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摘要
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%26apos;s performance. Emerging theory and technologies make adaptive goal setting and feedback interventions feasible. %26lt;br%26gt;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. %26lt;br%26gt;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, M=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%26apos;s performance and ensured goals were always challenging but within participants%26apos; abilities. The SI group received a static 10,000 steps/day goal with incentives linked to uploading the pedometer%26apos;s data. %26lt;br%26gt;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%26lt;.001) and a group by study phase interaction was observed (p = .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%26apos;s d = .74). %26lt;br%26gt;Conclusions: The adaptive intervention outperformed the static intervention for increasing PA. The adaptive goal and feedback algorithm is a %26quot;behavior change technology%26quot; that could be incorporated into mHealth technologies and scaled to reach large populations.
- 出版日期2013-12-9
