Patterns and Predictors of Engagement With a Mobile Mental Health Intervention Among Hurricane-Affected Adults

OBJECTIVE: Mental disorders commonly increase after disasters, yet access to care is often limited by logistical barriers. Digital mental health applications may help address these gaps by providing private, low-cost, and readily accessible support. The authors examined patterns and predictors of engagement with Bounce Back Now (BBN), a self-help mobile application (app) designed to address posttraumatic stress, depression, and sleep disturbance among disaster-affected adults. METHODS: Participants included 1,357 adults affected by Hurricanes Harvey, Irma, Maria, Florence, or Michael. Use data were passively collected in the BBN app. Generalized linear models were used to examine demographic and clinical predictors of number of logins to the app and frequency of module access. RESULTS: Of the 717 participants randomly assigned to BBN, most accessed the activate (84%) and coping tools (87%) modules. Income emerged as the only consistent demographic predictor of multiple outcomes. Lower income was significantly associated with more frequent access to the coping tools (b=-0.20, p=0.003) and activate (b=-0.03, p=0.035) modules, as well as greater odds of accessing these modules. CONCLUSIONS: These findings challenge assumptions about a persistent digital divide in engagement with mental health mobile apps and suggest that digital interventions designed to minimize access barriers may successfully reach economically marginalized populations after disasters. These results have important implications for the design and dissemination of scalable digital interventions to improve access to care in postdisaster contexts.