Using Machine Learning to Optimize User Engagement and Clinical Response to Digital Mental Health Interventions

Part of paid clinical trials in Boston, Massachusetts.

Sponsor
Boston University Charles River Campus
Study ID
NCT05567640
Status
Recruiting
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Conditions

  • Anxiety Disorders and Symptoms
  • Depressive Symptoms

Eligibility Criteria

Sex
ALL
Age
18 Years - N/A
Healthy Volunteers
Accepted

Interventions

  • The Unified Protocol for Transdiagnostic Treatment of Emotional Disorders (UP) — BEHAVIORAL
    This is a cognitive behavioral treatment (CBT) for emotional disorders. This transdiagnostic intervention consists of eight modules and can be effectively applied to various disorders and problems.
  • Space for depression — BEHAVIORAL
    Digital CBT program designed to minimize the impact of depression symptoms. Emphasizes CBT strategies and mindfulness through a series of seven structured modules.
  • Space for resilience — BEHAVIORAL
    This program is built from positive psychology principles and is designed to promote resilience and well-being through seven modules.

Study Details

Digital mental health interventions are a cost-effective and efficient approach to expanding the accessibility and impact of psychological treatments; however, little guidance exists for selecting the most effective program for a given individual. In the proposed study, decision rules will develop for selecting the digital program that is most likely to be the optimal intervention for each user. These treatment recommendations can be implemented in the context of large healthcare delivery systems to improve the delivery of digital mental health interventions at scale. The overarching aim of the current study is to better understand for whom and how leading digital interventions work in a large healthcare setting. The study builds on the existing literature and follows expert recommendations by using machine learning (ML) methods to develop precision treatment rules (PTRs) for three leading digital interventions for emotional disorders (e.g., anxiety, depression, and related mental health disorders). Specifically, ML methods will be used to develop PTRs to optimize clinical outcomes and associated intervention engagement. This study will leverage a unique partnership between Boston University (BU), SilverCloud Health (SC)--a leading provider of digital mental health care--and Kaiser Permanente (KP)--one of America's leading health care providers. A clinical trial (RCT) will be conducted to evaluate the relative effectiveness of three distinct empirically supported digital mental health interventions (from SC's existing library of programs) in a sample recruited from KP primary care and other clinical settings. Data from this trial will be used to develop theoretically and empirically informed, reliable selection algorithms for managing treatment delivery decisions. Algorithms will be validated in a separate "holdout" dataset by examining whether allocation to predicted optimal treatment is associated with superior outcomes compared to allocation to a non-optimal treatment. The role of user engagement will be determined, and other mechanisms in treatment outcome.

Key Dates

First listed
Oct 5, 2022
Start date
Apr 12, 2023
Status verified
Jul 2026
Primary completion
Jul 31, 2027
Completion
Jul 31, 2027

Study Design

Enrollment
1,800 participants (estimated)
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT

Arms

  • Experimental: The Unified Protocol for Transdiagnostic Treatment of Emotional Disorders (UP)
    Digital, transdiagnostic, emotion-focused CBT intervention that consists of five "core" modules or components that have been shown to target temperamental characteristics (i.e., neuroticism) and resulting emotion dysregulation that are believed to underlie all anxiety, depressive, and emotional disorders. The core components of the program are psychoeducation, mindfulness, cognitive flexibility, behavioral strategies to counter emotion-driven behaviors, interoceptive, and emotion exposures.
  • Active Comparator: Space from Depression (SFD)
    Digital CBT program designed to minimize the impact of depressive symptoms. This program emphasizes the use of cognitive behavioral strategies as well as mindfulness through a series of seven structured modules. The core components of the program include psychoeducation around the relationship between thoughts, feelings, and behaviors; cognitive behavioral practices aimed at restructuring negative beliefs; behavioral strategies to improve self-esteem; and mindfulness techniques that focus attention on the present moment.
  • Active Comparator: Space for Resilience (SFR)
    Digital program based on positive psychology principles and designed to promote resilience and well-being through a series of seven modules. The core components of the program include psychoeducation, values exploration, building relationships, promoting self-esteem and self-efficacy, and building gratitude and optimism.

Primary Outcome Measure

Change from baseline well-being at week 12 [ Time Frame: Baseline, 4-weeks following baseline, 8-weeks following baseline, and 12-weeks following baseline ]

Central Contacts

Locations (1)

FacilityCityStateZIPSite coordinators
Center for Anxiety and Related DisordersBostonMassachusetts02115
Todd Farchione, Ph.D.
[email protected]
617-353-9610

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By research site
Center for Anxiety and Related Disorders· Boston, MA

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