A Pilot Randomized Factorial Trial to Promote Physical Activity and Healthy Eating Among Young Adult Cancer Survivors
Phase: NA
Conditions: Physical Activity, Neoplasms, Cancer
Sponsor: UNC Lineberger Comprehensive Cancer Center

Complete Title: A pilot randomized factorial trial to promote physical activity and healthy eating 
among young adult cancer survivors 
Sponsor: University of North Carolina at Chapel Hill 
NCT number: NCT05887401 
Final Protocol Date: 09 July 2023 
 
University of North Carolina at Chapel Hill 
 
Study Principal Investigator: Erin Coffman, PhD 
Faculty Advisor: Carmina Valle, PhD 
CB#7440 Chapel Hill, NC 27599 
919-966-7546 
email: carmina.valle@unc.edu 
 
 
PROTOCOL TITLE: A pilot randomized factorial trial to promote physical activity and healthy eating 
among young adult cancer survivors 
Protocol Version: 3.0 
Version Date: 07/09/2024 
I confirm that I have read this protocol and understand it. 
Principal Investigator Signature: 

Page 2 of 37 
 
Table of Contents 
ABBREVIATIONS AND DEFINITIONS OF TERMS ............................................................................................ 3 
PROTOCOL SYNOPSIS ................................................................................................................................... 4 
BACKGROUND AND RATIONALE .................................................................................................................. 7 
1. STUDY OBJECTIVES ................................................................................................................................... 8 
2. INVESTIGATIONAL PLAN ........................................................................................................................... 9 
3. STUDY PROCEDURES ............................................................................................................................... 13 
4. STATISTICAL CONSIDERATIONS............................................................................................................... 15 
5. SAFETY MANAGEMENT ........................................................................................................................... 16 
6. DATA COLLECTION AND MANAGEMENT ................................................................................................ 20 
7. RECRUITMENT, CONSENT, AND RETENTION STRATEGY ......................................................................... 20 
8. PLANS FOR PUBLICATION ....................................................................................................................... 22 
9. REFERENCES............................................................................................................................................ 22 
APPENDIX ................................................................................................................................................... 27 

Page 3 of 37 
 
ABBREVIATIONS AND DEFINITIONS OF TERMS 
 
Abbreviation 
Definition 
API 
Application Program Interface 
BCT 
Behavior Change Technique 
BMI 
Body Mass Index 
DSMB 
Data Safety Monitoring Board 
DSM-IV-TR 
Diagnostic and Statistical Manual of Mental Disorders 
HEI 
Healthy Eating Index 
IMPACT 
IMproving Physical Activity after Cancer Treatment 
IRB 
Institutional Review Board 
MOST 
Multiphase Optimization Strategy 
MVPA 
Moderate to Vigorous Physical Activity 
PA 
Physical Activity 
NCI 
National Cancer Institute 
NIH 
National Institutes of Health 
OCT 
Office of Clinical Trials 
PAR-Q 
Physical Activity Readiness Questionnaire 
PAQ 
Paffenbarger Physical Activity Questionnaire 
REDCap 
Research Electronic Data Capture 
RCT 
Randomized Clinical Trial 
SCT 
Social Cognitive Theory 
SDT 
Self-Determination Theory 
SNAP 
Studies of Novel Approaches to Prevention 
TraCS 
North Carolina Translational and Clinical Scices 
YACS 
Young Adult Cancer Survivor 

Page 4 of 37 
 
PROTOCOL SYNOPSIS 
 
Study Title 
A pilot randomized factorial trial to promote physical activity and healthy eating among young 
adult cancer survivors  
 
Clinical Phase 
NA 
 
Study Rationale 
Few studies have tested PA interventions specifically for YACS, and no studies have tested 
nutrition interventions among this population. To accelerate science in this area, we 
propose a pilot randomized factorial trial as part of the preparation phase of the 
Multiphase Optimization Strategy (MOST) framework to test the feasibility, acceptability, 
and effects of a digital PA and nutrition intervention designed specifically for YACS. 
Previous interventions have demonstrated the importance of self-regulation (i.e., self-
monitoring of behaviors, small changes, goal setting and feedback) approaches to 
improve PA and nutrition behaviors among young adults and YACS. We aim to leverage 
our prior work to deliver a theory-based intervention using digital tools to simplify self-
monitoring of PA, and dietary monitoring to provide tailored behavioral goals and 
messages to support increased physical activity and diet quality among YACS. 
 
Study Objective(s) 
Primary 
• 
To evaluate the feasibility of a of a theory-based, mobile PA and nutrition 
intervention, as characterized by the participation, accrual, and retention rates at 3-
months. 
Secondary 
• 
To evaluate acceptability of the intervention as characterized by ratings of program 
acceptability and satisfaction at 3-months. 
• 
To evaluate the adherence to the intervention as measured by daily self-monitoring of 
activity (Fitbit) and dietary tracking (Traffic Light Approach) from baseline to 3-
months based on experimental conditions. 
• 
To estimate the main effects of each of four intervention components on changes in 
behavioral measures (Fitbit measured MVPA and healthy eating index (HEI) using 
the NCI’s 24-hour Recall (ASA-24)) at 3-months. 
• 
To estimate the main effects of each of four intervention components on changes in 
secondary outcomes including anthropometric (weight), clinical (Fried physical 
phenotype using the FRAIL index) and psychosocial measures (health related quality 
of life) at 3 months. 
 
 
Test Article(s) 
(If Applicable) 
All participants will receive a core 3-month physical activity and healthy eating  
intervention that includes evidence-based lessons, behavioral skills training, and daily self-
monitoring of physical activity and diet. 

Page 5 of 37 
 
 
 
Study Design 
The YACS HEAL (Young Adult Cancer Survivors Healthy Eating and Active Lifestyles) trial 
utilizes the Multiphase Optimization Strategy (MOST) framework and a 24 full factorial 
experimental trial to test the efficacy of 4 intervention components, to determine 
feasibility, acceptability, and effects of intervention components on physical activity (PA) 
and diet quality. Participants will receive a core 3-month digital PA and nutrition 
intervention delivered through a mobile app, with digital tools (PA tracker) and self-
monitoring of behaviors. All participants will receive evidenced based lessons and 
behavioral skills training focused on PA and nutrition behavioral change, skills training, 
goal-setting, and weekly feedback on progress.  Using the highly efficient, randomized 
factorial experimental design, we will test 4 intervention components--each with two 
levels-: 1) simplified dietary tracking based on the Traffic Light Approach (TLA, daily 
tracking of green (low-calorie, high nutrients) vs. red (high-calorie, high-fat, low nutrients) 
foods), 2) dietary goals using the TLA (daily green or red food goals vs. no goals), 3) 
supportive text messages (yes (≤5 per week) vs. no), and 4) lesson delivery (all provided 
once vs. weekly).  
 
Subject Population 
Inclusion Criteria 
1. Age ≥18-39 
2. Diagnosed with invasive cancer malignancy between the ages of 15-39 years 
3. Diagnosed with invasive malignancy in the last 10 years of diagnosis, and with no 
evidence of progressive disease or second primary cancers 
4. Completed active cancer directed therapy (cytotoxic chemotherapy, radiation therapy 
and/or definitive surgical intervention), except may be receiving “maintenance” therapy 
to prevent recurrences 
5. No pre-existing medical conditions(s) that preclude adherence to an unsupervised 
exercise program including cardiovascular disease, congestive heart failure, pulmonary 
conditions, renal disease, and severe orthopedic conditions 
6. Not currently meeting guideline recommendations of 150 MVPA minutes/week (self-
report) and guideline recommendations for fruit and vegetable consumption 
7. Have the ability to read, write and speak English 
 
8. Own a smartphone with a data and text messaging plan 
9. Willing to be randomized to any levels of the factors 
Exclusion Criteria 
1. History of heart attack or stroke within past 6 months 
2. Untreated hypertension, hyperlipidemia, or diabetes, unless permission is 
provided by their health care provider. 
3. Health problems which preclude ability to walk for PA 
4. Report a heart condition, chest pain during periods of activity or rest, or loss of 
consciousness on the Physical Activity Readiness Questionnaire (PAR-Q; items 1-4). 
5. Report a past diagnosis of or receiving treatment for a DSM-IV-TR eating disorder 
(anorexia nervosa or bulimia nervosa). 
6. Currently pregnant, pregnant within the past 6 months, or planning to become 
pregnant within the next 6 months. 
7. Hospitalization for depression or other psychiatric disorder within the past 12 
months. History of psychotic disorder or bipolar disorder. 
8. Plans for major surgery (e.g., breast reconstruction) during the study time 

Page 6 of 37 
 
frame 
9. Currently participating in a weight loss, nutrition or physical activity study or 
program or other study that would interfere with this study. 
10. Currently using prescription weight loss medications 
Number Of Subjects 
72 
Study Duration 
Each subject’s participation will last 4 months. 
 
Study Phases 
Screening 
Study Treatment 
Follow-Up 
 
(1) Screening: screening for eligibility and obtaining consent and (2) Baseline assessment 
(3) Intervention: study intervention/experimental treatment.  
(4) Follow-up assessment will occur at 6 weeks and 3 months 
 
 
 
Efficacy Evaluations 
NA 
 
Pharmacokinetic 
NA 
Evaluations 
Safety Evaluations 
All medical events and safety data will be assessed at assessment points or when interim 
events are reported by participants using a standard form used in our ongoing NIH funded 
trials. 
Statistical And Analytic 
Plan 
 
 
 
 
 
 
 
 
DATA AND SAFETY 
MONITORING PLAN 
The primary objective is to evaluate the feasibility and acceptability of a digital 
intervention components to increase PA and improve nutrition behaviors among YACS 
(n=80) at 3 months. Our primary feasibility measures are participation, accrual, and 
retention rates. 
The secondary outcome analysis will use mixed effects models to test for differences in 
MVPA and HEI by intervention components across time (baseline, 6 weeks, and 3 
months).24 Effects will be modeled as component x time interactions with the 3-month 
outcome as the primary endpoint.  
 
Data safety management in this trial is intended to achieve 4 objectives: 1) to minimize the 
occurrence of adverse effects, especially those related to intervention; 2) to effectively manage 
adverse events as they relate to the study; 3) to identify when the interventions should be 
suspended because of concerns for participant safety; and 4) to determine when interventions 
may be resumed after having been suspended. The Principal Investigator (Coffman) will have 
primary responsibility for the safety of participants as it relates to the study protocol, which must 
be approved by the UNC IRB prior to study initiation. In addition, the North Carolina 
Translational and Clinical Sciences (TraCS) Institute Data Safety Monitoring Board (DSMB) is 
responsible for reviewing data from clinical trials approved by the UNC Biomedical IRB. Because 
the risks to subjects participating in this phase of this study are expected to be low, we do not 
anticipate the need for a formal Data Safety Monitoring Board. The research team will provide 
continuous monitoring of participant safety and periodic reporting to the IRB as required, 
including any Unanticipated Problems/Severe Adverse Events that may have occurred during the 
study. If risk or complexity is significant, the UNC Office of Human Research Ethics may require   

Page 7 of 37 
 
 
Additional reporting or alternative data and safety monitoring. Data safety and monitoring 
activities continue until all participants have completed treatment and until all participants have 
been followed to the point at which study related adverse events would likely no longer be 
encountered. 
 

Page 8 of 37 
 
BACKGROUND AND RATIONALE 
 
1.1 
Introduction 
Adolescent and young adult cancer survivors are a historically understudied population that require interventions 
designed to address their unique care needs.1,2 Cancer treatment effects can lead to prolonged periods of sedentary 
behaviors, lack of PA, and poor nutrition, resulting in loss of lean muscle mass, and increases in fat mass, putting 
YACS at elevated risk for chronic diseases and premature frailty and mortality.3,4 
1.2 
Name and Description of Investigational Intervention 
The YACS HEAL (Young Adult Cancer Survivors Healthy Eating and Active Lifestyle) trial is a 24 full factorial 
experimental trial testing the efficacy of 4 intervention components (factors), each with two levels, to identify 
component levels that contribute to greater change in MVPA and diet quality among YACS. To do so, we will 
randomize 80 YAs with overweight or obesity to receive one level of each of the 4 intervention components, in 
addition to a core mobile-delivered behavioral physical activity and nutrition program. 
 
1.3 
Relevant Literature and Data 
Young adult cancer survivors (YACS) have historically been under-represented in research studies and clinical 
trials, and lack treatments and survivorship programs that address their specific needs.4 In comparison to 
children and older adults, adolescents and young adults have not experienced the same improvements in 
diagnosis, treatment and long-term survival rates, health disparities recognized by the National Cancer Institute 
(NCI).4,5 By 10 years post treatment, over 40% of YACS have ≥ 2 comorbidities compared to 20% for age-
matched peers without a history of cancer.8 Obesity, number of comorbidities (i.e., high cholesterol, 
hypertension, arthritis, diabetes), and anxiety or depression are all positively associated with increased frailty 
risk.9  Frailty is traditionally associated with age-related functional decline and has not been studied extensively 
in the YACS population, but recent research suggest that YACS have similar incidences of prefrailty and frailly 
similar to those of adults over the age of 60 without a history of cancer.10–13 Among adult cancer survivors PA 
has been shown to improve BMI, weight, muscle strength, peak oxygen consumption, gait speed, fatigue, 
depression, quality of life,14 and reduced cancer-specific and all-cause mortality.15 Dietary interventions among 
adult cancer survivors have led to significant improvements in dietary quality, reduction in dietary fat, and 
increased fruit and vegetable consumption.16 RCTs testing combined diet and exercise interventions have 
resulted in improved diet quality, increases in physical activity, physical function, health-related quality of life, 
and decreased obesity.17 Thus, there is a need to prospectively measure frailty and develop interventions to 
improve health outcomes in younger populations.   
To our knowledge, no interventions that concurrently promoted physical activity and nutrition have been 
developed specifically for YACS. Current international and US consensus guidelines for cancer survivors are 
based on the current evidence relating to diet, PA, and energy balance for cancer prevention and survival.18  
Adherence to guideline recommendations among cancer survivors has repeatedly been demonstrated to be 
associated with reduced risk of overall cancer incidence, all-cause mortality, non-cancer related mortality, and 
metabolic syndrome in large cohort studies, which have consisted primarily of white survivors and survivors of 
breast and prostate cancers.19–23 Despite the benefits of a healthy lifestyle, previous studies have found that 
majority of cancer survivors do not adhere to consensus guidelines. Less than half of YACS meet 
recommendations for vegetable consumption, two-thirds meet the recommendations for fruit consumption, 
and less than half meet recommendations for aerobic or resistance physical activity.24 Survivors ages 20-44 have 
poorer diet quality compared to older populations,25 and adherence to health behaviors decreases with 

Page 9 of 37 
 
survivorship duration.26 To date there have been few randomized controlled trials to promote PA among YACS, 
and none have addressed nutrition related behaviors or a combination of PA and nutrition related outcomes. 
Thus, there is a need for rigorous and systematically designed interventions targeting PA and dietary behaviors 
among YACS. 
Previous studies have shown that YACS have a desire to engage in healthy behaviors, and 92% use the internet 
to search for information relating to healthy eating and exercise.27 A large proportion report information needs 
related to PA, diet, and rehabilitation.28 However, they reported that there was too much information available, 
that the information was not specific to their needs as YACS, and that the information may not be 
trustworthy.27 Additional barriers to healthy diet and exercise among YACS include lack of resources (i.e. 
financial and professional services), negative thoughts and feelings (e.g., feeling embarrassed, frustrated, 
fatigue), and negative environmental and social influences (e.g., unhealthy snacks at work, lack of support from 
family and friends).27 Facilitators for YACS to engage in healthier lifestyles include cognitive motivators (e.g., 
health beliefs, fatigue, fear of recurrence, goals), “tools” to engage in health behaviors (e.g., access to farmer’s 
markets, gyms, wellness programs, skills-building), and positive environmental and social influences.29 
Digital behavior change interventions are an opportunity to deliver evidence-based tailored interventions that 
incorporate self-regulation via self-monitoring of PA, diet, and weight.30 The majority (78%) of YACS want 
support for PA and have a desire to increase their PA levels (70%), and prefer interventions that are individually 
tailored, home-based, consisting of a variety of modalities.31 Self-monitoring devices like PA trackers and digital 
scales to provide data-driven adaptive goal-setting and personalized feedback to promote self-efficacy and self-
regulation.32 Self-regulation of behaviors include self-monitoring of behaviors, evaluation of current behavior 
compared to goals, and reinforcement or self-correction of behaviors. The phases of self-regulation include goal 
selection/setting, active goal pursuit, and goal attainment/maintenance.33 
Our team has demonstrated the importance of self-weighing to promote self-regulation and weight 
management in young adults34–36 and cancer survivors.37,38 Additionally, small, discrete changes to dietary 
behaviors, promote the shaping and mastery of behaviors, and has been shown to be effect for weight gain 
prevention in young adults.39,40 Our previous interventions using a simplified dietary monitoring approach based 
on the Traffic Light approach (TLA) that categorizes foods as green (low-calorie, high nutrients), yellow 
(moderate calories, high nutrients), and red (high-calorie, high-fat, low nutrients) have shown promise for 
promoting adherence to dietary goals and weight loss in young adults.41,42 A simplified approach to dietary 
monitoring may improve adherence to dietary self-monitoring and lead to improvements in diet quality among 
YACS. Nutritional goals are an important for self-regulation, and self-efficacy beliefs directly shape goals, 
outcome expectations, and perceived barriers and facilitators of behavior change. Among YACS, higher self-
efficacy beliefs were associated with engaging in self-regulation behaviors and meeting ACS guideline 
recommendations for fruit and vegetable consumption and PA.26 Text message reminders serve as a powerful 
aid to promote self-monitoring and intervention engagement and may increase self-efficacy and feelings of 
social support and relatedness.43 
 
 
 

Page 10 of 37 
 
 
1. STUDY OBJECTIVES 
 
Few studies have tested PA interventions specifically for YACS, and no studies have tested nutrition interventions among this 
population. To accelerate science in this area, we propose a pilot randomized factorial trial to test the feasibility, acceptability, 
and effects of a digital PA and nutrition intervention designed specifically for young adult cancer survivors. Previous 
interventions have demonstrated the importance of self-regulation (i.e., self-monitoring of behaviors, small changes, goal 
setting and feedback) approaches to improve PA and nutrition behaviors among young adults and YACS. We aim to leverage our 
prior work to deliver a theory-based intervention using digital tools to simplify self-monitoring of PA, dietary monitoring, and 
self-weighing to provide tailored behavioral goals and messages to support increased physical activity and diet quality among 
YACS. 
 
Using a factorial experiment, we will test main effects of each of 4 candidate intervention components and interactions between 
the components, making efficient use of a smaller sample size than a typical RCT would require. We propose to conduct a 3-
month, 24 full factorial trial testing the effects of four intervention components--each with two levels— including (Table 1):  
1) simplified dietary tracking based on TLA (daily tracking of green (low-calorie, high nutrients) vs. red (high-calorie, high-fat, low 
nutrients) foods), 
2) dietary goals using TLA (daily goals vs. no goals),  
3) supportive text messages (yes vs. no), 
4) lesson delivery (all provided once vs. weekly).  
 
Proposed intervention strategies and components are based on previous nutrition and PA interventions that have been 
conducted in cancer survivors targeting these health behaviors. The conceptual model guiding intervention development 
includes theoretical constructs from social cognitive theory (SCT) and self-determination theory (SDT); intervention strategies 
will incorporate behavior change techniques (BCTs) that have demonstrated efficacy in previous behavioral interventions in 
cancer survivors. Intervention components will target the SDT and SCT constructs associated with improved dietary and PA 
behaviors: perceived autonomy (self-efficacy and self-regulation through goal setting and self-monitoring of behaviors), 
perceived competence (behavioral capability, perceived barriers, and outcome expectations targeted with behavioral lessons 
and feedback), and perceived relatedness (social support from text message and tailored feedback on goals and progress).  
 
The primary outcomes of interest are feasibility and acceptability. Secondary outcomes include change in minutes/week of 
MVPA and HEI dietary quality index score.  
 
Assessments of outcomes will be conducted at baseline, 6 weeks, and 3 months (post-intervention. We 
hypothesize that it will be feasible to recruit and retain post-treatment YACS Few studies have tested PA 
interventions specifically for YACS, and no studies have tested nutrition interventions among this population. To 
accelerate science in this area, we propose a pilot randomized factorial trial to test the feasibility, acceptability, 
and effects of a digital PA and nutrition intervention designed specifically for young adult cancer survivors. 
Previous interventions have demonstrated the importance of self-regulation (i.e., self-monitoring of behaviors, 
small changes, goal setting and feedback) approaches to improve PA and nutrition behaviors among young 
adults and YACS. We aim to leverage our prior work to deliver a theory-based intervention using digital tools to 
simplify self-monitoring of PA, dietary monitoring, and self-weighing to provide tailored behavioral goals and 
messages to support increased physical activity and diet quality among YACS. 
Using a factorial experiment, we will test main effects of each of 4 candidate intervention components and 
interactions between the components, making efficient use of a smaller sample size than a typical RCT would 
require. We propose to conduct a 3-month, 24 full factorial trial testing the effects of four intervention 
components--each with two levels— including (Table 1):  

Page 11 of 37 
 
1) simplified dietary tracking based on TLA (daily tracking of green (low-calorie, high nutrients) vs. red (high-
calorie, high-fat, low nutrients) foods), 
2) dietary goals using TLA (daily goals vs. no goals),  
3) supportive text messages (yes vs. no), 
4) lesson delivery (all provided once vs. weekly).  
Proposed intervention strategies and components are based on previous nutrition and PA interventions that 
have been conducted in cancer survivors targeting these health behaviors. The conceptual model guiding 
intervention development includes theoretical constructs from social cognitive theory (SCT) and self-
determination theory (SDT); intervention strategies will incorporate behavior change techniques (BCTs) that 
have demonstrated efficacy in previous behavioral interventions in cancer survivors. Intervention components 
will target the SDT and SCT constructs associated with improved dietary and PA behaviors: perceived autonomy 
(self-efficacy and self-regulation through goal setting and self-monitoring of behaviors), perceived competence 
(behavioral capability, perceived barriers, and outcome expectations targeted with behavioral lessons and 
feedback), and perceived relatedness (social support from text message and tailored feedback on goals and 
progress).  
The primary outcomes of interest are feasibility and acceptability. Secondary outcomes include change in 
minutes/week of MVPA and HEI dietary quality index score.  
Assessments of outcomes will be conducted at baseline, 6 weeks, and 3 months (post-intervention. We 
hypothesize that it will be feasible to recruit and retain post-treatment YACS for this intervention. We also 
hypothesize that the intervention will be acceptable to YACS participants. We will examine recruitment 
potential, retention, pilot intervention procedures, satisfaction, and adherence to the intervention. 
This proposed study presents an opportunity to deliver an evidence-based, theory-driven digital behavior 
intervention to YACS with the goal of improving nutrition and PA behaviors and subsequently reducing risk for 
adverse health outcomes. If the proposed aims are achieved, we will expand our understanding of ways to 
improve health outcomes among YACS, an underserved population of cancer survivors with unique needs. This 
study utilizes innovative intervention design frameworks to test multiple novel components that have not 
previously been tested in this population and will provide preliminary evidence for future optimization trials. 
Findings will enhance our understanding of promising intervention approaches to improve lifestyle behaviors in 
cancer survivors. 
 
2. INVESTIGATIONAL PLAN 
 
Study Design 
The intervention approach is based on previous PA and nutrition interventions that have been conducted in 
cancer survivors and targeting these health behaviors and outcomes including frailty. The present intervention 
incorporates theoretical constructs from multiple behavior change theories including the social cognitive theory 
(SCT), self-determination theory (SDT) and will incorporate behavior change techniques (BCTs) that have been 
demonstrated efficacy in previous behavioral interventions. Primary goals of the intervention are to improve PA 
and diet quality through self-regulation of PA and diet behaviors using evidence-based strategies that have 
been previously developed and tested by our study team. These self-regulation strategies include developing 
knowledge and skills through behavioral lessons, promoting daily self-weighing, self-monitoring of PA and small 
progressive increases in PA, and simplified dietary monitoring using the Traffic Light Approach. This intervention 

Page 12 of 37 
 
will target self-regulation via 1) self-monitoring of target behaviors (e.g., self-weighing, PA, green/red foods); 2) 
weekly self-evaluation of goal progress (e.g., weight change, weekly PA goal, daily green/red goal); and 3) self-
reinforcement if goal was met, or additional behavior change if goal was not. 
 
 
Elements Common to All Experimental Conditions 
Core Intervention (n=80) all participants will receive a core intervention that includes PA and nutrition education 
modified from concepts of the Diabetes Prevention Program 51, our prior studies37,42,52–55, and based around the 
consensus guideline recommendations for cancer survivors49. Participants will be given digital tools to facilitate self-
monitoring – a PA tracker and simplified diet tracking tool to support self-regulation of behaviors. The intervention 
will be delivered a mobile app integrated with Fitbit. Participants will receive an activity tracker (Fitbit) in the mail, 
followed by an introductory videochat session with an interventionist. In this session they will receive information 
relevant to their participation in the program, including an overview of how to use the study app (for both iOS and 
Android); Fitbit; the Traffic Light Log;  how to self-monitor to achieve their daily goals (eating, activity, and self-
weighing), and other information relevant to the conditions to which they have been randomized. Activity and 
dietary data are synced to study servers and activity is available in the app on pages that display participants’ daily 
and overall progress towards their goals. The study app also includes 12 behavioral lessons that are delivered based 
on randomization. These include behavioral strategies with specific instruction on core cognitive and behavioral 
skills (i.e., goal setting, self-monitoring, problem solving, overcoming barriers, reaching out for social support, and 
cognitive restructuring).  
 
Participants will receive qualitative feedback on self-monitoring behaviors and goal progress specific to MVPA and 
diet tracking (and goals). Feedback will include positive reinforcement and encouragement, strategies for barrier 
identification and overcoming barriers, and strategies for improving dietary and PA behaviors. Participants will 
receive tailored feedback summaries weekly. Participants will receive an email directing them to their feedback 
page. The feedback message will include feedback based on participants' number of days tracking diet and PA, and 
progress toward goals.  
Physical activity recommendation. The ultimate goal for the study will be to progress the participant to at least 150 
minutes of moderate to vigorous physical activity. The initial session and behavioral lesson materials will highlight 
both that the current physical activity guideline for cancer survivors is at least 150 minutes per week of moderate-
to-vigorous PA (MVPA) minutes/week (i.e., 3-5 metabolic equivalents (METs) and the benefits of light PA (e.g., every 
60-minute increase in light PA is associated with a 16% reduction in mortality). An initial goal will be based on 
baseline levels of activity as established during the baseline assessment for the study. Progression will occur each 
week based standard gradual physical activity plans we have previously used in distance-based intervention for YACS 
(LCCC1323, LCCC1709), and will be based on baseline PA activity levels and individual progress each week. 
 
Dietary recommendations. The specific recommendations for diet composition are consistent with American Cancer 
Society and American Institute for Cancer Research nutrition guidelines for cancer survivors49,58 and the Traffic Light 
Approach (TLA) concept we have used in previous interventions.41,42 This intervention component will test a 
simplified dietary monitory approach that emphasizes ACS49 recommended dietary approaches: eating foods that 
help achieve and maintain a healthy body weight such as a variety of whole fruit and vegetables in a variety of 
colors, fiber-rich legumes, and whole grains, and limiting or excluding red and processed meats, sugar-sweetened 
beverages, highly processed foods and refined grain products, and alcohol.  
 
Traffic Light Approach. To encourage long-term adherence to dietary monitoring, this study will use a simplified 
form of tracking based on the Traffic Light system. Rather than tracking calories as in traditional behavioral 
interventions, TLA is a simplified approach to dietary monitoring that categorizes food into colors of the stoplight 
based on their nutrient and calorie content.60,60 In the Traffic Light Approach, foods are categorized into green, 
yellow and red categories. Users are encouraged to increase green foods, moderate yellow foods, and limit red 

Page 13 of 37 
 
foods. Foods in the green category include fruits, vegetables, and certain very lean proteins, are low in calories, fat, 
and sugar and rich in nutrients. The high volume, water content and fiber in fruits and vegetables and the protein 
from the lean meats and egg whites in this category help promote satiety while consuming fewer calories. The 
yellow category includes moderately lean proteins, legumes, low fat dairy, whole grains, starchy vegetables, and 
low-fat dressings. These foods are higher in calories than green food but are still a good source of nutrients. Red 
foods are high in calories, fat, and/or sugar and are not a good source of nutrients. The goal in the Traffic Light 
Approach is to increase green foods and to limit red foods.  
 
 

Page 14 of 37 
 
 
Description of Intervention Components (Factors) 
In addition to the core intervention, participants will receive one level of each of four candidate intervention 
components to be evaluated: 1) simplified dietary tracking based on the Traffic Light Approach (daily tracking 
of green (low-calorie, high nutrients) vs. red (high-calorie, high-fat, low nutrients) foods), 2) dietary goals using 
Traffic Light Approach (daily green or red food goals vs. no goals), 3) supportive text messages (yes (≤5 per 
week) vs. no), and 4) lesson delivery (all provided once vs. weekly).  
Candidate intervention components and their levels are described in detail below: 
Simplified Dietary Tracking (green foods vs. red foods). Self-monitoring has repeatedly been shown as an 
essential self-regulation strategy for physical activity and dietary behavior change success. However, 
traditional dietary self-monitoring is challenging and time-consuming and adherence to self-monitoring 
typically declines over time.61,62 The TLA might be more feasible and sustainable since participants only 
monitor a subset of foods reducing participant burden to engaging with self-monitoring behaviors.42,63–65  
Simplified dietary tracking using the TLA targets self-regulation through specific cognitive behavioral strategies 
including—reducing intake of certain foods (red foods), increasing intake of healthier choices (green foods), 
planning meals in advance, regular mealtimes, self-monitoring of foods, etc. Dietary approaches that promote 
increased fruit and vegetable intake can lead to improved diet quality and displacement of calorie-dense 
foods.66 Our previous interventions that used a simplified dietary monitoring approach based on the TLA have 
shown promise for promoting adherence to dietary goals and weight loss in young adults.41,42 However, no 
interventions have compared the effects of maximizing green food consumption or minimizing red food 
consumption on diet quality in young adults. 
Participants will be randomized to use the TLA to either self-monitor their consumption of GREEN foods OR 
RED foods. 
 
Nutrition Goals (yes/no). Weekly goals will target self-efficacy by improving dietary behaviors gradually starting at 
baseline values, emphasizing self-monitoring of behaviors, and graded tasks by increasing with goal attainment. This 
intervention component will facilitate the evaluation of the main and interaction effects between dietary monitoring 
and goal setting for healthy eating. Provision of weekly goals by the study team may facilitate self-monitoring of 
behaviors, however, goal setting (by individual) may lead to greater feelings of autonomy, self-efficacy, and facilitate 
greater self-regulation. 
 
Participants will be randomized (yes/no) to receive weekly goals for their TLA assigned food group (e.g., increase 
green foods, decrease red foods) based on baseline intakes. If randomized to nutrition goals: participants in the RED 
food condition will be given a daily limit of 2-6 red foods based on their baseline intake of foods that are 
recommended to be avoided or limited by the American Cancer Society guidelines (e.g., processed foods, alcohol, 
etc.). Participants in the GREEN food condition will be given a daily goal of 6-12 green foods based their baseline 
intake of foods recommended by the ACS guidelines (e.g., fruits and vegetables, fiber). 
 
Text Messages (yes/no). The efficacy of text messages for behavior change are inconsistent due to heterogeneity of 
study designs the use of additional intervention components in complex multicomponent behavior change 
interventions.67 The factorial design allows for the examination of the independent effect of text messages in a 
multicomponent intervention. Text messages serve as a reminder for self-monitoring and engagement in health 
behaviors but may also increase participant burden and decrease intervention efficacy. Additionally YACS prefer 
interventions that provide social support, and text-messages may be an efficient and economical method to provide 
study-related content.31  

Page 15 of 37 
 
 
Text messages will include a variety of content including reminders of goals, self-monitoring behaviors, personal 
assessment of fatigue, energy levels and mood, PA nutrition tips, and positive reinforcement. Text messages will 
target perceived relatedness by providing social support, prompts and cues, and verbal persuasion about 
capabilities. 
 
Participants will be randomized (yes/no) to receive up to 5 weekly text messages pertaining to their personal goals 
and reminders to self-monitor based on intervention components.  
 
Behavioral Lessons (once/weekly). Due to heterogeneity of methods and lack of clarity in reporting, the efficacy of 
timing and delivery of behavioral lessons in mhealth interventions is unclear. Behavioral interventions typically 
provide content on a weekly basis. Providing lessons all at once gives YACS the autonomy to select content in the 
order and time of their choosing, increasing their self-efficacy and self-regulation of behavior change.   
 
Behavioral lessons will be delivered through using the mobile app. Lesson content will focus on behavioral 
strategies: self-monitoring, goal setting, barrier identification, problem solving and action planning to overcome 
barriers. Behavioral lessons are based on standard behavioral and weight management content and adapted from 
previous PA and weight management trials with YACS.37,42,51–53,68,69  
 
Participants will be randomized to receive all 12 lessons in week 1 or receive one lesson each Monday over 12 
weeks.  
 
 

Page 16 of 37 
 
Study Population 
Individuals will be eligible if they meet the following criteria: age ≥18-39 years at the time of consent, diagnosed 
with invasive cancer malignancy between the ages of 15-39 years, diagnosed with invasive malignancy in the last 
10 years, with no evidence of progressive disease or second primary cancers, completed active cancer directed 
therapy (cytotoxic chemotherapy, radiation therapy and/or definitive surgical intervention), except may be 
receiving “maintenance” therapy to prevent recurrences, have no pre-existing medical conditions(s) that 
preclude adherence to an unsupervised exercise program including cardiovascular disease, congestive heart 
failure, pulmonary conditions, renal disease, and severe orthopedic conditions, not currently meeting guideline 
recommendations of 150 MVPA minutes/week (self-report) and guideline recommendations for fruit and 
vegetable consumption, have the ability to read, write and speak English, have access to the Internet on at least 
a weekly basis, posess and usage of an Internet e-mail address or willingness to sign up for a free email account 
(e.g., Gmail), have smartphone with internet access and text messaging plan and be willing to be randomized to 
any condition.  
 
Exclusion criteria include a history of heart attack or stroke within past 6 months; untreated hypertension, 
hyperlipidemia, or diabetes, unless permission is provided by their health care provider; health problems which 
preclude ability to walk for PA; a heart condition, chest pain, or other health problems that influence the ability 
to follow physical activity recommendations; plans for major surgery (e.g., breast reconstruction) during the 
study time frame; currently using prescription weight loss medications; currently pregnant, pregnant within the 
past 6 months, or planning to become pregnant within the next 6 months; report a past diagnosis of or receiving 
treatment for an eating disorder; hospitalization for depression or another psychiatric disorder in the past 12 
months or history of bipolar or other psychotic disorder; currently participating in another weight loss, nutrition, 
or physical activity study or program; already adherent to the American Cancer Society’s recommendation of 
exercising > 150 minutes/week of moderate-to-vigorous intensity PA and the American Cancer Society’s 
recommendation of consuming > 5 servings of fruits and vegetables/day. 
Individuals that reported joint problems, some prescription medications, or current treatment for hypertension 
or hyperlipidemia will be required to obtain written physician consent to participate (Appendix). 
The trial is registered on clinicaltrials.gov (NCT05887401) and was approved by the Institutional Review Board at 
the University of North Carolina at Chapel Hill on August 18, 2022. 
 
 
3. STUDY PROCEDURES 
 
Data Collection 
All study participants will complete objective weight measurements, two dietary recalls, and self-administered 
online surveys at baseline and 3 months (post-intervention). At 6 weeks, only psychosocial measures and 
medical events will be collected via online survey and telephone. Participants will receive $25 for completing 
the 3-month assessments. Online surveys will include measures of theoretical mediators, other psychosocial 
constructs, and process measures. We will send emails and texts prompting participants to complete 
assessments, with follow-up emails and phone calls to non-respondents, as necessary. Measures and timing are 
described below. 
 
 
 

Page 17 of 37 
 
Measures 
 
Behavioral Measures (collected at baseline and 3 months) 
Device-measured physical activity will be assessed at baseline and 3 months using the study provided activity 
tracker. Participants will be instructed to wear the device at all times for a full week; monitoring for at least 600 
min/day for at least 4 days in the week (including at least one weekday and one weekend day) is considered 
adequate for analysis. Data are transmitted to Fitbit each time the participant syncs their tracker with the Fitbit app 
on their phone. Weekly minutes of moderate-to-vigorous activity (termed “active minutes” in Fitbit) and steps per 
day will be computed from the participant’s Fitbit data and averaged over a minimum of 4 out of a 7-day period. 
 
Self-reported physical activity will be assessed using the Paffenbarger Activity Scale (PAQ)76, which assesses leisure-
time activity. The PAQ provides an estimate of minutes per week of moderate-to-vigorous intensity, and 
calories/week of light (5 kcal/min), medium (7.5 kcal/min), and high (10 kcal/min) intensity activities. PAQ changes 
have been predictive of weight change. 
 
Dietary Intake: Changes in dietary intake will be assessed using the NCIs 24-hour Recall (ASA-24)46 by trained 
administrators. Participants will complete two self- reported recalls; one weekday and one weekend day. The 24 
recall is a validated method of measuring diet. Using two recalls permits adjustment of estimates for intraindividual 
variation and is superior to administering one recall. We will examine change in overall energy intake and saturated 
fat intake, as well as change in fruit and vegetable consumption, fiber consumption, and change in the Healthy 
Eating Index score.77 
 
Healthy Eating Index: The HEI-202078 consists of 13 component scores, including: total fruit, whole fruit, total 
protein foods, total vegetables, greens and beans, whole grains, dairy, seafood and plant proteins, fatty acids, 
sodium, refined grains, added sugars, and saturated fats. The summation of the 13 component scores ranges from 0 
to 100, with higher scores indicating closer compliance with the 2020 DGA. 
 
Anthropometric and clinical measures (collected at baseline and 3 months unless otherwise noted) 
Weight: will be objectively measured at the participant’s home. We will instruct participants to weigh 
themselves in light clothing, without shoes, on the cellular-enabled scales at each assessment time point. 
 
Height (baseline only): Participants will provide self-reported height using a single item from Behavioral Risk 
Factor Surveillance System and adapted for use in the NCI’s Health Information and National Trends Survey. 
 
Body Mass Index (BMI). Height and weight will be used to calculate BMI (kg/m2). 
 
Fried Frail Index.75 Frailty will be assessed among all participants with a Frailty Index (all participants) previously 
used by Co-I Dr. Smitherman among AYA survivors. The FRAIL Questionnaire includes five components that 
reliably predict declining health function and mortality without requiring a face-to-face evaluation. The items 
assess: 1) self-reported fatigue (PROMIS Fatigue Short form), 2) weight loss, 3) comorbidities (11 items from 
UNC Health Registry Comorbidity Assessment), 4) difficulty with ambulation (1 item from PROMIS Physical 
Function Short Form), and 5) ability to overcome resistance (1 item from PROMIS Physical Function)). The 
number of positive responses for these components is summed to create the FRAIL index (range 0–5) and 
characterized as 0-1: robust, 2: prefrail, and 3+: frail. Changes in individual components and overall frailty 
scores will be assessed.  
 
Psychosocial mediators and consequences (collected at baseline and 3 months). 
Health-related Quality of Life: will be assessed using the AYA PROMIS PRO Core battery,79 which has recently been 
recommended by the National Cancer Institute’s Clinical Trials Network AYA PRO Task Force. These measures assess 
relevant HRQoL domains for AYA cancer survivors that are most likely to be impacted during and after cancer 
treatment. The AYA PRO Core battery includes the following PROMIS short forms: depression, anxiety, fatigue, 

Page 18 of 37 
 
physical function, pain interference, emotional support, cognitive function, pain intensity. Each question has five 
responses ranging in value from 1 to 5, which are summed to find the total raw score. Total raw scores are 
translated to a T-score, standardized with a population mean of 50 and a standard deviation of 10. A higher PROMIS 
T-score represents more of the concept being measured.  
 
Depressive Symptoms: Depressive symptoms have been associated with changes in weight, diet, and PA and will be 
assessed with the PROMIS v1.0-Depression Short Form 4a.  
 
Anxiety. Anxiety will be assessed using the PROMIS v1.0-Emotional Distress Anxiety Short Short Form 4a. 
 
Fatigue. Fatigue will be assessed using the PROMIS v1.0-Fatigue Short form 4a. 
 
Physical Function. Physical function will be assessed using the PROMIS v2.0 Physical function Short Form 4a. 
 
Pain Interference. Pain interference will be assessed using the PROMIS Short form v1.1 Pain Interference Short Form 
4a. 
 
Emotional Support. Emotional support will be assessed using the PROMIS v2.0 Emotional Support Short form 4a. 
 
Cognitive Function. Cognitive function will be assessed using the PROMIS v2.0 Cognitive function short form 4a. 
 
Pain Intensity. Pain intensity will be assessed with the 1-tiem PROMIS v1.0 pain intensity 1a, “In the past 7 days, how 
would you rate your pain on average?” Scored 0-10 from no pain to worst imaginable pain. 
 
Competence for Exercise and Nutrition. The Perceived Competence Scale80 includes 4 items that measure perceived 
competence for nutrition and 4 items for physical activity. Ratings are on a 7-point Likert scale with higher scores 
indicating greater perceived competence for the behavior. 
 
Self-efficacy for Exercise and Nutrition: Nutrition and exercise self-efficacy will be measured using the Nutrition and 
Physical Activity Self-Efficacy Scale, a 10-item scale that assesses an individual’s confidence in their ability to eat 
healthy foods and exercise in the presence of barriers. Ratings are on a 4-point Likert scale with higher scores 
indicating greater self-efficacy for exercise and nutrition.81 
 
Self-Regulation for Exercise and Nutrition. The Treatment Self-Regulation Questionnaire (TSRQ)82 will be used to 
assess autonomous and controlled motivation for making changes in diet (15 items) and PA (15 items). Ratings are 
on a 7-point Likert scale indicating agreement with statements regarding motivation for behavior change such as, “I 
try to exercise on a regular basis: because I enjoy exercising.” This questionnaire contains four subscales: external 
regulation, introjected regulation, identified regulation, and intrinsic motivation which are determined by averaging 
the responses to each of the subscale’s items.  
 
Autonomy Support and Relatedness: The Virtual Care Climate Questionnaire (VCCQ)83 a 15-item measure of 
perceived support for autonomy provided by the study team in a virtual care setting. Ratings are on a 7-point Likert 
scale with higher scores indicating a higher level of perceived autonomy support. 
 
Other Supporting Measures (collected at baseline and 3 months unless otherwise indicated) 
Medical events. Will be collected at 3 months. Any responses will be reviewed by the study team to determine if a 
Serious Adverse Events report is required.  
 
 
 

Page 19 of 37 
 
Process and Adherence Measures (collected over 6 months) 
Dietary Monitoring. Engagement will be assessed via frequency of dietary monitoring. Each food entry 
will be time-stamped and automatically captured by the study website interface. Frequency of dietary 
monitoring will be defined as the total number of days during the 3-month intervention that participants 
tracked breakfast or lunch AND dinner, which will be considered a full (versus partial) day of tracking. 
 
PA monitoring. Objective self-monitoring of PA will be assessed via the Fitbit tracker data for all groups. Data 
will be collected using the Fitbit application program interface (API). We will collect data on frequency and 
number of days that activity was tracked using the Fitbit (steps>500/day). These data will be tabulated to 
derive a measure of percent of days in which activity was tracked, and average activity tracking days per 
week. We will also ask participants how often they viewed the Fitbit website/mobile app to access graphs of 
their physical activity trends.  
 
Adherence to Dietary Goals. Adherence will be measured as the total number of full days of tracking (i.e., breakfast 
or lunch AND dinner) during which participants met their dietary goal. Non-adherence will be assumed for partial 
days of tracking, or days where participants did not track. 
 
Engagement measures: Using REDcap and study specific TLA website analytics we will examine website logins, goal 
setting, viewing feedback, lesson views, calculated as average logins and component views per week. 
 
Program Acceptability and Satisfaction. Participants will be asked to complete a post-treatment program evaluation. 
Measures will be adapted from previous studies and assess intervention exposure and attention, perceived personal 
relevance, and satisfaction with intervention components. 
 
Other Measures (collected at baseline, 6 weeks, and 3 months, unless otherwise indicated) 
Medical events and symptoms will be collected at 6 weeks and 3 months in a self-report questionnaire. 
Responses will be reviewed to determine if follow up is necessary with a scheduled telephone call, and if a 
Serious Adverse Events form is required. 
 
Demographics. At baseline, standard demographic information will be collected 
 
Medical history, Medication use. Self-report of both prescription and non-prescription medications. 
 
Smoking, alcohol use. Smoking and alcohol use will be assessed at each time point. 
 
 

Page 20 of 37 
 
 
 
4. STATISTICAL CONSIDERATIONS 
 
Sample Size and Power 
 
The sample size is based on feasibility. Our primary feasibility measures are participation, accrual, and retention 
rates. Anticipated participation and retention rates are based on pilot feasibility studies among YACS and our 
previous work with this population. With an estimated participation rate of 85% (consistent with CO-I’s 
experience, LCCC 1707), we anticipate it will be feasible to recruit 80 YACS within 6 months. We will test the null 
hypothesis that the proportion recruited is 50% or lower versus the alternative that it is 65% or greater using a 
one-tailed test at an alpha level of 0.05 with 95% power, which would support that the study is feasible.  
 
We plan to accrue no more than 100 participants looking to have at least 80 evaluable participants, for 5 
participants in each of the 16 groups. We expect accrual to take a total of 10 months at a rate of approximately 
10 participants per month. We anticipate accruing 80 participants within 10 months, or 12-15 participants per 
month. A sample size of 80 achieves 99% power to detect a difference between a null hypothesis mean accrual 
rate of 12 participants/month and an alternative hypothesis mean of 15 participants/month with a significance 
level of 0.05 using a one-sided one-sample Poisson test.  
 
For retention, we will test the null hypothesis that the retention is 70% or below versus the alternative that the 
retention is 90% or greater using a one-tailed test at a 0.05 alpha level. With 80 participants we would reject 
the null hypothesis that the retention is 70% if at least 62 participants are retained with 90% power, which 
would support that the study is feasible. With 80 participants we would reject the null hypothesis that the 
retention is 70% if at least 66 participants are retained with 97% power, which would support that the study is 
feasible. 
 
All three endpoints must meet the proscribed criteria in order for the study to have successfully demonstrated 
feasibility. 
Statistical Analysis 
Primary Endpoint Analysis. Accrual rate will be defined as the number of YACS who agreed to participate divided 
by the number of weeks of recruitment. Participation rate will be defined as the percent of eligible participants 
who agreed to participate. Retention rate will be defined as the number of participants who completed 3-month 
measures divided by the number who consented to participate. Means and 95% confidence intervals will be 
computed for all rates. The proportion of patients who complete the 3-month assessment will be reported along 
with an exact 95% confidence interval. This percentage will also be reported for experimental condition 
individually. We will test the hypothesis that the recruitment rate is ≤50% vs. ≥65% using a one-sample test of 
proportions with a one-sided alpha level of 0.05. We will test the hypothesis that the retention rate is ≤70% vs. 
≥90% using a one-sample test of proportions with a one-sided alpha level of 0.05. We will use a one-sample 
Poisson test to compare the average number of participants accrued per month with a null hypothesis of 12 
participants per month and a one-sided alternative of 14 participants per month at an alpha level of 0.05. 
Secondary Endpoint Analysis. Ratings of program acceptability and satisfaction will be summarized using 
descriptive statistics (mean, standard deviations, frequencies) of the post-treatment evaluation (Objective 2.1.3). 
Descriptive statistics (percentages, medians, interquartile ranges) will be used to estimate adherence to daily 
self-monitoring (Objective 2.1.4), and study measures relating to demographics, anthropometric, clinical, 
behavioral, and psychosocial measurements (Objectives 2.1.5-2.1.7.). These study measures will be reported at 
baseline and 3 months. We will also estimate changes in weight, anthropometric, clinical, behavioral, and 
psychosocial measures from baseline to 3 months, and within each experimental condition, the change scores 

Page 21 of 37 
 
will be tested to see if they are significantly different from 0 using Wilcoxon Signed Rank tests. 
Using maximum likelihood methods, we will use mixed model analyses with repeated measures to estimate 
changes in MVPA and HEI (and other secondary measures; Objectives 2.1.5-2.1.7) at 3 months and to test for 
statistical differences across conditions in changes over time, adjusting for age, cancer type, and time since 
diagnosis. Models will include random intercept, time, component, and component*time variables. A proper 
error covariance structure will be chosen based on model fit indicated by model likelihood, Akaike Information 
Criterion and Bayesian information Criterion.  
 

Page 22 of 37 
 
 
Handling Missing Data 
While every effort will be made to avoid missing data, we expect some attrition. If the combined missingness on 
predictor variables is <5%, missing data will not be replaced. If levels of missing data are higher, we will examine 
the nature of the missing data and extent of bias it may introduce. We will compare respondents to non-
respondents to see if they differ on non-response systematically and assess if missing data are missing 
completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). Assuming the data 
are (MCAR/MAR), multiple imputation methods will be used. 
 
Strengths and Limitations. 
One of the biggest limitations of this study is the sample size. The use of a factorial design makes efficient use of 
the sample, but effect sizes may not provide conclusive evidence for efficacy to make determinations about 
component inclusion in future trials. However, it makes efficient use of resources and will provide valuable 
information about feasibility, acceptability, and implementation of intervention components, regardless of 
effect size. Another limitation that since the PA intervention is being included as a core component, it will not be 
possible to disentangle the main effects for this component alone.  This randomized factorial pilot trial is testing 
an innovative theory and evidence-based approach to promoting guideline concordant PA and nutrition in AYAs 
followed by 3 months of behavioral maintenance. A strength of the intervention is utilizing an existing 
intervention framework that has been shown to improve PA in this population. The factorial design allows for 
the simultaneous testing of multiple novel intervention components to examine their feasibility and 
acceptability while conserving resources. Intervention components will be developed around ACS guidelines for 
cancer prevention and cancer survivors, which has not been examined in this population. The goal of the 
intervention is to improve PA and nutrition behaviors among AYAs, which will be beneficial to all survivors, but 
may provide additional benefits for those that are least compliant and at highest risk for future morbidity and 
mortality. This is the first intervention design for AYAs to target determinants for frailty and could provide 
important information for future optimization trials and fully powered RCTs. Information from this pilot will be 
inform future trials to optimize timing, delivery, and dose of promising intervention components. This study will 
add to the field of cancer prevention and control by testing interventions targeting PA and nutrition behaviors 
with the goal of meeting guideline recommendations among AYAs improve survivorship outcomes and reduce 
health disparities. 
 
5. SAFETY MANAGEMENT 
 
Data safety management in this trial is intended to achieve 4 objectives: 1) to minimize the occurrence of 
adverse effects, especially those related to the intervention; 2) to effectively manage adverse events as they 
relate to the study; 3) to identify when the interventions should be suspended because of concerns for 
participant safety; and 4) to determine when interventions may be resumed after having been suspended. The 
Principal Investigator (Coffman) will have primary responsibility for the safety of participants as it relates to the 
study protocol, which must be approved by the UNC IRB prior to study initiation. In addition, the North Carolina 
Translational and Clinical Sciences (TraCS) Institute Data Safety Monitoring Board (DSMB) is responsible for 
reviewing data from clinical trials approved by the UNC Biomedical IRB. Because the risks to subjects 
participating in this phase of this study are expected to be low, we do not anticipate the need for a formal Data 
Safety Monitoring Board. The research team will provide continuous monitoring of participant safety and 
periodic reporting to the IRB as required, including any Unanticipated Problems/Severe Adverse Events that may 
have occurred during the study. If risk or complexity is significant, the UNC Office of Human Research Ethics may 
require additional reporting or alternative data and safety monitoring. Data safety and monitoring activities 
continue until all participants have completed treatment and until all participants have been followed to the 
point at which study related adverse events would likely no longer be encountered. 

Page 23 of 37 
 
 
Participant Safety 
The risks to human subjects in this study, including psychological and physical risks, are judged to be minimal. 
The anticipated benefits are great, insofar as the results will be used to determine if mobile-delivered behavior 
change strategies are effective for obesity risk reduction in a sample of young adults. 
Psychological Risks: We expect risk of harm, including psychosocial and emotional distress or breach of 
confidentiality, as a result of this study to be rare or infrequent. Most communication during the intervention 
period will take place via telephone, videochat, website, direct emails, or text messages—to deliver 
intervention components, conduct assessments, or remind participants to complete questionnaires. Mailing of 
materials to participants will be done with plain packaging. We do not anticipate any risks of harm as a result of 
these communications. As part of the intervention, participants may be asked about personal factors related to 
their PA, dietary behaviors, and weight. Participants are not required to share this information and can elect 
not to share sensitive and confidential information with the study. Intervention content teaches participants 
about the benefits of daily self-weighing for weight management, addressing certain myths regarding daily self-
weighing, and emphasizing the use of the smart scale as a tool or indicator of progress with diet and PA 
behaviors. Participants will be encouraged to contact the Principal Investigator if they feel they have incurred 
any emotional distress as a result of study participation. As this is a voluntary study, a participant is free to exit 
at any point if discomfort should occur.  
Participants with emotional concerns will be referred to survivor-specific support resources, including referral 
to: the UNC Comprehensive Cancer Support Program for psychological counseling; LIVESTRONG Navigation, 
which offers free, confidential, cancer navigation services to support individuals’ cancer journey; and 
CancerCare Counseling, through which oncology social workers offer telephone counseling to help cancer 
survivors cope with emotional and practical challenges of cancer.  
We will collect information at each assessment timepoint on hospitalizations for any psychiatric problem, 
including depression and eating disorders and make referrals as needed or suspend intervention if symptoms 
emerge that are study related or if the severity of the psychological symptoms warrant suspension or 
withdrawal. 
Physical Risks: There are no major physical risks associated with the PA or healthy eating interventions or data 
collection. There may be some risk to increasing physical activity, including, but not limited to, injuries to the 
muscles, ligaments, tendons, and joints of the body. Other risks include abnormal blood pressure, fainting, 
dizziness, disorders of heart rhythm, exacerbation of exercise-induced asthma symptoms, and very rare 
instances of heart attack, stroke, or even death. A consensus statement from the American College of Sports 
Medicine Roundtable on Exercise Guidelines for Cancer Survivors concluded that exercise training is safe after 
cancer treatments. Although increasing PA can have great benefits, participants may also experience some 
general fatigue and sore muscles or joints from exercising. Because several studies indicate that walking is the 
preferred exercise type for an estimated 55%-81% of survivors of varying cancer types,31,71 the study focuses on 
encouraging moderate-intensity walking. To help ensure safety, the study will follow guidelines and safety 
recommendations for PA set forth by the American College of Sports Medicine. PhD trained staff 
interventionists will oversee safety of exercise progression and suitability. Materials provided during the course 
of the intervention will educate participants on exercise safety and inform them of precautions to take when 
starting an exercise program and how to manage increasing amounts of exercise. Messages will encourage 
participants to start at a reasonable frequency and intensity of exercise based on their current level and to 
increase their exercise gradually. The informed consent will also clearly communicate the potential physical risks 
related to starting an exercise program and increasing PA. Participation is fully voluntary, and we will ask 
participants to report any problems to the study researchers. 
Unsafe Rate of Weight Loss: Participants will not receive an intervention designed to produce weight loss, 
however, making dietary changes and increasing PA can produce a gradual, modest rate of weight loss. The 
intervention focuses on making small daily changes to their eating in service of shaping dietary quality over 

Page 24 of 37 
 
time. Thus, although unlikely, it is still possible that some participants could experience excessive weight loss at 
a rate that is unsafe. We will carefully monitor rate of weight loss over the course of the study, and take 
appropriate action as detailed below to ensure participant safety. 
Risk of wearable devices. There is also the possibility of minor skin irritation associated with wearable activity 
tracker devices.  
Risk to privacy and/or confidentiality. We expect a breach of confidentiality to be rare or infrequent. Most 
communication during the study period will take place via telephone, direct emails, text messages, or through 
the smartphone app—either to deliver questions, conduct assessments, or remind participants to complete 
questionnaires. We do not anticipate any risks of harm as a result of these communications; however, there is 
always a possibility that their scale data, tracker data, or activity in the app will be intercepted. 
Adequacy of Protection Against Risks 
a. Informed Consent and Assent 
Recruitment and Informed Consent: Several different efforts will be used to recruit potential participants and 
obtain informed consent from those eligible participants. The research team is responsible for assuring that the 
UNC IRB has approved the study protocol and has reviewed and approved the informed consent document. 
Written approval must be obtained from the IRB before the study can begin. Once an individual has been 
deemed potentially eligible to participate through online and telephone screening, study staff will provide more 
study details and describe the concept of random assignment, the 36 study conditions, and assessment 
procedures over the telephone. Individuals who remain interested will receive an email directing them to a 
unique and secure REDCap link to an online informed consent. After clicking on the link, they will be directed 
through a series of screens that present the informed consent document. The online informed consent records 
will be retained, and recruitment of participants will be done by adhering to HIPAA regulations. Accrual reports 
will list enrolled participants and excluded participants throughout the stages of the research. This routine 
monitoring allows for early identification and resolution of potential problems during the recruitment phase. 
The anticipated recruitment duration is 30 months. All key personnel have attended the required courses on 
human subject protection and HIPAA regulations, and certificates of IRB training completion are on file with the 
University of North Carolina. 
b. Protections Against Risk 
Protections against Psychological Risks: As noted above, participants are not required to share this information 
and can elect not to share sensitive and confidential information with the study. Participants will be encouraged 
to contact the Principal Investigator if they feel they have incurred any emotional distress as a result of study 
participation. As this is a voluntary study, a participant is free to exit at any point if discomfort should occur. 
Protections against Physical Risks: To ensure medical readiness to begin physical activity, participants will 
complete a physical activity readiness questionnaire (PAR-Q). The PAR-Q assesses for the following medical 
conditions: heart problems, chest pains, faintness or dizzy spells, high blood pressure, bone or joint problems 
such as arthritis that has been or could be aggravated by exercise, prescription medication use, and other 
medical reasons why exercise would not be advisable. Participants endorsing yes to any items 1-4 on the PAR-Q 
(experience of heart problems, frequent chest pains, faintness or dizziness, bone or joint problems) will be 
excluded from the study. We also will monitor any major musculoskeletal problems that develop during the 
intervention (e.g. broken bones) using a medical events questionnaire at 6 weeks and 3 months and will 
determine whether these appear related to our study. Participants who develop musculoskeletal problems or 
other health problems that may affect safe participation will be instructed to stop exercising until the problem 
resolves and their physician approves resumption of physical activity. 
All participants will be advised about safe weight loss practices including dietary change and increasing physical 
activity at their initial study visit. Participants will be advised to gradually increase their physical activity and to 

Page 25 of 37 
 
use walking as a primary form of activity and will be taught that the appropriate rate of weight loss is 1 to 2 
pounds per week. In addition, we will carefully monitor changes in weight during our trial. We will collect 
information at each assessment timepoint on hospitalizations for any psychiatric problem, including depression 
and eating disorders.  
Protections to Ensure Confidentiality of Participants: All identifying information collected from participants, 
including names, email addresses, Internet protocol address numbers, and zip code, will not be directly linked 
with research data. Instead, each study participant will be assigned a unique ID number. Identifying information 
will only be collected in order to contact participants. To ensure confidentiality of participants, all surveys and 
records related to a participant’s involvement in this research study will be stored in a locked file cabinet and/or 
in encrypted files on servers that adhere to the University policy on storage and transmission of sensitive data. 
Participant identity on these records will be indicated by an ID number, and the information linking these 
numbers with participant identity will be kept separate from the research records. In addition, all research 
databases will have password-controlled access, and this will be controlled by critical staff. 
During online data collection, questionnaire data will be temporarily stored on the REDCap data server. The 
REDCap database system provides for secure web-based data entry with the data stored on servers that are 
maintained by NC TraCS. The data is encrypted during transmission. The servers are located in a secure campus 
area with all appropriate physical security measures in place. The web and database servers are monitored by 
the TraCS IT staff, patched frequently, and scanned by a third -party vendor to ensure that they are protected 
against known vulnerabilities. The scanning application is the standard service for the entire campus. Access is 
by individual user id and is restricted to the forms and/or functions that the user needs to have. Data will be 
erased from the REDCap data server after online data collection is completed and the data is transferred to the 
study database in a secure password protected area. 
CareEvolution's MyDataHelps™ mobile app data is securely stored in the HIPAA compliant, Meaningful Use 
Certified EDC HIEBus™ platform. CareEvolution® platforms comply with the security and privacy controls 
defined by NIST 800-53 Rev. 4 at the FISMA Moderate baseline. They regularly undergo external formal 
assessments by a FedRAMP-accredited Third Party Assessment Organization (3PAO). The platform has been 
granted an Authorization to Operate (ATO) by the National Institutes of Health (NIH) and is FDA Part 11 
Compliant (info@careevolution.com). MyDataHelps integrates Fitbit using the Fitbit API. 
Fitbit data will be collected using the Fitbit API (Application Programming Interface), which maintains data 
security by requiring an Access Token when the Fitbit user authorizes our study to access their data. During the 
consent process, participants will consent for the data from their specific device IDs (Fitbits) to be accessed by 
our study in order to drive tailored messages. Access to any individual’s Fitbit account is protected by user login 
and password authentication. The risk of breach of confidentiality over the Fitbit website will be partly subject 
to each individual’s comfort in sharing information in their individual profile. To further minimize the risk of 
breach of confidentiality, all participant activities on the Fitbit website will be voluntary. Consent forms will also 
clearly communicate the risk of this type of disclosure to participants. 
If members of the research team wish to work with the data, they will only receive non-identifiable data. 
Although unanticipated, if study data need to be transmitted between study staff, encrypted files will be sent. 
Files will be password protected prior to sending or uploading. Only study staff will have knowledge of file 
passwords. 
Removal of Patients from Protocol. Participants will be removed from the protocol should they experience 
greater than minimal risk as detailed above. Participants will be reminded that their participation is voluntary 
and that they may withdraw from the study at any time without consequence by contacting the Principal 
Investigator. In addition, we expect that pregnancies will occur. We will stop all intervention activities for 
anyone who becomes pregnant. 
 
 

Page 26 of 37 
 
Potential Benefits of the Proposed Research to Research Participants and Others 
Intervention participants are expected to improve dietary behaviors, physical activity, ultimately reducing their 
risk of secondary cancers or cancer recurrence, developing type 2 diabetes, cardiovascular disease, and other 
chronic diseases. In addition, the results of this study could be used to develop an optimized behavioral 
intervention to be tested in a similar population and, if effective, could be generalizable to a larger population 
of young adult cancer survivors. If successful, an automated program that uses a smartphone and other 
relatively low-cost digital technologies that is optimized for effective treatment components could be 
disseminated in a larger-scale program, thus benefiting a greater number of young adults cancer survivors. 
Importance of the Knowledge to be Gained 
There are currently few trials that have used a strictly mobile weight loss intervention that have resulted in 
clinically significant weight loss in young adults. Should this trial prove effective, it will have great public health 
relevance, as young adulthood is a critical period for excessive weight gain that is associated with future 
This will be one of the first entirely remotely delivered interventions using digital tools to improve physical 
activity and diet behaviors simultaneously among YACS. This study addresses unmet needs of YACS and their 
demand for healthy lifestyle programs, uses the ubiquity of smartphones and technology, and employs their 
preference for remotely delivered health interventions. Further, the study design and digital intervention will 
enable nationwide recruitment and enhance external validity. This technology-based approach has the 
potential to deliver a novel intervention with high-reach, at low cost, with the potential for scaling the 
intervention for future efforts. This approach holds promise at improving body composition, physical function, 
and quality of life for YACS  and thus has the potential for strong public health impact. 
 

Page 27 of 37 
 
 
 
6. DATA COLLECTION AND MANAGEMENT 
7.  
The rigorous system used for participant tracking, data collection, management, and quality control procedures 
for our previous projects (SNAP: U01HL090864; IMPACT: R01CA204965) will be modified for this study. All data 
will be entered using the REDCap computer-assisted program that has built-in validation checks for range, data 
type, and completeness. All measurements will undergo consistency and outlier checks, compared with raw 
data, and edited as required. REDCap includes detailed audit logs that document any database changes, and we 
will document protocols for cleaning data. 
 
Participants will provide physical data (objective weight and self-reported height), self-reported questionnaire 
data online, and self-reported physical activity data over the telephone specifically for research purposes. All 
self-report online data will be collected using secure REDCap surveys. Food log data will be collected using 
CareEvolution's MyDataHelps™ mobile app. Data is securely stored in the HIPAA compliant, Meaningful Use 
Certified EDC HIEBus™ platform. CareEvolution® platforms comply with the security and privacy controls 
defined by NIST 800-53 Rev. 4 at the FISMA Moderate baseline. They regularly undergo external formal 
assessments by a FedRAMP-accredited Third Party Assessment Organization (3PAO). The platform has been 
granted an Authorization to Operate (ATO) by the National Institutes of Health (NIH) and is FDA Part 11 
Compliant (info@careevolution.com). MyDataHelps integrates Fitbit using the Fitbit API. 
 
Identifiable information collected in online surveys will include the participant’s name, address, phone 
numbers, and date of birth. 
Risk to privacy and/or confidentiality 
We expect a breach of confidentiality to be rare or infrequent. Most communication during the study period will 
take place via telephone, direct emails, text messages, or through the smartphone app—either to deliver 
questions, conduct assessments, or remind participants to complete questionnaires. We do not anticipate any 
risks of harm as a result of these communications; however, there is always a possibility that their scale data, 
tracker data, or activity in the app will be intercepted. 
 
8. RECRUITMENT, CONSENT, AND RETENTION STRATEGY 
 
Recruitment 
Based on previous trials among young adult cancer survivors (YACS), successful recruitment and timely 
enrollment of 62 YACS for the THRIVE trial (LCCC1709) and 280 YACS for IMPACT trial (LCCC1707, R01CA204965) 
our team has demonstrated the ability to recruit this population. Based on our previous experiences and 
estimates of YACS served by UNC, participants will be recruited on a rolling basis and randomized to 1 of 16 
intervention conditions. We will use a multi-method recruitment strategy that demonstrated previous success 
to reach individuals across the United States, and include the following strategies: 
A) Direct mailings to individuals identified through the local UNC tumor registry. Used by Dr. Valle to 
recruit young adult cancer survivors (LCCC 1707, LCCC1709) and African American breast cancer 
survivors (LCCC1323), we will obtain a list of potentially eligible participants from the Carolina Data 
Warehouse for Health (CDW-H), which accesses UNC tumor registry data (i.e., participants from the 
UNC tumor registry are provided in a list from CDW-H). Potentially eligible survivors will be sent a letter 
of approach from the study PI, inviting them to participate and a study brochure describing the study 
and what it means to participate. The letters will invite interested individuals to visit a study 

Page 28 of 37 
 
recruitment website or call research study staff to be screened by phone. We may follow up with up to 
two additional mailings if necessary. 
B) Local and national community-based programs. We will work with our AYA Oncology Program 
members, local programs and national community-based organizations dedicated to YACS with whom 
we have established relationship (e.g., Teen Cancer America, Ulman Foundation, Cervivor, Stupid 
Cancer, A Ballsy Sense of Tumor) to share and post recruitment information on various communication 
channels (e.g., Facebook, Instagram, Twitter, listservs, email). Unpaid Facebook posts by cancer 
organizations or friends was the primary approach used in our IMPACT study (LCCC 1707) to recruit 280 
participants in 14 months.  
C) Advertisements. We will place sponsored posts on social media, ads on relevant internet sites, and use 
email listservs. We will produce flyers, posters, brochures, and presentations to advertise the study at 
community events and cancer conferences dedicated to young adults (e.g., CancerCon, AYA Global 
Congress). We will work with the UNC AYA Oncology clinical staff with whom we have established 
relationships (i.e., Co-Investigator Dr. Smitherman, Medical Director, UNC AYA Oncology; Lauren Lux) to 
advertise the study and recruit potentially interested individuals.  
 
All recruitment efforts will direct participants to a study website with a more detailed description of the 
research study and FAQs about participating in research. Interested individuals will click through to a REDCap 
web screening survey to determine initial eligibility. Those who are preliminarily eligible will be contacted by 
trained study staff and will undergo additional phone screening. Following an approach we have used in the 
past, eligible and interested individuals will receive an email directing them to a unique REDCap link to an online 
informed consent. After providing consent, participants will be instructed on procedures for 1) completing one 
weekday and one weekend self-reported 24-hour dietary recall, 2) completing baseline online questionnaires, 
and 3) completing their baseline weight measurement. The Fitbit activity tracker will be mailed to the 
participant. After completion of baseline assessments, the participant will be randomized to one level of each of 
the 4 factors, or one of the 16 conditions (Table 1). The participant will be scheduled for a one-on-one 
telephone/videochat session during which they will be informed of condition assignments, assisted with 
downloading of the study smartphone app, and questions about getting started will be answered. 
 
Inclusion of Women and Minorities 
All individuals will be eligible for enrollment independent of sex/gender and race/ethnicity.  
 
Retention 
Participant retention strategies have been used effectively in our other studies. A systematic protocol will be 
followed to minimize dropouts. Top priority will be for assessment visits. At baseline, names and addresses of 
several friends and family members who can be contacted are obtained for use if we lose touch with the 
participant. Participants will be paid a $25 honorarium for completing assessments at 3 months. For each 
assessment visit, we will send participants an email that will include a link to online questionnaires with an 
expected completion date. Missed questionnaires will be immediately followed up by phone and rescheduled. 
 

Page 29 of 37 
 
In addition to our assessment retention protocol, participants who complete all three assessments will be able 
to keep their Fitbit and scale at the end of the study, which we will believe will also encourage retention to 
assessment measures.  
9. PLANS FOR PUBLICATION 
As Principal Investigator of this grant application and clinical trial, Dr. Coffman will comply with the clinical trial 
information dissemination expectations of the NIH policy to register and submit summary results at 
ClinicalTrials.gov. Consistent with the terms and conditions of NIH funding, we will ensure the submission and 
updating of registration and results information for this clinical trial in the timeframes established by the Final 
Rule. Registration and results reporting in ClinicalTrials.gov will be 
completed within the following timeframes: 
• 
Registration of the trial at ClinicalTrials.gov no later than 21 days after enrolling the first participant 
• 
All submitted information will be updated at least once a year. 
• 
Any apparent errors, deficiencies, and/or inconsistencies identified by NIH as part of the quality control 
review process and any other errors identified will be addressed by the responsible party. 
• 
Corrections to submitted information will be made within 15 days for registration information and 25 days 
for results information. 
• 
Trial results will be submitted no later than one year after the primary completion date. 
Informed Consent Documents for the clinical trial will include a specific statement relating to posting of clinical 
trial information at ClinicalTrials.gov. The UNC IRB has template language explaining that the study will be 
posted on ClinicalTrials.gov that investigators are required to include in the consent document for the trial. 
As PI, Dr. Coffman will work closely with the UNC-CH Office of Clinical Trials (OCT), which serves as the 
university’s internal PRS. The OCT has responsibility for ensuring that clinical trial registration and reporting 
occurs in compliance with NIH policies. The OCT has support staff to facilitate the process of registration and 
results reporting to ClinicalTrials.gov, and our team will work closely with them to register this trial and to 
submit summary results to the website in a timely manner, in keeping within the required timeframes. Once 
data collection is complete, our research team will work to prepare and submit trial results no later than one 
year after the primary completion date. 
 

Page 30 of 37 
 
 
10. REFERENCES 
 
1.    Telles CM. A scoping review of literature: What has been studied about adolescents and young adults (AYAs) 
with cancer? Cancer Treatment and Research Communications. 2021;27:100316. 
doi:10.1016/j.ctarc.2021.100316 
2.    Hayashi RJ. Adolescent and young adult cancer survivorship: The new frontier for investigation. Cancer. 
2019;125(12):1976-1978. doi:10.1002/cncr.32064 
3.    National Institutes of Health. Cancer Stat Facts: Cancer Among Adolescents and Young Adults (AYAs) (Ages 15–
39). National Cancer Institute Surveillance, Epidemiology, and End Results (SEER) Program. Accessed March 25, 
2022. https://seer.cancer.gov/statfacts/html/aya.html 
4.    Sodergren SC, Husson O, Robinson J, et al. Systematic review of the health-related quality of life issues facing 
adolescents and young adults with cancer. Qual Life Res. 2017;26(7):1659-1672. doi:10.1007/s11136-017-1520-x 
5.    Warner EL, Nam GE, Zhang Y, et al. Health behaviors, quality of life, and psychosocial health among survivors of 
adolescent and young adult cancers. J Cancer Surviv. 2016;10(2):280-290. doi:10.1007/s11764-015-0474-7 
6.    RFA-CA-20-028: Research to Reduce Morbidity and Improve Care for Pediatric, and Adolescent and Young Adult 
(AYA) Cancer Survivors (R21 Clinical Trial Optional). Accessed September 11, 2020. 
https://grants.nih.gov/grants/guide/rfa-files/rfa-ca-20-028.html 
7.    Adams SC, Herman J, Lega IC, et al. Young Adult Cancer Survivorship: Recommendations for Patient Follow-up, 
Exercise Therapy, and Research. JNCI Cancer Spectr. 2021;5(1). doi:10.1093/jncics/pkaa099 
8.    Chao C, Bhatia S, Xu L, et al. Chronic comorbidities among survivors of adolescent and young adult cancer. J Clin 
Oncol. 2020;38(27):3161-3174. doi:10.1200/JCO.20.00722 
9.    Signorelli C, Fardell JE, Wakefield CE, Webber K, Cohn RJ. The cost of cure: chronic conditions in survivors of 
child, adolescent, and young adult cancers. In: Koczwara B, ed. Cancer and Chronic Conditions. Springer 
Singapore; 2016:371-420. doi:10.1007/978-981-10-1844-2_13 
10.   Ness KK, Krull KR, Jones KE, et al. Physiologic frailty as a sign of accelerated aging among adult survivors of 
childhood cancer: a report from the St Jude Lifetime cohort study. J Clin Oncol. 2013;31(36):4496-4503. 
doi:10.1200/JCO.2013.52.2268 
11.   Ness KK, Kirkland JL, Gramatges MM, et al. Premature physiologic aging as a paradigm for understanding 
increased risk of adverse health across the lifespan of survivors of childhood cancer. J Clin Oncol. 
2018;36(21):2206-2215. doi:10.1200/JCO.2017.76.7467 
12.   Smitherman AB, Anderson C, Lund JL, Bensen JT, Rosenstein DL, Nichols HB. Frailty and Comorbidities Among 
Survivors of Adolescent and Young Adult Cancer: A Cross-Sectional Examination of a Hospital-Based Survivorship 
Cohort. J Adolesc Young Adult Oncol. 2018;7(3):374-383. doi:10.1089/jayao.2017.0103 
13.   Smitherman AB, Wood WA, Mitin N, et al. Accelerated aging among childhood, adolescent, and young adult 
cancer survivors is evidenced by increased expression of p16INK4a and frailty. Cancer. 2020;126(22):4975-4983. 
doi:10.1002/cncr.33112 
14.   Fong DYT, Ho JWC, Hui BPH, et al. Physical activity for cancer survivors: meta-analysis of randomised controlled 
trials. BMJ. 2012;344:e70. doi:10.1136/bmj.e70 
15.   Friedenreich CM, Stone CR, Cheung WY, Hayes SC. Physical Activity and Mortality in Cancer Survivors: A 
Systematic Review and Meta-Analysis. JNCI Cancer Spectr. 2020;4(1):pkz080. doi:10.1093/jncics/pkz080 
16.   Pekmezi DW, Demark-Wahnefried W. Updated evidence in support of diet and exercise interventions in cancer 
survivors. Acta Oncol. 2011;50(2):167-178. doi:10.3109/0284186X.2010.529822 
17.   Adams RN, Mosher CE, Blair CK, Snyder DC, Sloane R, Demark-Wahnefried W. Cancer survivors’ uptake and 
adherence in diet and exercise intervention trials: an integrative data analysis. Cancer. 2015;121(1):77-83. 
doi:10.1002/cncr.28978 
18.   Simmonds A, Mitrou P, Wiseman MJ. Food, nutrition, physical activity and cancer - keeping the evidence 
current: WCRF/AICR Continuous Update Project. Nutr Bulletin. 2014;39(1):126-131. doi:10.1111/nbu.12077 
19.   Kaluza J, Harris HR, Håkansson N, Wolk A. Adherence to the WCRF/AICR 2018 recommendations for cancer 
prevention and risk of cancer: prospective cohort studies of men and women. Br J Cancer. 2020;122(10):1562-

Page 31 of 37 
 
1570. doi:10.1038/s41416-020-0806-x 
20.   Petimar J, Smith-Warner SA, Rosner B, Chan AT, Giovannucci EL, Tabung FK. Adherence to the world cancer 
research fund/american institute for cancer research 2018 recommendations for cancer prevention and risk of 
colorectal cancer. Cancer Epidemiol Biomarkers Prev. 2019;28(9):1469-1479. doi:10.1158/1055-9965.EPI-19-
0165 
21.   Turati F, Dalmartello M, Bravi F, et al. Adherence to the world cancer research fund/american institute for 
cancer research recommendations and the risk of breast cancer. Nutrients. 2020;12(3). 
doi:10.3390/nu12030607 
22.   Kohler LN, Garcia DO, Harris RB, Oren E, Roe DJ, Jacobs ET. Adherence to diet and physical activity cancer 
prevention guidelines and cancer outcomes: A systematic review. Cancer Epidemiol Biomarkers Prev. 
2016;25(7):1018-1028. doi:10.1158/1055-9965.EPI-16-0121 
23.   Cifu G, Arem H. Adherence to lifestyle-related cancer prevention guidelines and breast cancer incidence and 
mortality. Ann Epidemiol. 2018;28(11):767-773.e1. doi:10.1016/j.annepidem.2018.09.002 
24.   Darabos K, Barakat LP, Schapira M, Hill-Kayser C, Schwartz LA. Association of Demographic and Cancer-Specific 
Factors on Health Behavior Recommendations Specific to Cancer Prevention and Control Among Adolescent and 
Young Adult Survivors of Childhood Cancer. J Adolesc Young Adult Oncol. Published online November 18, 2020. 
doi:10.1089/jayao.2020.0130 
25.   Lee E, Zhu J, Velazquez J, et al. Evaluation of Diet Quality Among American Adult Cancer Survivors: Results From 
2005-2016 National Health and Nutrition Examination Survey. J Acad Nutr Diet. 2021;121(2):217-232. 
doi:10.1016/j.jand.2020.08.086 
26.   Skiba MB, Jacobs ET, Crane TE, Kopp LM, Thomson CA. Relationship Between Individual Health Beliefs and Fruit 
and Vegetable Intake and Physical Activity Among Cancer Survivors: Results from the Health Information 
National Trends Survey. J Adolesc Young Adult Oncol. Published online August 5, 2021. 
doi:10.1089/jayao.2021.0078 
27.   Mooney R, Samhouri M, Holton A, et al. Adolescent and young adult cancer survivors’ perspectives on their 
internet use for seeking information on healthy eating and exercise. J Adolesc Young Adult Oncol. 2017;6(2):367-
371. doi:10.1089/jayao.2016.0059 
28.   Thorsen L, Bøhn S-KH, Lie HC, Fosså SD, Kiserud CE. Needs for information about lifestyle and rehabilitation in 
long-term young adult cancer survivors. Support Care Cancer. Published online August 1, 2021. 
doi:10.1007/s00520-021-06418-z 
29.   Wu YP, Yi J, McClellan J, et al. Barriers and facilitators of healthy diet and exercise among adolescent and young 
adult cancer survivors: implications for behavioral interventions. J Adolesc Young Adult Oncol. 2015;4(4):184-
191. doi:10.1089/jayao.2015.0028 
30.   Daniel CL, Emmons KM, Fasciano K, Fuemmeler BF, Demark-Wahnefried W. Needs and lifestyle challenges of 
adolescents and young adults with cancer: summary of an institute of medicine and livestrong foundation 
workshop. Clin J Oncol Nurs. 2015;19(6):675-681. doi:10.1188/15.CJON.19-06AP 
31.   Adams SC, Petrella A, Sabiston CM, et al. Preferences for exercise and physical activity support in adolescent and 
young adult cancer survivors: a cross-sectional survey. Support Care Cancer. 2021;29(7):4113-4127. 
doi:10.1007/s00520-020-05897-w 
32.   Singh B, Zopf EM, Howden EJ. Effect and feasibility of wearable physical activity trackers and pedometers for 
increasing physical activity and improving health outcomes in cancer survivors: A systematic review and meta-
analysis. J Sport Health Sci. Published online July 24, 2021. doi:10.1016/j.jshs.2021.07.008 
33.   Maes S, Karoly P. Self-Regulation Assessment and Intervention in Physical Health and Illness: A Review. Applied 
Psychology. 2005;54(2):267-299. doi:10.1111/j.1464-0597.2005.00210.x 
34.   Wing RR, Tate DF, Espeland MA, et al. Innovative Self-Regulation Strategies to Reduce Weight Gain in Young 
Adults: The Study of Novel Approaches to Weight Gain Prevention (SNAP) Randomized Clinical Trial. JAMA Intern 
Med. 2016;176(6):755-762. doi:10.1001/jamainternmed.2016.1236 
35.   LaRose JG, Tate DF, Lanoye A, et al. Adapting evidence-based behavioral weight loss programs for emerging 
adults: A pilot randomized controlled trial. J Health Psychol. 2019;24(7):870-887. 
doi:10.1177/1359105316688951 
36.   Gokee-Larose J, Gorin AA, Wing RR. Behavioral self-regulation for weight loss in young adults: a randomized 

Page 32 of 37 
 
controlled trial. Int J Behav Nutr Phys Act. 2009;6:10. doi:10.1186/1479-5868-6-10 
37.   Valle CG, Deal AM, Tate DF. Preventing weight gain in African American breast cancer survivors using smart 
scales and activity trackers: a randomized controlled pilot study. J Cancer Surviv. 2017;11(1):133-148. 
doi:10.1007/s11764-016-0571-2 
38.   Martin CL, Tate DF, Valle CG. Nonadherence to daily self-weighing and activity tracking is associated with weight 
fluctuations among African American breast cancer survivors. PLoS ONE. 2018;13(6):e0199751. 
doi:10.1371/journal.pone.0199751 
39.   Gokee LaRose J, Tate DF, Gorin AA, Wing RR. Preventing weight gain in young adults: a randomized controlled 
pilot study. Am J Prev Med. 2010;39(1):63-68. doi:10.1016/j.amepre.2010.03.011 
40.   Wing RR, Tate D, Espeland M, et al. Weight gain prevention in young adults: design of the study of novel 
approaches to weight gain prevention (SNAP) randomized controlled trial. BMC Public Health. 2013;13:300. 
doi:10.1186/1471-2458-13-300 
41.   Nezami BT, Ward DS, Lytle LA, Ennett ST, Tate DF. A mHealth randomized controlled trial to reduce sugar-
sweetened beverage intake in preschool-aged children. Pediatr Obes. 2018;13(11):668-676. 
doi:10.1111/ijpo.12258 
42.   Valle CG, Nezami BT, Hatley KE, Power JM, Tate DF. A micro-randomized trial of just-in-time adaptive 
intervention messages nudging weight-related behaviors in young adults. Ann Behav Med. 2020;54(Suppl 
1):S747-S747. 
43.   Idalski Carcone A, Ellis DA, Eggly S, et al. Improving diabetes management in emerging adulthood: an 
intervention development study using the multiphase optimization strategy. JMIR Res Protoc. 
2020;9(10):e20191. doi:10.2196/20191 
44.   Mobley EM, Moke DJ, Milam J, et al. Interventions to address disparities and barriers to pediatric cancer 
survivorship care: a scoping review. J Cancer Surviv. 2022;16(3):667-676. doi:10.1007/s11764-021-01060-4 
45.   Thomas S, Reading J, Shephard RJ. Revision of the Physical Activity Readiness Questionnaire (PAR-Q). Can J Sport 
Sci. 1992;17(4):338-345. 
46.   National Cancer Institute. ASA24® Dietary Assessment Tool | EGRP/DCCPS/NCI/NIH. Accessed October 4, 2019. 
https://epi.grants.cancer.gov/asa24/ 
47.   Collins LM, ed. Optimization of Multicomponent Behavioral, Biobehavioral, and Biomedical Interventions: The 
Multiphase Optimization Strategy (MOST). Springer; 2018. 
48.   Collins LM, Murphy SA, Nair VN, Strecher VJ. A strategy for optimizing and evaluating behavioral interventions. 
Ann Behav Med. 2005;30(1):65-73. doi:10.1207/s15324796abm3001_8 
49.   Rock CL, Thomson CA, Sullivan KR, et al. American Cancer Society nutrition and physical activity guideline for 
cancer survivors. CA Cancer J Clin. 2022;72(3):230-262. doi:10.3322/caac.21719 
50.   Collins LM, Dziak JJ, Kugler KC, Trail JB. Factorial experiments: efficient tools for evaluation of intervention 
components. Am J Prev Med. 2014;47(4):498-504. doi:10.1016/j.amepre.2014.06.021 
51.   Diabetes Prevention Program (DPP) Research Group. The Diabetes Prevention Program (DPP): description of 
lifestyle intervention. Diabetes Care. 2002;25(12):2165-2171. doi:10.2337/diacare.25.12.2165 
52.   Valle CG, Tate DF, Mayer DK, Allicock M, Cai J. A randomized trial of a Facebook-based physical activity 
intervention for young adult cancer survivors. J Cancer Surviv. 2013;7(3):355-368. doi:10.1007/s11764-013-
0279-5 
53.   Salsman J, Tooze JA, Diamond M, et al. A randomized pilot trial of an mhealth physical activity intervention for 
adolescent and young adult cancer survivors: Feasibility, acceptability, and psychological well-being outcomes. 
PSYCHO-ONCOLOGY. 2020;29(1, SI):90-91. 
54.   Valle CG, Pinto BM, LaRose JG, et al. Promoting physical activity in young adult cancer survivors using mHealth 
and adaptive tailored feedback strategies: Design of the Improving Physical Activity after Cancer Treatment 
(IMPACT) randomized controlled trial. Contemp Clin Trials. 2021;103:106293. doi:10.1016/j.cct.2021.106293 
55.   Valle CG, Nezami BT, Tate DF. Designing in-app messages to nudge behavior change: Lessons learned from a 
weight management app for young adults. Organ Behav Hum Decis Process. 2020;161:95-101. 
doi:10.1016/j.obhdp.2020.10.004 
56.   Steinberg DM, Tate DF, Bennett GG, Ennett S, Samuel-Hodge C, Ward DS. The efficacy of a daily self-weighing 
weight loss intervention using smart scales and e-mail. Obesity (Silver Spring). 2013;21(9):1789-1797. 

Page 33 of 37 
 
doi:10.1002/oby.20396 
57.   Wing RR, Tate D, LaRose JG, et al. Frequent self-weighing as part of a constellation of healthy weight control 
practices in young adults. Obesity (Silver Spring). 2015;23(5):943-949. doi:10.1002/oby.21064 
58.   Clinton SK, Giovannucci EL, Hursting SD. The world cancer research fund/american institute for cancer research 
third expert report on diet, nutrition, physical activity, and cancer: impact and future directions. J Nutr. 
2020;150(4):663-671. doi:10.1093/jn/nxz268 
59.   Jensen MD, Ryan DH, Apovian CM, et al. 2013 AHA/ACC/TOS guideline for the management of overweight and 
obesity in adults: a report of the American College of Cardiology/American Heart Association Task Force on 
Practice Guidelines and The Obesity Society. Circulation. 2014;129(25 Suppl 2):S102-38. 
doi:10.1161/01.cir.0000437739.71477.ee 
60.   Turner-McGrievy GM, Wilcox S, Kaczynski AT, et al. Crowdsourcing for self-monitoring: Using the Traffic Light 
Diet and crowdsourcing to provide dietary feedback. Digit Health. 2016;2:2055207616657212. 
doi:10.1177/2055207616657212 
61.   Burke LE, Wang J, Sevick MA. Self-monitoring in weight loss: a systematic review of the literature. J Am Diet 
Assoc. 2011;111(1):92-102. doi:10.1016/j.jada.2010.10.008 
62.   Burke LE, Swigart V, Warziski Turk M, Derro N, Ewing LJ. Experiences of self-monitoring: successes and struggles 
during treatment for weight loss. Qual Health Res. 2009;19(6):815-828. doi:10.1177/1049732309335395 
63.   Epstein LH, Paluch RA, Beecher MD, Roemmich JN. Increasing healthy eating vs. reducing high energy-dense 
foods to treat pediatric obesity. Obesity (Silver Spring). 2008;16(2):318-326. doi:10.1038/oby.2007.61 
64.   Nezami BT, Power JM, Hurley L, Tate DF. A randomized controlled trial of two diet monitoring approaches in a 
mobile weight loss intervention. In: Poster Presented at: Obesity Week 2020. Virtual Conference. The Obesity 
Society; 2020. 
65.   Nezami BT, Hurley L, Power J, Valle CG, Tate DF. A pilot randomized trial of simplified versus standard calorie 
dietary self-monitoring in a mobile weight loss intervention. Obesity (Silver Spring). 2022;30(3):628-638. 
doi:10.1002/oby.23377 
66.   Spees CK, Braun AC, Hill EB, et al. Impact of a tailored nutrition and lifestyle intervention for overweight cancer 
survivors on dietary patterns, physical activity, quality of life, and cardiometabolic profiles. J Oncol. 
2019;2019:1503195. doi:10.1155/2019/1503195 
67.   He Z, Wu H, Yu F, et al. Effects of Smartphone-Based Interventions on Physical Activity in Children and 
Adolescents: Systematic Review and Meta-analysis. JMIR Mhealth Uhealth. 2021;9(2):e22601. 
doi:10.2196/22601 
68.   Allicock M, Campbell MK, Valle CG, Carr C, Resnicow K, Gizlice Z. Evaluating the dissemination of Body & Soul, an 
evidence-based fruit and vegetable intake intervention: challenges for dissemination and implementation 
research. J Nutr Educ Behav. 2012;44(6):530-538. doi:10.1016/j.jneb.2011.09.002 
69.   Valle CG, Diamond M, Pinto BM, et al. IMPACT: A Randomized Controlled Trial of an mHealth Physical Activity 
Intervention for Young Adult Cancer Survivors. Cancer Epidemiol Biomarkers Prev. 2022;31(7):1509. 
doi:10.1158/1055-9965.EPI-22-0471 
70.   Radloff LS. The CES-D scale: A self-report depression scale for research in the general population. Appl Psychol 
Meas. 1977;1(3):385-401. doi:10.1177/014662167700100306 
71.   Bélanger LJ, Plotnikoff RC, Clark AM, Courneya KS. Determinants of physical activity in young adult cancer 
survivors. Am J Health Behav. 2012;36(4):483-494. doi:10.5993/AJHB.36.4.5 
72.   Ceniccola GD, Castro MG, Piovacari SMF, et al. Current technologies in body composition assessment: 
advantages and disadvantages. Nutrition. 2019;62:25-31. doi:10.1016/j.nut.2018.11.028 
73.   Bradley LE, Smith-Mason CE, Corsica JA, Kelly MC, Hood MM. Remotely delivered interventions for obesity 
treatment. Curr Obes Rep. 2019;8(4):354-362. doi:10.1007/s13679-019-00356-y 
74.   Hood KM, Marr C, Kirk-Sorrow J, et al. Validity and reliability of a Wi-Fi smart scale to estimate body 
composition. Health Technol (Berl). 2019;9(5):839-846. doi:10.1007/s12553-019-00359-8 
75.   Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: Evidence for a phenotype. J Gerontol A Biol Sci Med 
Sci. 2001;56(3):M146-56. doi:10.1093/gerona/56.3.m146 
76.   Paffenbarger RS, Hyde RT, Wing AL, Lee IM, Jung DL, Kampert JB. The association of changes in physical-activity 
level and other lifestyle characteristics with mortality among men. N Engl J Med. 1993;328(8):538-545. 

Page 34 of 37 
 
doi:10.1056/NEJM199302253280804 
77.   Guenther PM, Kirkpatrick SI, Reedy J, et al. The Healthy Eating Index-2010 is a valid and reliable measure of diet 
quality according to the 2010 Dietary Guidelines for Americans. J Nutr. 2014;144(3):399-407. 
doi:10.3945/jn.113.183079 
78.   Healthy Eating Index (HEI) | USDA-FNS. Accessed December 8, 2019. 
https://www.fns.usda.gov/resource/healthy-eating-index-hei 
79.   Roth ME, Parsons SK, Ganz PA, et al. Inclusion of a core patient-reported outcomes battery in adolescent and 
young adult cancer clinical trials. J Natl Cancer Inst. Published online October 21, 2022. doi:10.1093/jnci/djac166 
80.   Williams GC, Freedman ZR, Deci EL. Supporting autonomy to motivate patients with diabetes for glucose control. 
Diabetes Care. 1998;21(10):1644-1651. doi:10.2337/diacare.21.10.1644 
81.   Schwarzer R, Renner B. Health-specific self-efficacy scales. Freie Universität Berlin. 2009;14. 
82.   Levesque CS, Williams GC, Elliot D, Pickering MA, Bodenhamer B, Finley PJ. Validating the theoretical structure 
of the Treatment Self-Regulation Questionnaire (TSRQ) across three different health behaviors. Health Educ Res. 
2007;22(5):691-702. doi:10.1093/her/cyl148 
83.   Smit ES, Dima AL, Immerzeel SAM, van den Putte B, Williams GC. The virtual care climate questionnaire: 
development and validation of a questionnaire measuring perceived support for autonomy in a virtual care 
setting. J Med Internet Res. 2017;19(5):e155. doi:10.2196/jmir.6714 
 

Page 35 of 37 
 
APPENDIX 
 
Table 1. Experimental conditions in the 24 factorial trial 
 
Core 
Intervention 
Components 
(ALL) 
Experimental 
Condition 
Nutrition 
Tracking 
(Green/Red 
Foods) 
Nutrition 
Goals 
(Yes/No) 
Text 
Messages 
(Yes/No) 
Lessons 
(Once/Weekly) 
 
Activity Tracker 
Traffic Light 
Log 
Weekly MVPA 
Goals 
Weekly 
Feedback 
1 
Green 
Yes 
Yes 
Once 
2 
Green 
Yes 
Yes 
Weekly 
3 
Green 
Yes 
No 
Once 
4 
Green 
Yes 
No 
Weekly 
5 
Green 
No 
Yes 
Once 
6 
Green 
No 
Yes 
Weekly 
7 
Green 
No 
No 
Once 
8 
Green 
No 
No 
Weekly 
9 
Red 
Yes 
Yes 
Once 
10 
Red 
Yes 
Yes 
Weekly 
11 
Red 
Yes 
No 
Once 
12 
Red 
Yes 
No 
Weekly 
13 
Red 
No 
Yes 
Once 
14 
Red 
No 
Yes 
Weekly 
15 
Red 
No 
No 
Once 
16 
Red 
No 
No 
Weekly 
 
 
 
Deborah Tate, PhD 
Principal Investigator 

Page 36 of 37 
 
PHYSICIAN CONSENT TO PARTICIPATE IN A DIET AND EXERCISE 
PROGRAM AT UNC-CHAPEL HILL 
 
 
TO: 
 
 
 
 
 
 
 
 
 
 
 
 
Telephone Number 
 
Your patient ______________________________ has asked to participate in a lifestyle program at the University 
of North Carolina at Chapel Hill specifically designed for young adult cancer survivors to help them adopt 
healthier eating and activity habits.  
 
The 3-month program is delivered using a study smartphone app and digital health tools. All participants will 
receive a standard program using the study smartphone app, which will have weekly lessons and feedback on 
progress.  Each participant will receive a combination of tools to help them keep track of their diet and meet 
healthy eating and physical activity goals. The combination of tools will vary the method of dietary 
monitoring, physical activity goals, and personalization of study messages in the app for each study 
participant. 
 
 All participants groups will receive the following intervention: 
1. A lifestyle program that focuses on physical activity and nutrition behavioral change, skills training, goal 
setting, and weekly feedback. 
2. The physical activity program that will be primarily home-based.  The exercise will gradually be progressed to 
150 minutes per week with activities akin to brisk walking.   
3. Behavioral modification techniques for changing eating and exercise behaviors. 
4. Digital tools including a smartphone app and Fitbit activity tracker. 
 
Please indicate below if this program seems appropriate for your patient or if you see any contraindications 
for his/her participation (please check the appropriate box below). 
 
I know of no contraindications to this patient participating in any of the above components of the 
program. 
 
I feel that this program would not be appropriate for this patient for the following reason(s): 
 
_____________________________________________________________________________________ 
_____________________________________________________________________________________ 
 
 
Signature of Physician 
 
Physician’s Name 
 RETURN TO: 
Karen Hatley, MPH, RD 
UNC Weight Research Program 
Lineberger Comprehensive Cancer Center 
1700 Martin Luther King Jr. Blvd., CB 7294 
Address 
Chapel Hill, NC 27514 
Telephone: (919) 966-5853 
City 
State 
Zip 
FAX: 
(919) 843-6663 
Email: 
agilestudy@unc.edu 
( 
) 

Page 37 of 37 
 
 
Inclusion Criteria: 
1. Age 18-39 
2. Diagnosed with invasive cancer malignancy between the ages of 15-39 years 
3. Diagnosed with invasive malignancy in the last 10 years, with no evidence of progressive disease or 
second primary cancers 
4. Completed active cancer-directed therapy (cytotoxic chemotherapy, radiation therapy and/or definitive 
surgical intervention), except may be receiving “maintenance” therapy to prevent recurrences 
5. No pre-existing medical conditions(s) that preclude adherence to an unsupervised exercise program 
including cardiovascular disease, congestive heart failure, pulmonary conditions, renal disease, and severe 
orthopedic conditions 
6. English-speaking and writing 
7. Own a smartphone with a data and texting plan 
8. Achieve less than 150 minutes/week of moderate-to-vigorous intensity activity and/or consume less than 5 
servings of fruits and vegetables per day 
9. Willing to be randomized to any levels of the factors 
 
Exclusion Criteria: 
1.  History of heart attack or stroke within past 6 months 
2. Untreated hypertension, hyperlipidemia, or diabetes, unless permission is provided by their health care 
provider. 
3. Health problems which preclude ability to walk for physical activity. 
4. Report a diagnosis of psychiatric diseases (schizophrenia, bipolar disorder, depression leading to 
hospitalization in the past year), drug or alcohol dependency. These conditions may potentially limit 
intervention adherence. 
5. Report a past diagnosis of or treatment for a DSM-IV-TR eating disorder (anorexia nervosa or bulimia 
nervosa). 
6. Plans for major surgery (e.g., breast reconstruction) during the study time frame. 
7. Current participation in another PA or weight control program 
8. Currently using prescription weight loss medications 
9. Currently pregnant, pregnant within the past 6 months, or planning to become pregnant within the next 6 
months. 
 
No exclusion criteria shall be based on race, ethnicity, or gender.