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.
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 blended care models offer promising solutions, but 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 digital data collected throughout the intervention period. Designed based on theories of engagement and working alliance, GLOW aims to retain the “human touch” of blended care to support engagement while maximizing scalability through digital interventions and task-sharing.
Objective:
This pilot study aims to assess the feasibility and acceptability of GLOW. As a secondary aim, we will explore the contribution of human support to user engagement by comparing two delivery models, GLOW as designed (Full GLOW) and GLOW without human support (Unguided GLOW).
Methods:
The study uses a mixed-methods, parallel-group randomized controlled design. A sample of 30 participants with mild-to-moderate anxiety will be recruited and randomized to either Full GLOW or Unguided GLOW. Qualitative and quantitative measures of feasibility and acceptability will be assessed. Post-intervention semi-structured interviews will be conducted to explore participants' experiences and identify specific barriers and facilitators to engagement across both delivery models. Participant engagement is measured through self-reported and other-reported measures, and objective app usage logs. Preliminary clinical and process outcomes as well as smartphone digital phenotyping data are assessed.
Results:
Data collection began on July 31, 2026, and 19 participants had been enrolled by September 30, 2026. Data collection is expected to be completed in December 2026.
Conclusions:
The findings from this study will provide preliminary insights to inform future evaluations of the value and cost-effectiveness of integrating human support into Digital Mental Health Interventions, including its potential to make 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.