Gina L Tripicchio1,2, Alice S Ammerman1,2, Cody Neshteruk1,2, Myles S Faith3, Kelsey Dean4,5, Christie Befort6, Dianne S Ward1,2, Kimberly P Truesdale1, Kyle S Burger1, Ann Davis4,7. 1. 1 Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill , Chapel Hill, NC. 2. 2 Center for Health Promotion and Disease Prevention, University of North Carolina at Chapel Hill , Chapel Hill, NC. 3. 3 Department of Counseling, School, and Educational Psychology, University at Buffalo-SUNY , Buffalo, NY. 4. 4 Center for Children's Healthy Lifestyles & Nutrition , Kansas City, MD. 5. 5 Children's Mercy Hospital , Kansas City, MD. 6. 6 Preventive Medicine & Public Health, University of Kansas Medical Center , Kansas City, KS. 7. 7 Department of Pediatrics, University of Kansas Medical Center , Kansas City, KS.
Abstract
BACKGROUND: Strategies to treat pediatric obesity are needed, especially among high-need populations. Technology is an innovative approach; however, data on technology as adjuncts to in-person treatment programs are limited. METHODS: A total of 64 children [body mass index (BMI) ≥85th percentile, mean age = 9.6 ± 3.1 years, 32.8% female, 84.4% Hispanic] were recruited to participate in one of three cohorts of a family-based behavioral group (FBBG) treatment program: FBBG only, TECH1, and TECH2. Rolling, nonrandomized recruitment was used to enroll participants into three cohorts from May 2014 to February 2015. FBBG began in May 2014 and received the standard, in-person 12-week treatment only (n = 21); TECH1 began in September 2014 and received FBBG plus a digital tablet equipped with a fitness app (FITNET) (n = 20); TECH2 began in February 2015 and received FBBG and FITNET, plus five individually tailored TeleMed health-coaching sessions delivered via Skype (n = 23). Child BMI z-score (BMI-z) was assessed at baseline and postintervention. Secondary aims examined weekly FBBG attendance, feasibility/acceptability of FITNET and Skype, and the effect of technology engagement on BMI-z. RESULTS: FBBG and TECH1 participants did not show significant reductions in BMI-z postintervention [FBBG: β = -0.05(0.04), p = 0.25; TECH1: β = -0.006(0.06), p = 0.92], but TECH2 participants did [β = -0.09(0.02), p < 0.001] and TeleMed session participation was significantly associated with BMI-z reduction [β = -0.04(0.01), p = 0.01]. FITNET use and FBBG attendance were not associated with BMI-z in any cohort. Overall, participants rated the technology as highly acceptable. CONCLUSIONS: Technology adjuncts are feasible, used by hard-to-reach participants, and show promise for improving child weight status in obesity treatment programs.
BACKGROUND: Strategies to treat pediatric obesity are needed, especially among high-need populations. Technology is an innovative approach; however, data on technology as adjuncts to in-person treatment programs are limited. METHODS: A total of 64 children [body mass index (BMI) ≥85th percentile, mean age = 9.6 ± 3.1 years, 32.8% female, 84.4% Hispanic] were recruited to participate in one of three cohorts of a family-based behavioral group (FBBG) treatment program: FBBG only, TECH1, and TECH2. Rolling, nonrandomized recruitment was used to enroll participants into three cohorts from May 2014 to February 2015. FBBG began in May 2014 and received the standard, in-person 12-week treatment only (n = 21); TECH1 began in September 2014 and received FBBG plus a digital tablet equipped with a fitness app (FITNET) (n = 20); TECH2 began in February 2015 and received FBBG and FITNET, plus five individually tailored TeleMed health-coaching sessions delivered via Skype (n = 23). Child BMI z-score (BMI-z) was assessed at baseline and postintervention. Secondary aims examined weekly FBBG attendance, feasibility/acceptability of FITNET and Skype, and the effect of technology engagement on BMI-z. RESULTS:FBBG and TECH1 participants did not show significant reductions in BMI-z postintervention [FBBG: β = -0.05(0.04), p = 0.25; TECH1: β = -0.006(0.06), p = 0.92], but TECH2 participants did [β = -0.09(0.02), p < 0.001] and TeleMed session participation was significantly associated with BMI-z reduction [β = -0.04(0.01), p = 0.01]. FITNET use and FBBG attendance were not associated with BMI-z in any cohort. Overall, participants rated the technology as highly acceptable. CONCLUSIONS: Technology adjuncts are feasible, used by hard-to-reach participants, and show promise for improving child weight status in obesity treatment programs.
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