Wearable-Based Assessment for Relapse Prediction Following Repetitive Transcranial Magnetic Stimulation for Depression: Protocol for a Feasibility Study (WARN-D Study) (Preprint)
Bibliographic record
Abstract
Abstract Background A significant proportion of patients with major depressive disorder do not achieve remission after 2 antidepressant trials and are considered to have treatment-resistant depression (TRD). Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for TRD. However, relapse rates among remitters within the first year post treatment are significant, and there are no validated markers of relapse. Wearable devices have shown positive results for longitudinal monitoring of health metrics, and may be a promising tool for early detection of relapse following rTMS treatment. Objective This study aims to evaluate the feasibility of a wearable device (Oura Ring) to monitor individuals receiving rTMS treatment for depression. We will also explore the utility of wearable-derived data as preliminary markers of treatment response and depressive relapse in a 6-month follow-up period. Methods This single-arm pilot study will recruit 25 outpatients with a major depressive episode receiving rTMS at 2 tertiary hospitals in Ontario, Canada. Participants will be required to use a smart ring throughout the treatment course and during the 6-month follow-up. Clinical assessments including the Montgomery-Åsberg Depression Rating Scale (MADRS), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), Insomnia Severity Index (ISI), Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5), and World Health Organization-Five Well-Being Index (WHO-5) will be collected at baseline, treatment end, and 3- and 6-month follow-ups, alongside biweekly PHQ-9 and GAD-7 scores. The primary outcomes will be defined by feasibility measures (ie, recruitment, adherence, retention, missing data, and usability). Exploratory outcomes will include the assessment of preliminary associations between wearable-derived features and clinical outcomes and depressive relapse. Results The study was funded in December 2025, and data collection will commence following research ethics approval. At the time of manuscript submission, the study protocol was under review at the Research Ethics Board of the participating institutions, and no study-related activities, including participant recruitment, had started. Data analysis and dissemination of findings will occur following completion of data collection. Conclusions This study will provide initial evidence on the feasibility and utility of wearable-based digital phenotyping in individuals receiving rTMS for TRD. Our findings will inform the design of future large-scale studies aimed at wearable-supported relapse prevention and precision monitoring in depression care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".