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

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT02659163
Recruitment Status : Unknown
Verified August 2017 by Nicolas M. Oreskovic, MD, MPH, Massachusetts General Hospital.
Recruitment status was:  Not yet recruiting
First Posted : January 20, 2016
Last Update Posted : September 1, 2017
Sponsor:
Collaborator:
Massachusetts Institute of Technology
Information provided by (Responsible Party):
Nicolas M. Oreskovic, MD, MPH, Massachusetts General Hospital

Brief Summary:
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.

Condition or disease Intervention/treatment Phase
Obesity Health Behavior Behavioral: mHealth wristband Behavioral: mHealth scale Behavioral: EMA Behavioral: mHealth app Behavioral: Health Behavior Feedback Behavioral: Integrated closed-loop feedback system Early Phase 1

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.

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 68 participants
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Single (Participant)
Primary Purpose: Prevention
Official Title: An Integrated Closed-loop Feedback System for Pediatric Cardiometabolic Disease
Estimated Study Start Date : October 2017
Estimated Primary Completion Date : October 2018
Estimated Study Completion Date : October 2018

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 Name: sensors

Behavioral: mHealth scale
A wireless scale used by participants to measure and record daily weight.
Other Name: wireless sscale

Behavioral: EMA
Self-reported information on sugar sweetened beverage consumption collected via mobile messaging
Other Name: 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 Name: sensors

Behavioral: mHealth scale
A wireless scale used by participants to measure and record daily weight.
Other Name: wireless sscale

Behavioral: EMA
Self-reported information on sugar sweetened beverage consumption collected via mobile messaging
Other Name: 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




Primary Outcome Measures :
  1. BMI, Child [ Time Frame: 6 months ]
    mean change in BMI z-score


Secondary Outcome Measures :
  1. 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.

  2. BMI, Adult [ Time Frame: 6 months ]
    mean change in BMI z-score



Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


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Ages Eligible for Study:   6 Years to 12 Years   (Child)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   Yes
Criteria

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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT02659163


Contacts
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Contact: Nicolas M Oreskovic, MD, MPH 617.726.0593 noreskovic@mgh.harvard.edu
Contact: John D Knutsen, PhD 617.726.6721 jknutsen@mgh.harvard.edu

Sponsors and Collaborators
Massachusetts General Hospital
Massachusetts Institute of Technology
Investigators
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Principal Investigator: Nicolas M Oreskovic, MD, MPH Massachusetts General Hospital
Additional Information:
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Responsible Party: Nicolas M. Oreskovic, MD, MPH, Assistant Professor of Pediatrics, Massachusetts General Hospital
ClinicalTrials.gov Identifier: NCT02659163    
Other Study ID Numbers: R21HS024001 ( U.S. AHRQ Grant/Contract )
First Posted: January 20, 2016    Key Record Dates
Last Update Posted: September 1, 2017
Last Verified: August 2017

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Nicolas M. Oreskovic, MD, MPH, Massachusetts General Hospital:
cardiometabolic risk
obesity
mHealth
health behaviors
Additional relevant MeSH terms:
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Obesity
Overnutrition
Nutrition Disorders
Overweight
Body Weight