Translational Investigation of Growth and Everyday Routines in Kids (TIGER Kids)
Conditions: Childhood Obesity
Sponsor: Pennington Biomedical Research Center

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Protocol Title: Translational Investigation of Growth 
and Everyday Routines in Kids (TIGER Kids)
Principal Investigator: Amanda Staiano, PhD, MPP
Medical Investigator: Daniel Hsia, MD
Co-Investigators: Peter Katzmarzyk, PhD, Robert L. Newton, Jr., PhD, 
Stephanie Broyles, PhD, Catherine Champagne, PhD, RD, LDN
Protocol Version Date: 3/1/2021
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Purpose and Objectives
Project Summary 
Increasing physical activity and reducing sedentary behavior are key recommendations of the Dietary 
Guidelines for Americans as a way to balance calories to manage healthy weight.  However, few youth 
meet recommended levels of physical activity, and interventions to increase physical activity often have 
low adherence and high drop-out rates. Furthermore, health behaviors may be particularly compromised 
in children with severe obesity, which is the fastest growing sub-category of obesity. Severe obesity 
affects three times more African American children than White children.1 
Precision medicine is a transformative approach to change health behaviors that considers individual 
characteristics and environmental context. Our interdisciplinary team at Pennington Biomedical Research 
Center will establish a prospective pediatric cohort to identify intervention targets based on the location, 
timing, barriers, and facilitators of current physical activity and sedentary behavior in a child’s day. 
Examining activity profiles and contexts by obesity status and race will allow interventions to be further 
tailored for high risk groups. We will accomplish the project’s aims by conducting a prospective 
examination of up to 345 African American and White girls and boys aged 10 to 16 years, including 
approximately 50% who are classified as severely obese. We will use state-of-the-art technology 
including accelerometry to quantify physical activity, magnetic resonance imaging to quantify fat 
accumulation, and global positioning system (GPS) information and ecological momentary assessment to 
identify environmental and socio-emotional barriers and facilitators. This project will identify 
opportunities in a child’s day to naturally integrate physical activity and reduce sedentary behavior to 
achieve the overall goal of creating actionable strategies to improve health. 
Study Objectives
Precision medicine is an emerging medical model that customizes healthcare decisions based on an 
individual patient’s characteristics, aimed at delivering the right treatment at the right time.2 A population 
approach to precision medicine takes into account environmental exposures.3 As emphasized in the 
Scientific Report of the 2015 Dietary Guidelines for Americans,4 multiple determinants of health from an 
individual’s physical and socio-emotional characteristics to the environment in which they live influence 
treatment adherence and efficacy. The “Exercise is Medicine” initiative calls for healthcare providers to 
prescribe physical activity for the prevention, treatment, and management of chronic diseases.5 An 
opportunity lies in leveraging precision medicine to create individualized physical activity plans for the 
prevention and treatment of obesity, a disease affecting 23% of Louisiana’s children. To determine 
optimal, individually tailored exercise prescriptions, we must first identify the contexts within a child’s 
day that facilitate physical activity and minimize time spent in sedentary pursuits, particularly among 
children with obesity. To achieve this aim, we will achieve the following objectives: 
 
Objective 1:  Determine the effect of severe obesity on physical activity, sedentary behavior, and 
cardiometabolic risk factors during childhood and adolescence and whether these associations are 
modified by race (NP 107, C4, PS 4A). 
Hypothesis 1.A: Severe obesity will be associated with greater changes in physical activity, 
sedentary behavior, and cardiometabolic risk factors.  
 Hypothesis 1.B: Associations of severe obesity with physical activity, sedentary behavior, and 
cardiometabolic risk factors will be stronger among African American versus White youth. 
Objective 2:  Investigate the contribution of (total, regional, and depot-specific) fat accumulation on 
changes in physical activity, sedentary behavior, and cardiometabolic risk factors during childhood and 
adolescence (NP 107, C4, PS 4A). 
Hypothesis 2:  The accumulation of total, trunk, and visceral fat will be significantly associated 
with changes in physical activity, sedentary behavior, and cardiometabolic risk factors during 
childhood and adolescence. 
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Objective 3:  Determine barriers and facilitators of physical activity that are related to obesity, including 
environmental and socio-emotional factors. (NP 107, C4, PS 4A). 
Hypothesis 3.A: Environmental factors will be associated with physical activity. 
Hypothesis 3.B: Socio-emotional factors will be associated with physical activity. 
We specifically focus on health disparities among severely obese African American and White youth 
living in the Lower Mississippi Delta (LMD) region. The LMD represents one of the most medically 
underserved, at-risk populations living in the U.S, being characterized by high levels of poverty, food 
insecurity, obesity, and related diseases. Our Steps Ahead randomized clinical trial with LMD adults, 
supported by the ARS-funded Delta Obesity Prevention Research Unit, observed weight loss following a 
12-week behavior change intervention, with those who received enhanced physical activity counseling 
losing more weight.6 Realizing the need for prevention earlier in life, our plan will target LMD 
adolescents through a highly innovative and rigorously designed project using state-of-the-art technology, 
including accelerometers to objectively measure physical activity, global positioning system (GPS) 
devices to track location of the child’s physical activity, and ecological momentary assessment to quantify 
the social context of the child’s physical activity. This project is urgently needed to better design 
interventions that can be integrated into a child’s daily life to produce clinically meaningful changes in 
children’s weight status and cardiometabolic health.  
The research will address a critical public health need: to identify times, locations, and contexts in a 
child’s day that are optimal opportunities to increase physical activity. We will also examine whether 
activity patterns vary by obesity status and race in order to tailor physical activity prescriptions to groups 
at highest risk for obesity-related health complications. Once equipped with a precise profile of children’s 
daily activities and environmental and socio-emotional contexts, we can create actionable strategies to 
improve children’s health behaviors. 
Description of Problem to be Solved: Severe obesity, defined as having a body mass index (BMI) ≥ 
120% of the 95th percentile, continues to rise among children and adolescents, with one-third of obese 
children currently being classified as severely obese.1 Racial disparities in severe obesity exist: African 
American children have a 2 to 3 times higher prevalence of severe obesity than White children.7 Though 
the role of physical inactivity as a contributor to the development of obesity is well established, we know 
far less about how obesity alters physical activity levels. Considering 17% of children are obese,1 we 
urgently need to identify the impact of obesity on children’s physical activity so that we can intervene 
early in the disease. The Scientific Report of the 2015 Dietary Guidelines Advisory Committee4 
recommends increasing physical activity and reducing sedentary behavior as a way to manage weight. 
Further, physical activity is recommended as first-line obesity treatment for children.8 Yet only 8% of 12-
15 year-olds meet national physical activity guidelines of 60 minutes of daily moderate-to-vigorous 
physical activity (MVPA).9 Our research team has demonstrated that adolescents instead spend 7.5 hours 
per day sitting, with sitting more common among obese youth.10 Physical activity programs are often 
hindered by high attrition (27% to 73% in a recent review11), due to inconvenience, transportation 
barriers, and scheduling conflicts.12,13 Our project examines the influence of body fat and fat accumulation 
on the presence, proximity, location, and timing of physical activity and sedentary behavior in a child’s 
day and whether these patterns vary by race. By examining facilitators to and barriers of physical activity, 
our project will inform new strategies about how to target children to increase physical activity and 
reduce sedentary behavior, particularly those children who rapidly accumulate body fat.  
 
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Background
Research Gaps 
Our project will address two key research needs highlighted in the Scientific Report of the 2015 Dietary 
Guidelines Advisory Committee4 by doing the following: 1) conducting prospective research to examine 
the relationships of quantity, patterns, and changes of physical activity and sedentary behavior with body 
weight, adiposity, and health in a diverse population of African American and White adolescents; and 2) 
using objective measures to assess the physical activities and sedentary behaviors in which adolescents 
regularly engage, without a reliance on self-report measures.  Through the present project, we address the 
following gaps in scientific knowledge: the impact of severe obesity and fat accumulation on children’s 
physical activity, sedentary behavior, and cardiometabolic risk factors; the activity profiles of children 
and how these profiles vary by obesity status and race; and barriers and facilitators of children’s activity 
based on socio-emotional and environmental contexts. We will address these gaps by using state-of-the-
art technology to quantify behaviors, environmental context, and health outcomes including 
accelerometry, clinical chemistry, magnetic resonance imaging (MRI), dual energy x-ray absorptiometry 
(DXA), global positioning system (GPS) information, and ecological momentary assessment (EMA). 
Each technological device and its potential for informing the identified research gaps are described below. 
 
Impact of Severe Obesity and Fat Accumulation on Children’s Physical Activity 
We focus on severe obesity, the fastest growing sub-category of obesity7,14 affecting between 4% and 6% 
of youth in the U.S.15 This group of children is at highest risk for medical complications including heart 
disease, type 2 diabetes, and cancer.15 Our research team, among others, has identified the importance of 
physical activity and low levels of sedentary behavior for children’s health outcomes. In 2010-11, our 
research team conducted the WAIST study, which was a cross-sectional examination of 423 children and 
adolescents aged 5 to 18 years that were evenly distributed by age, sex, race (African American or 
White), and obesity status (PI: Peter Katzmarzyk, PhD, who is an Investigator on this project). Thus far 
we have published 8 manuscripts from this cohort.16-23 We demonstrated that engagement in at least 60 
minutes/day of physical activity on at least 4 days/week was related to lower body fat and lower visceral 
adiposity.17 Conversely, sedentary behavior characterized by TV viewing was related to higher visceral 
adiposity and cardiovascular risk.18 Indeed, 24% of the WAIST cohort already had at least 2 
cardiometabolic risk factors at baseline (elevations in waist circumference, blood pressure, triglycerides, 
or fasting glucose, or low levels of high density lipoprotein cholesterol). The present project will build 
upon our prior work by investigating these relationships among adolescents who are classified as severely 
obese.  
Although behavior modification that employs physical activity is considered the first-line treatment for 
severe obesity,15 we know very little about current patterns of physical activity or the barriers and 
facilitators of physical activity among children with severe obesity. A scientific statement from the 
American Heart Association in 2013 issued an appeal for innovative behavior-based treatment and 
minimally invasive procedures to treat severe obesity in children and adolescents.15 Few lifestyle 
modification studies have focused on children with severe obesity, and interventions that do include this 
population have not taken into account environmental or socio-emotional factors related to children’s 
health behaviors. Family-based behavioral treatment that employs physical activity, dietary counseling, 
and behavioral strategies to implement and sustain changes have had limited long-term success,24-27 with 
many children returning to baseline weight following the completion of the intervention.24,25,28  
In response to the paucity of data and the lackluster response to lifestyle interventions, two urgent 
research needs emerged: 1) to characterize the relationship of severe obesity with children’s behavioral 
and environmental contexts and 2) to identify weight-gain trajectory data that differentiate children at 
high risk who need intensive intervention to promote healthy behaviors.15 The present project will provide 
important data for both the association of severe obesity with children’s physical activity and sedentary 
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behavior (Objectives 1 and 3) and the identification of fat accumulation as a risk factor for inactivity 
(Objective 2).  
Obesity and fat accumulation are often assessed using anthropometric measures such as body mass index 
z-score (BMIz) based on height and weight. Though useful to classify obesity status at a population level, 
we will use more precise and direct quantification of fat accumulation via MRI and DXA. Health 
consequences associated with obesity differ based on where adipose tissue is stored, for instance 
subcutaneously between the skin and muscle versus internally, such as within the chest, abdomen, and 
pelvis (i.e. visceral fat).29 Using the WAIST cohort, our research team observed that trunk fat, which is fat 
located centrally in the body excluding the extremities, affects adolescents’ metabolic risk.18 Recent 
advances in body composition imaging allow for the examination of specific depots of fat in the pediatric 
population, providing sufficient precision to evaluate individual and group differences and to track 
changes in fat mass over time. Our research team previously identified through an extensive literature 
review that there are little data available on visceral fat in children and adolescents, and even less data 
measured in a longitudinal fashion over time.30 Through an examination of the cross-sectional data from 
the WAIST cohort, we identified ethnic and sex differences in visceral fat.16 However, we do not know 
how the accumulation of trunk or visceral fat affects physical activity, sedentary behavior, or 
cardiometabolic risk factors. A systematic review of 32 studies indicated a relationship among children’s 
fat gains with reduced physical activity and increased sedentary behavior, but stronger measurement 
methods and stratification by subgroup are needed.31 Therefore, the present project will provide 
important, timely data on the association between fat accumulation and children’s activity levels using 
MRI and DXA technologies in a diverse sample of children and adolescents. 
 
Activity Profiles and How Activity Varies by Obesity Status and Race 
There is a dearth of data on the activity profiles of children and adolescents, particularly those with severe 
obesity and from ethnic minority groups. We will gather information on the location, frequency, intensity, 
and timing of physical activity and sedentary behavior in children during free-living conditions, providing 
precise profiles of children’s activity patterns. To do so, we will use accelerometry, which is the gold 
standard to assess free-living physical activity and highly correlated with oxygen consumption or direct 
observation of physical activity.32  
 
This project makes three novel contributions to profiling children’s activity: 1) by quantifying sedentary 
behavior with accelerometry, GPS, and EMA simultaneously, extensively characterizing free-living 
activity; 2) by examining differences in activity profiles by obesity status; and 3) by focusing on African 
American youth who are at particularly high risk for obesity and obesity-related health problems. 
Sedentary behavior, which is activity occurring at the lowest end of the intensity spectrum typically in the 
posture of sitting or lying down, has emerged as an independent risk factor for obesity-related 
comorbidities by our team33 and others.34 Identifying the contexts and facilitators of sedentary behavior 
will inform actionable strategies to reduce sedentary behavior in children’s daily lives.  
Obesity is an important risk factor for low physical activity and high sedentary behavior, but it is not clear 
how activity profiles differ based on obesity status. A study of 507 children aged 9 to 11 years 
demonstrated that accelerometer-measured MVPA was inversely associated with adiposity regardless of 
sedentary time or sleep duration,35 yet associations were cross-sectional and adiposity was measured by 
bioelectrical impedance rather than DXA or MRI. The present project will build on prior work by 
examining the influence of longitudinal changes in fat, including depot-specific fat in the trunk and 
visceral regions, and subsequent health behaviors, with a focus on children with severe obesity. 
We also focus on health disparities between African American and White youth and whether differences 
may occur in the association between obesity and health behaviors. To do so, we leverage our diverse 
population in the Louisiana LMD and target African American youth, a group with high levels of severe 
obesity14 and therefore at particularly high risk for obesity-related health problems. Approximately 44% 
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of African American men and women have cardiovascular disease, compared to 36% of Whites. African 
Americans experience higher prevalence of hypertension (48% vs. 24%) and almost double the 
prevalence of stroke (4.5% vs. 2.4%).36 In addition, there is a higher prevalence of obesity (37.1% vs. 
32.4%) and severe obesity (6.9% vs. 3.8%) among African Americans compared to Whites.37,38 These 
differences in chronic disease prevalence translate into greater morbidity and mortality for African 
Americans.39,40 Our research team has previously demonstrated that compared to Whites, African 
American adolescents have more total body fat but less visceral adiposity.16 Prevention efforts are clearly 
needed to reduce racial disparities in cardiovascular disease, and understanding health disparities in 
behaviors at a younger age will inform prevention and treatment strategies for African Americans.   
  
Barriers and Facilitators of Children’s Activity  
The barriers and facilitators of children’s activity are not well documented and have largely relied on self-
reported data. We employ two novel methods to examine the environmental and socio-emotional context 
of children’s activity. 
 
Global positioning system (GPS). GPS technology has recently emerged as a way to measure the 
location of behaviors and subsequently link these location data to proximal physical activity facilitators 
such as parks and sidewalks or barriers such as neighborhood crime. It is becoming increasingly 
recognized that social determinants contribute to health disparities.41,42 For example, African Americans 
are more likely to live in lower socio-economic status neighborhoods, which have been characterized by 
poor sidewalk conditions,43 unpleasant aesthetics,44-46 lower safety,45-46 higher social disorder and lower 
social capital,46 and less access to physical activity facilities.45,47-49 Also, these social determinants are 
related to lower levels of physical activity and higher levels of obesity; for example, lower access to 
physical activity facilities is associated with decreased odds of engaging in the recommended amount of 
MVPA and higher prevalence of obesity.47 In the WAIST cohort, our research team demonstrated that 
children living in high poverty/crime neighborhoods were at higher risk for elevated C-reactive protein, a 
marker of cardiovascular risk.19  
Both obesity and low levels of physical activity contribute to poor cardiovascular health, and therefore, 
are targets for intervention. However, the effectiveness of interventions targeting physical activity or 
obesity may be affected by environmental factors.50-52 In an intervention that substituted physical activity 
for sedentary behavior, Epstein and colleagues showed that children living within 0.5 miles of a park 
increased their physical activity more than children not living near parks.53 Also, Investigators Robert 
Newton, PhD and Stephanie Broyles, PhD conducted a pilot randomized trial comparing an intensive and 
a minimal mobile phone-based intervention designed to promote physical activity in children (Industry 
funded; Newton, PI).54 Data from this pilot study (P-Mobile, n=27) found that lower neighborhood crime 
was associated with a larger increase in physical activity and that treatment response was highly 
correlated with crime in the intensive intervention group (manuscript under review). The project will 
examine the specific environmental factors that contribute to and impede physical activity and sedentary 
behavior in order to better design effective interventions. 
Ecological momentary assessment (EMA). EMA is emerging as a real-time method to measure the 
subjective and contextual barriers and facilitators of physical activity and sedentary behavior. EMA 
involves sending messages over a mobile device throughout the day for the participant to record 
descriptions of activities undertaken as well as socio-emotional factors such as who the child is with and 
how the child is feeling. Responses are provided in real-time in a naturalistic setting, offering minimal 
interference in a child’s typical day. Importantly, EMA has been validated with objective physical activity 
measures in children55 and has demonstrated acceptable compliance (76.8% response rate among 121 
children sampled over 4 days).56 EMA is an alternative to traditional recall instruments that are limited by 
participants’ memory errors and biases and that are not completed in an environment where the behavior 
occurred. EMA is also an alternative to observational methods, which are limited to a specific setting (e.g. 
a park, an artificial environment created in a laboratory) and cannot capture subjective perceptions such as 
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mood. In summary, EMA has been demonstrated to be a valid, reliable, feasible approach to quantify 
physical activity and sedentary behavior.57 
Because EMA is an emerging technology, its potential to profile children’s free-living activities remains 
untapped. Dunton and colleagues applied EMA to a sample of 121 children aged 9 to 13 years and 
uncovered several barriers and facilitators to physical activity in this age group: physical activity most 
often occurred in a social context when other people were present,56 children were more active in settings 
where they perceived less traffic,58 and children’s feelings of tiredness and energy predicted physical 
activity, which in turn predicted subsequent emotional states.59 Though the sample surveyed was 
ethnically diverse (i.e. 38% Hispanic/Latino), African American youth were not represented, and the 
sample was younger (9 to 13 years) and had a healthier weight status (61% were normal weight or 
underweight, 18% were overweight, and 21% were obese) than the proposed sample. Indeed, the present 
project will be the first to our knowledge to apply EMA to a large sample of children with severe obesity 
and to use EMA among a sample composed of 50% African American children. 
  
Inclusion and Exclusion Criteria
We will include adolescents aged 10 to 16 years. We selected this age range because adolescence is a 
developmental period of rapid fat gain and changes in activity patterns. By choosing the maximum age as 
16 years at baseline, the children are likely to still live in Baton Rouge and attend high school at the 
second clinic visit (age 18 years), facilitating follow-up. 
Inclusion Criteria:

Age 10-16 years

Body weight < 500 lbs.

Ability to understand instructions and complete all study procedures
Exclusion Criteria:

Pregnant

On a restrictive diet due to illness

Significant physical or mental disabilities that impede walking, wearing accelerometer or GPS, or 
responding to EMA.
In addition to the exclusion criteria listed above, participants who refuse to provide written assent, or 
whose parents or legally authorized representative refuse to sign an informed consent document, will be 
excluded from the study. Consent/assent will be obtained upon each participant’s arrival at the PBRC 
Pediatric Research Clinic before participating in any study procedures. To further safeguard participant 
safety and internal validity of the study, inclusion and exclusion criteria will be reassessed by PBRC 
clinic staff following the completion of required assent/consent forms. The principal investigator and 
medical investigator have the right to withdraw a participant from the study at any point for any reason. 
Number of Participants
We will identify and recruit up to 345 boys and girls aged 10 to 16 years. The sample will be screened on 
the telephone in attempt to achieve a comparable sex and race distribution. We will oversample for 
severely obese youth so they comprise approximately 50% of the sample (with the other 50% being 
distributed among normal weight, overweight, and obese status).  
Recruitment Methods
Recruiting children for clinical studies is challenging, particularly those from high risk populations such 
as with severe obesity and from ethnic minority groups. However, we are confident we can recruit up to 
345 children within 2 years. A key recruitment strategy will be to enroll participants who completed the 
WAIST Study.16 Of 423 initial participants who were assessed for the WAIST Study in 2010-11, 386 
(91.3%) agreed to be re-contacted. Of these, 193 participants will meet the age criteria for the follow-up 
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study (i.e. age 10 to 16 years in the year 2016). Therefore, we will have a sample of approximately 193 
participants to immediately recruit into this new study. 
Participants will also be recruited from the Greater Baton Rouge area via website advertisements and 
emails. We will use a variety of recruitment strategies include flyers in pediatric clinics (affiliated with 
Our Lady of the Lake), health fairs, community events, local schools, local businesses/organizations, and 
other strategies. For recruitment in schools, we will obtain permission to promote the study at each school 
from the school district superintendent (if necessary) and individual school leaders (principals, 
headmasters, directors, etc.) prior to recruiting in that school. School recruiting efforts will include 
communications about the study that may be distributed to students or sent to parents directly. These 
communications may include informational flyers, posters, letters, emails, newsletters, school call 
services, and consents through which parents may provide contact information and request to learn more 
about the study, among others. Study staff may also attend schools in person to promote the study at 
school events which the school deems appropriate, including attending physical education/health classes, 
lunch periods, school assemblies, parent nights, and other school events. In-person efforts may include, 
but are not limited to, making announcements about the study, distributing informational materials, and/or 
demonstrating procedures used in the study. We will work closely with each school to tailor recruiting 
efforts based on each school’s rules and preferences. 
The Pennington Biomedical recruitment core will execute a plan to advertise through branches of the 
public library system, seek the support of community leaders, issue personal appeals through social 
organizations, place advertisements in places of congregation (i.e. libraries, churches), and conduct direct 
mailings, as well as advertisements via newspaper, radio, and television. Our recruitment plan is based on 
strategies that have resulted in investigators successfully reaching their recruitment goals: 1023 children 
and adolescents for cross-sectional obesity screening studies (Investigator Katzmarzyk)16 and 1043 
children and adolescents into physical activity and dietary interventions (Project Lead Staiano and 
Investigator Newton).78-81 Baton Rouge area has an estimated 2013 population of 445,277 and almost 
46% of the population is African American.82 Even with anticipated loss to follow-up, we will have 
sufficient power in overall logistic regression analyses. 
Recruitment will begin in the spring of 2016 and is expected to end in the spring of 2018. 
Study Timeline
Participants will be enrolled on a continual basis over the course of 24 months. Participation in the study 
will last approximately 2 years. It is estimated that the investigators will complete primary analysis by 
December 2019. The study timeline is reported in Table 1.
Table 1. Study Timeline
2016
2017
2018
2019
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Q4
Q1
Q2
Q3
Personnel Training
Participant Recruitment
Baseline Testing, Y0
Follow Up Testing, Y2
Data Management
Data Analysis Y0
Paper Writing & 
Submission, Y0
Data Analysis Y2
Paper Writing & 
Submission, Y2
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Procedures Involved
Study Design 
We will conduct a prospective epidemiological study to determine the effect of severe obesity on physical 
activity, sedentary behavior, and cardiometabolic risk factors among African American and White 
children and adolescents. Physical activity, sedentary behavior, cardiometabolic risk factors, and body 
composition will be assessed in each participant using gold standard, objective measures, with the first 
assessment (Year 0) in 2016-17 and the second assessment (Year 2) in 2018-19. We previously observed 
that children’s physical activity and other health behaviors vary throughout the year based on school term 
versus school holiday.60 Therefore, each child’s Year 0 and Year 2 clinic visits will be scheduled at a 
consistent time of year to protect against changes due to the child being in school term or on a school 
holiday. Furthermore, we will use a scheduled enrollment to ensure that clinic visits are representative of 
both school term and school holidays. 
Following an initial recruitment telephone screen or web-screen (followed by a telephone screen) to 
assess initial eligibility criteria, eligible participants will be scheduled for their Orientation visit at PBRC 
with their parent or legally authorized representative where participants will be formally oriented to the 
study by receiving detailed information on the purposes, goals, procedures, participant flow, and study 
timeline. Staff will obtain signed informed consent and child assent before participants take part in any of 
the study-related procedures. Participants will be given the GPS and accelerometer devices as well as 
instructions on how to wear them for the next week. Participants will also be given the EMA application 
on their personal cell phones or a provided mobile device along with instructions on how to respond to 
the prompts for the next week. Participants will be introduced to the ASA-24 dietary intake tool and 
provided instructions on how to complete two separate recalls from their home computer prior to the Year 
0 clinic visit. Participants and parents may be asked to complete surveys on the devices while waiting. At 
the conclusion of the Orientation session, participants will be scheduled for their Year 0 clinic visit within 
8-21 days after the Orientation. Parents may receive reminders from staff for all tasks required of 
participants between orientation and Year 0.
Participants will be fasting at the Year 0 visit. Measurements including anthropometry, blood draw, urine 
pregnancy test, body composition, dietary intake, activity monitor tracking equipment collection, and all 
remaining surveys will take place during the clinic visits. Participants must be accompanied by a parent or 
legally authorized representative at the clinic visits. 
Approximately 10 days prior to the Year 2 visit, participants will pick up an accelerometer and GPS 
device to wear for 7 days and return at the Year 2 clinic visit. Participants will also be given instructions 
on how to complete two trials of the ASA-24 dietary intake tool from their home computer prior to the 
Year 2 clinic visit. Participants will return to clinic approximately 2 years (+/- 6 months) after their Year 
0 visit for a Year 2 visit, in which all procedures collected during Year 0 will be repeated. Table 2 
includes a list of all measurements occurring at both visits. Parents may receive reminders from staff for 
all tasks required of participants between orientation and Year 2.
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Measurements 
Orientation
Year 0
Year 2
Informed consent/assent
X
Anthropometry (Height, weight, waist size)
X
X
Blood Pressure
X
X
Body Composition (DXA, MRI)
X
X
Blood Draw
X
X
Urine Pregnancy Test (eligible females only)
X
X
Dietary Intake (ASA-24)
X
X
Family environment surveys
X
X
Neighborhood environment surveys
X
X
Body image and mood surveys
X
X
Global Positioning System (GPS)
7-days
7-days
Physical Activity (Actigraph GT3X+)
7-days
7-days
Ecological momentary assessment (eMA)
7-days
Table 2. Study procedures 
Description of tests and procedures
Anthropometry
Weight- Anthropometry will be measured to the nearest 0.1 cm (0.1 kg for weight). All measures are 
taken twice with the analyzed value being the average of both measurements. If the measures differ by 
greater than 0.5 units, a third measurement will be taken to replace the discrepant value. Participants wear 
only a hospital gown and undergarments while being weighed.
Height- Standing height will be measured using a Harpenden stadiometer (Holtain Limited, Crymych, 
UK). Participants will remove shoes, stand upright with heels and back against the stadiometer, inhale, 
and hold breath while an assessor provides traction to align the participant’s head along the Frankfort 
Horizontal Plane during measurement. Weight will be measured using a Michelli GSE 460 scale (G.T. 
Michelli Co., Baton Rouge, LA). 
BMIz- BMI percentile and BMIz will be calculated based on the participant’s age, height, and weight 
using the CDC 2000 growth charts.61 Severe obesity will be categorized as ≥ 120% of the 95th BMI 
percentile.15  
Waist circumference - will be measured at the natural waist (halfway between inferior border of rib cage 
and superior aspect of iliac crest) with clothing moved out of the way. 
Body composition
DXA- Total body fat and trunk fat will be assessed by whole-body DXA using a GE iDXA scanner (GE 
Medical Systems, Milwaukee, WI) to quantify total and regional body fat (including trunk fat). 
DXA Instructions given to participant: This scan measures the amount of bone, muscle, 
and fat in your body.  The scan will be performed using a whole-body scanner.  You will 
be required to wear a hospital gown, to remove all metal-containing objects from your 
body, and to lie down on the table.  You will be carefully positioned on the table, and 
your legs will be placed together using two Velcro straps.  A scanner emitting low energy 
X-rays and a detector will pass along your body.  You will be asked to remain completely 
still while the scan is in progress.  The scan takes approximately ten minutes.  
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MRI - Visceral fat, i.e. visceral adipose tissue, will be assessed by water-fat shifting MRI using the 
General Electric Discovery 750w 3.0 Tesla (GE Medical Systems, Milwaukee, WI). IDEAL-IQ imaging 
technique will be used to generate water-only, fat-only, in-phase, and out-of-phase echoes in a single 
acquisition with a 20-second breath-hold. For female subjects ages 12 and above or females under the age 
of 12 who are menstruating as verbally confirmed by a parent or legal guardian, a negative urine 
pregnancy test will be required prior to DXA or MRI scans.
MRI instructions given to participants: For this test, you will be asked to change into a hospital 
gown.  You will be given earplugs.  You may be given a blanket to keep you warm as needed.  
You will lie on a table in the MR scanner and lie as still as possible to measure the amount of fat, 
muscle, and other tissues inside your abdomen as well as the size and functioning of internal 
organs.  Foam padding will be provided as needed to insure that you are able to lie down 
comfortably.  A device called a “coil” will be placed over your abdomen to collect the images.    
After the coil is placed over your abdomen, you will be asked to lie as still as you can for about 
10 to 50 minutes while the machine collects images of your body.
You may be asked to hold your breath repeatedly for 10 to 15 seconds during this scan.  The 
purpose of holding your breath is to collect higher quality images of your abdomen.  Breathing 
causes your internal organs to move up and down in a rhythmic and natural way.  For some 
magnetic resonance scanning techniques, this natural motion makes the images blurry.  If holding 
your breath makes you dizzy or lightheaded, the scan will be discontinued for your safety.
During the scan, you may be asked to wear devices that record the beating of your heart, your 
breathing, and the circulation of blood to your extremities. Sticker-like electrocardiography pads 
will be attached to your chest and wires will be attached to those pads.  This device will record 
the beating of your heart.  A belt-like breathing monitor will be strapped around your chest to 
record the rise and fall of your chest due to your natural breathing.  A pulse oxygenation monitor 
is a small device that will clip onto your fingertip to record how much oxygen is in the blood that 
circulates from your heart to your fingertips.  If any of these devices cause you discomfort, the 
scan will be discontinued.  Some magnetic resonance scanning techniques are affected by the 
rhythm of your breath and the rhythm of blood flow from the heart. These devices help us to 
remove this effect from the scans so that we can better measure the structure and function of 
bodily tissues.
You may be asked to come back to the Pennington Center and repeat this test if we have an 
unforeseen problem collecting the scan on your first visit. 
Blood draw
A fasting sample of blood will be taken by a trained phlebotomist following standardized clinic 
procedures. The participant will be offered EMLA cream (lidocaine 2.5% and prilocaine 2.5%) to 
minimize pain related to blood draws. Serum concentrations of glucose, triglycerides, low density 
lipoprotein cholesterol (LDL-C), and high density lipoprotein cholesterol (HDL-C) will be obtained from 
a DXC600 manufactured by Beckman Coulter.  Serum CRP levels are determined by immunoassay with 
chemiluminescent detection, run on an Immulite 2000 manufactured by Siemens. Insulin will be assayed 
on the Siemens Immulite 2000. Approximately two teaspoons of blood will be collected.  
An aliquot of whole blood and aliquot of serum will be stored in case further tests are needed and if 
further consent is obtained from the subject. 
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Cardiometabolic risk factors
Resting blood pressure – will be assessed using standard clinical procedures on a standard 
sphygmomanometer.62  
Cardiometabolic risk factors - Elevated cardiometabolic risk (i.e. metabolic syndrome) will be 
categorized as the presence of three or more cardiometabolic risk factors using the established criteria in 
the 2011 National Institutes of Health Guidelines for Cardiovascular Health and Risk Reduction in 
Children and Adolescents.63  
24-hour Dietary Recall (ASA-24)
The focus of this project is physical activity and sedentary behavior, yet dietary intake is a critical 
component to energy balance and weight gain.4,15 Therefore, dietary intake will be assessed to 
characterize children’s energy balance so that the effects of physical activity and sedentary behavior on 
obesity can be isolated from dietary intake. Dietary intake will be monitored by the NCI Self-
administered 24-hour Dietary Recall (ASA24 ™). The 24-hour dietary recall is considered the gold 
standard to assess free-living diet in children.69 The ASA24™, which has been used in over 300 clinical 
trials,70 was developed to be automated and self-administered, enabling the efficient administration of 
multiple recalls in large-scale epidemiological studies such as the present study. The format and design 
are adapted from the interviewer-administered Automated Multiple-Pass Method (AMPM) developed by 
the USDA. The ASA24™ has been validated with 80% accuracy vs. 83% in AMPM, based on 
inconspicuous observation of adults consuming an ad libitum buffet meal.71 Parents will assist children in 
completing the instrument, and trained staff will be available to answer questions. Participants will 
complete the ASA24 survey outside of clinic two more times at each visit time point within 
approximately one week of the completed clinic visit. Surveys will be completed twice on a week day and 
once on a weekend at Year 0 and again at Year 2. Dietary patterns derived from principal components 
analysis will be used as a covariate and tested as a mediator in the proposed analyses. 
Accelerometer
Physical activity and sedentary behavior will be measured by a triaxial accelerometer (Actigraph GT3X+, 
Actigraph of Ft. Walton Beach, FL). The participant will be instructed to wear the accelerometer on an 
elasticized belt, on the left mid-axillary line. The Actigraph is one of the most common accelerometers 
used for scientific purposes. Participants will be encouraged to wear the accelerometer 24-hours per day 
for at least 7-days (plus an initial familiarization day and the morning of the final day), including 2 
weekend days. A 7-day monitoring protocol provides reliable estimates of children’s free-living physical 
activity behavior.64 The nocturnal sleep period will be identified and removed based on a fully automated, 
validated algorithm.65,66 The minimal amount of accelerometer data that will be considered acceptable is 4 
days with at least 10 hours of awake wear time per day (excluding the sleep period), including at least one 
weekend day. The 10 hour/day minimum is the standard in the field and was used by nearly all of the 54 
articles reviewed in a recent systematic review.67 The requirement of at least 1 weekend day was selected 
because children’s physical activity varies between weekday vs. weekend day.64,67 Following the final day 
of data collection, the research team will verify the data for completeness using the most recent version of 
the ActiLife software (version 5.6 or higher; ActiGraph, Pensacola, FL). The research team will ask 
children to wear the accelerometer for additional days (to a maximum of 14 days) to ensure that the 
minimal data requirements are met. Cutpoints will be assigned based on Evenson et al.,68 with 0 to 25 
counts per 15-second epoch (CPE) classified as sedentary, 26 to 573 CPE as light, 574 to 1002 CPE as 
moderate, and ≥ 1003 CPE as vigorous. The Evenson et al. cutpoints were selected because data are 
collected in 15-second epochs which allows for fine-tuned analysis of the sporadic nature of children’s 
physical activity. For the purposes of statistical analysis, sedentary behavior will be defined as ≤ 25 CPE 
and MVPA as ≥ 574 CPE. 
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Portable global positioning system (GPS) device
To identify where physical activity occurs, children will wear the QStarz BT Q1000XT data logger for 7 
days concurrent with the ActiGraph GT3X+. This GPS device will attach to the same elastic belt holding 
the ActiGraph GT3X+. This portable GPS works up to 20 hours (in our experience other devices have a 
longer battery life, but poorer satellite receptivity) when set at 15 sec epoch acquisition. Because the GPS 
data will be matched to the accelerometry data, the minimal amount of GPS data that will be considered 
acceptable is identical to that of the accelerometry data: 4 days with at least 10 hours of awake wear time 
per day (excluding the nocturnal sleep period), including at least one weekend day. Parents will be 
provided a charger to charge the GPS at night after removal. With reminder calls and e-mails from 
research staff (including links to the device-use video), this procedure has worked well previously. GPS 
allows for the identification of physical activity locations and routes.83 Portable GPS technology appears 
particularly accurate at detecting outdoor walk trips where the GPS device has sky view, with accuracy 
only slightly attenuated when the device is obstructed.84 The introduction of more reliable and less 
expensive equipment to provide location and speed data in time-linked formats85 has led to GPS adding 
significantly to physical activity duration and intensity data obtained through accelerometry, including 
improvements in estimating distance and time, as well as improved accuracy in detecting walking 
trips.86,87 
Surveys
Medical History Survey
Parents will complete a survey detailing the participant’s medical history and medication use as well as 
the biological parents’ medical history, if known. 
Lifestyle Survey
Participants will complete a survey to collect information about lifestyle, including diet and physical 
activity habits, quality of life, friends, confidence, smoking status, and media use. Participants will also be 
asked to report on their stage of sexual maturation, based on a series of standardized, validated drawings 
depicting progressive stages of pubertal development from 1 (no development) to 5 (complete 
development) for female breasts or male genitalia and from 1 (no development) to 6 (complete 
development) for pubic hair development (Tanner, 1986). 
IWQOL-Kids
Participants will complete a survey to collect information about how the participants body weight affects 
their quality of life, such as physical comfort, body esteem, social life, and family relations.
Resilience Scale (CD-RISC 10)
Participants will complete the 10-item Connor-Davidson Resilience Scale survey to collect information 
on resilience, which is the ability to thrive despite adversity. The 10-item unidimensional scale has 
showed excellent psychometric properties including good internal consistency and construct validity.118 
Sociodemographics Survey 
Parents will report race, ethnicity (i.e. Hispanic), household income, and parental education. These 
variables will be used as potential covariates in analyses. 
Family Environment Survey 
Several aspects of the family environment will be measured via parent-reported questionnaire.88,89 The 
survey we use pertains to children living in a household with parents. The baseline maximum age is 16 
years, so we expect all participants to be 18 years and still enrolled in high school at follow-up. However, 
if any participants no longer live at home, the participant will only complete the physical activity 
equipment part of this survey. Parents will report the following: 
a) physical activity equipment: presence of items at home that facilitate physical activity; internal 
consistency for this scale has been shown (α = 0.75);89  
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b) family structure: marital status and the number and age of children living in the home;  
c) household rules: parental rules regarding physical activity;88 
d) parent physical activity support: parental support for their child’s physical activity over the 
course of a typical week;88 these four items have been shown to be internally consistent (α = 
0.75);89 
e) parent physical activity behavior: the Aerobics Center Longitudinal Study Physical Activity 
scale is a self-report measure of current levels of physical activity; this measure asks about 
various activities and the amount of time spent on each activity, on average and has been shown 
to be psychometrically sound;90 and 
f) sibling physical activity behavior: amount of physical activity for each child over the course of 
a typical week, including frequency, time, location and type.91   
Constrained Behaviors Questionnaire
Parents will be asked to answer questions regarding their constrained behavior, which are parenting 
behaviors that restrict their child from outdoor play in their neighborhood (i.e. avoidance and defensive 
behaviors). This measure was validated by Carver et al., 2010.-119
Neighborhood Impact on Kids (NIK) Survey 
Parents (or participant if age is ≥ 18 years) will be asked to report on various aspects of the neighborhood 
environment, via a questionnaire adapted from the Neighborhood Impact on Kids (NIK) study survey (PI 
Dr. Brian Saelens, who serves as Consultant on this project),91 which drew on questions from other 
validated instruments. We have used this questionnaire in other studies.54,95 The questionnaire includes 
items related to neighborhood social capital (collective efficacy and the degree to which neighbors know 
each other and engage socially) and the neighborhood physical activity environment and built 
environment (access to and use of various places where a child can be physically active, and the 
suitability of the neighborhood environment for walking and physical activity). To measure the child’s 
perception of the neighborhood environment, we will use a short validated survey for the child to report 
his/her perceptions of access to neighborhood destinations, perceptions of neighborhood aesthetics and 
safety, and perceptions of the neighborhood social environment.96  
Body Image Assessment Procedure for Preadolescents (BIA-P) and Mood and Feelings Questionnaire
The participating child will complete the Body Image Assessment Procedure for Preadolescents (BIA-
P),99 which has been demonstrated as reliable and valid in the adolescent age period.99,100 The child will 
also complete the Short Mood and Feelings Questionnaire (SMFQ), a 13-item scale which has been 
validated in a community sample of children and adolescents to self-report depressive symptoms.
Body dissatisfaction and negative mood have both been shown to influence health behaviors and weight 
status in adolescents, making both of these factors relevant in the present investigation. Research has 
shown that a higher level of body dissatisfaction not only perpetuates health-compromising behaviors, 
such as unhealthy weight control methods and binge eating, but also hinders health-promoting behaviors, 
such as vegetable intake and participation in physical activity.102 In general, research indicates that 
negative body image (higher body dissatisfaction) does not serve as a motivator for engaging in healthy 
weight management behaviors, but rather predicts the use of behaviors that may place adolescents at risk 
for overall poorer health and weight gain.102  
Health-Related Qualify of Life (KIDSCREEN-10 Index)
Health-related quality of life will be assessed using the KIDSCREEN-10 Index, designed to evaluate 
quality of life in children and adolescents with a chronic illness.115,116 The index includes questions on 
energy, sadness, loneliness, fun with friends, and performance at school. The psychometric properties of 
this index are such that the distribution of raw-scores resembles the theoretical expected normal 
distribution. Thus the index provides a good discriminatory power along the HRQoL-trait-continuum, 
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shows only few ceiling/floor effects, and even raw-scores may provide interval-scaled HRQoL 
measurement. In addition the good internal consistency reliability (Cronbach’s Alpha = .82) and the good 
test-retest reliability / stability (r = .73; ICC=.72) enables a precise and stable HRQoL measurement. 
Additional statistical analyses show that the KIDSCREEN-10 Index is able to differentiate between 
different groups. Children and adolescents with a low score on the family affluence scale (FAS, effect 
size d=.47), with behavioral problems (SDQ, effect size d=.1.30) and with a high number of 
psychosomatic complaints (d=1.69) display significantly lower health related quality of life in comparison 
to the respective comparison group.
Food Security Questionnaire
Parents of participants will complete the American Academy of Pediatrics’ Food Security Questionnaire, 
a self-reported assessment of the family’s financial situation in regards to food security within the past 
year. 
Stress Questions (PECK)
The Personal Experiences Checklist provides a self-report assessment of a young person’s personal 
experience of being bullied. The measure is suitable for both girls and boys aged 8 to 16 years and it 
covers the full range of bullying behaviors, including covert relational forms of bullying and cyber 
bullying. There are four subscales: physical, relational, cultural, and cyber.
•
Hunt, C., Rapee, R.M., & Peters, L. (2012). Development of a measure of the experience of being 
bullied in youth. Psychological Assessment, 24 (1), 156-165.  
Food Craving Inventory (FCI) 
The food craving survey is a self-reported measurement of how often the participant experiences a 
craving for listed foods. The survey is 33 items long, and includes various foods and beverages. 
Participants will indicate on the survey how many cravings he or she has had in the past month.  

White M, et al. Development and validation of the food-craving inventory, Obes Res. 
2002;10:107-114.

Geiselman P et al. Reliability and validity of a macronutrient self-selection paradigm and a food 
preference questionnaire. PHYSIOL BEHAV. 1998; 63(5): 919-928.
Neighborhood Perception Survey
Participants will complete this validated survey to measure the child’s perception of his or her 
neighborhood environment. The self-reported survey indicates the child’s perception of access to 
neighborhood destinations, perceptions of neighborhood aesthetics and safety, and perceptions of 
neighborhood social environment.

Hume C, Salmon J, Ball K. Associations of children’s perceived neighborhood environment 
with walking and physical activity. Am J Health Promot. 2007; 21:201-207.
Sibling Relationship Questionnaire (SRQ)
Participants with sibling(s) who are also enrolled in the study will complete a shortened version of the 
Sibling Relationship Questionnaire (Furman and Buhrmesters 1985) for each sibling in the study. 
Participants will be asked to gauge the quality of their sibling relationships through questions related to 
the constructs of prosocial, affection, companionship, similarity, intimacy, admiration of sibling, and 
admiration by sibling. A psychometric analysis of this questionnaire was examined among a sample of 
428 adolescents and confirmed that stated seven constructs form an overall factor of warmth/closeness.  

Derkman MS, Scholte RHJ, Van der Veld WM, Engels RCME. Factorial and construct 
validity of the Sibling Relationship Questionnaire. Eur J Psychol Assessm 
2010;26(4):277-283.

Furman W & Buhrmester D. Children’s perceptions of the qualities of sibling 
relationships. Child Development 1985;56(2):448-461.
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mHealth Weight Management Program Acceptability Survey
Parents will complete a survey assessing program acceptability for an mHealth weight management 
program that teaches the parent and child dietary, physical activity, and behavioral change strategies. 
Cyber Cycling Lifestyle Program Acceptability Survey
Parents will complete a survey via REDCap assessing acceptability for an exercise and healthy eating 
program for adolescents and young adults.  
COVID-19 Survey
Parents will be asked to complete a survey related to the stay at home order that families were under due 
to the COVID-19 pandemic. The survey will be sent out via email using REDCap, where parents can 
click that they consent to completing the additional survey. Participants that have already completed the 
Year 2 visit will also receive the survey link, with an option to complete it. Parents will be asked to report 
on information related to their child’s behavior habit changes, such as sleep, physical activity, diet and 
screen time.
Environmental Assessment and Analysis
Neighborhood environment
A participant's neighborhood will be defined as the 3/4-mile street network buffers around each child’s 
residence (walkable distance from home), and neighborhood features will be enumerated within the 
buffers (see Colabianchi et al., 2014 as an example).92 Physical activity patterns will be characterized 
according to whether the physical activity occurs within 3/4-mile of the child’s residence or not. As a 
point of comparison, the secondary definition of neighborhood environment will be the Census Block 
Group (BG) in which he/she resides. This definition has been used in other recent work assessing 
environmental influences on children’s physical activity93,94 and is expected to be a good scale on which 
to measure the influence of neighborhood disorder and social processes.93  
Objective measures: park access and crime. Park access has been determined based on a 
comprehensive list of public parks in the four parishes (counties) surrounding Baton Rouge, LA 
maintained by Dr. Broyles (Investigator for the present project). This list is constantly updated 
and will be verified prior to recruitment. A BG will be considered to have park access if any 
boundary of a park is located either within the BG or within a 0.25-mile buffer around the BG, 
corresponding to similar research.94 Distance from participant residence to nearest park will be 
calculated within the ArcGIS 9.2 software program and will be used descriptively. Level of 
neighborhood crime will be measured via an index of total crime derived from Uniform Crime 
Report data (CrimeRisk, Applied Geographic Solutions).  
Physical activity locations. GPS and accelerometry data will be matched in 15-sec epoch pairs 
based on date and time stamp, using the most recent version of SAS statistical software. GPS-
ActiGraph GT3X+ matched data points will be compiled in global positioning systems software 
(ArcGIS 9.2) and combined with other spatial data (e.g., locations of parks, community centers). 
Project Consultants Dr. Brian Saelens and Dr. Philip Hurvitz of University of Washington are 
leading experts in the combination and interpretation of GPS-accelerometry matched data, and 
they will advise on data collection and the management and analysis of these combined data 
within the ArcGIS software (see letters of support). Participant PA locations will be categorized 
(e.g., park, school, home, other’s home, etc.),97 and the route distance between the PA location 
and the participant’s home will also be calculated to facilitate distance-based categorization.  
Neighborhood-level data
Neighborhood-level data will be collected from a variety of existing data sources. Measures will describe 
the neighborhood food environment, neighborhood physical activity facilities, and neighborhood physical 
and social disorder.  
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Physical activity opportunities. The procedures used to create measures of opportunities for 
physical activity closely follow those used by others.47,98 A list of physical activity facilities (i.e., 
businesses associated with certain Standard Industrial Classification (SIC) codes will be compiled 
for zip codes associated with participant neighborhood buffers from a business list developed by 
ESRI. Opportunities for physical activity will be measured and examined as the number of 
available facilities, concentration of facilities (per neighborhood population), and facility density 
(per square mile). 
Geocoding of address information. In the present study, accurate geocoding will depend mainly 
on the accuracy and completeness of the address information obtained from the environmental 
data files. Investigator Dr. Broyles has extensive experience in geocoding and will perform all 
address mapping. Further, Dr. Brian Saelens and Dr. Philip Hurvitz are subject matter experts in 
GPS data analysis and will act as consultants for this project (see letters of support). 
 
Ecological momentary assessment app
EMA data will be collected using the participant’s personal mobile device with a custom version of the 
EMA mobile application  installed. The mobile application is compatible with iOS or Android powered 
devices. If the participant does not have or does not wish to use his/her own mobile phone, we will loan a 
mobile device with disabled voice and data services other than as required by the mobile application. The 
HIPAA-compliant mobile application will be programmed to display question sequences and response 
options on the mobile device screen, and de-identified data are encrypted and stored on the software 
platform’s cloud-based storage database until downloaded by the investigator. Upon hearing the signal, 
children will be instructed to complete a short electronic survey taking approximately 2 to 3 minutes. The 
methodology, questions, and responses have been previously examined for validity, feasibility, and 
reliability by comparing time-matched accelerometer data to mobile phone-delivered EMA responses of 
activity type.55,106 In a pilot focus group (n=8), children indicated that the device was easy to use and that 
they would be willing to participate in an EMA study in the future.106 In a study of children ages 9 to 13 
years (n=121), accelerometer counts of MVPA were significantly higher during EMA-reported physical 
activity than sedentary behaviors, independent of weight status (p<0.0001). Additionally, children 
responded to an average of 80% of EMA prompts.106
Table 3. Sampling schedule for the EMA measurements.
Note. Question sequences are prompted at a random time within each interval. 
We will use a signal-contingent sampling schedule that includes randomly delivered prompts including 
one prompt during each 2-hour window for a total of 2 prompts each weekday (outside of school time) 
and 6 prompts each weekend day (see Table 3 for the EMA sampling schedule). Though the schedule 
will be standardized across participants, the schedule will vary each day within the time window, and the 
participant will not know the schedule ahead of time to prevent them from altering their behavior in 
anticipation of a prompt.  
 
The participant will be asked a series of questions via the mobile application during each prompt. The 
questions have been previously validated on samples of children and adolescents using EMA.55,56,59 The 
final ten questions are selected from the Positive and Negative Affect Schedule for Children.107 See Table 
4 for questions that will be included in each EMA prompt. 
Sunday 
Monday 
Tuesday 
Wednesday 
Thursday 
Friday 
Saturday 
8 - 10am 
X 
 
 
 
 
 
X 
10am - 12pm 
X 
 
 
 
 
 
X 
12 - 2pm 
X 
 
 
 
 
 
X 
2 - 4pm 
X 
 
 
 
 
 
X 
4 - 6pm 
X 
X 
X 
X 
X 
X 
X 
6 - 8pm 
X 
X 
X 
X 
X 
X 
X 
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What were you doing right before the beep went off?
Reading/computer/homework, watching tv/movies, playing video 
games, active play/sports/exercising, other
If other, what was this other activity?
Eating/drinking, talking/on the phone, chores, riding in a car, 
something else
Were you alone right before the beep went off?
Yes, No
If not alone, were you with your (parent, sibling, friend, etc.)
Mom or dad, brother(s) or sister(s), other family members (cousins, 
aunts, uncles), friend(s), classmates, people you don’t know
Where were you just before the beep went off?
Home indoors, Home outdoors, School, Car/van/truck, Outdoors not at 
home, Restaurant, Store/mall, Someone else’s house, Gym/rec center, 
Someplace else
How joyful / cheerful / happy / lively / proud / miserable / mad / afraid 
/ scared / sad were you feeling just before the beep went off?
0-5 Likert scale
Table 4. Question prompts and answer choices for the EMA measurements.
Retention
We expect challenges in following up with this cohort 2 years after the Year 0 clinic visit. We will 
employ several retention strategies between Years 0 and 2 that have been successful in prior trials, 
including mailing birthday cards to child participants, sending quarterly newsletters on family and 
children’s health topics, inviting families to the annual Pennington Biomedical 5K, and telephoning the 
parent 2 months prior to the Year 2 clinic visit to schedule the visit and answer questions. The child will 
receive a stipend after each completed clinic visit and upon returning the accelerometer/GPS tracker, and 
the child will receive a certificate of completion upon finishing the study. Additionally, Pennington 
Biomedical has an excellent retention rate for clinical trials. The population of Baton Rouge is relatively 
stable,82 and the majority of children will still be enrolled in K-12 school. Offering assessments during 
summer and school holidays will increase the likelihood of participation. We successfully followed up 
with children in prior prospective trials: 87% of children aged 10 to 14 years returned for a 2-year follow-
up clinic visit in our Baton Rouge Children’s Study (114 of the initial 131).77 Our retention for adult 
clinical trials is also excellent: we examined 14 trials conducted at Pennington Biomedical with a total of 
1581 participants and observed a 70% retention rate over an average 4.8 year follow-up (range 1 year to 
13 years). We expect an even higher retention rate due to the shorter follow-up time (2 years).  
Pennington Biomedical staff may contact participants every 3-6 months by phone or email to maintain 
contact between Year 0 and Year 2 clinic visits as well as schedule the Year 2 visit.
Anticipated Dropout 
The 25% dropout rate is a combined estimate for both loss to follow-up and exclusions that occur during 
the clinic visit. Based on attrition at 2-year follow-up for Pennington Biomedical’s Baton Rouge 
Children’s Study,77 13% of the sample is expected to not report back for the 2-year follow up. An 
additional 12% is expected to be excluded due to exceeding weight limitations or the refusal of certain 
procedures (e.g. MRI, DXA) based on refusal rates in Pennington Biomedical’s WAIST Study.20  
Statistical Analysis 
Objective 1:
The hypotheses will be tested using a series of generalized linear models with severe obesity as the 
independent variable. First, linear mixed effect models will be used to test for effects between BMIz and 
each dependent continuous variable (change in: min/day sedentary behavior, min/day MVPA, and each 
cardiometabolic risk factor). Next, logistic regression models will be used to determine the association 
between BMIz with the likelihood of not meeting recommended behaviors (i.e. top quartile of min/day of 
sedentary behavior; MVPA < 60 min/day) or being at elevated cardiometabolic risk. Finally, the cohort 
will be stratified into two groups based on obesity at Year 0: severely obese (SO) and not severely obese 
(NSO). Logistic regression models will be used to determine associations between SO (vs. NSO) and 
each dependent variable (i.e. change in: minutes/day sedentary time, minutes/day MVPA, and each 
continuous cardiometabolic risk factor). Covariates will include age, sex, dietary intake, and race. School 
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term vs. school holiday will be considered as an additional covariate if time of year is significantly 
associated with the independent and dependent variables. 
 
Secondary analysis will use MRI and DXA as covariates in the model (namely the visceral, trunk, and 
total body fat). These measurements, along with BMIz, may be highly correlated.  Various variable select 
criteria (including LASSO and support vector machine) will be used to determine which combination of 
these variables best explains the variance in the data. The full model will enable the comparison of 
imaging-based body composition measures vs. BMIz alone and their associations with physical activity, 
sedentary behavior, and cardiometabolic risk factors in children. 
As exploratory analyses, dietary intake will be tested as a potential mediator to explain significant 
associations between the predictor variable (BMIz) on outcomes (physical activity, sedentary behavior, 
and cardiometabolic risk factors). Series of sequential models will be used to test each pathway using a 
path analysis model based on the Baron and Kenny causal steps for mediation.73,74 
 
Objective 2:
Analyses to test Hypothesis 2 will be conducted using a series of linear regression models (i.e. linear 
mixed effects models). Based on difference score between Years 0 and 2 for total body fat, the group will 
be divided into two dichotomous groups using percentiles: high fat gainer (HFG) and low fat gainer 
(LFG). These variables will be used to predict changes in physical activity, sedentary behavior, and each 
cardiometabolic risk factor in linear mixed effect models. Covariates will include age, sex, dietary intake, 
and race. School term vs. school holiday will be considered as an additional covariate if time of year is 
significantly associated with the independent and dependent variables. Secondary analysis will use MRI 
and DXA as covariates in the model (namely the visceral, trunk, and total body fat). As previously 
reported, variable select criteria (including LASSO and support vector machine) will be used to determine 
which combination of these variables best explains the variance in the data. The full model will enable the 
comparison the accumulation of imaging-based body composition measures vs. BMIz alone and their 
associations with change in physical activity, sedentary behavior, and cardiometabolic risk factors in 
children. Finally, there are no standard accepted definitions of pediatric severe obesity to dichotomize 
participants based on MRI or DXA measures of body fat. Therefore, receiver operating characteristic 
curves will be created to estimate the value at which visceral, trunk, and body fat best correspond with the 
BMI definition of severe obesity (i.e. BMI ≥ 120% of the 95th percentile1). Clinically relevant cut-points 
of severe obesity based on total, trunk, and visceral body fat will be established among this diverse cohort 
of adolescents. 
Power analysis
Objective 1: 
The primary outcome for the first objective is change in minutes of sedentary behavior between Year 0 
and Year 2 (i.e. Year 2 values minus Year 0 values). Baseline estimates for the two groups were 
calculated using the WAIST cohort data (from the 2010-11 assessment). Based on data from Berkey et 
al.,75 the SO group is assumed to increase their sedentary time by 30 minutes/day at year 2, while smaller 
increases in sedentary time is expected for the NSO group (Table 5). A minimum of 334 participants is 
needed to ensure a well-powered study with significance level of α = 0.05, 80% power to detect a 24 
minute difference change in sedentary behavior between the two groups, and allowing for a dropout rate 
of 25%. Therefore, our sample size of up to 345 participants will be sufficient. 
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Table 5. Difference in changes in sedentary time based on severe obesity status.  
Note. NSO is the group classified as not severely obese; SO is the group classified as severely obese.
 
Secondary analyses will include change in daily MVPA minutes between years and change in days/week 
of physical activity (≥ 60 minutes MVPA/day) between years. Power analysis was conducted on these 
outcomes, using the WAIST cohort and data from Nigg et al.,76 and our sample size will have power of 
93.8% and 90.4% to detect a significant difference in change in daily MVPA minutes and change in 
days/week of physical activity, respectively. 
Contingencies  
Based on national reference data, it is possible that very few or no severely obese children meet the 
healthy behavior or cardiometabolic health criteria (e.g. no severely obese child attains ≥ 60 minutes of 
MVPA/day; no severely obese child has a waist circumference < 90th percentile). If this is the case, the 
dichotomous dependent variable will be adjusted to be quartiles, so that the analyses is interpreted as the 
likelihood of a severely obese child being in the top (healthiest) quartile of the health behavior or 
cardiometabolic health criteria for the overall cohort. 
Objective 2: 
The primary outcome for the second objective is the relationship between total fat accumulation and 
change in sedentary behavior. Based on data from Berkey et al.,75 we estimate an overall change in 
sedentary behavior of 12 minutes/day increase in the HFG and 3.6 minutes/day increase in the LFG over a 
two-year period. Table 6 shows sample sizes required to detect various differences for the change 
between groups. With a sample size of up to 345, this will provide at least 80% power for detecting a 
difference in the change between groups of 8.4 minutes/day, allowing for the anticipated 25% dropout. 
Therefore, with our sample size of up to 345 participants, we will have sufficient power to detect a 
difference between the HFG and LFG groups. 
Table 6. Difference in changes in sedentary time between the high fat vs. low fat gainer group. 
Note. HFG is the group classified as high fat gainers; LFG is the group classified as low fat gainers. 
Secondary analyses based on the second objective will include modeling the relationship between fat 
mass accumulation and change in physical activity. Using data from Berkey et al.,75 we estimate an 
overall decrease of 19.2 minutes/day of MVPA in the HFG and a decrease of 4.8 minutes/day of MVPA 
in the LFG over a two-year period. With a sample size of up to 345 (including anticipated dropout), this 
will provide 93.4% power for detecting a difference in the change between groups of 14.4 minutes/day. 
 
Change in 
minutes/day of 
sedentary time for 
NSO group 
Change in 
minutes/day of 
sedentary time for 
SO group 
Difference in 
change between 
groups 
Standard deviation 
of change between 
groups 
Minimum sample 
size 
Total sample size 
(allows for 25% 
dropout) 
2.4 
30.0 
27.6 
69.6 
202 
253 
3.6 
30.0 
26.4 
69.6 
222 
278 
4.8 
30.0 
25.2 
69.6 
242 
303 
6.0 
30.0 
24.0 
69.6 
266 
334 
Change in minutes 
of sedentary time 
for HFG 
Change in minutes 
of sedentary time 
for LFG 
Change between 
groups 
Standard deviation 
of change between 
groups 
Minimum sample 
size 
Total sample size 
(allows for 25% 
dropout) 
12 
2.4 
9.6 
24 
194 
259 
12 
3 
9.0 
24 
220 
293 
12 
3.6 
8.4 
24 
255 
Up to 345
12 
4.2 
7.8 
24 
292 
389 
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Contingencies 
 The DXA instrument has a maximal weight limit of 204 kg and the Signa Excite MRI has a maximal 
weight limit of 159 kg; therefore, no participants exceeding this weight will be allowed on the respective 
instrument. Furthermore, the MRI instrument has a maximal waist diameter limit of 60 cm with a 48 cm 
field of view. Based on the WAIST cohort, in which 4 of 423 participants exceeded weight limits, we 
anticipate that few participants for the present project will exceed the weight limits. However, we have 
accounted for 12% of the sample to exceed weight limits in thed sample, and we also have a contingency 
plan in place should this occur. The clinic coordinator will remove the DXA and/or MRI scan from the 
participant’s schedule following the anthropometric measurement to minimize participant discomfort or 
embarrassment, and the anthropometric measures will be used to classify obesity status.  
Objective 3:
Analyses to test the hypotheses will be conducted using linear models. The primary independent variable 
will be BMIz and the dependent variable will be physical activity (MVPA). The c statistic will be 
evaluated to assess the contributions of BMIz and the environmental (or socio-emotional) factors to 
increase the accuracy in model-based prediction of events using data collected at Year 0, Year 2, and the 
changes between Years 0 and 2. Linear models will also be used to examine if there are changes in 
environmental or socio-emotional factors between Years 0 and 2. Finally, severe obesity (vs. all others) 
will be tested as a dichotomous variable with each environmental or socio-emotional factor to determine 
associations with physical activity. School term vs. school holiday will be considered as an additional 
covariate if time of year is significantly associated with the independent and dependent variables. 
 For the models investigating environmental factors, the independent variables will include the family 
environment, park access and crime, perceived neighborhood environment, physical activity locations, 
and physical activity opportunities. Family environment subscales related to physical activity will initially 
be summed to create a single continuous score, with higher values indicating higher family support for 
physical activity. Additionally, the average minutes/day of MVPA will be summarized by location type. 
To examine socio-emotional factors, the independent variables will include body image, mood, and the 
data collected from the EMA including the three affect states (i.e. happy, stressed, energetic) and the 
social context of the activity (i.e. activity type and who was with the participant). For both the 
environmental and the socio-emotional factors, associations that are positively related to physical activity 
will be identified as facilitators of each, and inverse associations will be identified as barriers of each. 
 
Contingencies  
GPS and EMA are emerging technologies that have shown compliance and acceptability by children and 
adolescents.106 However, we have considered the potential challenges that these devices present to 
participants and have created several contingency plans. EMA in particular may be perceived as 
burdensome and time-consuming since the participants receive several messages over several days.57 
Therefore, in the present project, children and their parents will receive an extensive introduction to the 
EMA technology and can request changes to the scheduled time interval to receive messages (e.g. if the 
child’s bedtime is before 8 pm, the parents can request that messages end at the bedtime). In addition to 
stipends for completing the clinic visits, we also allocated $22 per participant to incentivize response to 
EMA queries, so the participant can earn $1 per finished survey. This incentive structure has been 
effective in prior EMA studies to encourage the participant to continue providing data.106 We specifically 
designed questions that are short in length with few response options in order to minimize the time 
required of the participant. By limiting all EMA/GPS measures to 2 weeks (one week during Year 0 and 
one week during Year 2), interference to the participant’s daily life is minimized. 
We selected the EMA application over other EMA options due to its functionality and low cost. The 
EMA provides several strengths to maximize the collection of valid, reliable data:57 reminder prompts are 
used for unanswered/unfinished entries in order to improve validity of the responses and minimize the 
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time lapse between response and activity; research staff members have real-time access to data to monitor 
compliance so they can immediately contact the participant by phone call or text message if a participant 
skips a survey response; questions employ skip sequences and branching to allow participants to skip 
questions that are not relevant; question sequence can be randomized to reduce participant boredom; 
participants are alerted via in-app push notifications, avoiding text messages that may incur an additional 
fee; and wireless internet connection is not required, so if the participant does not have access to wifi they 
will still receive prompts and answer the questions. 
We will provide resources to participants to ensure that technical difficulties do not impede data 
collection (e.g. we will provide participants with a charger and regular reminders to charge the device to 
preserve power; we will provide a phone hotline and email address to provide technical assistance). It is 
possible that reactance may affect participants’ responses, meaning that participants become more aware 
of behaviors, emotions, and cognitions as a result of being surveyed on a regular basis.108,109 Therefore, 
we will not assess behavior-specific cognitions, and we limit the number of survey questions and the 
frequency of the prompts. 
Despite our numerous efforts to maximize participant compliance, we anticipate a small amount of 
missing data from the accelerometer, GPS, and EMA measurements due to participants not completely 
complying with the protocol by either not wearing the devices for a sufficient period of time or not 
responding to EMA survey prompts. Multiple imputation has been shown to work well with this type of 
data110 and will be used here. Sensitivity analysis will be conducted to determine if patterns exist in the 
missing data.  
 
Data and Specimen Banking and Management
Blood samples will be banked for future research. These samples will be stored at Pennington Biomedical 
Research Center in a secure, locked storage freezer. Storage and disposal of biospecimens will be 
conducted in a manner conforming to the appropriate care and handling of biological specimens as 
outlined through the Institutional Biohazard Committee Guidelines. Only the investigators, study staff, 
and collaborators (including external to Pennington) explicitly given access to the samples by the 
principal investigator, will have access to these specimens.  
Each participant will be issued an ID number that will be utilized throughout the study. A secure master 
file linking names, addresses and ID numbers will be maintained in a confidential computer file 
accessible only to the investigators. Access to data files can be made only with permission of the Principal 
Investigator. Privacy in the context of this study includes confidentiality of data and personal information. 
During interviews, measurements, and the study staff will ensure full privacy of participants and will 
ensure that the data are stored in a secured area. Hard copies of study data will be kept in a secure, locked 
cabinet at PBRC and electronic copies of study data will be kept in the secure PBRC and RedCap 
databases. All study staff must be HIPAA certified. Only de-identified data will be used for analysis.
PBRC’s Data Management Core will be responsible for database design, database implementation, and 
manual data entry of clinic visit data. PBRC’s Clinic Core will be responsible for managing clinic visit 
data using the REDCap secure, web-based database/survey application. PBRC’s Biostatistics Core will 
cooperate with the data manager who will manage all data entry to ensure data quality.  
Provisions to Monitor the Data to Ensure the Safety of Participants
This research requires slightly more than minimal risks to subjects due to the small radiation exposure in 
DXA and MRI, therefore does not warrant the establishment of an independent Data and Safety 
Monitoring Board (DSMB). This Data and Safety Monitoring plan describes the safety monitoring 
procedures for the proposed study, including a description of how often and to whom serious and 
unexpected adverse events will be reported. The plan will help ensure the safety of all participants. The PI 
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will communicate via electronic submission to the IRB all unanticipated problems as defined by the IRB 
and all serious adverse events (SAEs) to the medical investigator within 24 hours. All less serious adverse 
events will be reported to the medical investigator within 3 days of occurrence. 
Subject Accrual and Compliance
1)
Measurement and reporting of subject accrual, adherence to inclusion/exclusion criteria, 
protocol adherence, and rates of study completion  – Review of subject accrual, 
adherence to inclusion/exclusion criteria and study procedures as listed in the protocol, 
and rates of study completion will occur monthly. These data will be reviewed by the 
study PI.  The recruitment goals per month are listed in the table below.
Table 7. Recruitment goals per month
2)
Stopping rules – Data on subject accrual and completion rates will be synthesized and 
evaluated yearly to determine if the study should be terminated. One of the most likely 
reasons for early termination is the failure to recruit or retain participants; therefore, these 
data will be evaluated yearly to determine if failure to recruit or attrition is jeopardizing 
the ability to empirically test the study aims. 
3)
AE rates and out of range laboratory data – AE rates will be evaluated quarterly and out 
of range laboratory data will be evaluated yearly by the study PI and Medical 
Investigator, to ensure proper AE reporting and to regulate procedures to protect 
participant safety.
Withdrawal of Participants
During the course of the study, participants may be withdrawn from the study for the following reasons:

Unwillingness on behalf of the child or adolescent to participate in the study or cooperate with 
study staff

Unwillingness on behalf of the parent or legally authorized representative to cooperate with study 
staff

Presentation of significant medical symptoms that would warrant termination of study 
participation to protect the participant’s safety

Termination of the study by the sponsor
Data that have already been collected during the course of study participation from a withdrawn 
participant will be used, unless a specific request is otherwise received.
Risks to Participants
Blood sampling
There may be pain, light-headedness, infection, bleeding or bruising at the needle insertion site; however, 
aseptic (sterile) technique and trained personnel minimize these risks. The participant may feel hungry or 
weak during the time she is required to fast before the measurement. The amount of blood drawn is about 
2 tablespoons. If the participant chooses to use the EMLA cream, the treated skin may temporarily block 
all sensations. The participant should avoid scratching, rubbing, or exposure to extreme hot or cold 
temperatures until complete sensation has returned. If the participant has a history of sensitivity to local 
anesthetics, we do not recommend that the child uses the EMLA cream.
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There is a risk of loss of confidentiality and/or privacy associated with banking blood samples for future 
research. These blood samples may be given to other investigators for future research. The future research 
may occur at sites other than Pennington Biomedical Research Center and may be reviewed by other 
Institutional Review Boards when applicable. The research done with the specimens may help to develop 
new products in the future, or may be used to establish a cell line or test that could be patented or 
licensed. Participants will not receive any financial compensation for any patents, inventions or licenses 
developed from this research. Storage and disposal of biospecimens will be conducted in a manner 
conforming to the appropriate care and handling of biological specimens as outlined through the 
Institutional Biohazard Committee Guidelines.  
Blood pressure
The participant may experience discomfort during blood pressure recordings due to the pressure of the 
cuff on their arm.
DXA Scan
The amount of radiation used during the whole-body dual energy x-ray absorptiometry scan is very small.  
The radiation dose for this scan is equivalent to the radiation you are naturally exposed to in the 
environment in less than one day.  Scans will not be performed on any participant who is pregnant, and all 
females must submit to a urine pregnancy test prior to the scan.    
MRI
There are no known biological risks associated with magnetic resonance scanning.  It has been used 
routinely for over 20 years.  It produces side effects in very few situations.  Side effect description given 
to participants includes:
 
Metal: Because the magnetic resonance machine uses a magnetic field, it can move any metallic 
objects that are inside the body.  This disruption of metal inside the body is extremely dangerous 
to you and may even be life threatening.  If you think you may have a cardiac stent, metallic 
implant, metallic piercings, shrapnel, or any other metallic material in your body, it is of utmost 
importance that you alert the study coordinator or MR technician. If you have metallic materials 
in your body that cannot be removed, we will exclude you from this study for your safety.  
Electronics: Magnetic resonance imaging involves the use of radio frequency energy that can 
disrupt the functioning of electronic devices.  If you think you might possess a pacemaker or any 
other electronic medical device inside your body, it is of utmost importance that you inform the 
study coordinator or MR technician.  If you have any such electronic devices we will exclude you 
from this study for your safety.
Tattoos and cosmetics: Some tattoos and cosmetics contain metallic materials that can heat up 
during scanning, especially if they are located on the part of the body being scanned.  If the 
metallic material heats up enough, you may feel an uncomfortable burning sensation, and a skin 
burn may develop.  If you have any tattoos or cosmetics that might contain metallic materials, 
please alert the study coordinator or MR technician.  If you feel a burning sensation on your skin, 
alert the study coordinator or MR technician.  In some cases, the amount of metallic material in 
the area being scanned is so excessive that the scan must be stopped.  In other cases, a cold 
compress placed over the metallic material will be used to prevent the burning sensation.
Confinement: During the MR scan, you will be lying down on a table inside of a metal tube.  
The metal tube is a confined place.  This might produce a feeling of claustrophobia, which can be 
distressing.  If you have experienced claustrophobia in the past, you might become too distressed 
to complete the scan.  If you become distressed during the scan due to confinement in the scanner 
tube, please alert the MR technician and the scan will be halted.
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Noise: The MRI machine creates a loud, rhythmic noise that sounds like grinding or churning.  
This can be distressing to those who are sensitive to loud noises.  You will be provided with 
earplugs to reduce the noise.  But if you find the machine noises distressing, alert the MR 
technician and the scan can be halted.
Peripheral nerve stimulation: During the MR scan, the magnetic field around your body goes 
through rapid changes.  These changes are all within safety limits set by the Food and Drug 
Administration.  But, some people experience twitching in the nerves of their arms or legs as a 
result of these magnetic field changes.  This twitching is generally not painful, and it stops at the 
end of the MR scan.  But the feeling of inadvertent muscle twitching may make you feel 
disoriented or uncomfortable.  If you experience this and wish to stop the scan as a result, please 
tell the MR technician.
Carbon dioxide: The level of carbon dioxide in the air during gas delivery experiments is similar 
to what you would find at high elevation. There is a slight risk that the carbon dioxide could lead 
to a mild headache or dizziness. If you experience this and wish to stop the scan as a result, please 
tell the MR technician.
Venous thromboembolism: In some elderly or obese individuals, lying down perfectly still for 
multiple hours can slightly increase the risk that blood clots develop in the blood vessels.  These 
blood clots can be hazardous to your health.  The technologist will make every effort to keep your 
time in the MRI machine as short as possible to reduce this risk.  Also, you will have breaks 
during your time in the MRI machine, and during these breaks the technologist will ask you to 
move your arms and legs and reposition your body to get comfortable.  Moving around in this 
way reduces your risk of blood clots.
Potential Benefits to Participants
No other benefits for participating in the TIGER study can be promised. 
Vulnerable Populations
This study will involve children as participants (10-16 years of age). As such, their parents and/or legally 
authorized representative will have to provide written informed consent allowing their child to participate 
in the study. In addition, participating children will provide written assent.
All participants will be explicitly told that their participation is voluntary and that they may end (stop) 
their participation at any time. If a participant indicates that they wish to stop participating, all study 
procedures they are undertaking at that time will stopped to protect their rights and welfare.
Sharing of Results with Participants
Individual participant results will be provided if the participant or parent requests them. Results from the 
study will be submitted for manuscripts in scholarly journals and presentations. All study reports for 
publication will present only aggregated data to minimize the risks that a participant can be identified 
form their participation in the study.
Setting
Potential participants will be recruited utilizing the recruiting services (Recruitment Core) of the 
PBRC. All clinic visits will be performed at the PBRC in the Outpatient Clinic and/or the 
Translational Research Center for Children (TReCC). 
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Resources Available
Physical Resources 
The Pennington Biomedical Research Center (PBRC) is a 187-acre campus of the Louisiana State 
University System located in Baton Rouge, Louisiana. The PBRC is a model for clinical and translational 
research, since it houses basic, clinical, and population research programs in one facility. PBRC houses 
53 research laboratories, 19 core service laboratories, and more than $20 million in technologically 
advanced equipment. The following core units will support this project, operating as cost centers.  
 
Outpatient clinic. The four-story Clinical Science Building (92,530 GSF) opened in 2010 and the 
adjoining Translational Research Clinic for Children (TReCC) opened in 2014. The outpatient clinic is 
overseen by Frank Greenway, M.D. All clinic visits will occur in these two facilities. A clinic coordinator 
will provide complete oversight of all clinical visits including scheduling visits, preparing medical charts, 
coordinating visits, reporting adverse events, and following up with participants. The clinic services 
include but are not limited to informed consent, anthropometry, vitals, blood draw, concomitant 
medication collection, adverse event assessment, telephone visits, questionnaires, and ordering/processing 
of participant stipends ($50 per clinic visit per participant disbursed as check or loadable debit card). 
Data management and analysis. The PBRC Biostatistics and Data Management Core resides in the 
Population and Public Health Sciences program, headed by Peter Katzmarzyk, PhD (Investigator). The 
Biostatistics and Data Management Core is divided into two units: the Biostatistics Group and the 
Research Computing Group (RCG). Dr. William Johnson is directly responsible for overseeing the 
Biostatistics Group. In addition, Dr. Johnson supervises two other faculty biostatisticians (including 
Robbie Beyl, PhD, Collaborator), as well as several MS level biostatisticians. Further, Stephanie Broyles, 
PhD (Investigator) serves as an additional doctoral level biostatistician. Together, this team serves the 
research design and analysis needs for PBRC scientists. The Core is equipped with state-of-the art 
computers, and the E-mail and data transfer needs are supported by the PBRC Technology Services 
Group. The standard software used for statistical analysis is the most recent version of SAS, presently 
Version 9.4. 
The Research Computing Group (RCG) is a unit within the department of Computing Services.  RCG’s 
primary responsibility is the continuing development of a proprietary web-based portal to the clinical 
research database.  The clinical research database is a Microsoft SQL Server database secured on the 
Pennington Biomedical network.  Direct access to this database is restricted to authorized personnel 
within the RCG and Computing Services. Security is managed by the Pennington Biomedical Computing 
Services Infrastructure Security Group and is administered in accordance with established standard 
operating procedures. Active Directory (AD) credentials serve to authenticate, authorize and facilitate 
accountability for a user’s access to PBRC information systems.  The RCG team interfaces with 
researchers to ensure the efficient and accurate transfer of data from observation to electronic files for 
storage and analysis; monitors the data processing throughout each study’s duration; and provides 
investigators with study specific data sets via web-based desktop data access.  The team has developed 
custom applications for expedited creation of study specific data sets that may contain both PBRC data 
and Non-PBRC data.  This development and data storage paradigm allows the team to work with both 
intramural and extramural researchers.  
Guidelines for Good Clinical Practices as they relate to data handling have been documented and 
implemented in daily tasks.  The group maintains current HIPAA Security Rule training and works 
closely with the Director of Intellectual Property, Legal and Regulatory Affairs.  Programmers are 
provided with the latest software and hardware which allow them to perform their work efficiently.   
Clinical chemistry. The Clinical Research Laboratory is directed by Jennifer Rood, PhD, (Collaborator). 
Dr. Rood has initiated rigid quality control systems in the lab, and the laboratory is accredited by the 
Health Care Financing Administration (HCFA) and the College of American Pathologists (CAP). The 
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laboratory also participates in the lipid standardization program of the Centers for Disease Control and 
Prevention (CDC). The Clinical Research Laboratory is staffed by licensed medical technologists, 
phlebotomists, and accessioners. The laboratory is well-equipped for performing routine and specialized 
tests on clinical participants. In 2013 over 360 different analytical procedures were performed by the lab 
for a total of 300,000 assays. The major instrument housed at PBRC that will used on this project for 
assaying fasting glucose, triglycerides, and HDL cholesterol is the DXC600 manufactured by Beckman 
Coulter.  
Imaging. The Imaging Center, directed by Owen Carmichael, PhD (Collaborator) is a new 30,000 square 
foot two-story Imaging Center that is connected to the clinic and to the Clinical Research Building and 
opened in 2013. The Core personnel currently includes 3 licensed radiological technologists (an MR 
technologist, a CT technologist/Image analyst, and a DXA technologist), an ultrasound technologist, an 
engineer (M.S.) for the optical spectrometer, and a Baton Rouge Radiology Group serves as a consultant 
for interpretation of scans as needed. The Imaging Core will provide acquisition and analysis services for 
the DXA and MRI scans. 
Recruiting/Advertising. The Recruiting Core is composed of 3 full-time recruiters, 3 to 6 part-time 
recruiters, an outreach coordinator, and a full-time manager who coordinates advertising. The department 
receives 3200 calls and 829 complete web screenings per month from potential volunteers that are 
facilitated by a Uniform Call Distributor system and online survey. The online screening survey 
established in 2013 provides an additional outlet for parents to register their children for studies. Monies 
are allocated to produce brochures, purchase advertisements, conduct active recruiting efforts including 
online marketing and at other venues, and phone screen participants for the study. 
Supplies. Monies are allocated to purchase GPS devices, accelerometers, and mobile devices (for those 
children who do not have a mobile device or do not wish to use theirs for the purposes of the study), as 
well as the contracted usage of the EMA software. 
 
Human Resources 
There are currently 64 full-time faculty members at PBRC, in addition to several hundred post-doctoral 
fellows and other research staff.  
Investigators. This project is led by Amanda Staiano, PhD (0.30 FTE; developmental psychology) and 
team members Peter Katzmarzyk, PhD (0.15 FTE; physical activity epidemiology), Robert Newton, PhD 
(0.20 FTE; physical activity and ethnic minority health), Stephanie Broyles, PhD (0.20 FTE; contextual 
risk factors), and Catherine Champagne, PhD (0.10 FTE; nutritional epidemiology). Specific roles are 
detailed in the Project Management and Evaluation section.  
Collaborators. Several collaborators among the Pennington Biomedical faculty have been identified (see 
letters of support). Robbie Beyl, PhD, 0.05 FTE, is an Assistant Professor of Biostatistics and will 
conduct all of the statistical analysis for the project. Daniel Hsia, MD, is an Assistant Professor, a 
pediatrician and an internist, and board certified in adult and pediatric endocrinology. Dr. Hsia will 
review participants’ risk factor profiles and refer abnormal values to appropriate medical care. Jennifer 
Rood, PhD, is Professor and Associate Executive Director for Cores and Resources, and she will 
supervise the collection of blood and analysis of all clinical chemistry assays. Owen Carmichael, PhD, is 
Associate Professor and Director of the Biomedical Imaging Center and will supervise the acquisition and 
analysis of the DXA and MRI scans. Corby Martin, PhD, is Associate Professor and Director of the 
Ingestive Behavior Laboratory and will supervise the contract with the EMA vendor, will contribute to 
the manual of procedures for the EMA measures, and will contribute to the analysis and interpretation of 
results. Tiffany Stewart, PhD, is Associate Professor and Director of the Behavior Technology Laboratory 
on Eating Disorders and Obesity, and she will supervise the analysis and interpretation of the socio-
emotional instruments especially the body image, mood, and affect assessments. 
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Technical support staff. In addition, several support personnel are available for the project. A project 
manager will provide overall project coordination. Research specialists will coordinate accelerometry, 
EMA, and GPS data collection. Two external consultants (Brian Saelens, PhD, Seattle Children’s 
Hospital and University of Washington; Philip Hurvitz, PhD, Northwest Geospatial, LLC) will provide 
consultation for the management and analysis of the GPS data.  
Compensation
The child will receive a $25 stipend after each completed clinic visit. In addition to stipends for 
completing the clinic visits, we also allocated $25 per participant to incentivize response to EMA queries 
as well as wearing and returning the accelerometer/GPS trackers. The total possible compensation 
received per participant will be $100. The child will also receive a certificate of completion upon 
finishing the study. Participants that are offered the COVID-19 survey will be eligible for a $20 stipend 
upon completion of the survey.
Confidentiality
All data collected in this project will be subject to the same confidentiality requirements that are in place 
for other studies at the PBRC. Study files will be kept in locked cabinets and access restricted to study 
staff. All PBRC staff sign a confidentiality statement. Personal identifiers are not included in computer 
files. No individual’s data will be released without their specific written consent. 
Provisions to Protect the Privacy Interests of Participants
Privacy in the context of this study includes confidentiality of data and personal information. During 
interviews, measurements, and intervention, the study staff will ensure full privacy of participants and 
will ensure that the data are stored in a secured area. Clinic measurements will be conducted individually 
in private, with only the clinic assessor, the participant, and the participant’s parent/legal guardian to 
protect the privacy interests of the participant.
Compensation for Research-Related Injury
No form of compensation for medical treatment or for other damages (i.e., lost wages, time lost from 
work, etc.) will be available for this research study.  In the event of injury or medical illness resulting 
from the research procedures, participants will be referred to a treatment facility.
Economic Burden to Participants
Participants and their parent/legal guardian will be required to bear the cost of transportation to and from 
PBRC for clinic visits. Due to the design of this study, the burden associated with travel is low, with 
participants only attending 2 visits at PBRC over a 2-year span. 
Consent Process
Informed consent will be obtained from the parent/legal guardian and assent will be obtained from the 
child by PBRC clinic staff prior to conducting any study procedures. Because there is not a greater than 
minimal risk, informed consent will be obtained from one parent even if the other parent is alive, known, 
competent, reasonably available, and shares legal responsibility for the care and custody of the child.
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Written informed consent and assent will be collected in a private room of the Outpatient Clinic or 
TReCC during the screening visit. The study procedures will be explained to parent and prospective 
participant (10-16 years of age). Both will be asked if they have any questions about the study and will be 
given adequate time to review and ask questions. The parent/legally authorized representative will be 
given an informed consent form to read and sign indicating their permission to allow their child to 
participate in the study. Following the obtainment of written parental consent, participants 10-16 years of 
age will be given a written assent form to read and sign before participating in any study procedures. The 
study staff member administering consent may read the assent to the participant if requested, or if the 
participant has a low literacy level.
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