Literature DB >> 27417980

Design and rationale of the STRIVE trial to improve cardiometabolic health among children and families.

Nicolas M Oreskovic1, Richard Fletcher2, Mona Sharifi3, John D Knutsen4, Ani Chilingirian5, Elsie M Taveras6.   

Abstract

BACKGROUND: Many of the health behaviors known to contribute to cardiometabolic risk and disease (CMRD), including physical activity, diet, sleep, and screen time, begin during childhood. Given the population-wide burden of CMRD, novel ways of assessing risk and providing feedback to support behavior change are needed.
PURPOSE: This paper describes the design and rationale for the Study for using Technology to Reach Individual Excellence (STRIVE), a randomized controlled trial testing the use of an integrated, closed-loop feedback system that incorporates longitudinal, patient-generated, mobile health technology (mHealth) data on health behaviors and provides clinical recommendations to help manage CMRD among at-risk families.
METHODS: STRIVE is a 6-month trial among 68 children, ages 6-12year olds with a body mass index≥85th percentile from Massachusetts with at least one parent with CMRD. Data on several health behaviors will be collected daily over 6months. Children and parents will each wear wristbands that collect objective physical activity, sleep, and screen time data via accelerometry, noise, and infrared detection. Sugar sweetened beverage consumption will be assessed by self-report via a smartphone application. Weight will be collected using a wireless scale. Intervention group parents receive feedback on their child's health behaviors and personalized CMRD counseling via mobile messaging. Control parents receive standard of care recommendations and weekly health behavior reports for self-guided care.
CONCLUSION: The STRIVE trial will test the use of mHealth and closed-loop feedback systems to improve health behaviors among families at-risk for or with established CMRD.
Copyright © 2016 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Cardiometabolic; Childhood; Health behaviors; Obesity; Technology; mHealth

Mesh:

Year:  2016        PMID: 27417980      PMCID: PMC4969164          DOI: 10.1016/j.cct.2016.07.012

Source DB:  PubMed          Journal:  Contemp Clin Trials        ISSN: 1551-7144            Impact factor:   2.226


  25 in total

1.  Pediatric metabolic syndrome predicts adulthood metabolic syndrome, subclinical atherosclerosis, and type 2 diabetes mellitus but is no better than body mass index alone: the Bogalusa Heart Study and the Cardiovascular Risk in Young Finns Study.

Authors:  Costan G Magnussen; Juha Koskinen; Wei Chen; Russell Thomson; Michael D Schmidt; Sathanur R Srinivasan; Mika Kivimäki; Noora Mattsson; Mika Kähönen; Tomi Laitinen; Leena Taittonen; Tapani Rönnemaa; Jorma S A Viikari; Gerald S Berenson; Markus Juonala; Olli T Raitakari
Journal:  Circulation       Date:  2010-10-04       Impact factor: 29.690

2.  Parental recall of doctor communication of weight status: national trends from 1999 through 2008.

Authors:  Eliana M Perrin; Asheley Cockrell Skinner; Michael J Steiner
Journal:  Arch Pediatr Adolesc Med       Date:  2011-12-05

3.  Feasibility and acceptability of a 1-page tool to help physicians assess and discuss obesity with parents of preschoolers.

Authors:  Susan J Woolford; Sarah J Clark; Sana Ahmed; Matthew M Davis
Journal:  Clin Pediatr (Phila)       Date:  2009-05-29       Impact factor: 1.168

4.  Delivering "Just-In-Time" Smoking Cessation Support Via Mobile Phones: Current Knowledge and Future Directions.

Authors:  Felix Naughton
Journal:  Nicotine Tob Res       Date:  2016-05-28       Impact factor: 4.244

5.  Comparative effectiveness of childhood obesity interventions in pediatric primary care: a cluster-randomized clinical trial.

Authors:  Elsie M Taveras; Richard Marshall; Ken P Kleinman; Matthew W Gillman; Karen Hacker; Christine M Horan; Renata L Smith; Sarah Price; Mona Sharifi; Sheryl L Rifas-Shiman; Steven R Simon
Journal:  JAMA Pediatr       Date:  2015-06       Impact factor: 16.193

6.  Texting your way to healthier eating? Effects of participating in a feedback intervention using text messaging on adolescents' fruit and vegetable intake.

Authors:  Susanne Pedersen; Alice Grønhøj; John Thøgersen
Journal:  Health Educ Res       Date:  2016-02-05

7.  Use of a web-based risk appraisal tool for assessing family history and lifestyle factors in primary care.

Authors:  Heather J Baer; Louise I Schneider; Graham A Colditz; Hank Dart; Analisa Andry; Deborah H Williams; E John Orav; Jennifer S Haas; George Getty; Elizabeth Whittemore; David W Bates
Journal:  J Gen Intern Med       Date:  2013-01-31       Impact factor: 5.128

Review 8.  Early childhood health promotion and its life course health consequences.

Authors:  Bernard Guyer; Sai Ma; Holly Grason; Kevin D Frick; Deborah F Perry; Alyssa Sharkey; Jennifer McIntosh
Journal:  Acad Pediatr       Date:  2009 May-Jun       Impact factor: 3.107

9.  Automated Behavioral Text Messaging and Face-to-Face Intervention for Parents of Overweight or Obese Preschool Children: Results From a Pilot Study.

Authors:  Lisa Militello; Bernadette Mazurek Melnyk; Eric B Hekler; Leigh Small; Diana Jacobson
Journal:  JMIR Mhealth Uhealth       Date:  2016-03-14       Impact factor: 4.773

10.  Estimated time spent on preventive services by primary care physicians.

Authors:  Kathryn I Pollak; Katrina M Krause; Kimberly S H Yarnall; Margaret Gradison; J Lloyd Michener; Truls Østbye
Journal:  BMC Health Serv Res       Date:  2008-12-01       Impact factor: 2.655

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