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. 2019 Nov 15;7(11):e14458.
doi: 10.2196/14458.

Impact of a Mobile App-Based Health Coaching and Behavior Change Program on Participant Engagement and Weight Status of Overweight and Obese Children: Retrospective Cohort Study

Affiliations

Impact of a Mobile App-Based Health Coaching and Behavior Change Program on Participant Engagement and Weight Status of Overweight and Obese Children: Retrospective Cohort Study

Victor Cueto et al. JMIR Mhealth Uhealth. .

Abstract

Background: Effective treatment of obesity in children and adolescents traditionally requires frequent in-person contact, and it is often limited by low participant engagement. Mobile health tools may offer alternative models that enhance participant engagement.

Objective: The aim of this study was to assess child engagement over time, with a mobile app-based health coaching and behavior change program for weight management, and to examine the association between engagement and change in weight status.

Methods: This was a retrospective cohort study of user data from Kurbo, a commercial program that provides weekly individual coaching via video chat and supports self-monitoring of health behaviors through a mobile app. Study participants included users of Kurbo between March 2015 and March 2017, who were 5 to 18 years old and who were overweight or obese (body mass index; BMI ≥ 85th percentile or ≥ 95th percentile) at baseline. The primary outcome, engagement, was defined as the total number of health coaching sessions received. The secondary outcome was change in weight status, defined as the change in BMI as a percentage of the 95th percentile (%BMIp95). Analyses of outcome measures were compared across three initial commitment period groups: 4 weeks, 12 to 16 weeks, or 24 weeks. Multivariable linear regression models were constructed to adjust outcomes for the independent variables of sex, age group (5-11 years, 12-14 years, and 15-18 years), and commitment period. A sensitivity analysis was conducted, excluding a subset of participants involuntarily assigned to the 12- to 16-week commitment period by an employer or health plan.

Results: A total of 1120 participants were included in analyses. At baseline, participants had a mean age of 12 years (SD 2.5), mean BMI percentile of 96.6 (SD 3.1), mean %BMIp95 of 114.5 (SD 16.5), and they were predominantly female 68.04% (762/1120). Participant distribution across commitment periods was 26.07% (292/1120) for 4 weeks, 61.61% (690/1120) for 12-16 weeks, and 12.32% (138/1120) for 24 weeks. The median coaching sessions (interquartile range) received were 8 (3-16) for the 4-week group, 9 (5-12) for the 12- to 16-week group, and 19 (11-25) for the 24-week group (P<.001). Adjusted for sex and age group, participants in the 4- and 12-week groups participated in -8.03 (95% CI -10.19 to -5.87) and -9.34 (95% CI -11.31 to -7.39) fewer coaching sessions, compared with those in the 24-week group (P<.001). Adjusted for commitment period, sex, and age group, the overall mean change in %BMIp95 was -0.21 (95% CI -0.25 to -0.17) per additional coaching session (P<.001).

Conclusions: Among overweight and obese children using a mobile app-based health coaching and behavior change program, increased engagement was associated with longer voluntary commitment periods, and increased number of coaching sessions was associated with decreased weight status.

Keywords: behavior change; child obesity; health behavior; health coaching; mHealth; mobile apps; self-monitoring.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Mobile app platform.
Figure 2
Figure 2
Cohort flow diagram.

Comment in

References

    1. Ogden CL, Carroll MD, Lawman HG, Fryar CD, Kruszon-Moran D, Kit BK, Flegal KM. Trends in obesity prevalence among children and adolescents in the United States, 1988-1994 through 2013-2014. J Am Med Assoc. 2016 Jun 7;315(21):2292–9. doi: 10.1001/jama.2016.6361. http://europepmc.org/abstract/MED/27272581 - DOI - PMC - PubMed
    1. Skinner AC, Ravanbakht SN, Skelton JA, Perrin EM, Armstrong SC. Prevalence of obesity and severe obesity in US children, 1999-2016. Pediatrics. 2018 Sep;141(3):e20173459. doi: 10.1542/peds.2018-1916. http://pediatrics.aappublications.org/cgi/pmidlookup?view=long&pmid=3017... - DOI - PMC - PubMed
    1. Ajala O, Mold F, Boughton C, Cooke D, Whyte M. Childhood predictors of cardiovascular disease in adulthood. A systematic review and meta-analysis. Obes Rev. 2017 Sep;18(9):1061–70. doi: 10.1111/obr.12561. - DOI - PubMed
    1. Kelly AS, Barlow SE, Rao G, Inge TH, Hayman LL, Steinberger J, Urbina EM, Ewing LJ, Daniels SR, American Heart Association Atherosclerosis‚ Hypertension‚ Obesity in the Young Committee of the Council on Cardiovascular Disease in the Young‚ Council on Nutrition‚ Physical Activity and Metabolism‚Council on Clinical Cardiology Severe obesity in children and adolescents: identification, associated health risks, and treatment approaches: a scientific statement from the American Heart Association. Circulation. 2013 Oct 8;128(15):1689–712. doi: 10.1161/CIR.0b013e3182a5cfb3. - DOI - PubMed
    1. American Academy of Pediatrics: Institute for Health Childhood Weight. 2015. [2019-07-12]. Algorithm for the Assessment and Management of Childhood Obesity in Patients 2 Years and Older https://ihcw.aap.org/Documents/Assessment%20%20and%20Management%20of%20C....

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