Currently accepted at: JMIR Research Protocols
Date Submitted: Mar 17, 2026
Date Accepted: Oct 31, 2026
This paper has been accepted and is currently in production.
It will appear shortly on 10.2196/95555
The final accepted version (not copyedited yet) is in this tab.
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Guided Learning for Ongoing Worries (GLOW) for Adults: Protocol for a Pilot Randomized Controlled Trial Comparing a Guided and Unguided Digital Mental Health Intervention for Anxiety
ABSTRACT
Background:
Anxiety disorders affect millions globally, yet barriers such as cost and clinician shortages limit treatment access. Digital Mental Health Interventions (DMHIs) and hybrid models offer promising solutions, but they suffer from low engagement and limited scalability. To address these challenges, we developed a guided DMHI for anxiety, Guided Learning for Ongoing Worries (GLOW). GLOW consists of one clinician check-in, a 4-week self-guided intervention delivered on an app, and weekly meetings with a Digital Health Navigator to review active and passive data collected throughout the intervention period. Designed based on theories of engagement and working alliance, GLOW aims to retain the “human touch” of hybrid care to support engagement while maximizing scalability through digital interventions and task-sharing.
Objective:
This pilot study aims to evaluate the feasibility, engagement, and preliminary efficacy of GLOW by comparing two delivery models: GLOW as designed (Full GLOW) and GLOW without human support (Unguided GLOW). The study also aims to explore the underlying mechanisms of change by examining the relationships between digital working alliance, engagement, skill acquisition, and clinical outcomes.
Methods:
The study uses a mixed-methods, parallel-group randomized controlled design. A sample of 176 participants with mid-to-moderate anxiety will be recruited and randomized to either Full GLOW or Unguided GLOW. Participant engagement is measured through recruitment/retention rate, self-reported and other-reported measures, and objective app usage logs. Clinical outcomes are assessed through anxiety symptoms and life satisfaction measured at pre-, mid-, and post intervention. Process outcomes, including digital alliance, emotion regulation, self-efficacy, and skill-acquisition, will be collected repeatedly during the intervention period to compare trajectories between the two groups. Furthermore, smartphone digital phenotyping data are collected to identify exploratory behavioral markers of symptom change. Post-intervention semi-structured quality interviews will be conducted to explore participants' experiences and identify specific barriers and facilitators to engagement across both delivery models.
Results:
The study is currently being reviewed by the institutional review board (IRB) for the Beth Israel Deaconess Medical Center (Protocol ID: 2026P000052).
Conclusions:
The findings from this study will provide critical insights into the value and cost-effectiveness of integrating human support into DHMIs, making evidence-based mental health support more scalable and engaging.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.