Tailored Daily Activity: An Adaptive Physical Activity Smartphone Intervention.

Background:Nontailored and static goals may hinder behavior change. We investigated the feasibility and acceptability of an adaptive proof-of-concept smartphone-delivered intervention by using real-world movement data capture of physical activity (PA) and sedentary behavior (SB) to inform behavior change content delivery.Materials and Methods:A single-group 8-week study with pre- and post-intervention assessments was conducted in Auckland, New Zealand. Participants aged 17-69 years who owned an Android smartphone were recruited and used the application (app). Usage data, self-reported acceptability and PA and SB were assessed. Daily repeated measurement of PA and SB outcomes were analyzed through random-effects mixed models.Results:Participants (n = 69) were predominantly female (78%) with a mean age of 34.5 years (range 18-61). On average, participants opened the app on 11.4 days throughout the 8 weeks. Use decreased over time; 20% of participants opened the app every day. Feedback on behavior (73%), behavior substitution (71%), discrepancy between behavior and goal (58%) and goal setting (54%) were rated as the most useful behavior change techniques by participants. Time spent on light, moderate-to-vigorous intensity and total PA increased post-intervention, whereas time spent on SB decreased.Conclusions:The adaptive proof-of-concept app was considered acceptable, with preliminary support for its positive effects on PA and SB.

Using the Intervention Mapping and Behavioral Intervention Technology Frameworks: Development of an mHealth Intervention for Physical Activity and Sedentary Behavior Change.

Few interventions to promote physical activity (PA) adapt dynamically to changes in individuals' behavior. Interventions targeting determinants of behavior are linked with increased effectiveness and should reflect changes in behavior over time. This article describes the application of two frameworks to assist the development of an adaptive evidence-based smartphone-delivered intervention aimed at influencing PA and sedentary behaviors (SB). Intervention mapping was used to identify the determinants influencing uptake of PA and optimal behavior change techniques (BCTs). Behavioral intervention technology was used to translate and operationalize the BCTs and its modes of delivery. The intervention was based on the integrated behavior change model, focused on nine determinants, consisted of 33 BCTs, and included three main components: (1) automated capture of daily PA and SB via an existing smartphone application, (2) classification of the individual into an activity profile according to their PA and SB, and (3) behavior change content delivery in a dynamic fashion via a proof-of-concept application. This article illustrates how two complementary frameworks can be used to guide the development of a mobile health behavior change program. This approach can guide the development of future mHealth programs.