An Integrated Closed-loop Feedback System for Pediatric Cardiometabolic Disease

Study Overview
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Brief Summary
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The high prevalence and burden of cardiometabolic disease underlie the urgent need to identify novel approaches to managing and preventing cardiometabolic disease and risk. This project will test an innovative use of mobile health technology to implement a closed-loop feedback system that collects objective patient-generated data and provides clinical recommendations to modify contributing health behaviors. In addition to improving care for cardiometabolic disease, the tools and methods developed by this study for collecting patient data and providing clinical feedback could also easily be adapted and applied to a range of other health conditions, and are thus highly relevant to public health.

Detailed Description
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Cardiometabolic disease - a clustering of medical conditions and risk factors which includes obesity, diabetes, impaired liver function, and an increased risk in children for adult-onset cardiovascular disease - represents a major population-wide health burden in the United States. Management of cardiometabolic disease also imposes a substantial financial burden on the economy and ties up significant healthcare resources. It is well-known that many of the lifestyle and health behaviors that contribute to cardiometabolic disease are difficult to modify once established, and childhood represents an opportune time for promoting healthy behaviors. Patient-centered outcomes research (PCOR) has identified certain health behaviors as important and actionable in modifying cardiometabolic risk, namely weight management, physical activity, screen-time, sleep, and consumption of sugar-sweetened beverages. Mobile health technology (mHealth) could be used to monitor and counsel on common health behaviors associated with cardiometabolic risk, which may facilitate the inclusion of PCOR evidence on cardiometabolic disease into clinical practice. The overall goal of this research is to use mHealth technology to accelerate the uptake of PCOR findings on treatment of cardiometabolic disease. To achieve our goal, this study will develop a novel set of mHealth tools capable of collecting health behavior information and determine to what extent providing clinical feedback on these health behaviors improves obesity and health behaviors among children ages 6-12 year and their families. In this study we will develop, implement, and test the comparative clinical effectiveness of a closed-loop feedback system for collecting patient data and providing recommendations. The specific aims of this study are: 1) to develop an integrated closed-loop feedback system that incorporates longitudinal mHealth data in managing cardiometabolic disease among at-risk families, and 2) to determine the extent to which an integrated closed-loop system that provides feedback on objective patient-generated data improves cardiometabolic risk, as measured by changes in body mass index and health behaviors including, physical activity, screen-time, sleep, and sugar-sweetened beverage consumption. This research will develop novel mHealth tools and approaches that will allow healthcare providers and patients to better understand disease risk and improve disease management by collecting patient data 1) repeatedly over time, 2) simultaneously, and 3) objectively. This study is innovative because it will use mHealth tools to simultaneously collect longitudinal data on multiple health behaviors known to be associated with cardiometabolic risk, and it will offer a new approach to implementing and disseminating PCOR findings via a novel closed-loop feedback system. The high prevalence of cardiometabolic disease makes this innovative closed-loop system very relevant to public health. The mHealth tools and methods developed by this study for collecting patient data and providing clinical feedback could also easily be adapted and applied to a range of other health conditions.

Official Title
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An Integrated Closed-loop Feedback System for Pediatric Cardiometabolic Disease

Conditions
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Obesity, Health Behavior

Intervention / Treatment
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* Behavioral: mHealth wristband
* Behavioral: mHealth scale
* Behavioral: EMA
* Behavioral: mHealth app
* Behavioral: Health Behavior Feedback
* Behavioral: Integrated closed-loop feedback system


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: ages 6-12 years body mass index categorized as overweight or obese followed for obesity care an adult household family member with one or more elevated cardiometabolic risk, as defined by established or documented increased risk of cardiometabolic disease (overweight, obesity, hypertension, coronary artery disease, diabetes or glucose intolerance, dyslipidemia, non-alcoholic fatty liver disease, cerebrovascular disease) participating parent must own Android Smartphone Wi-Fi access at home speak and read English Exclusion Criteria: n/a

Ages Eligible for Study
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Minimum Age: 6 Years
Maximum Age: 12 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Prevention
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: intervention<br>Intervention subjects will receive feedback on their health behaviors along with clinical recommendations. | Behavioral: mHealth wristband<br>* A wristband containing several sensors worn by participants to collect daily objective patient-generated health behavior data on physical activity, sleep, and screen time<br>* Other names: sensors;Behavioral: mHealth scale<br>* A wireless scale used by participants to measure and record daily weight.<br>* Other names: wireless sscale;Behavioral: EMA<br>* Self-reported information on sugar sweetened beverage consumption collected via mobile messaging<br>* Other names: Sugar Sweetened Beverage Assessment;Behavioral: mHealth app<br>* A mobile application that houses study data and provides two-way messaging between the study team and study participants.<br>Behavioral: Integrated closed-loop feedback system<br>* Daily feedback and weekly e-report cards on patient-generated longitudinal health behaviors along with clinical recommendations via mobile messaging<br>|
| Active Comparator: control<br>Control subjects will receive feedback on their health behaviors for self-guided care. | Behavioral: mHealth wristband<br>* A wristband containing several sensors worn by participants to collect daily objective patient-generated health behavior data on physical activity, sleep, and screen time<br>* Other names: sensors;Behavioral: mHealth scale<br>* A wireless scale used by participants to measure and record daily weight.<br>* Other names: wireless sscale;Behavioral: EMA<br>* Self-reported information on sugar sweetened beverage consumption collected via mobile messaging<br>* Other names: Sugar Sweetened Beverage Assessment;Behavioral: mHealth app<br>* A mobile application that houses study data and provides two-way messaging between the study team and study participants.<br>Behavioral: Health Behavior Feedback<br>* Provide feedback on patient-generated health behaviors data, along with standard of care recommendations, for self-guided<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| BMI, Child | mean change in BMI z-score | 6 months | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Health Behaviors Index, Child and Adult | Cardiometabolic risk will be reported as an index score, a continuous variable calculated as the sum of Z-scores of mean daily moderate-to-vigorous physical activity (minutes), mean daily sleep (minutes), mean daily screen time (minutes), and mean weekly sugar sweetened beverage intake. | 6 months | 
| BMI, Adult | mean change in BMI z-score | 6 months | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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cardiometabolic risk, obesity, mHealth, health behaviors