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 the frequency 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 compared effects when hybrid mobile intervention did versus did not target each behavior in the Make Better Choices 1 (MBC1) trial ( = 204). Participants had all of four risk behaviors: low moderate-vigorous physical activity (MVPA) and fruit and vegetable consumption (FV), and high saturated fat (FAT) and sedentary leisure screen time (SED). Models estimated the mean (location), between-subjects variance, and within-subject variance (scale).

RESULTS

Treatment by time interactions showed that location increased for MVPA and FV (s = 1.68, .61; s < .001) and decreased for SED and FAT (s = -2.01, -.07; s < .05) more when treatments targeted the behavior. Within-subject variance modeling revealed group by time interactions for scale (taus = -.19, -.75, -.17, -.11; s < .001), indicating that all behaviors grew more consistent when targeted.

METHOD

Study 2: In the MBC2 trial ( = 212) we examined correlations between location, scale, self-efficacy, and automaticity for the three targeted behaviors.

RESULTS

For SED, higher scale (less consistency) but not location correlated with lower self-efficacy ( = -.22, = .014) and automaticity ( = -.23, = .013). For FV and MVPA, higher location, but not scale, correlated with higher self-efficacy (s = .38, .34, s < .001) and greater automaticity (s = .46, .42, s < .001).

CONCLUSIONS

Location scale mixed modeling suggests that both habit and self-regulation changes probably accompany acquisition of complex diet and activity behaviors. (PsycInfo Database Record (c) 2022 APA, all rights reserved).