Enhancing engagement with digital mental health care abstract digital mental health (DMH) is the use of technology to improve population well-being through rapid disease detection, outcome…

Enhancing engagement with digital mental health care abstract digital mental health (DMH) is the use of technology to improve population well-being through rapid disease detection, outcome measurement, and care 1. Although several randomized clinical trials have demonstrated that digital mental health tools are highly effective 2-6, most consumers do not sustain their use of these tools 7-9. The field currently lacks an understanding of DMH tool engagement, how engagement is associated with well-being, and what practices are effective at sustaining engagement. In this partnership between mental health america (MHA), talkspace (TS) and the university of washington (UW), we propose a naturalistic and experimental, theory-driven program 10,11 of research, with the aim of understanding 1) how consumer engagement in self-help and clinician assisted DMH varies and what engagement patterns exist, 2) the association between patterns of engagement and important consumer outcomes, and 3) the effectiveness of personalized strategies for optimal engagement with DMH treatment. This study will prospectively follow a large, naturalistic sample of MHA and TS consumers, and will apply machine learning, user-centered design strategies, and micro-randomized and sequential multiple assignment randomized (smart) trials to address these aims. As is usual practice for both platforms, consumers will complete online mental health screening and assessment, and we will be able to classify participants by disease status and symptom severity. The sample we will be working with will not be limited by diagnosis or CO-morbidities. Participants will be 10 years old and older and enter the MHA and TS platforms prospectively over 4 years. In order to test the first aim, we will identify a minimum of 100,000 consumers who have accessed MHA and TS platforms in the past. Participant data will be analyzed statistically to reveal differences in engagement and dropout across groups based on demographics, symptoms and platform activity. For aim 2, we will use supervised machine learning techniques to identify subtypes based on consumer demographics, engagement patterns with DMH, reasons for disengagement, success of existing MHA and TS engagement strategies, and satisfaction with the DMH tools, that are predictive of future engagement patterns. Finally, based on the outcomes from aim 2, in aim 3 we will conduct focus groups applying user-centered design strategies to identify and CO-build potentially effective engagement strategies for particular client subtypes. We will then conduct a series of micro-randomized and smart trials to determine which theory-driven engagement strategies, CO-designed with users, have the greatest fit with subtypes developed under aim 2. We will test the effectiveness of these strategies to 1) prevent disengagement from those who are more likely to have poor outcomes after disengagement, 2) improve movement from motivation to volition and, 3) enhance optimal dose of DMH engagement and consequently improve mental health outcomes. These data will be analyzed using longitudinal mixed effects models with effect coding to estimate the effectiveness of each strategy on client engagement behavior and mental health outcomes.