Longitudinal Patterns of Engagement and Clinical Outcomes: Results From a Therapist-Supported Digital Mental Health Intervention

Abstract

Objective Digital mental health interventions (DMHI) are an effective treatment modality for common mental disorders like depression and anxiety; however, the role of intervention engagement as a longitudinal “dosing” factor is poorly understood in relation to clinical outcomes. Methods We studied 4,978 participants in a 12-week therapist-supported DMHI (June 2020- December 2021), applying a longitudinal agglomerative hierarchical cluster analysis (HCA) to the number of days per week of intervention engagement. The proportion of people demonstrating remission in depression and anxiety symptoms during the intervention was calculated for each cluster. Multivariable logistic regression models were fit to examine associations between the engagement clusters and symptom remission adjusting for demographic and clinical characteristics. Results Based upon clinical interpretability and stopping rules, four clusters were derived from the HCA (in descending order): 1) sustained high engagers (45.0%); 2) late disengagers (24.1%); 3) early disengagers (22.5%); and 4) immediate disengagers (8.4%). Bivariate and multivariate analyses supported a dose-response relationship between engagement and depression symptom remission, while the pattern was partially evident for anxiety symptom remission. In multivariable logistic regression models, older age groups, males, and Asians had increased odds of achieving depression and anxiety symptom remission, while higher odds of anxiety symptom remission were observed among gender expansive individuals. Conclusions Segmentation based on the frequency of engagement performs well in discerning timing of intervention disengagement and a dose-response relationship with clinical outcomes. The findings among the demographic subpopulations indicate therapist-supported DMHIs may be effective in addressing mental health problems among patients who disproportionately experience stigma and structural barriers to care. Machine learning models can enable precision care by delineating how heterogeneous patterns of engagement over time relate with clinical outcomes. This empirical identification may help clinicians personalize and optimize interventions to prevent premature disengagement.

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