorts from the Fun 5 study (86,87),
which had adopted a longitudinal cohort design, and included three consecutive surveys
- baseline (BL), one-year follow-up (T1), and five-year follow-up (T2). In cohort 1, 259
students from 4th-6th grades (9-12 years old) were randomly selected in 2006 for the BL
35
survey. Out of 259 students, 172 participated in T1 (2007), and 116 in T2 (2011). In
cohort 2 248 students participated in the BL survey in 2007, 176 in T1 (2008), and 88 in
T2 (2012). We replicated the findings using two cohorts of students.
Description of Fun 5 study
The Fun 5 study is an evidence-based PA and nutrition promotion program,
which has been implemented in over 160 Hawai‘i state-legislated elementary A+ after-
school programs for ten years. The program has been implemented with the aim of
promoting PA and FVC among children. The program trained after-school staff on PA,
nutrition, and sustainability. The nutrition component included lessons on benefits of FV,
managing portion sizes, and sugar intake. The staff was also encouraged to be good role
models in terms of snacking during A+ time, and to acknowledge when the children
brought healthy snacks. To promote FVC, the program developed and distributed an
activity booklet for students, which included word searches, cross words, coloring pages,
and word/picture matching activities addressing local culturally appropriate FV. In order
to increase the children’s PA, the program offered after school students a variety of
organized, non-competitive, non-gender-specific, and fun physical activities in which
children of all skill levels could participate and experience success (86, 87).
Data collection procedure
After obtaining a written consent from parents, the BL surveys were
administered in after-school classes by the Fun 5 staff at the beginning of the school
year. It took about 15 minutes for the students to complete the survey. At the end of
36
the school year, T1 was administered in after-school classes by the Fun 5 staff. For the
T2 survey, the research staff mailed the BL participants at the addresses they provided
on the consent form. The mail included a cover letter, a survey form, and a self-
addressed and stamped envelope. The students who did not return the surveys were
reminded twice through mail, once every two weeks. Data collection was stopped after
three months. Each student received a $10 gift card for returning the follow-up survey.
The study was approved by the University of Hawai‘i Human Studies Program.
Measures
Demographics Children reported their age, grade, and gender in the BL survey.
At T2, they also reported their height in feet and inches, and weight in pounds. This
information was converted into centimeters and kilograms; BMI percentile was
calculated using the age- and sex-specific growth curves from the CDC (88). They also
reported their ethnicity at T2.
FVC To measure FVC, children were asked to report the number of servings of
fruits and the number of servings of vegetables they consumed daily. Responses were
given using whole numbers from zero servings to 10+ servings. Examples of servings
sizes were provided (e.g. one serving of fruit = size of one baseball). These items have
documented validity and reliability in adolescents (128). Servings for fruits and servings
for vegetables were summed for a combined daily fruit and vegetable consumption
score. Only children who reported both fruit and vegetable servings were counted in the
analyses using this variable. Children reported their FVC at BL, T1, and T2.
37
TPB Constructs A five point Likert type scale (from “disagree a lot” to “agree a
lot”) was used to measure the TPB constructs. The intention, attitude, subjective norms,
and PBC were assessed by asking whether the children agree or disagree with these
statements “I plan to eat five or more servings of fruits and vegetables every day”;
“Eating fruits and vegetables is fun”; “Most people who are important to me think I
should eat five or more servings of fruits and vegetables every day; “I believe I can eat
five or more servings of fruits and vegetables every day” respectively. Children reported
their intention, attitude, subjective norms, and PBC towards FVC at BL, and T1.
Statistical Analysis
Descriptive statistics were produced for age, sex, ethnicity, BMI, and predictor
and outcome variables. To examine hypothesis 1, we developed piecewise growth mixer
modeling (92-95) of FVC during BL to T1, and T1 to T2, with 2-level hierarchical linear
modeling. We also used a random coefficient model to validate our results. These more
complex analytic methods provide both the average trajectory across a group of
individuals and capture heterogeneity in individual trajectories if it exists.
To examine hypothesis 2, structural equation modeling was used. We repeated
the same model for BL data point of two cohorts. The overall fit of the models were
assessed using a number of goodness of fit indices representing absolute, comparative
and residual aspects of fit, specifically: chi-square index, comparative fit index (CFI), root
mean squared error of approximation (RMSEA), and weighted root mean square
residual (WRMR). The larger the probability associated with the chi-square, the better
38
the fit of the model to the data (96).The CFI >.95 indicates excellent model fit (97). An
RMSEA of <.05 is excellent (96). The cut-off point for WRMR is <.90. Hypothesis 2a. was
evaluated by the significance of standardized path coefficients of attitude, subjective
norms, and PBC on intention; and R2 on intention. Hypothesis 2b. was evaluated by the
significance of standardized path coefficients of PBC on intention and childhood FVC.
Hypothesis 2c. was examined by the significance of standardized path coefficient of
intention on childhood FVC; and R2 on childhood FVC. Hypothesis 2d. was examined by
the significance of standardized path coefficients of attitude, subjective norms on
childhood FVC.
A three-step multivariate linear regression was used to examine hypothesis 3.
Covariates were entered into step one, childhood FVC into step two, and adolescent FVC
into step three. Based on the available variables of the Fun 5 study data set, age, sex,
ethnicity were assessed for statistical significance and were included in each FVC level
model as covariates if significant at p < .05. In cohort 1, 259 students participated in BL
survey and only 116 students followed up till the end of the study and in cohort 2, out of
248 students, only 88 students followed up till the end of the study. Comparisons of BL
predictor and outcome variables of students who continued the study and who lost to
follow up were examined by t-test. Two cohorts of data were analyzed separately using
the same analytical process.
39
Results
Descriptive statistics
A summary of individual characteristics is provided in Table 6. Average age of
students of cohort 1 was 14.70 (SD=.87), and cohort 2 was 14.61 (SD=.92), almost half
were girls for both cohorts, most of them were from fourth grade (45.2% for cohort 1,
and 42.6% for cohort 2), the largest ethnic group represented was Asian (68.5% for
cohort 1, and 60.5% for cohort 2), and the mean BMI percentile for age for cohort 1 was
58.42 (SD = 27.28), and cohort 2 was 67.35 (SD=25.08). Overall, students reported mid
level attitude, subjective norms, intention, and PBC for both cohorts (Table 6).
40
Table 6 Descriptive statistics for respondents, predictors, and outcome variables
Characteristics Baseline(BL) One year follow-up(T1) Five year follow-up(T2)
Cohort 1
(2006)
Cohort 2
(2007)
Cohort 1
(2007)
Cohort 2
(2008)
Cohort 1
(2011)
Cohort 2
(2012)
n 259 248 172 176 116 88
Age (mean in
years and SD)
14.70(.87) 14.61(.92)
Sex (in %)
Boys 51.2 52.9
Girls 48.8 47.1
Education (in %)
4th Grade
45.2 42.6
5th Grade
39.0 40.1
6th Grade
15.8 17.4
Ethnicity
Native Hawaiian 15.7 11.1
Asian 68.5 60.5
White 11.1 19.8
Others 4.7 8.6
41
(Table continued)
Predictor and outcome variables Mean(SD)
Attitude 3.33(1.43) 3.73(1.23)
Subjective
norms
3.68(1.27) 3.91(1.25)
PBC 3.62(1.32) 3.72(1.28)
Intention 3.50(1.29) 3.58(1.31)
FVC (servings
per day)
6.84(4.53) 7.40(4.78) 7.04(4.68) 6.65(4.23) 4.64(2.87) 4.07(2.75)
BMI
percentile
for age
58.42(27.28) 67.35(25.08)
SD = standard deviation, PBC= perceived behavioral control, FVC = fruit and vegetable consumption
Hypothesis one
Figure 4a, and 4b present findings of observed FVC for randomly selected
samples of 10% of students over time from cohort 1 and cohort 2 respectively. Students’
reported FVC showed a decreasing pattern for change over time. Table 7 presents
findings from the piecewise growth mixer modeling. Mean FVC for cohort 1 students
was 6.84 servings per day, which slightly increased 0.26 servings per day from time BL to
T1, and decreased 2.50 servings per day from time T1 to T2. Mean FVC for cohort 2
students was 7.41 servings per day, which decreased 0.78 servings per day from time BL
to T1, and further decreased 2.30 servings from time T1 to T2. Decrease in all time
42
points were statistically significant. However, the increase in FVC of cohort 1 from BL to
T1 was not statistically significant. Results from the random coefficient model revealed
similar findings for FVC change over time (table not shown).
Figure 4a Observed FVC of 10% randomly selected students from Cohort 1 over time
Figure 4b Observed FVC of 10% randomly selected students from Cohort 2 over time
43
Table 7 Piecewise growth mixer modeling for growth trajectories in FVC over time for
cohort 1 and cohort 2
Time Coefficient Standard
error t-ratio df p
Cohort 1
Intercept 6.84 0.29 23.60 246 <0.001
BL to T1 0.26 0.36 0.73 246 0.464
T1 to T2 -2.50 0.38 -6.56 246 <0.001
Cohort 2
Intercept 7.41 0.31 23.56 235 <0.001
BL to T1 -0.75 0.36 -2.05 235 0.042
T1 to T2 -2.30 0.41 -5.65 235 <0.001
Hypothesis two
The TPB model was tested separately for both cohorts. The bivariate correlations
among study variables are provided in table 8. Significant intercorrelations were
observed among TPB constructs, and all the constructs were associated with childhood
FVC. The hypothesized model revealed an excellent representation of the data in
practical terms for both cohorts (table 9). Figure 5a, 5b present the path coefficients of
TPB constructs explaining children’s FVC and intention at BL of cohort 1 and cohort 2
respectively. Attitude, subjective norms, and PBC explained 50-51% of the variance of
intention to childhood FVC, and intention explained 15-17% of the variance of childhood
FVC. Path coefficients revealed that attitude, subjective norms, and PBC were positively
related with intention for childhood FVC; and PBC, and intention were also positively
44
related to FVC. Path coefficients also revealed that subjective norm or attitude did not
directly predict FVC, rather were mediated by intention.
Table 8 Bivariate correlation among TPB constructs to explain childhood FVC
Cohort 1
Childhood FVC Intention Attitude SN PBC
Childhood FVC -
Intention .42** -
Attitude .25** .36** -
SN .28** .51** .33** -
PBC .45** .65** .31** .46** -
Cohort 2
Childhood FVC Intention Attitude SN PBC
Childhood FVC -
Intention .32** -
Attitude .26** .43** -
SN .22** .51** .24** -
PBC .34** .60** .39** .41** -
** = significant at the 0.01 level; SN = Subjective norms; PBC = Perceived behavior control
45
Table 9 Goodness of fit indices for the models to explain childhood FVC by TPB
constructs
Time Chi2 RMSEA
CFI WRMR Value df p Estimate 90%CI Pro<=0.5
Cohort 1
BL 0.82 1 .37 .00 .00-.16 .50 1.0 .25
Cohort 2
BL 0.25 1 .62 .00 .00-.14 .71 1.0 .14
RMSEA= root mean squared error of approximation; CFI= comparative fit index; WRMR= weighted root
mean square residual
Figure 5a Standardized path coefficients of TPB constructs with 95% confidence interval
explaining FVC of cohort 1-BL participants (n=248)
PBC=perceived behavioral control
Attitude
PBC
Intention Childhood FVC
Subjective norms
.14(.05-.22)
.60(.50-.70)
.27(.16-.38)
.55(.03-.1.08)
.75(.37-1.13)
R2 =.50
R2 =.17
46
Figure 5b Standardized path coefficients of TPB constructs with 95% confidence interval
explaining FVC of cohort 2-BL participants (n=243)
PBC=perceived behavioral control
Hypothesis three
Results from three step linear regression revealed that controlling for gender,
both childhood FVC and adolescent FVC were not statistically significant predictors of
adolescent BMI (table 10).
Table 10 Predicting adolescent obesity by childhood FVC, adolescent FVC, and
demographic variables
Variable B Std. Er. β 95%CI of B R2 ΔR2
Cohort 1
Step 1 .06
Sex -120.86 45.82 -.25 -211.70, -30.01
Attitude
PBC
Intention Childhood FVC
.26(.15-.36)
.45(.35-.54)
.37(.21-.49)
.60(.04-1.16)
.60 (.12-1.09)
R2 =.51
R2 =.15
Subjective norms
47
(Table continued)
Step 2 .06 .000
Sex -120.40 46.10 -.25 -211.82, -28.98
Childhood FVC -.96 5.35 -.02 -11.57, 9.65
Step 3 .06 .000
Sex -120.48 46.36 -.25 -212.43, -28.53
Childhood FVC -1.04 5.67 -.02 -12.28, 10.21
Adolescent FVC .37 8.66 .01 -16.81, 17.55
Cohort 2
Step 1 .03
Sex -72.71 49.29 -.16 -170.72, 25.30
Step 2 .03 .008
Sex -68.81 49.62 -.15 -167.50, 29.88
Childhood FVC -4.21 5.18 -.09 -14.51, 6.09
Step 3 .06 .026
Sex -72.83 49.31 -.16 -170.93, 25.26
Childhood FVC -6.42 5.34 -.13 -17.04, 4.21
Adolescent FVC 14.07 9.32 .17 -4.46, 32.61
Discussion
Several studies have revealed tracking FVC from childhood to adolescence (27,
123, 129-134), and some of them have observed decreasing FVC (27, 123, 129-131, 134)
over time. However, none of these studies have examined whether the decrease is
48
statistically significant or not. They just focused on tracking FVC from childhood to
adolescence. They defined tracking as the “stability overtime”. In other word, they
either measured the tracking as the stability in the rank at a group label, or calculated
the proportion of participants that stayed in the same group over time. Since FVC
stability study cannot provide a direction of significant change, findings lack insight to
develop age appropriated intervention, for example, findings do not suggest whether
intervention is necessary to increase FVC and or what age to target. However, two
studies (28, 133) have examined the significant change of FVC over time. But, they have
only used two time points, which also lack the tracking pattern. This study, therefore,
tried to study the FVC from childhood to adolescence with three time points’ data,
which would help to determine the ideal age for intervention.
The specific aims of this study were to investigate FVC from childhood to
adolescence, and to evaluate if the TPB constructs to explain childhood FVC among
children from Hawai’i. The secondary aim was to examine the ability of childhood and
adolescent FVC to predict adolescent obesity. While previous studies examined FVC
over time, as well as TPB constructs to explain childhood FVC, very few studies have
sought to understand these factors among NHOPI. NHOPI populations consist of diverse
minority groups and are especially prone to obesity and its consequences (31-33).
Studies also reveal a high prevalence of health risk behaviors and obesity/overweight
among Hawaiian youth (68).
Piecewise growth mixer modeling and random coefficient modeling revealed
significant decline of FVC from childhood to adolescent for both cohorts. Several studies
49
observed the same results (27, 28, 123, 129-131, 133), although they did not test the
significance. Wang and colleagues (132) noted that food intake and dietary habits
normally change during this time due to individual factors like physiological
development, changes in parental influence and social and environmental changes.
Patterson and colleagues (27) also argued that children are restricted by what parents
and schools provide them to eat, while, adolescents to a lesser degree as both
autonomy and the influence of peers increases. At the same time, young adults are
usually independent and may have more control over their food choice. For example,
soft drinks consumption increases in young adulthood (135, 136). The findings of
decreasing trend of FVC from childhood to adolescence imply that interventions need to
target children to increase their FVC, as well as continue over time. In this regard, it is
important to know the determinants of childhood FVC, which is another aim of this
study.
This study applied TPB constructs to explain childhood FVC among NHOPI
populations. Overall, the model was an excellent fit to the data for both cohorts,
meaning that our results support the TPB as an appropriate theoretical model for
explaining childhood FVC behaviors. The model accounted for 50-51% of the variance in
intention, and 15-17% of the variance in FVC behavior, which is similar to most TPB-FVC
studies in children and adolescent populations (125-127, 137, 138). Our findings indicate
that when a child holds a positive attitude toward FVC, perceives significant others
believe he or she should consume more, and feels that he or she has the requisite ability
to consume, that individual will form a strong intention. Similar findings have been
50
reported by many studies (125-127, 137, 138). Strong intentions will then lead to more
consumption. For both cohorts, PBC was found to make the largest contribution to
predicting FVC intention followed by subjective norms. However, several studies found
attitude is a stronger predictor of FVC intentions compared to subjective norms (125,
137), which contradicts with this study. This might be due to the age of the respondents.
Those studies were conducted on youth, who have more control over their food choice
(27), while this study was conducted on elementary school children. This study revealed
that a subjective norm is a stronger predictor if FVC intention, which is reported in
several studies (125, 139). The subjective norm finding is likely due to the parental
influence of eating behaviors during childhood.
The findings of this study provide us insights for tailoring future intervention
designs for this population. To achieve more FVC among children, future interventions
should implement strategies that reflect our findings on the importance of subjective
norms and PBC. Findings of this study suggest that subjective norms and PBC have
important role on FVC intention, therefore, encouraging parents and schools to enable
access to more FV, providing self regulation behavioral skills for FVC, as well as
encouraging them by informing the benefits of FVC seem beneficial.
Strengths and limitations of the study
The longitudinal design is a major strength of this study, which allowed us to
evaluate changes over time and conclude regarding the predictors of change. In addition,
two different cohorts allowed us to replicate our findings and validate our conclusions.
51
However, there were limitations that should be acknowledged when interpreting the
results. First, data were collected by self-report in a supervised group setting (BL and T1)
and on mail-return questionnaires (T2), which might have introduced social desirability
and recall bias. However, Baranoski and Domel have found that children are able to give
accurate dietary information and are well aware of the foods they have eaten (140).
Moreover, FVC measures have documented reliability and validity (128). Second, the
ethnic composition of this population might limit the generalizability. Third, like all
longitudinal studies, this study experienced lost to follow-up; however, the BL
comparisons between participants who lost to follow up and who continued the study
showed no statistically significant difference in predictor and outcome variables. These
non significant differences however might not be enough to minimize the selection bias.
Fourth, single indicators were used for the TPB constructs, which might have potentially
limited the content validity of the constructs. However, at least one other study (109)
has successfully used single items to measure subjective norms and intention; and
another study (126) to measure PBC. Moreover, our findings are similar to other TPB
FVC studies. Finally, this study did not collect data on some possible confounding
variables, for example, socio-economic status, parents’ motivation towards FVC,
availability of FV at home and school, environmental factors, etc.
52
PAPER THREE: PREDICTING ADOLESCENTS’ OBESITY RELATED BEHAVIORS: THE
FUN 5 STUDY
Introduction
Childhood obesity has dramatically increased and become a growing concern of the
United States over the last three decades (1). There is no single or simple solution to
addressing the childhood obesity epidemic, but a healthy lifestyle, including healthy
eating and physical activity (PA), can lower the risk of becoming obese (140). Recent
literature document that regular PA improves reductions in body adiposity, increases
aerobic fitness, and contributes to a range of other health benefits from decreased
blood pressure to increased bone mass in adulthood (16-19). PA also benefits children
physically, mentally, emotionally, and socially (7, 8, 10, 17-19). Studies reveal that time
spent in moderate to vigorous physical activity (MVPA) is negatively associated with the
change of childhood BMI (35, 36). Considering the health benefits of PA, the Centers for
Disease Control and Prevention (CDC) recommends at least 60 minutes of MVPA for
children every day (37). However, a significant number of US children do not follow this
recommendation (38, 39). According to Beighle (40), less than half of 6-11 year old
children met the recommendation. Moreover, the percentage of children meeting
recommended PA levels declines with age (21-23). In a pooled analysis of 26 studies,
Dumith and colleagues (21) reveal a 7% PA decline per year among adolescents.
Conversely, several studies document that sedentary behavior (SB), especially TV
watching, is associated with multiple risk factors for negative health outcomes, including
53
obesity (24, 25, 41-43). In addition, Proctor and colleagues (44) reveal that eating meals
in front of the TV, especially, salty snacks, pizza, and soft drinks may influence energy
intake. Despite the negative health consequences, TV watching appears to increase with
age (24, 25).
Healthy diets also have positive effects on health, including obesity (45). The
Dietary Guidelines for Americans recommend a diet rich in fruit and vegetable, whole
grains, and fat-free and low-fat dairy products for individuals 2 years and older. The
guidelines also recommend limiting the intake of solid fats for children, adolescents, and
adults (46). However, most young people are not following these recommendations (46-
48). Moreover, studies further reveal a tracking pattern of fruit and vegetable
consumption (FVC) from childhood to adolescence to adulthood (26-28).
Several studies document that PA, SB, and FVC are associated with each other
(29, 30, 49) and influence each other over time (49, 50). For example, Driskell and
colleagues (30) document that healthy diet, intention to be active, and PA preferences
cluster with PA. Lippke and colleagues (51) also reveal a strong correlation between
nutrition and PA. These associations of obesity risk behaviors imply that a group of risk
behaviors tend to occur together, and may be tied with common underlying factors. An
intervention that addresses these behaviors together could potentially be more
effective in the long run. Considering the interactions, researchers (52) suggest that
multiple behavior change interventions may have a greater impact than single behavior
change interventions.
54
Since PA, SB and FVC change over time from childhood to adolescences, it is also
important to identify the age when these behaviors change. Findings of such studies
may be helpful to develop age appropriate interventions. It is also important to find out
how these behaviors interact in changing from childhood to adolescence, as during
adolescence, individuals start to make their own choices and develop a personalized
lifestyle.
Among the US, the native Hawaiian and other Pacific Islander (NHOPI)
populations consists of diverse minority groups and are especially prone to obesity and
its consequences (31-33). Novotny, Oshiro, and Wilkens found that almost one third of 5-
8 year old NHOPI children are either obese or overweight (34). Studies reveal a high
prevalence of health risk behaviors and obesity/overweight among Hawaiian youth (68).
According to the 2008 Hawai‘i Physical Activity and Nutrition Surveillance Report, 71%
of the middle school children do not follow the recommended 60 minutes of MVPA and
most (78 %) do not consume the recommended amount (five daily servings) of FV.
Moreover, this report shows that Pacific Islander and Asian middle school students are
more inactive (those engaging in no MVPA in the past seven days) than all other
ethnicities (68). Failure to meet recommended guidelines for PA, FVC, and SB places
children at an increased risk for overweight/obesity and for chronic conditions such as
cardiovascular disease and diabetes (1). Understanding how multiple health risk
behaviors track overtime is essential to developing effective disease prevention
strategies in populations with high percentages of NHOPI population. Thus, the primary
55
aim of this study was to find out how PA, FVC, and SB influence with each other in
tracking from childhood to adolescence. We hypothesized that:
a. children with higher PA, FVC, and lower SB will have higher PA at adolescents
compared to those with lower PA, FVC, and higher SB. We examined this from BL
to T1, T1 to T2, and BL to T2.
b. children with higher PA, FVC, and lower SB will have higher FVC at adolescents
compared to those with lower PA, FVC, and higher SB. We examined this from BL
to T1, T1 to T2, and BL to T2.
c. children with higher PA, FVC, and lower SB will have lower SB at adolescents
compared to those with lower PA, FVC, and higher SB. We examined this from BL
to T1, T1 to T2, and BL to T2.
Table 11 presents the hypotheses in an abridged format.
56
Table 11 Hypotheses to predict adolescents’ obesity related behaviors
Hypotheses BL to T1 Hypotheses T1 to T2 Hypotheses BL to T2
BL T1 T1 T2 BL T2
Hypothesis a
PA + PA PA + PA PA + PA
FVC + PA FVC + PA FVC + PA
SB - PA SB - PA SB - PA
Hypothesis
b
PA + FVC PA + FVC PA + FVC
FVC + FVC FVC + FVC FVC + FVC
SB - FVC SB - FVC SB - FVC
Hypothesis c
PA - SB PA - SB PA - SB
FVC - SB FVC - SB FVC - SB
SB + SB SB + SB SB + SB
BL = baseline, T1 = one-year follow-up, T2 = five-years follow-up, PA = physical activity, FVC = fruits and
vegetable consumption, SB = sedentary behavior
Methods
Design and sample
For this study, we used the data of two cohorts from the Fun 5 study (86,87),
which had adopted a longitudinal cohort design, and included three consecutive surveys
- baseline (BL), one-year follow-up (T1), and five-year follow-up (T2). In cohort 1, 259
students from 4th-6th grades (9-12 years old) were randomly selected in 2006 for the BL
57
survey. Out of 259 students, 172 participated in T1 (2007), and 116 in T2 (2011). In
cohort 2 248 students participated in the BL survey in 2007, 176 in T1 (2008), and 88 in
T2 (2012). We replicated the findings using two cohorts of students.
Description of Fun 5 study
The Fun 5 study is an evidence-based PA and nutrition promotion program,
which has been implemented in over 160 Hawai‘i state-legislated elementary A+ after-
school programs for ten years. The program has been implemented with the aim of
promoting PA and FVC among children. The program trained after-school staff on PA,
nutrition, and sustainability. The nutrition component included lessons on benefits of FV,
managing portion sizes, and sugar intake. The staff was also encouraged to be good role
models in terms of snacking during A+ time, and to acknowledge when the children
brought healthy snacks. To promote FVC, the program developed and distributed an
activity booklet for students, which included word searches, cross words, coloring pages,
and word/picture matching activities addressing local culturally appropriate FV. In order
to increase the children’s PA, the program offered after school students a variety of
organized, non-competitive, non-gender-specific, and fun physical activities in which
children of all skill levels could participate and experience success (86, 87).
Data collection procedure
After obtaining a written consent from parents, the BL surveys were
administered in after-school classes by the Fun 5 staff at the beginning of the school
58
year. It took about 15 minutes for the students to complete the survey. At the end of
the school year, T1 was administered in after-school classes by the Fun 5 staff. For the
T2 survey, the research staff mailed the BL participants at the addresses they provided
on the consent form. The mail included a cover letter, a survey form, and a self-
addressed and stamped envelope. The students who did not return the surveys were
reminded twice through mail, once every two weeks. Data collection was stopped after
three months. Each student received a $10 gift card for returning the follow-up survey.
The study was approved by the University of Hawai‘i Human Studies Program.
Measures
Demographics Children reported their age, grade, and gender in the BL survey.
At T2, they also reported their height in feet and inches, and weight in pounds. This
information was converted into centimeters and kilograms; BMI percentile was
calculated using the age- and sex-specific growth curves from the CDC (88). They also
reported their ethnicity at T2.
PA The Fun 5 study adopted the Godin Leisure-Time Exercise Questionnaire to
measure the PA of the children (89, 90). To fill in the questionnaire, children had to
indicate the number of days per week, and the minutes per day in which they engaged
in strenuous, moderate, and mild PA (not including physical education). The minutes per
day were listed as 10-minute increments starting with zero minutes and ending with 60+
minutes. All PA levels were defined and examples were given. The number of days
children engaged in each PA level was multiplied by the number of minutes spent in
59
each PA level each day, resulting in a separate score of minutes per week for strenuous,
moderate, and mild PA. Strenuous PA minutes per week and moderate PA minutes per
week were added together and then divided by seven to calculate MVPA minutes per
day. Only the children who reported both moderate and strenuous activities were
included in the MVPA calculation. The Godin Leisure-Time Exercise Questionnaire has
documented test-retest reliability (r = 0.81) and adequate validity (r = 0.39) when
compared to kilocalories expended per day in a sample of 5th, 8th, and 11th graders
(91). Children reported their PA at BL, T1, and T2.
FVC To measure FVC, children were asked to report the number of servings of
fruits and the number of servings of vegetables they consumed daily. Responses were
given using whole numbers from zero servings to 10+ servings. Examples of servings
sizes were provided (e.g. one serving of fruit = size of one baseball). These items have
documented validity and reliability in adolescents (128). Servings for fruits and servings
for vegetables were summed for a combined daily fruit and vegetable consumption
score. Only children who reported both fruit and vegetable servings were counted in the
analyses using this variable. Children reported their FVC at BL, T1, and T2.
SB To measure sedentary behavior, children were asked to report the number of
hours per day that they watch TV, play videogames, and use the Internet (not for
homework). Responses were given using whole hours from zero hours to 10+ hours.
This measure has documented test-retest reliabilities in a sample of college students
(141). Children reported their SB at BL, T1, and T2.
60
Statistical Analysis
Descriptive statistics were produced for age, sex, ethnicity, BMI, and predictor
and outcome variables. Baseline demographic characteristics and outcome variables
were compared between the students who completed three time points and the
students lost to follow up. Assumptions of multivariate normality were assessed by
comparing two estimating methods: maximum likelihood method, and maximum
likelihood robust method. To examine the hypothesis structural equation modeling was
used. We repeated the same model for both cohorts. The overall fit of the models were
assessed using a number of goodness of fit indices representing absolute, comparative
and residual aspects of fit, specifically: chi-square index, comparative fit index (CFI), root
mean squared error of approximation (RMSEA), and standardized root mean square
residual (SRMR). The larger the probability associated with the chi-square, the better
the fit of the model to the data (96). The CFI >.95 indicates excellent model fit (97). An
RMSEA of <.05 is excellent (96). The cut-off point for SRMR is <.90. Hypothesis a. was
evaluated by the significance of standardized path coefficients of BL PA, FVC, and SB on
T1 PA; standardized path coefficients of T1 PA, FVC, and SB on T2 PA; and standardized
path coefficients of BL PA, FVC, and SB on T2 PA. Hypothesis b. was evaluated by the
significance of standardized path coefficients of BL PA, FVC, and SB on T1 FVC;
standardized path coefficients of T1 PA, FVC, and SB on T2 FVC; and standardized path
coefficients of BL PA, FVC, and SB on T2 FVC. Hypothesis c. was examined by the
significance of standardized path coefficient of BL PA, FVC, and SB on T1 SB;
61
standardized path coefficients of T1 PA, FVC, and SB on T2 SB; and standardized path
coefficients of BL PA, FVC, and SB on T2 SB.
Results
Descriptive statistics
A summary of individual characteristics is provided in Table 12. Average age of
students of cohort 1 was 14.70 (SD=.87), and cohort 2 was 14.61 (SD=.92), almost half
were girls for both cohorts, most of them were from fourth grade (45.2% for cohort 1,
and 42.6% for cohort 2), the largest ethnic group represented was Asian (68.5% for
cohort 1, and 60.5% for cohort 2), and the mean BMI percentile for age for cohort 1 was
58.42 (SD = 27.28), and cohort 2 was 67.35 (SD=25.08). On average students of cohort 1
had 42.89 (SD = 29.12) minutes of MVPA per day at BL, 44.92 (SD = 30.78) at T1, and
47.04 (SD = 28.98) at T2. Students of cohort 2 had 38.40 (SD = 29.13), 40.20 (SD = 30.95),
and 45.06 (SD = 28.68) minutes of MVPA at BL, T1, and T2 respectively. Students of
cohort 1 consumed 6.84 (SD = 4.53), 7.04 (SD = 4.68), and 4.64 (SD =2.87) servings of FV
per day at BL, T1and T2 respectively. Whereas, cohort 2 students consumed 7.40 (SD =
4.78), 6.65 (SD = 4.23), and 4.07 (SD = 2.75) servings of FV at same time points
respectively. On average students of cohort 1 watched TV 4.05 (SD = 2.82) hours per day
at BL, 3.76 (SD = 2.99) at T1, and 3.16 (SD = 1.77) at T2. Students of cohort 2 watched
3.99 (SD = 3.27), 4.29 (SD = 3.22), and 3.61 (SD = 2.31) hours per day at BL, T1, and T2
respectively (Table 12). Comparison of maximum likelihood and maximum likelihood
robust estimation revealed that the assumptions of multivariate normality were met.
62
Table 12 Descriptive statistics for respondents, predictors, and outcome variables
Characteristics Baseline(BL) One year follow-up(T1) Five year follow-up(T2)
Cohort 1
(2006)
Cohort 2
(2007)
Cohort 1
(2007)
Cohort 2
(2008)
Cohort 1
(2011)
Cohort 2
(2012)
n 259 248 172 176 116 88
Age (mean in
years and SD)
14.70(.87) 14.61(.92)
Sex (in %)
Boys 51.2 52.9
Girls 48.8 47.1
Education (in %)
4th Grade
45.2 42.6
5th Grade
39.0 40.1
6th Grade
15.8 17.4
Ethnicity
Native Hawaiian 15.7 11.1
Asian 68.5 60.5
White 11.1 19.8
Others 4.7 8.6
63
(Table continued)
Predictor and outcome variables Mean(SD)
MVPA (in
minutes/day)
42.89(29.12) 38.40(29.13) 44.92(30.78) 40.20(30.95) 47.04(28.98) 45.06(28.68)
FVC
(servings per
day)
6.84(4.53) 7.40(4.78) 7.04(4.68) 6.65(4.23) 4.64(2.87) 4.07(2.75)
Hours of TV
watching/day
4.05(2.82) 3.99(3.27) 3.76(2.99) 4.29(3.22) 3.16(1.77) 3.61(2.31)
BMI
percentile for
age
58.42(27.28) 67.35(25.08)
SD = standard deviation, MVPA = moderate to vigorous physical activity, FVC = fruit and vegetable
consumption
Comparative analysis on baseline demographic characteristics and outcome variables
revealed no significant differences between the students who completed three time
points and the students lost to follow up (table not shown).
Hypothesis testing
The hypothesized model revealed a poor fit of the data in practical terms for
both cohorts (table 13). The model was revised with the non significant paths deleted
and the revised model revealed a better fit of the data (table 14). Figure 6a, and 6b
present the revised model’s path coefficients of childhood obesity related behaviors on
adolescent obesity related behaviors for cohort 1 and cohort 2 respectively. In cohort 1,
64
T1 PA was significantly predicted by BL PA and FVC, but not BL SB. For T2 PA, T1 PA, FVC
and SB did not significantly predict T2 PA. However, BL PA significantly predicted T2 PA
(Figure 6a). For cohort 2, T1 PA was significantly predicted by BL PA and FVC like cohort
1, however, the direction of BL FVC to T1 PA was negative. T2 PA was significantly
predicted by T1 PA, and BL FVC. T1 SB did not predict T2 PA (Figure 6b).
Only BL FVC significantly predicted T1 FVC, and T1 FVC predicted T2 FVC in
cohort 1. In cohort 2, BL FVC significantly predicted T1 FVC, but T1 FVC did not predict
T2 FVC. However, BL FVC significantly predicted T2 FVC. Only BL SB significantly
predicted T1 SB, and T1 SB predicted T2 SB in cohort 1. In cohort 2 BL SB significantly
predicted T1 SB, but T1 SB did not predict T2 SB.
Table 13 Goodness of fit indices for the models to explain adolescent PA, FVC, and SB by
childhood PA, FVC, and SB with all paths
Time Chi2 RMSEA
CFI SRMR Value df p Estimate 90%CI Pro<=0.5
Cohort 1 9.47 3 .02 .11 .03-.19 .09 .93 .03
Cohort 2 8.16 3 .04 .10 .02-.18 .13 .96 .03
RMSEA= root mean squared error of approximation; CFI= comparative fit index; SRMR= standardized root
mean square residual
To improve power and interpretability, the possibility of combining the cohorts was
explored. Baseline demographics and all three behaviors were compared between
cohorts at each time points using t-tests. None of the comparisons were significant
65
(p>.05). Thus the two cohorts were merged. Similar to the individual cohort analysis, the
overall model fit was not adequate. The model was revised with non-significant paths
deleted and revealed a better fit of the data (table 15). Figure 6c presents the revised
model’s path coefficients of childhood obesity related behaviors on adolescent obesity
related behaviors for both cohorts combined. BL PA predicted T1 PA and T2 PA. T2 PA
was also predicted by BL FVC. BL FVC predicted T1 FVC and T2 FVC. T2 FVC was also
predicted by T1 FVC. BL SB predicted T1 SB, and T1 SB predicted T2 SB.
Table 14 Goodness of fit indices for the revised models to explain adolescent PA, FVC,
and SB by childhood PA, FVC, and SB with significant paths only
Time Chi2 RMSEA
CFI SRMR Value df p Estimate 90%CI Pro<=0.5
Cohort 1 24.70 19 .17 .04 .00-.08 .62 .94 .06
Cohort 2 19.81 20 .47 .00 .00-.06 .86 1.00 .06
RMSEA= root mean squared error of approximation; CFI= comparative fit index; SRMR= standardized root
mean square residual
Table 15 Goodness of fit indices for the model to explain adolescent PA, FVC, and SB by
childhood PA, FVC, and SB combining both cohorts
Time Chi2 RMSEA
CFI SRMR Value df p Estimate 90%CI Pro<=0.5
With all paths 14.13 3 .01 .10 .05-.16 .05 .95 .03
Significant paths only 23.79 21 .30 .02 .00-.05 .96 .99 .04
RMSEA= root mean squared error of approximation; CFI= comparative fit index; SRMR= standardized root
mean square residual
66
Figure 6a Standardized path coefficients of childhood behaviors with 95% confidence interval explaining adolescent
behaviors of cohort 1 participants in revised model (n=192), significant paths only
BL = baseline, T1 = one-year follow-up, T2 = five-years follow-up, PA=physical activity, FVC = fruit and vegetable consumption, SB = sedentary activity
BL PA
BL FVC
T1 PA
T1 FVC
T2 PA
T2 FVC
Childhood Adolescence
.30(.17, .43)
.37(.26, .49)
.15(.03, .28)
BL SB T1 SB T2 SB
.41(.30, .52)
R2 = .11
R2 = .17
R2 = .14
.20(.03, .38)
.36(.20, .52)
R2 = .05
R2 = .15
R2 = .04
.23(.09, .37)
67
Figure 6b Standardized path coefficients of childhood behaviors with 95% confidence interval explaining adolescent
behaviors of cohort 2 participants in revised model (n=182), significant paths only
BL = baseline, T1 = one-year follow-up, T2 = five-years follow-up, PA=physical activity, FVC = fruit and vegetable consumption, SB = sedentary activity
BL PA
BL FVC
T1 PA
T1 FVC
T2 PA
T2 FVC
Childhood Adolescence
.35(.24, .47)
.46(.36, .56)
-.18 (-.31, -.06)
BL SB T1 SB T2 SB
.46(.36, .56)
R2 = .13
R2 = .21
R2 = .21
.38(.21, .56)
-.21(-.41, -.01)
.29(.09, .49)
R2 = .24
R2 = .17
.38(.21, .56)
.36(.20, .51)
68
Figure 6c Standardized path coefficients of childhood behaviors with 95% confidence interval explaining adolescent
behaviors of participants for both cohorts in revised model (n=374), significant paths only
BL = baseline, T1 = one-year follow-up, T2 = five-years follow-up, PA=physical activity, FVC = fruit and vegetable consumption, SB = sedentary activity
BL PA
BL FVC
T1 PA
T1 FVC
T2 PA
T2 FVC
Childhood Adolescence
.32(.24, .41)
.42(.34, .49) BL SB T1 SB T2 SB
.42(.35, .50)
R2 = .10
R2 = .18
R2 = .17
.20(.07, .34)
.16(.03, .30)
R2 = .10
R2 = .10
R2 = .04
.16(.04, .29)
.17(.06, .29)
.21(.09, .33)
69
Discussion
With an aim to evaluate the predictability of childhood obesity related behaviors
(PA, FVC, and SB) to adolescent behaviors (PA, FVC, and SB), this study used the data of
the Fun 5 study. While several cross-sectional and longitudinal studies examined the
relationship between PA and FVC (51, 142-158), or SB and FVC (159-162), or PA and SB
(149, 163-168), very few longitudinal study examined these three (PA, FVC, and SB)
behaviors together. Moreover, longitudinal study on the relationship of these three
behaviors targeting NHOPI populations is very limited. NHOPI populations consist of
diverse minority groups and are especially prone to obesity and its consequences (31-
33). Studies also document a high prevalence of health risk behaviors and
obesity/overweight among Hawaiian youth (68).
This study hypothesized and found that high PA, high FVC, and low SB in
childhood predict high PA, high FVC, and low SB in adolescence, which means that all
behaviors themselves are stable from childhood to adolescents. This is consistent with
other studies that have found that childhood PA and FVC are predictive of adolescent PA
and FVC (65, 132, 169, 170), and childhood SB is predictive of adolescent SB (49, 66).
Findings imply that obesity related behaviors are developed in early childhood and
continue to adolescence.
Looking at the cross behavioral prediction, our hypotheses are not largely
confirmed. Only 1 path was significant (BL FVC to T2 PA) out of 18 paths of cross
behavioral prediction. Findings reveal that SB in childhood, especially TV watching is not
related with PA. Other longitudinal studies (165, 166) documented same findings.
70
Robinson and colleagues (165) followed 279 adolescents for two years and observed
that TV watching was not associated with the change of PA levels. Neumark-Sztainer
and colleagues (166) followed 201 adolescents for 8 months and documented similar
results. Anderson and colleagues (163), and Nigg and colleagues (164) also did not find
any meaningful relationship between TV watching and PA. However, several cross-
sectional studies revealed an inverse association between times spent watching TV and
playing video games with PA (149, 167, 168).
Findings of this study also revealed that SB and FVC are not associated with each
other. Olivares and colleagues (162) found similar results between nutritional status and
television viewing. However, several longitudinal studies (159-161) found inverse
relationship between SB and FVC.
This study provides some indication that childhood FVC predicts adolescents PA
(one of two paths was significant). This is consistent with other studies examining the
relationship between FVC and PA that documented significant positive relationship
(142-150), whereas, several others found no relationship or moderate relationship (151-
158). For example, Emmonos and colleagues (150) found that PA were associated with
improved dietary behaviors, including FVC, while Dutton and colleagues (151), in their
analysis of a PA intervention's impact on dietary quality found no effect on FVC or
dietary fat intake. Wilcox and colleagues (152), however revealed significant decreases
in calories, fat, and cholesterol in result of a PA intervention, but no changes for FVC or
fiber intake.
71
This study documented inconclusive results regarding the cross behavioral
prediction from childhood to adolescents. This might be due to small sample size, and a
focus solely on these three behaviors. For example, while predicting adolescent PA, this
study only used childhood PA, FVC, and SB, and did not consider behavioral, social, and
environmental factors that might influence adolescents PA. Therefore, further
longitudinal studies are recommended to examine the predictability of childhood
obesity related behaviors to adolescent behaviors. Findings of which might be useful to
develop age appropriate interventions to fight against obesity epidemic.
The major strength of this study was the longitudinal design, which allowed us to
predict the adolescent behaviors by childhood behaviors. In addition, two different
cohorts allowed us to replicate our findings and validate our conclusions. However,
there were limitations that should be acknowledged when interpreting the results. First,
data were collected by self-report in a supervised group setting (BL and T1) and on mail-
return questionnaires (T2), which might have introduced social desirability and recall
bias. However, Baranoski and Domel have found that children are able to give accurate
dietary information and are well aware of the foods they have eaten (139). Moreover,
PA (91), FVC (128), and SB (141) measures have documented high reliability and validity.
Second, the ethnic composition of this population might limit the generalizability. Third,
like all longitudinal studies, this study experienced loss to follow-up. However, we
compared the baseline characteristics of lost to follow-up students with all students and
found no statistically significant difference in behaviors and demographics. These non
significant differences might not be enough to minimize the selection bias due to lost to
72
follow up. Finally, this study solely focused on PA, FVC, and SB; did not collect data on
some possible confounding variables, for example, socio-economic status, parents’
motivation towards specific behavior, environmental factors, etc.
The results of this study provide evidence that childhood obesity related
behaviors predict same adolescent behaviors, which means that these behaviors
develop in childhood, and continue to be stable through adolescence. Therefore,
interventions in childhood to increase PA, and FVC, and decrease SB are recommended
to fight against the obesity epidemic. This study, however, did not find cross behavioral
prediction from childhood to adolescent, which implies that further study is necessary
to study these relationships over time.
CONCLUSION
Obesity in the United States has increased over the past three decades, and has
become a public health crisis. Studies showed that healthy lifestyle, including healthy
eating, increased PA, and decreased SB, can lower the risk of becoming obese, as well as
developing obesity related disease and negative psycho-social consequences. In order to
explore the changing pattern of PA and FVC from childhood to adolescence, as well as
investigate how childhood PA, FVC, and SB behaviors influence adolescence PA, FVC,
and SB behaviors, this study utilized the data of Fun 5 study. The Fun 5 study is an
evidence-based PA and nutrition promotion program, which has been implemented in
over 160 Hawai‘i state-legislated elementary A+ after-school programs for ten years in
order to increase PA and FVC among elementary school children.
73
This dissertation is divided into three papers. In paper one, we tried to explore
the changing pattern of PA from childhood to adolescence, as well as examine the TPB
constructs to explain childhood PA. Paper one also examined the impact of childhood
and adolescent PA on adolescent obesity. Using piecewise growth mixer modeling, and
random coefficient model, paper one did not find any significant pattern of PA change
from childhood to adolescent. Structural equation modeling revealed the applicability of
TPB. Findings also documented that PBC is the strongest variable to influence childhood
PA followed by attitudes. Paper one did not find any impact of childhood and adolescent
PA on adolescent BMI.
In paper two, we tried to explore the changing pattern of FVC from childhood to
adolescence, as well as examine the TPB constructs to explain childhood FVC. Paper two
also examined the impact of childhood and adolescent FVC on adolescent obesity. Using
piecewise growth mixer modeling, and random coefficient model, paper two revealed a
declining pattern of FVC from childhood to adolescent. Structural equation modeling
revealed the applicability of TPB. Findings also documented that PBC is the strongest
variable to influence childhood FVC followed by subjective norms. Paper two did not
find any impact of childhood and adolescent FVC on adolescent BMI.
In paper three, we tried to examine the predictability of childhood PA, FVC, and
SB on adolescent PA, FVC, and SB. Structural equation modeling revealed that all three
childhood behaviors predicted same adolescent behaviors, which implies that these
behaviors develop at childhood and continue to be stable over time. Paper three,
74
however, did not find cross behavioral prediction, except BL FVC to T2 PA, in this
population.
The overall findings of this dissertation point to the following intervention
strategies to fight against childhood obesity:
• Target early childhood for intervention activities regarding increasing PA, FVC,
and decreasing SB
• Address children’s self confidence, attitudes, and subjective norms towards PA
• Address children’s self confidence, subjective norms, and attitudes towards FVC
This study also recommends:
• Further study to explore the change of obesity related behaviors from childhood
to adolescent, which should be a longitudinal study, with objectively measured
indicators. Advanced statistical technique (piecewise growth mixer modeling,
random coefficient model for example) is recommended for data analysis.
• Further study to examine the relationships of childhood and adolescent PA and
FVC with adolescent obesity. Longitudinal study with objectively measured PA,
FVC, and obesity should be adopted for such kind of study.
• Further study to explore the condition where cross behavioral relationships may
exist.
75
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