Digital motivational message framing effects on physical activity

Lee, A. M., Hojjatinia, S., Courtney, J. B., Brunke-Reese, D., Hojjatinia, S., Lagoa, C. M., & Conroy, D. E. (2023). Motivational Message Framing Effects on Physical Activity Dynamics in a Digital Messaging Intervention: Secondary Analysis. JMIR Formative Research, 7(1), e41414.

We conducted a 6-month intervention to promote increases in step counts in insufficiently active young adults via digital messages. This secondary, exploratory analysis compared intervention responses to affectively framed, social cognitively framed, and inspirational quotes messages to identify if one message type elicited a consistently greater intervention response after the delivery of one message. Using system identification, we generated person-specific dynamical models of physical activity and found that step responses did not statistically significantly differ by message type, but the speed and momentary magnitude of intervention and step response was greater on weekends compared with weekdays for all message types. We also observed significant participant heterogeneity such that some participants achieved their highest steady state from affective messages (weekdays: 35.6%, weekends: 37.8%), some from social-cognitive messages (weekdays: 26.7%, weekends: 35.6%), and some from inspirational quotes (weekdays: 35.6%, weekends: 26.7%). Thus, this exploratory analysis suggests that personalizing message types for participants in an intervention may be a worthy endeavor for generating greater step responses over time.

The median effect of digital physical activity interventions in adults is 943 steps per day [5]; thus, if a future intervention included multiple messages per day, knowledge of optimal participant response could become meaningful because approximately one-third of this sample showed a minimum of a 250-step difference between message types. This heterogeneity between participants indicates that future interventions can benefit from methods that can both explore the effects of multiple message types on physical activity and exploit the most effective message types for an individual once identified. Given that messages have proximal effects on behavior in the minutes and hours after message delivery, the use of wearable devices for measuring physical activity behavior provides a rich source of information about behavioral dynamics. Harnessing this technology, system identification and dynamical modeling can inform future work that continuously tunes interventions based on participants’ responses over time

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