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Currently submitted to: JMIR mHealth and uHealth

Date Submitted: Jun 10, 2019
Open Peer Review Period: Jun 13, 2019 - Aug 8, 2019
(currently open for review and needs more reviewers - can you help?)

Examining Variations of Digital Behavior Change Techniques for Physical Activity Using an Adaptive Intervention Design

  • Nicole Brainard; 
  • Mohammad Soltani; 
  • Heather Cole-Lewis; 
  • Claudia Hernandez; 
  • Shawn T. Mason; 
  • David Metcalf; 
  • Jennifer Turgiss



To foster physical activity behavior, technology often incorporates evidence-based behavior change techniques (BCTs). However, a gap exists on how to apply BCTs for optimal behavior change, and do so in time-varying adaptive interventions.


This study evaluated BCT variations using an adaptive intervention design that randomly assigned participants to a different intervention version based on whether participants met a self-determined physical activity goal.


The study contained three intervention versions (individual pursuit, community comparison, and team competition). Each version included variations of 4 BCTs (goal setting, action planning, feedback, and prompts & cues). The individual pursuit version was the control, while versions two and three received variations of the social competition/comparison BCT. BCTs were delivered via phone app, phone texts, and a Garmin vivofit 3™. Participants who did not increase physical activity in the first 21 days as compared to their baseline were re-randomized into a different intervention version, reassessed at 42 days, and re-randomized again if physical activity did not increase. Ecological momentary assessments were conducted for secondary measures of self-efficacy, barriers, expectations, motivation, mood, social support, and well-being.


A total 158 adults in central Florida with low to moderate levels of physical activity, were randomized into one of three intervention versions. Based on a subsample analysis of 87 participants, those who received the team competition intervention version first, followed by community comparison, and individual pursuit, saw the greatest increase in their overall physical activity as compared to other intervention orders. In addition, five distinct behavioral pattern subgroups were identified. We also predicted the likelihood of a participant being active or inactive 14 days into observation and with >80% precision. There was also evidence that app usage in the first 21 days of observation was positively associated with physical activity behavior at study conclusion.


The way BCTs are designed and the sequence in which they are delivered can impact physical activity behavior. Additional work is needed on determinants of physical activity behavior, as well as longevity of BCT novelty and user engagement. Clinical Trial: N/A


Please cite as:

Brainard N, Soltani M, Cole-Lewis H, Hernandez C, Mason ST, Metcalf D, Turgiss J

Examining Variations of Digital Behavior Change Techniques for Physical Activity Using an Adaptive Intervention Design

JMIR Preprints. 10/06/2019:14987

DOI: 10.2196/preprints.14987


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