Personalized models of physical activity responses to text message micro-interventions: A proof-of-concept application of control systems engineering methods. (March 2019)

• Record Type: Journal Article
• Authors: Conroy, David E.; Hojjatinia, Sarah; Lagoa, Constantino M.; Yang, Chih-Hsiang; Lanza, Stephanie T.; Smyth, Joshua M.
• Is Part Of: Psychology of sport and exercise. Volume 41(2019)
• Page Start: 172
• Page End: 180
• Publication Date: 2019-03
• Subjects: Precision medicine -- Short message service (SMS) -- Computational model -- System identification; Sports -- Psychological aspects -- Periodicals; Exercise -- Psychological aspects -- Periodicals; Psychology -- Periodicals; Sports -- Periodicals; Exercise -- Periodicals; Societies, Medical -- Periodicals; Psychology; Sports; Exercise; Societies, Medical; Sports -- Aspect psychologique -- Périodiques; Exercice -- Aspect psychologique -- Périodiques; 613.71019
• DOI: 10.1016/j.psychsport.2018.06.011
• Languages: English
• ISSNs: 1469-0292

Abstract

Abstract: Objectives: The conceptual models underlying physical activity interventions have been based largely on differences between more and less active people. Yet physical activity is a dynamic behavior, and such models are not sensitive to factors that regulate behavior at a momentary level or how people respond to individual attempts at intervening. We demonstrate how a control systems engineering approach can be applied to develop personalized models of behavioral responses to an intensive text message-based intervention. Design & method: To establish proof-of-concept for this approach, 10 adults wore activity monitors for 16 weeks and received five text messages daily at random times. Message content was randomly selected from three types of messages designed to target (1) social-cognitive processes associated with increasing physical activity, (2) social-cognitive processes associated with reducing sedentary behavior, or (3) general facts unrelated to either physical activity or sedentary behavior. A dynamical systems model was estimated for each participant to examine the magnitude and timing of responses to each type of text message. Results: Models revealed heterogeneous responses to different message types that varied between people and between weekdays and weekends. Conclusions: This proof-of-concept demonstration suggests that parameters from this model can be used to develop personalized algorithms for intervention delivery. More generally, these results demonstrate the potential utility of control systems engineering models for optimizing physical activity interventions. Highlights: Customizing messaging interventions has not increased effects on physical activity. Tools from control systems engineering can be applied to model behavior change. Behavioral responses to physical activity messages are largely idiosyncratic. Different decision rules can trigger different interventions for different people.