Microtemporal Processes Underlying Health Behavior Adoption and Maintenance

University Of Southern California, Los Angeles CA

Investigators

Linked publications, trials & patents

Abstract

SUMMARY ? Parent U01 Emerging adulthood (ages 18-29 years) is marked by substantial weight gain, leading to increased lifetime risks of cancer and other chronic diseases. Engaging in sufficient levels of physical activity and sleep, and limiting sedentary time are important contributors to the prevention of weight gain. Traditional health behavior theories provide limited guidance regarding factors underlying behavior maintenance; therefore, to ad- dress this gap, our work suggests that dual-process models of decision-making and behavior can shed light on differences in the mechanisms underlying adoption versus maintenance. Reflective processes (e.g., efficacy, self-control) may be activated to a greater extent during behavior adoption. In contrast, reactive processes (e.g., contextual cues, automaticity) may play a greater role in behavior maintenance. However, re- active processes are difficult to measure because they can unfold on a micro-timescale (i.e., change across minutes or hours). To solve this problem, we propose to use real-time mobile technologies to collect inten- sive longitudinal data examining differences in the micro-temporal processes underlying the adoption and maintenance of physical activity, low sedentary time, and sufficient sleep duration. We will conduct intermittent self-report (i.e., ecological momentary assessment) of reflective variables; and continuous, sensor- based passive monitoring of reactive variables (e.g., location) and behaviors (i.e., physical activity, sedentary time, sleep) using smartwatches and smartphones. Data will be used to predict within-subject variation (within- days, between-days) in the likelihood of behavior ?episodes? (e.g., ?10 min of physical activity) and ?lapses? (i.e., failure to achieve recommended levels ?7 days). The specific aims are to (1) idiographically use machine learn- ing to identify person-specific combinations of time-varying reflective and reactive factors that predict behavior episodes and lapse; and (2) nomothetically determine whether there are group-level patterns of time-varying predictors, and whether those patterns predict successful behavior maintenance outcomes. The data and meth- ods will contribute to the U01/U24 Intensive Longitudinal Health Behavior Network?s collective goal to build more predictive health behavior theories that specify targets for personalized interventions.