title: A computational cognitive model of self-efficacy and daily adherence in mHealth.
abstract: Mobile health (mHealth) applications provide an excellent opportunity for collecting rich, fine-grained data necessary for understanding and predicting day-to-day health behavior change dynamics. A computational predictive model (ACT-R-DStress) is presented and fit to individual daily adherence in 28-day mHealth exercise programs. The ACT-R-DStress model refines the psychological construct of self-efficacy. To explain and predict the dynamics of self-efficacy and predict individual performance of targeted behaviors, the self-efficacy construct is implemented as a theory-based neurocognitive simulation of the interaction of behavioral goals, memories of past experiences, and behavioral performance.
authors.affiliation: Palo Alto Research Center, Palo Alto, CA, 94304, USA. pirolli@parc.com.
authors.forename: Peter
authors.initials: P
authors.lastname: Pirolli
mesh_terms: D000328:Adult; D003071:Cognition / Q000502:physiology*; D003198:Computer Simulation; D015444:Exercise; D005260:Female; D019983:Guideline Adherence; D015438:Health Behavior; D006801:Humans; D008297:Male; D008875:Middle Aged; D020377:Self Efficacy; D017216:Telemedicine / Q000379:methods* / Q000706:statistics & numerical data
keywords: ACT-R; Adherence; Computational model; Self-efficacy