Clinical trial NCT04507360: Enhancing Engagement With Digital Mental Health Care
Official title: Enhancing Engagement With Digital Mental Health Care
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Engagement, Patient
Keywords: Digital Mental Health
Lead sponsor: University of Washington
Sponsor class: OTHER
Collaborators: name: Mental Health America
name: Talkspace
name: National Institute of Mental Health (NIMH)
First posted: 2020-08-11
Enrollment: 73533
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Allocation: RANDOMIZED
Intervention model: SEQUENTIAL
Intervention model description: 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 anticipate enrolling 10,000 Mental Health America consumers in Study 1 (the sequential multiple assignment randomized trial; SMART), with a subset of 250 participating in Study 2, a randomized controlled trial of an Artificial Intelligence (AI)-assisted self-help tool compared to a non-AI self help tool and to psychoeducational materials only control group.
Primary purpose: HEALTH_SERVICES_RESEARCH

Brief 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.

Detailed 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 trials (SMART) 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 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 month-long 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.

Eligibility criteria: Inclusion Criteria:

* Phase 3b Study 1 (Sequential Multiple Assignment Randomized Trial; SMART): Users of the Mental Health America (MHA) website, engaging from Internet Protocol (IP) addresses in the United States, who have chosen to start the PHQ-9 depression screener in English. Must be those who can read English.
* Phase 3b Study 2 (Do-It-Yourself; DIY): PHQ-9 or GAD-7 score of 10 or greater, users of MHA website, 18 years of age or older.

Exclusion Criteria:

* Phase 3b Study 1 (SMART): None
* Phase 3b Study 2 (DIY): Younger than 18 years old, Non-English or Non-Spanish speaking, PHQ-9 less than 10, outside of US, have more than a little familiarity with the concept of cognitive reframing.

Primary outcomes:
measure: Study 1: Mental Health America Engagement
timeFrame: Through active web session, an average of 10 minutes
measure: Study 1: Mental Health America Engagement
timeFrame: Through active web session, an average of 10 minutes
measure: Study 1: Mental Health America Engagement
timeFrame: Through active web session, an average of 10 minutes
measure: Study 1: Mental Health America Disengagement
timeFrame: Through active web session, an average of 10 minutes
measure: Study 1: DIY Completion Rate
timeFrame: Week 1, Week 2, Week 3, Week 4
measure: Study 2: Engagement (Dosage)
timeFrame: Week 1, Week 2, Week 3, Week 4
measure: Study 2: Tool Use Helpfulness
timeFrame: Week 1, Week 2, Week 3, Week 4, Week 8
measure: Study 2: Emotion Mechanisms
timeFrame: Week 1, Week 2, Week 3, Week 4, Week 8
measure: Study 2: Tool Mechanisms
timeFrame: Week 1, Week 2, Week 3, Week 4
measure: Study 2: Tool Mechanisms (Part 2)
timeFrame: Week 1, Week 2, Week 3, Week 4
measure: Study 2: Hopefulness
timeFrame: Week 1, Week 2, Week 3, Week 4, Week 8
measure: Study 2: DIY Skill Use: Competencies of Cognitive Therapy Scale - Self-Report
timeFrame: Week 1, Week 2, Week 3, Week 4, Week 8
Secondary outcomes:
measure: Study 2: Patient Health Questionnaire (PHQ-9)
timeFrame: Baseline, Week 1, Week 2, Week 3, Week 4, Week 8
measure: Study 2: Generalized Anxiety Disorder (GAD-7)
timeFrame: Baseline, Week 1, Week 2, Week 3, Week 4, Week 8
Other outcomes:
Locations:
city: New York
country: United States
facility: Groop Internet Platform DBA Talkspace
state: New York
city: Alexandria
country: United States
facility: Mental Health America
state: Virginia
city: Seattle
country: United States
facility: University of Washington
state: Washington
Study officials:
affiliation: University of Washington
name: Michael Pullmann, PhD
role: PRINCIPAL_INVESTIGATOR
References: