An Integrated Closed-loop Feedback System for Pediatric Cardiometabolic Disease (STRIVE)

August 31, 2017 updated by: Nicolas M. Oreskovic, MD, MPH, Massachusetts General Hospital
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.

Study Overview

Status

Unknown

Conditions

Intervention / Treatment

Detailed Description

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.

Study Type

Interventional

Enrollment (Anticipated)

68

Phase

  • Early Phase 1

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

6 years to 12 years (Child)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Prevention
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Single

Arms and Interventions

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

What is the study measuring?

Primary Outcome Measures

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

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Health Behaviors Index, Child and Adult
Time Frame: 6 months
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
Time Frame: 6 months
mean change in BMI z-score
6 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Massachusetts General Hospital

Collaborators

Massachusetts Institute of Technology

Investigators

  • Principal Investigator: Nicolas M Oreskovic, MD, MPH, Massachusetts General Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Anticipated)

October 1, 2017

Primary Completion (Anticipated)

October 1, 2018

Study Completion (Anticipated)

October 1, 2018

Study Registration Dates

First Submitted

January 12, 2016

First Submitted That Met QC Criteria

January 14, 2016

First Posted (Estimate)

January 20, 2016

Study Record Updates

Last Update Posted (Actual)

September 1, 2017

Last Update Submitted That Met QC Criteria

August 31, 2017

Last Verified

August 1, 2017

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • R21HS024001 (U.S. AHRQ Grant/Contract)

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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