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An adaptive physical activity intervention for overweight adults: a randomized controlled trial
Adams MA, Sallis JF, Norman GJ, Hovell MF, Hekler EB, Perata E
PLoS ONE 2013 Dec;8(12):e82901
clinical trial
6/10 [Eligibility criteria: Yes; Random allocation: Yes; Concealed allocation: No; Baseline comparability: Yes; Blind subjects: No; Blind therapists: No; Blind assessors: No; Adequate follow-up: Yes; Intention-to-treat analysis: Yes; Between-group comparisons: Yes; Point estimates and variability: Yes. Note: Eligibility criteria item does not contribute to total score] *This score has been confirmed*

BACKGROUND: Physical activity (PA) interventions typically include components or doses that are static across participants. Adaptive interventions are dynamic; components or doses change in response to short-term variations in participant's performance. Emerging theory and technologies make adaptive goal setting and feedback interventions feasible. OBJECTIVE: To test an adaptive intervention for PA based on Operant and Behavior Economic principles and a percentile-based algorithm. The adaptive intervention was hypothesized to result in greater increases in steps per day than the static intervention. METHODS: Participants (n = 20) were randomized to one of two 6-month treatments: (1) static intervention (SI) or (2) adaptive intervention (AI). Inactive overweight adults (85% women, mean 36.9 +/- 9.2 years, 35% non-white) in both groups received a pedometer, email and text message communication, brief health information, and biweekly motivational prompts. The AI group received daily step goals that adjusted up and down based on the percentile-rank algorithm and micro-incentives for goal attainment. This algorithm adjusted goals based on a moving window; an approach that responded to each individual's performance and ensured goals were always challenging but within participants' abilities. The SI group received a static 10,000 steps/day goal with incentives linked to uploading the pedometer's data. RESULTS: A random-effects repeated-measures model accounted for 180 repeated measures and autocorrelation. After adjusting for covariates, the treatment phase showed greater steps/day relative to the baseline phase (p < 0.001) and a group by study phase interaction was observed (p = 0.017). The SI group increased by 1,598 steps/day on average between baseline and treatment while the AI group increased by 2,728 steps/day on average between baseline and treatment; a significant between-group difference of 1,130 steps/day (Cohen's d = 0.74). CONCLUSIONS: The adaptive intervention outperformed the static intervention for increasing PA. The adaptive goal and feedback algorithm is a "behavior change technology" that could be incorporated into mHealth technologies and scaled to reach large populations. TRIAL REGISTRATION: ClinicalTrials.gov NCT01793064.

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