Digitally Characterizing the Dynamics of Multiple Health Behavior Change

Objective: We applied the ORBIT model to digitally define dynamic treatment pathways whereby intervention improves multiple risk behaviors. We hypothesized that effective intervention improves thefrequency and consistency of targeted health behaviors and that both correlate with automaticity (habit)and self-efficacy (self-regulation). Method: Study 1: Via location scale mixed modeling we comparedeffects when hybrid mobile intervention did versus did not target each behavior in the Make BetterChoices 1 (MBC1) trial (n = 204). Participants had all of four risk behaviors: low moderate-vigorousphysical activity (MVPA) and fruit and vegetable consumption (FV), and high saturated fat (FAT) andsedentary leisure screen time (SED). Models estimated the mean (location), between-subjects variance,and within-subject variance (scale). Results: Treatment by time interactions showed that locationincreased for MVPA and FV (Bs 1.68,.61; ps <.001) and decreased for SED and FAT (Bs = -2.01, -.07; ps <.05) more when treatments targeted the behavior. Within-subject variance modelingrevealed group by time interactions for scale (taus=-.19, -.75, -.17, -.11; ps <.001), indicating thatall behaviors grew more consistent when targeted. Method: Study 2: In the MBC2 trial (n = 212) weexamined correlations between location, scale, self-efficacy, and automaticity for the three targetedbehaviors. Results: For SED, higher scale (less consistency) but not location correlated with lowerself-efficacy (r = -.22, p <.014) and automaticity (r = -.23, p <.013). For FV and MVPA, higherlocation, but not scale, correlated with higher self-efficacy (rs =.38,.34, ps <.001) and greaterautomaticity (rs =.46,.42, ps <.001).