CommitFit App to Facilitate Health Behavior Change in Clinic Adolescents
Phase: NA
Conditions: Overweight Adolescents
Sponsor: University of Missouri-Columbia

Study Protocol and Statistical Analysis Plan 
 
NCT ID not yet assigned 
 
Document Date: 4/25/25 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
SOCIAL/BEHAVIORAL/EDUCATIONAL RESEARCH PROTOCOL  
UNIVERSITY OF MISSOURI 
 
Project Title: CommitFit App to Facilitate Health Behavior Change in Clinic Adolescents 
IRB Number:  2092610 
Version Number: 13 
Version Date: 4/25/25 
Replacing File ID: 657592 
Principal Investigator: Amy Braddock 
Funding Source: University of Missouri, School of Medicine, Coulter 
 
 
 
 
Adolescent obesity continues to be a significant public health problem in the United States 
with 35% of children ages 12-19 years having overweight or obesity in 2012.1 During the 
COVID-19 pandemic, child obesity is expected to increase by 2.4%.2 Because adolescents with 
obesity are more likely to have obesity as adults,3 they are at increased risk of developing 
obesity-related diseases such as heart disease, type 2 diabetes, hypertension, and cancer.4  
Mobile health (mHealth) apps have shown potential to improve health behaviors and improve 
short-term Body Mass Index (BMI) in adolescents. Unfortunately, most mHealth apps lack 
scientific evidence, 6,7 are not grounded in behavior change theory,8,9 and were not developed by 
health care experts.6 Involving health care providers in health behavior change results in better 
and more sustained weightloss10,11 and a combination of mHealth an in-person provider 
counseling is the best means of achieving a healthy weight.12 Current mHealth apps lack 
engagement with or tailored advice from the adolescent’s health care provider, despite provider 
willingness to utilize mHealth for child obesity management.13 Importantly, EHR synced apps 
can also save providers time and improve patient care by reducing the burden of collecting, 
analyzing, and documenting data.14-16  
Building on our expertise in adolescent development and health care patient/provider 
communication, experience with gamification and user-centered design of electronic decision 
support, we have developed the “CommitFit” smartphone/tablet app which uses gamification 
techniques to motivate adolescents to set and achieve their health behavior goals. This current 
project evaluates the use of CommitFit app through a feasibility and pilot study utilizing 
interviews, surveys, and analytic software to measure adolescent frequency of use and opinions 
of the app. 
 
Pilot study – We will recruit 30 adolescents and30 caregivers to beta test CommitFit and 
use software analytics. We will evaluate changes in health behaviors using surveys, and 
measured health metrics. Participants will be recruited from a Primary Care Provider 
I. 
Research Objectives/Background 

approved clinic list. Body Mass Index and Blood Pressure (BP) from participants recruited 
will be collected in clinic (on site) 3 times - at the beginning of the study, at the end of 3rd 
month and at the end of the study. There will be three arms; Control, CommitFit, and 
CommitFit$. (Each group is described in study design). 
 
SA 3.      Pilot study – There will be three groups in the study: 1) Control 2) CommitFit and 3) 
CommitFit$. 30 adolescents and their caregivers (30, one caregiver per adolescent) will 
participate in this study. For participants in the CommitFit and CommitFit$ groups, software 
analytics will be utilized to evaluate how the app was used (frequency of goals set and behaviors 
logged), duration of use, etc., to evaluate app features and gamification techniques. We will 
evaluate changes in health behaviors using online/paper surveys, and measure health metrics 
(height and weight which will be used to calculate Body Mass Index (BMI), and blood pressure) 
(see study design). 
 
Device 
 
 
CommitFit is a smartphone/tablet app which uses gamification techniques to motivate 
adolescents to set and achieve their health behavior goals. CommitFit promotes positive health 
behaviors. It is not a weight loss app and does not log or track weight. 
 
CommitFit is a lifestyle management software app and not a medical device. It does not require 
an IDE.  
 
 
 
Pilot study:   
We will partner with South Providence, to recruit 13-15-year-olds adolescents and their 
caregivers to use and test the CommitFit app.  
 
We will get the names and email addresses of potential participants from a list of adolescent 
patients from MU Family Medicine and Child Health clinics to review with the patient’s Primary 
Care Provider (PCP). PCP will remove any patients they do not want contacted to participate in 
the study. The goal will be to recruit one adolescent along with one of their caregivers to 
participate in the study. We will contact caregivers of (13-15 old) adolescents through email and 
mailed letter with information about the study, screening survey, and consent forms. We will 
obtain consent by having the interested caregiver sign the consent form electronically or signing 
using Docusign/ Redcap, and/or mailing us back for themself and their adolescent. We will also 
have adolescent participants read and understand the assent form, and we will answer any 
questions after explaining the study. We will only enroll adolescent participants after they have 
read the assent forms and caregivers have signed the consent forms.  
 
Randomization: 
Adolescents of 13-15 years old- and their caregivers will be randomly assigned to each group 
(Control, CommitFit and CommitFit$) with a total of 20 adolescents and caregivers per group.  
III. 
Recruitment Process 
II. 
Device 

 
 
 
 
 
For the Aim 3 portion of the study, we will contact caregivers of (13-15 old) adolescents through 
email/phone and mailed letter with information about the study, screening survey, and consent 
forms. We will obtain consent by having the interested caregiver sign the consent form 
electronically or signing using Docusign/ Redcap, and/or mailing us back for themself and their 
adolescent or if the caregiver can bring them to clinic, we will do the consent and screening 
there. We will also have adolescent participants read and understand the assent form, and we will 
answer any questions after explaining the study. We will only enroll adolescent participants after 
they have read the assent forms, and caregivers have signed the consent forms.  
 
 
 
 
Inclusion Criteria:  
For specific aim 3, adolescents will be 13-15 years old recruited from MU South Providence 
Family Medicine and Child Health clinics. 
 
For every adolescent interested in participating in the study, one of their caregivers (18 years and 
above) is also required to participate. Only one caregiver per child will be allowed to participate. 
 
All participants with mild or controlled anxiety and/or depression will be included. 
 
All participants will be required to speak English fluently, read at the 6th grade level or higher, 
and be proficient with smart phone app use to participate. 
 
Exclusion Criteria: We will screen and exclude adolescents and caregivers diagnosed with severe 
mental health disorders (other than mild or controlled anxiety and/or depression), intellectual 
disabilities, or eating disorders via a screening survey. For adolescents, their caregiver will be 
filling out the screening survey.  
 
 
 
 Pilot study – 30 adolescents and 30 caregivers. For every adolescent interested in participating 
in the study, one of their caregivers is also required to participate. The study will be limited to 
one participating adolescent and one of their caregivers. 
 
 
 
 
We will recruit 30 adolescents and 30 caregivers to beta test CommitFit. The study will be 
limited to one participating adolescent and one of their caregivers. There will be three groups in 
the study: 1) Control 2) CommitFit and 3) CommitFit$.  
VII. 
Study Procedures/Study Design 
VI. 
Number of Subjects 
IV. 
Consent Process 
V. 
Inclusion/Exclusion Criteria 

All participants will be required to complete a screening survey at the beginning of the study. 
The eligible participants will be contacted via email/ phone and will give their consent after 
taking the screening survey, and then they will take the baseline demographic survey for their 
adolescents and themselves. 
Our screening form will include1) list of exclusion and inclusion criteria for participants like 
English fluency, reading level, severe mental health disorders (other than mild or controlled 
anxiety and/or depression), intellectual disabilities, or eating disorders.  
Our baseline demographic survey form will include 1) demographic information like name, 
gender, race etc. 2) phone number, mailing address 3) SSN, 4) preferred vendor of eGift card, 
and 5) email address to send the eGift card, at the beginning of the study after they have signed 
the consent form. We’ll use RedCap to capture all the information as an emailed survey.  
After the participants in the CommitFit and CommitFit$ groups download the app, they will be 
shown how to pick a health behavior (fruits/vegetables, water, decreased sugary beverages, 
physical activity, sleep) and then set a goal and a time period to work on their goal. They will 
receive a daily reminder to log their health behaviors. At the end of their goal period, they will be 
rewarded with points for logging behaviors and successfully achieving their goals. Points will be 
used to rank the participant on the leaderboard using the username that they registered with. 
Users will be encouraged not to use or share their real names or any identifying personal 
information. Users can also use points to unlock gear for their avatar in the gear store. It will take 
less than 15 minutes to download and learn CommitFit and less than 1 minute each day to log 
their behavior. Control group participants will receive email handouts with healthy lifestyle tips 
which they could use if they choose to. They will not use the CommitFit app. All participants 
will be compensated through eGift cards. 
Software analytics will be utilized to evaluate how the app was used (frequency of goals set and 
behaviors logged), duration of use, etc., to evaluate app features and gamification techniques. We 
will evaluate changes in health behaviors using online surveys, and measure health metrics 
(height and weight which will be used to calculate Body Mass Index (BMI), and blood pressure) 
in the clinic.  
In the CommitFit and CommitFit$ groups, we will have the adolescents take 7 surveys-GAD7, 
PedQL, Reasons why I think about being healthy, Behavior economics survey, Gamification 
Survey, CommitFit App Survey, and Commercialization Survey for adolescent subject. Each 
clinic visit will last from 30 to 60 minutes. Similarly, we will have the caregivers take 4 surveys 
Gamification Survey, CommitFit App Survey, Behavior economics survey, and 
Commercialization Survey for caregiver subject. In the control group, adolescents will only take 
4 surveys- GAD7, PedQL, Reasons why I think about being healthy, and Behavior economics 
survey. Caregivers in the control group will take only the Behavioral Economic survey. All 
participants will take the surveys at the beginning of the study, after end of 3rd month, and at the 

end of the 4th month (follow-up visit). All surveys will be available in online and paper format 
and will take 15 to 30 minutes to complete per participant. Weight, height and blood pressure 
from all adolescents and their caregivers will also be collected at the clinical site. If your 
adolescent chooses to stop participating in the study before its completion, you can still remain 
enrolled in the study, and you will take surveys and clinic visits at the end of 3rd and 4th months. 
All participants will complete the 5-10 minute paper exit survey at their final 4-month clinic 
visit. 
We will use logic and branching reasoning in Redcap that would allow participants to skip 
questions. Participants can skip questions that they do not want to answer and can submit without 
getting an error message if a question is left blank. Questions will include demographics, 
environmental factors that could influence health behaviors, and technology and mHealth app 
use. We will be measuring changes in health behaviors (physical activity, water, sugary 
beverages, fruits/vegetables, and sleep) by analyzing data logged into the app. 
There will be three groups (1) Control (2) CommitFit group using the app where the intervention 
will not include extra monetary incentives and (3) CommitFit$ group using the app which will be 
pushed with extra monetary incentives. 30 adolescents will be randomly assigned to three groups 
with each group having 10 adolescents. Their respective caregivers will also join the same 
groups as their adolescents. Participants will download the app on their iPhone or iPad and if the 
participant doesn’t have either of them, an iPad will be temporarily provided for the time 
participants are in the study which will be returned to the researchers after the study, we will use 
a spreadsheet to capture all the information in one place for each participant. 
The purpose of this study is to evaluate “CommitFit” a smart phone app to help teens develop 
and maintain positive health behaviors. We will be measuring changes in health behaviors 
(physical activity, water, sugary beverages, fruits/vegetables, and sleep) in CommitFit and 
CommitFit$ groups. The app will only be tracking goal progress and nothing else. 
• In the control group (10 adolescents, randomly chosen and their caregivers), we will 
email handouts with healthy lifestyle tips which participants could use if they choose to. 
During this time there will be no CommitFit app use by participants. They will also not 
receive any extra monetary incentives. 
• In the CommitFit group, participants (10 adolescents, randomly chosen, and their 
caregivers) will use regular CommitFit app without extra monetary incentives. 
Researchers will assist participants to download the CommitFit app. Participants will be 
asked to use the app for 3 months, there will be no penalties if they don’t use the app for 
the entire duration or only use it frequently. The adolescents and their caregivers will get 
an app reminder each day to update their health behavior goals once in morning (8 am) 
and once in the evening (5pm) on which the participant will spend approximately 1 min 
in a day.  

• In CommitFit$ group, participants (10 adolescents, randomly chosen and their caregivers) 
will use the CommitFit app just as the CommitFit group for 3 months. In addition to the 
eGift cards, adolescents will receive 5 cents for every point they earn during the study. 
There are a maximum of 1050 possible points that can be earned, so the most they can 
receive is $52.50 in cash at the end of 3rd month of their participation in this CommitFit$ 
group This will be calculated by tracking the points at the beginning and end of each 
month for the CommitFit$ group. Only adolescents will receive these extra monetary 
incentives as this group is set up to simulate adolescent app users receiving incentives 
from their caregivers for adopting healthy lifestyle behaviors in the real world.  
For all three groups (Control, CommitFit and CommitFit$) – participants – adolescents 
and their caregivers will come to the clinic and have their height, weight, and blood 
pressure measured at the beginning of the study, after the third month, and after the 
fourth month (follow-up visit). These will be conducted in a private exam or conference 
room by research staff, and for adolescents their caregivers will also be present.  
 
Training: Participants in the CommitFit and CommitFit$ groups will be assisted in downloading 
the CommitFit app. Researchers will guide them through logging in, show them how to use the 
app including picking their first goal, setting a behavior level, and setting a time period (1-2 
weeks for the first time period), logging their behavior daily, setting notifications, leaderboard, 
avatar, progress report, gear store, and resources.  
 
Monthly visits: Participating adolescents and caregivers will visit the clinic in person during the 
3 visits to provide height, weight, and BPs and fill out online or paper surveys. This will occur at 
the beginning of the study, after the 3rd month, and after the 4th month (follow-up visit). For all 
participants, it will take 15 to 30 minutes to complete the surveys. If we obtain any information 
from the participant, including blood pressure that concerns us, we will work with the clinic 
nurse provider to develop a treatment plan. For adolescents the cutoff to notify the caregivers 
will be: 1) For BP, elevated blood pressure (> 130/>=80 mm Hg)17, 2) For anxiety, a score of 
>=10 on the GAD-7 represents a reasonable cut point for identifying cases of GAD18. 
Follow-up visit: At the end of the fourth month, adolescents and their caregivers will once again 
visit the clinic in person to get their height, weight, Blood Pressure measured, and fill out 
electronic or paper surveys.  
 
 
There are minimal risks expected for participants in this study. We will do everything we can to 
help participants feel comfortable when completing the surveys, and height, weight, and blood 
pressure checks. However, any participant can skip any questions or not participate in any part of 
the study. If any participant feels uncomfortable at any time, we will encourage them to notify 
the research staff and we will inform the caregivers and debrief them and their adolescent 
VIII. Potential Risks 

immediately. If we obtain any information from participants, including elevated blood pressures 
that concerns us, we will work with the patient’s PCP and notify the participant immediately.  
We have programmed CommitFit to set limits on health behavior goals to prevent extreme or 
unhealthy goal setting and behaviors. The CommitFit App will not collect any personal or 
sensitive information from any participant. CommitFit promotes positive health behaviors. It is 
not a weight loss app and will not ask any participant to log or track their weight. 
 
We will be collecting information about all participants from surveys. We will check health 
metrics (blood pressure, height, weight). We are collecting this information to determine if using 
the CommitFit app has any influence on the user’s health. All of this information will be kept 
private to researchers in the study, but there is always a small risk of loss of privacy if there is an 
accidental disclosure of participants’ information, including logged health behaviors and 
information provided in the surveys. We will reduce this risk by removing participants’ names 
and any other identifying information from their results and taking the steps below to protect 
their privacy. 
 
We will be asking the adolescent about their anxiety and the adolescent’s answers may be 
reported to the caregiver and their PCP if needed. Also, if we obtain any information from the 
participant, including elevated blood pressure that concerns us, we will work with the PCP and 
notify the participant/caregiver immediately. The cut off to notify the PCP and 
participant/caregiver will be 1) For BP, elevated blood pressure (> 130/>=80 mm Hg), 2) For 
anxiety, a score of >=10 on the GAD-7 represents a reasonable cut point for identifying cases of 
GAD. 
There is also a small potential risk of harm from accidental breach of personal and health 
information and data. We will remove identifying information from all data before analyzing, 
sharing, or publishing any results. We will keep participants' data and participation status 
confidential and secured on a password protected laptop and server. We will assign a code to 
each participant and keep the code key in a locked office drawer. We will also keep back-up files 
on an USB drive in a locked office drawer. We will report any unanticipated problems or 
deviations to the IRB within 5 days. 
 
 
 
 
There will be no direct benefits to the individual participant, but they may benefit indirectly from 
the health behavior education and behaviors promoted through app use. Society will benefit 
directly and indirectly through new innovative technology that will, if deemed effective, improve 
health behaviors of adolescents, and their caregivers. 
 
 
 
 
We will offer each adolescent and caregiver $50 eGift cards at the time of enrollment, $50 at the 
3 months visit, and $50 at completion of the study only (4-month visit) for a total of $150 e-gift 
cards for each adolescent and caregiver. Caregivers will receive payment via gift card codes 
IX. 
Anticipated Benefits 
X. 
Compensation 

emailed to them for their and their adolescents’ participation. Potential vendors are Walmart, 
Amazon, and Target.10 participating adolescents in the CommitFit$ group will also receive 5 
cents per point up to $52.50 in extra monetary incentive in cash at the 3-month visit. 
 
 
Cost 
 
All cost will be covered by the research study.  
For participants who will receive an iPad for the study, they will be required to return the 
functioning iPad to receive their last $50 eGift card. However, if you have borrowed an iPad for 
this study and you lose or break it than the third payment of $50 will be withheld to offset the 
expense of it.  
 
 
 
 
The Principal Investigator (Dr. Braddock) and research team will be responsible for monitoring 
the safety of this trail, executing the Data and Safety Monitoring plan, and complying with the 
reporting requirements. The Data and Safety Monitoring report will include participants’ socio-
demographic characteristics, expected-versus-actual recruitment rates, any quality assurance, and 
any actions or changes regarding the protocol. All personnel involved with this study will 
complete appropriate IRB training. We will maintain certification of this training.  
 
We will use the Research Electronic Data Capture (REDCap) online database management 
system that the University of Missouri provides. In this secure network, we will store all study 
data, data collection instruments, monitor enrollment, and manage secure storage. If a 
participant's privacy is breached or there is an unexpected adverse outcome, these events will be 
reported to the IRB within five days, once we are made aware of the event. 
 
 
 
 
Element 1 Policy for Data Management and Sharing (DMS Policy): We are committed to 
transparency, collaboration, strengthening analyses through combined datasets, and enhancing 
the value of research and public knowledge. We support the public dissemination and sharing of 
our research results. Furthermore, we recognize that the proposed project may result in novel 
ideas for new methods and data that could benefit the public health community and result in new 
frontiers of discovery, while being mindful that the confidentiality and privacy of participants in 
research must be protected at all times.  
 
A. Types and amount of scientific data expected to be generated in the project: The proposed 
research will include data from approximately 30 dyads (60 total participants, adolescents and 
caregivers). The final dataset will include survey data, analytic data generated from the mHealth 
CommitFit app software, anthropometrics (height, weight, blood pressure, waist circumference), 
lab values (adults only). To protect the identities of participants, all data will be deidentified 
before final storage and sharing.  
 
XIII. Data Safety Monitoring Plan 
XIV. Data Management and Sharing Plan 
XII. 
COST 

B. Scientific data that will be preserved and shared, and the rationale for doing so: All 
quantitative data will be collected via REDCap and stored via the HIPAA protected REDCap 
server.  
 
 
Element 2: Standards: We will adhere to NIH, HIPAA, and IRB standards for data management 
and sharing. In the absence of formal standards for the management, use, and sharing of research 
data or other materials, best practices will be used. Information about research processes, 
including data analysis, will be maintained using formalized, documented procedures. This 
information will be accessible to all members of the research team and will be shared alongside 
the final dataset. 
 
 Element 3: Data Preservation, Access, and Associated Timelines: Timelines for distribution of 
data will vary depending on any required restrictions in accordance with federal and/or 
institutional policies and guidelines. We expect the data will be disseminated through 
publications, presentations at scientific symposiums, and seminars within a year of project 
completion.  
 
 A. Repository where scientific data and metadata will be archived: We will include publications 
resulting from the study/grant and dataset used for the analysis, during and after the grant period, 
in an archived repository. A long-term data sharing and preservation plan will be used to store 
and make publicly accessible, the deidentified data beyond the life of the project. Completed data 
will be deposited into the Data Repository for the University of Missouri (MOspace Institutional 
Repository). MOspace Institutional Repository is the digital institutional repository for the 
University of Missouri System and is a joint initiative of the University Libraries, the Office of 
the Library Systems, and the Division of Information Technology. It is a free service for MU 
students and faculty and provides permanent storage and dissemination for deidentified research 
data.  
 
B. How scientific data will be findable and identifiable: MOspace items will include appropriate 
metadata and are identifiable through a permanent URL. Items will be findable via the MOspace 
web site at https://mospace.umsystem.edu, and searchable via Google and other search engines. 
Data will be disseminated in open formats to the greatest extent possible and will be 
accompanied by robust documentation and metadata to maximize reusability.  
 
C. When and how long the scientific data will be made available: Data and corresponding 
documentation will first be made available after publication of primary outcomes, in accordance 
with journal policies. Materials deposited in MOspace will be made available permanently and 
for future research needs.  
 
Element 4: Access, Distribution, or Reuse Considerations:  
 
A. Factors affecting subsequent access, distribution, or reuse of scientific data: Only IRB-
approved project staff, researchers, or other IRB-approved entities will have access to 
identifiable data. We will enforce strict protocols for protection from unauthorized access to 
physical and electronic data. We will specifically include appropriate language in our informed 

consent documents to request broad sharing of deidentified participant data through MOspace 
and upon request from researchers.  
 
B. Control of access to scientific data: All data will be shared within IRB and HIPAA-approved 
parameters. The PI will monitor and restrict access of identified data to IRB-approved staff. All 
significant contributors to the data will be credited. Deidentified data submitted to MOspace and 
upon request, will be publicly accessible.  
 
Data that are unable to be sufficiently deidentified will not be made available for sharing, to 
protect the identity of the participant. Although the final dataset will have identifiers removed 
prior to release for sharing, because of the small sample size, we believe that there remains the 
possibility of deductive disclosure of subjects with unusual characteristics. Data that cannot be 
sufficiently deidentified will not be shared.  
 
Element 5: Oversight of Data Management and Sharing: Study Principal Investigator, Dr. 
Braddock, will be responsible, before, during, and after the study, for data collection, 
management, storage, and sharing. She will maintain the Data Management and Sharing Plan 
with updates when necessary. At the conclusion of the study, Dr. Braddock will deposit all 
deidentified data and corresponding documentation into MOspace and destroy any identified 
data not required for additional analysis or publications and in compliance with IRB protocols 
 
 
 
Descriptive statistics were used to examine baseline demographics and to examine study 
outcomes over time between study arms. Chi-square analysis was used to compare group 
differences for categorical outcomes and t-test for continuous outcomes for baseline 
demographics and BMI, as well as study outcomes. An a priori value of p<0.05 was set for 
statistical significance.  
 
 
 
 
1. 
Maternal and Child Health Bureau. Child Health USA 2014: Adolescent overweight and 
obesity. U. S. Department of Health and Human Services and Health Resources and 
Services Administration. https://mchb.hrsa.gov/chusa14/health-status-
behaviors/adolescents/adolescent-overweight-obesity.html. Published 2014. 
Accessed2020. 
2. 
An R. Projecting the impact of the coronavirus disease-2019 pandemic on childhood 
obesity in the United States: A microsimulation model. J Sport Health Sci. 
2020;9(4):302-312. DOI:10.1016/j.jshs.2020.05.006. 
3. 
Gordon-Larsen P, The NS, Adair LS. Longitudinal trends in obesity in the United States 
from adolescence to the third decade of life. Obesity (Silver Spring). 2010;18(9):1801-
1804. DOI:10.1038/oby.2009.451. 
4. 
Jensen MD, Ryan DH, Apovian CM, Ard JD, Comuzzie AG, Donato KA, Hu FB, 
Hubbard VS, Jakicic JM, Kushner RF, Loria CM, Millen BE, Nonas CA, Pi-Sunyer FX, 
Stevens J, Stevens VJ, Wadden TA, Wolfe BM, Yanovski SZ. 2013 AHA/ACC/TOS 
XVI. References 
XV. 
Statistical Analysis Plan 

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. J Am Coll Cardiol. 2014;63(25 Pt B):2985-3023. 
DOI:10.1016/j.jacc.2013.11.004. 
5. 
Chen JL, Wilkosz ME. Efficacy of technology-based interventions for obesity prevention 
in adolescents: a systematic review. Adolesc Health Med Ther. 2014;5:159-170. 
DOI:10.2147/AHMT.S39969. 
6. 
Rivera J, McPherson A, Hamilton J, Birken C, Coons M, Iyer S, Agarwal A, Lalloo C, 
Stinson J. Mobile apps for weight management: a scoping review. JMIR Mhealth 
Uhealth. 2016;4(3):e87. DOI:10.2196/mhealth.5115. 
7. 
Wearing JR, Nollen N, Befort C, Davis AM, Agemy CK. iPhone app adherence to 
expert-recommended guidelines for pediatric obesity prevention. Child Obes. 
2014;10(2):132-144. DOI:10.1089/chi.2013.0084. 
8. 
Chen J, Cade JE, Allman-Farinelli M. The Most Popular Smartphone Apps for Weight 
Loss: A Quality Assessment. Journal of Medical Internet Research. 2015;17(12):1-17. 
9. 
Cho YM, Lee S, Islam SMS, Kim SY. Theories Applied to m-Health Interventions for 
Behavior Change in Low- and Middle-Income Countries: A Systematic Review. Telemed 
J E Health. 2018;24(10):727-741. DOI:10.1089/tmj.2017.0249. 
10. Lenoir L, Maillot M, Guilbot A, Ritz P. Primary care weight loss maintenance with 
behavioral nutrition: An observational study. Obesity (Silver Spring). 2015;23(9):1771-
1777. DOI:10.1002/oby.21157. 
12. Vine M, Hargreaves MB, Briefel RR, Orfield C. Expanding the role of primary care in 
the prevention and treatment of childhood obesity: a review of clinic- and community-
based recommendations and interventions. Journal of Obesity. 2013. 
DOI:10.1155/2013/172035. 
13. Harris LM, Mounsey A, Nashelsky J. Clinical Inquiries: Can mobile technology improve 
weight loss in overweight and obese patients? J Fam Pract. 2017;66(2):111-113. 
14. SanGiovanni BC, Morella K, Roberts JR. Primary care providers welcome smartphone 
apps that assist in pediatric weight management. Clin Pediatr (Phila). 2019;58(6):665-
670. DOI:10.1177/0009922819832642. 
15. Kawamoto K, Kukhareva P, Shakib JH, Kramer H, Rodriguez S, Warner PB, Shields D, 
Weir C, Del Fiol G, Taft T, Stipelman CH. Association of an electronic health record 
add-on app for neonatal bilirubin management with physician efficiency and care quality. 
JAMA Netw Open. 2019;2(11):e1915343. DOI:10.1001/jamanetworkopen.2019.15343. 
15. Austin L, Sharp CA, van der Veer SN, Machin M, Humphreys J, Mellor P, McCarthy J, 
Ainsworth J, Sanders C, Dixon WG. Providing 'the bigger picture': benefits and 
feasibility of integrating remote monitoring from smartphones into the electronic health 
record. Rheumatology (Oxford). 2020;59(2):367-378. 
DOI:10.1093/rheumatology/kez207. 
16. Cohen DJ, Keller SR, Hayes GR, Dorr DA, Ash JS, Sittig DF. Integrating patient-
generated health data Into clinical care settings or clinical decision-making: lessons 
learned from Project HealthDesign. JMIR Hum Factors. 2016;3(2):e26. 
DOI:10.2196/humanfactors.5919. 
17. Joseph T. Flynn, MD; David C. Kaelber, MD; Carissa M. Baker-Smith, MD; Douglas 
Blowey, MD; Aaron E. Carroll, MD; Stephen R. Daniels, MD; Sarah D. de Ferranti, MD; 
Janis M. Dionne, MD; Bonita Falkner, MD; Susan K. Flinn, MA; Samuel S. Gidding, 

MD; Celeste Goodwin; Michael G. Leu, MD; Makia E. Powers, MD; Corinna Rea, MD; 
Joshua Samuels, MD; Madeline Simasek, MD; Vidhu V. Thaker, MD; Elaine M. Urbina, 
MD. Clinical Practice Guideline for Screening and Management of High Blood Pressure 
in Children and Adolescents. Pediatrics (2017) 140 (3): e20171904. 
https://doi.org/10.1542/peds.2017-1904 
18. Robert L Spitzer 1, Kurt Kroenke, Janet B W Williams, Bernd Löwe. A brief measure for 
assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006 May 
22;166(10):1092-7. 
19. K Kroenke 1, R L Spitzer, J B Williams. The PHQ-9: validity of a brief depression 
severity measure. Gen Intern Med. 2001 Sep;16(9):606-13. DOI: 10.1046/j.1525-
1497.2001.016009606.x.