Feasibility of a 12-Week, Therapist-Independent, Smartphone-Based Biofeedback Treatment for Episodic Migraine in Adults: Single-Center, Open-Label, 1-Armed Trial
Bibliographic record
Abstract
Background: Biofeedback is an established treatment principle for migraine, but home-based therapy with proven efficacy is not available. Objective: This study aims to assess the feasibility, usability, and safety of 12 weeks of daily use of a novel medical device (Cerebri; Nordic Brain Tech AS) for therapist-independent multimodal biofeedback preventative treatment in adults with episodic migraine. Methods: In this open-label, one-armed trial, 20 adult participants with episodic migraine used Cerebri for 12 weeks. The primary outcome was the feasibility of the Cerebri system, measured by the level of adherence to daily biofeedback and electronic headache diary (eDiary) entries. Secondary outcomes were safety, usability, and efficacy. Results: Initial adherence to biofeedback was high (16/20, 80% in weeks 1-4), declining to 20% (4/20) by weeks 9-12. eDiary adherence remained high (15/20, 75% in weeks 9-12). Reduction in migraine days was not significant (-0.6, 95% CI -2.4 to 1.1 days; P=.47). App usability was impacted by software issues. No safety concerns were reported. Conclusions: Cerebri demonstrates potential in self-managed migraine treatment, with strong initial engagement and high safety. Usability issues, including technical bugs, were identified as the most important modifiable cause for the decline in adherence. This highlights the need for further app refinement to sustain user engagement.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".