Enhancing Engagement With Digital Mental Health Care

  • End date
    Nov 30, 2024
  • participants needed
  • sponsor
    University of Washington
Updated on 7 December 2021
Accepts healthy volunteers


This proposal is a partnership between Mental Health America (MHA), a nonprofit mental health advocacy and resource organization, Talkspace (TS), a for-profit, online digital psychotherapy organization, and the University of Washington's Schools of Medicine and Computer Science Engineering (UW). The purpose of this partnership is to create a digital mental health research platform leveraging MHA and TS's marketing platforms and consumer base to describe the characteristics of optimal engagement with digital mental health treatment, and to identify effective, personalized methods to enhance motivation to engage in digital mental health treatment in order to improve mental health outcomes. These aims will be met by identifying and following at least 100,000 MHA and TS consumers over the next 4 years, apply machine learning approaches to characterizing client engagement subtypes, and apply micro-randomized trials to study the effectiveness of motivational enhancement strategies and response to digital mental health treatment.


Digital mental health (DMH) is the use of technology to improve population well-being through rapid disease detection, outcome measurement, and care. Although several randomized clinical trials have demonstrated that digital mental health tools are highly effective, most consumers do not sustain their use of these tools. 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), the investigators propose a naturalistic and experimental, theory-driven program 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 the investigators will be able to classify participants by disease status and symptom severity. The sample that the investigators 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, the investigators 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, the investigators 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 the investigators will conduct focus groups applying user centered design strategies to identify and co-build potentially effective engagement strategies for particular client subtypes. The investigators 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. The investigators 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.

Condition Engagement, Patient
Treatment Engagement Strategies TBD
Clinical Study IdentifierNCT04507360
SponsorUniversity of Washington
Last Modified on7 December 2021


Yes No Not Sure

Inclusion Criteria

Phase 1 (100K) and Phase 2 (50K): MHA and TS consumers who are naturalistically seeking services; Aged 14 and older; English or Spanish speaking
Focus Groups: Adult MHA and TS consumers, English speaking or bilingual English & Spanish speakers
Phase 3a (10,000): TS consumers, 18 years old and older, English or Spanish speaking
Phase 3b (250): TS consumers, 18 years old and older, PHQ-9 or GAD-7 of 10 or greater, English or Spanish speaking

Exclusion Criteria

Phase 1 (100K) and Phase 2 (50K): Younger than 14 years old; Non-English or Non-Spanish speaking
Focus Groups: Younger than 18 years old; Non-English speaking or Non-bilingual (English & Spanish)
Phase 3a (10,000): Younger than 18 years old, Non-English or Non-Spanish speaking
Phase 3b (250): Younger than 18 years old, Non-English or Non-Spanish speaking, PHQ-9 or GAD-7 of less than 10
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