Mental health mobile applications are increasingly used to address psychological distress and promote well-being, yet evidence on how and when these tools exert their effects over time and what constitutes meaningful engagement remains mixed. This study examined whether use of the Social Activity Guardian and Intervention Project (SAGIP) mental health mobile application predicts differential growth trajectories in psychological well-being and psychological distress among members of an academic community, and whether app engagement explains individual differences in these trajectories. Using an experimental repeated-measures design, participants from a university academic community were randomly assigned to a SAGIP app group or a waitlist control group and assessed at baseline (Month 0) and monthly for three months (Months 1 to 3). Psychological well-being and psychological distress were analyzed using latent growth curve modeling and mixed-effects piecewise growth models to capture nonlinear change. Compared to waitlist controls, SAGIP users demonstrated significantly greater improvements in psychological well-being and larger reductions in psychological distress during the randomized phase. Piecewise models indicated phase-specific change, with distress reduction strongest early in the intervention and well-being gains emerging more gradually and strengthening later. Frequency and duration of app use were associated with baseline mental health status but did not predict growth trajectories. Supplementary analyses provided additional support for the robustness of the findings: descriptive post-randomized analyses suggested that the experimental group generally maintained gains in psychological well-being and stabilized psychological distress below baseline levels through Month 6, whereas the control group showed modest improvements in well-being and distress after crossover to app access. Sensitivity analyses restricted to student participants replicated the primary pattern of intervention effects, while exploratory moderation analyses found no clear evidence that intervention effects differed between students and employees. These findings suggest that the therapeutic impact of self-guided digital interventions may depend more on the timing and quality of engagement than on the quantity of use and highlight the importance of modeling nonlinear change when evaluating digital mental health interventions.
Rilveria et al. (Sat,) studied this question.