Exploring behavioral markers of long-term physical activity maintenance: a case study of system identification...

Efficacious interventions to promote long-term maintenance of physical activity are not well understood. Engineers have developed methods to create dynamical system models for modeling idiographic (i.e., within-person) relationships within systems. In behavioral research, dynamical systems modeling may assist in decomposing intervention effects and identifying key behavioral patterns that may foster behavioral maintenance. The Active Adult Mentoring Program was a 16-week randomized controlled trial of a group-based, peer-delivered physical activity intervention targeting older adults. Time-intensive (i.e., daily) physical activity reports were collected throughout the intervention. We explored differential patterns of behavior among participants who received the active intervention (N = 34; 88% women, 64.1 ± 8.3 years of age) and either maintained 150 minutes/week of moderate to vigorous intensity physical activity (MVPA; n = 10) or did not (n = 24) at 18 months following the intervention period. We used dynamical systems modeling to explore whether key intervention components (i.e., self-monitoring, access to an exercise facility, behavioral initiation training, behavioral maintenance training) and theoretically plausible behavioral covariates (i.e., indoor vs. outdoor activity) predicted differential patterns of behavior among maintainers and nonmaintainers. We found that maintainers took longer to reach a steady-state of MVPA. At week 10 of the intervention, nonmaintainers began to drop whereas maintainers increased MVPA. Self-monitoring, behavioral initiation training, percentage of outdoor activity, and behavioral maintenance training, but not access to an exercise facility, were key variables that explained patterns of change among maintainers. Future studies should be conducted to systematically explore these concepts within a priori idiographic (i.e., N-of-1) experimental designs.