Mobile Technology for Obesity Prevention

Abstract

Background Mobile technologies have wide-scale reach and disseminability, but no known studies have examined mobile technologies as a stand-alone tool to improve obesity-related behaviors of at-risk youth. Purpose To test a 12-week mobile technology intervention for use and estimate effect sizes for a fully powered trial. Methods Fifty-one low-income, racial/ethnic minority girls aged 9–14 years were randomized to a mobile technology (n=26) or control (n=25) condition. Both conditions lasted 12 weeks and targeted fruits/vegetables (FV; weeks 1–4), sugar-sweetened beverages (SSB; weeks 5–8), and 2 screen time (weeks 9–12). The mobile intervention prompted real-time goal setting and self-monitoring and provided tips, feedback, and positive reinforcement related to the target behaviors. Controls received the same content in a written manual but no prompting. Outcomes included device utilization and effect sizes estimates of FV, SSB, screen time, and BMI. Data were collected and analyzed in 2011–2012. Results Mobile technology girls used the program on 63% of days and exhibited trends toward increased FVs (+0.88, p=0.08) and decreased SSBs (−0.33, p=0.09). The adjusted difference between groups of 1.0 servings of FV (p=0.13) and 0.35 servings of SSB (p=0.25) indicated small to moderate effects of the intervention (Cohen’s d=0.44 and −0.34, respectively). No differences were observed for screen time or BMI. Conclusions A stand-alone mobile app may produce small to moderate effects for FV and SSB. Given the extensive reach of mobile devices, this pilot study demonstrates the need for larger-scale testing of similar programs to address obesity-related behaviors in high-risk youth.

Efficacy of interventions that use apps to improve diet, physical activity and sedentary behaviour: a systematic review

“…Most studies were randomised controlled trials ( n  = 19) with 2-group [29, 31, 35, 40, 43–46, 48, 50, 52–55, 58] or 3-group [33, 34, 42, 47, 56, 57] study designs. The remaining studies were controlled trials ( n  = 3) [33, 37, 49], randomised trials ( n  = 1) [39] or pre-post studies ( n  = 4) [36, 38, 41].…”

“…± 4,121vs CG: 6,274 ± 2,106, p  < 0.001). Study quality FairCONSORT score: 14.5Percentage of fulfilled criteria: 59.2%Maher et al 2015 [50]Australia Study design 2-group RCT Duration Intervention exposure: 8 weeks (September 2013 - July 2014)Measurement points: baseline, 8 weeks, 20 weeksAttrition rate: 13%SampleAdults N  = 11035.6 years/18–65 years29% (M), 71% (F)Random Behaviour change theory Theory of planned behaviour, fun theory App features Newly designed app:Facebook app (Active Team) including goal setting (10,000 steps/day), self-monitoring of physical activity (calendar to log daily steps), performance feedback via tally board to monitor individual and teammates’ progress; team message board to allow team members to communicate with one another; gamification in the form of awards for individual and team step-logging and step-count achievement, as well as sending virtual gifts to teammates; peer social support through Facebook friends (Active Teams) Intervention group Used the app, automated computer-tailored emails to summarise progress and encourage continued participation, use of pedometer to encourage achieve 10,000 steps/day Control group Wait-list control Multi-component versus stand-alone app intervention Multi-component Outcome Physical activity (moderate, vigorous, walking; minutes/week) Other relevant outcomes Quality of life Measures Questionnaires Physical activity 8-week follow-up:Significant between-group increase in mean weekly minutes of overall PA in IG (528 ± 391 vs CG: 391 ± 371, effect size: 0.39, 95% CI:0.01–0.76) and walking (332 ± 289 vs CG: 160 ± 185, effect size: 0.69, 95% CI: 0.30–1.07)20-week follow-up:Physical activity remained higher compared to baseline, and higher in IG compared to CG. But within-group and between-group differences were not significant. Quality of life No significant changes in quality of life at 8-week and 20-week follow-ups. Study quality HighCONSORT score: 19Percentage of fulfilled criteria: 77.6%Mummah et al 2016 [45]USA Study design 2-group RCT Duration Intervention exposure: 12 weeksMeasurement points: baseline, 12 weeksAttrition rate: 24% Sample Adults N  = 1742.05 years/18–50 years35% (M), 65% (F)Random Behaviour change theory Behavioural theory App features Newly designed app:Goal setting for and self-monitoring of vegetable consumption (i.e., vegetable logging by tapping on different vegetable icons and recording the number of servings consumed); performance feedback via graphs, social comparison with friends via leaderboard, consumption challenges delivered via push notifications, prompts to log vegetables via push notifications Intervention group Used the app Control group Wait-list control Multi-component versus stand-alone app intervention Stand-alone Outcome Diet (daily vegetable consumption/servings) Measures Questionnaires (Food Frequency Questionnaire) Diet Significant between-group increase in vegetable consumption in intervention group compared to control group (adjusted mean difference: 7.4 servings; 95% CI: 1.4–13.5; p  = 0.02) Study quality HighCONSORT score: 17.5Percentage of fulfilled criteria: 72.9%Nollen et al 2014 [40]USA Study design 2-group RCT Duration Intervention exposure: 12 weeks (wee...…”

A Systematic Review to Assess Sugar-Sweetened Beverage Interventions for Children and Adolescents across the Socioecological Model

“…Nearly three-quarters of the interventions and policies (39; 71%) were implemented in schools or multiple settings including schools 46, 49–55, 57–64, 70–72, 76, 80, 86–102, 104–108, 110–111, 114–133 . Additional settings included home 66–67,109,112–113 (4; 7%), clinic 47–48,56,65,68–69,73–75,77–79 (7; 13%), and community-based organizations 81–85,103 (5; 9%). Twenty of 55 (36%) studies identified SSB as their sole focus 56, 62–64, 70–72, 84–85, 90, 96, 98–99, 105–106, 109, 112–113, 120–125, 133 .…”

“…Twenty-two reported using a behavioral theory during development (40%) 46–54, 57–61, 64–68, 77–79, 82–83, 96–98, 110, 114–118, 121–124, 127–129, 133 . Finally, 21 (38%) reported focusing their programs or policies toward one or more at-risk groups: those of low socioeconomic status 54,108,120–124,133 (5; 9%), racial and/or ethnic minorities 47 (1; 2%), those who are overweight or obese 55,65,68,73–75,77–79,109,112,113 (8; 15%), those living in rural areas 56,72,98 (3; 5%), or multiple at-risk groups 66–67,81,84–85 (4; 19%). Supplemental Table 1 describes characteristics of the 55 individual studies, and Supplemental Table 2 summarizes these characteristics across all studies.…”

Staying Young at Heart: Cardiovascular Disease Prevention in Adolescents and Young Adults

“…Technological supports are useful in altering health behaviors [49], [50], [51] and providers who are privy to both the technology usage patterns and CV health behaviors of teens can inform the development of such supports.…”