Optimizing Engagement in Digital Mental Health Services among Sexual and Gender Minority Consumers

PROJECT SUMMARY Sexual and gender minority (SGM) populations are disproportionately impacted by mental health concerns relative to their heterosexual and cisgender peers. SGM populations, however, continue to report unmet mental health needs because they cannot or do not access mental health services. Digital Mental Health (DMH) has

been recognized as a feasible, economical, and effective approach to broaden the availability of mental health care to consumers who face barriers to mental health help-seeking. SGM consumers cite a preference for DMH resources, and this delivery format holds promise to attend to major access barriers experienced by this

consumer group. Yet, the availability of DMH content tailored to the needs of SGM consumers is limited, and a dearth of research examines SGM populations' actual engagement with DMH services. A potential solution to fully understand how SGM populations utilize DMH services would be to characterize their engagement within a

natural setting. Leveraging an established partnership with Mental Health America (MHA), a non-profit mental health advocacy group offering free, evidence-based screenings and self-guided DMH resources, this study will follow a large, naturalistic sample of SGM DMH consumers with the aims to: 1) characterize consumers' current

and predictive patterns of engagement with MHA's DMH resources and examine consumer characteristics associated with these different engagement patterns (Aim 1); 2) employ human-centered design approaches to gain insights related to group-specific processes not captured by MHA's meta-data that impact DMH

engagement among SGM consumers and integrate data to design and build prototype engagement strategies for consumer evaluation (Aim 2), and; 3) test the tailored engagement strategies with MHA's SGM consumers using a micro-randomized trial (MRT) design (Aim 3). To accomplish these aims and prepare for a larger R01

trial, the Principal Investigator will receive training in: 1) the theoretical, substantive, and methodological underpinnings of DMH; 2) big data management and analytic methods, specifically supervised machine learning; 3) human-centered design approaches for designing tailored DMH interventions, and; 4) experimental design

and data analysis for evaluating adaptive DMH interventions. The candidate is an Assistant Professor of Social Work at the University of Washington whose long-term career goal is to become an expert in the field of mental health services research, specializing in digital interventions to improve service access for SGM consumers

through increased availability and enhanced acceptability. The proposed study advances this objective by aligning with the care preferences of SGM consumers, leveraging the infrastructure of an existing DMH platform at minimal burden to consumers, and contributing evidence towards optimized engagement outcomes for SGM

consumers through the use of tailored strategies.