Using Machine Learning to Optimize User Engagement and Clinical Response to Digital Mental Health Interventions
Using Machine Learning to Optimize User Engagement and Clinical Response to Digital Mental Health Interventions
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.
Kaiser Permanente (KP) members referred to SilverCloud Health (SC) through established operating procedures will be eligible to participate. After referral, potential participants will be informed of the study through the SC digital health platform. Interested participants will be provided information about the study through a digital version of an approved consent form. After signing consent, participants will complete baseline assessment questionnaires through Qualtrics, an online data collection platform.
After completing the baseline assessment, eligible participants will be randomly assigned to one of three existing digital mental health interventions hosted by SC: (1) Unified Protocol for Transdiagnostic Treatment of Emotional Disorders (UP), (2) Space from Depression (SFD), and (3) Space for Resilience (SFR). A SC program supporter will also be established at that time.
This differs from standard operating procedures at SC in that participants are generally able to select their preferred program from SC's offerings. There is nothing that would preclude participants from seeking a different SC treatment following study participation.
Potential participants will have up to two weeks to enroll in the study, complete the baseline assessment, and begin a treatment program. If the participant does not initiate treatment within two weeks, they will be withdrawn from the study though they may continue with clinical services at SC.
Following randomization, study staff at BU will send participants Qualtrics questionnaires via email throughout the treatment phase of the study, at 4-, 8-, and 12-weeks after treatment is initiated. The participant completes these questionnaires online. Regular asynchronous written support will be provided by SC supporters, consistent with their standard procedures.
Inclusion Criteria:
tfarchio@bu.edu(617) 353-9610
ajrosell@bu.edu(617) 353-9610