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An adaptive physical activity intervention for overweight adults: a randomized controlled trial
Marc A Adams1, James F Sallis2, Gregory J Norman3
1School of Nutrition and Health Promotion, Arizona State University, Phoenix, Arizona, United States of America ; Graduate School of Public Health, San Diego State University, San Diego, California, United States of America.
Plos One
|December 19, 2013
Summary
Adaptive physical activity interventions significantly increased daily steps more than static approaches. This personalized, technology-driven method offers a scalable solution for public health.
Area of Science:
- Behavioral science
- Health technology
- Exercise physiology
Background:
- Traditional physical activity (PA) interventions use static components, unlike adaptive interventions that dynamically adjust based on individual performance.
- Emerging technologies facilitate adaptive goal setting and feedback for PA interventions.
- A study tested an adaptive intervention for PA using Operant and Behavior Economic principles and a percentile-based algorithm.
Purpose of the Study:
- To evaluate an adaptive physical activity intervention against a static intervention.
- To determine if adaptive interventions yield greater increases in daily steps compared to static interventions.
Main Methods:
- Twenty inactive overweight adults were randomized into either a static intervention (SI) or an adaptive intervention (AI) group for six months.
- Both groups received a pedometer and regular communication; the AI group received dynamically adjusted daily step goals and micro-incentives.
- The AI group's goals were adjusted using a percentile-rank algorithm based on a moving window, ensuring personalized challenge.
Main Results:
- The adaptive intervention group increased daily steps by an average of 2,728 steps, compared to 1,598 steps in the static intervention group.
- A significant between-group difference of 1,130 steps/day was observed, favoring the adaptive intervention (Cohen's d = .74).
- Statistical analysis confirmed a significant increase in steps/day from baseline to treatment (p<.001) and a significant group by study phase interaction (p=.017).
Conclusions:
- The adaptive intervention demonstrated superior effectiveness in increasing physical activity compared to the static intervention.
- The adaptive goal and feedback algorithm represents a "behavior change technology" with potential for integration into mHealth solutions.
- This adaptive approach is scalable and could be widely implemented to enhance PA in large populations.
