Enhancing Engagement With Digital Mental Health Care

Engagement, Patient Clinical Trial

Official title: Enhancing Engagement With Digital Mental Health Care

Status Active, not recruiting

Clinical Trial Summary

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.

Clinical Trial Description

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. For aim 1, 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 3a 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.

The purpose of aim 3b is to identify effective engagement strategies tailored to client needs and demographics to increase MHA website engagement, and to better understand how self-help mental health resources can help people overcome negative thinking and support healthier thought processes. The investigators will compare effective engagement strategies tailored to subtypes developed under aim 2 to study the mediated impact of engagement strategies on consumer mental health outcomes. The study team will determine if engagement strategies targeted to consumer engagement subtype will enhance engagement and in turn result in improved clinical outcomes. These will be compared to generic strategies that are not subtype targeted.

All aim 3b activities will occur with MHA, broken down into two parts: (Study 1) a sequential multiple assignment randomized trial (SMART) and (Study 2) a Do-It-Yourself (DIY) tool longitudinal randomized control trial (RCT). Study 1 will use a SMART to examine methods to optimize engagement with MHA's website, and Study 2 will recruit participants for a longitudinal monthlong study where they are randomly assigned to a control group, the use of a DIY tool without Artificial Intelligence (AI), or the use of a DIY tool with AI to examine the efficacy of using a digital tool to improve mental health functioning. An AI tool that uses machine learning/Natural Language Processing (NLP)/AI methods was developed to personalize and tailor an intervention to improve engagement and completion outcomes. The study focuses on a specific, popular DIY tool that teaches cognitive restructuring. Pilot work showed that (1) engagement and completion rates on DIY tools can be low, and (2) a pilot AI tool had significantly higher engagement and completion rates. These differences may arise due to AI support, User Interface/User Experience/design differences, other factors, or a combination thereof. Additionally, the efficacy of the digital tool to improve mental health functioning is unknown. Study 2 will recruit participants who will be randomly assigned to one of three groups for a longitudinal month-long study: thrice-weekly DIY tool use with AI, without AI, or a control group. ;

Related Conditions & MeSH terms

• Engagement, Patient

• NCT number: NCT04507360
• Study type: Interventional
• Source: University of Washington
• Status: Active, not recruiting
• Phase: N/A
• Start date: October 15, 2021
• Completion date: November 30, 2024