Visual respiratory biofeedback to improve visuospatial cognition and cardiac interoception in migraineurs: a study protocol for a randomized controlled trial
Bibliographic record
Abstract
Objectives Migraine is a complex neurological disorder that typically presents with unilateral cephalgia associated with cognitive impairment and reduced interoception. These symptoms result in socio-economic repercussions due to reduced productivity, efficiency, and work performance. Therefore, along with headache management, improving cognition and interoception should also be significant therapeutic targets to effectively manage migraine. To achieve this, we propose to explore the role of a yoga-based visual respiratory biofeedback (VRB) as a possible therapeutic strategy. Methods and analysis At least 64 participants will be recruited for the trial after screening for eligibility criteria, using the migraine screening questionnaire and Montreal cognitive assessment test. They will be randomly allocated (1:1) to either the experimental group receiving a 20-min session of yoga-based VRB or the control group who will be asked to watch a documentary film for the same duration. Visuospatial cognition will be assessed by the Corsi block-tapping task, and cardiac interoceptive accuracy will be assessed by the heartbeat counting task at baseline and immediately after the intervention. Based on the distribution and variance of the data obtained, analysis will be conducted based on linear mixed models using SPSS version 28.0.1.0, with a two-sided p-value of < 0.05 considered to be statistically significant. Discussion To the best of our knowledge, this is the first study to design and assess the effects of yoga-based biofeedback therapy on cognition and cardiac interoception in migraineurs. Furthermore, we postulated that pranayama's therapeutic effects might be enhanced by using visual yogic respiratory biofeedback. Considering the socio-economic burden of migraine, if found effective, VRB investigated in the trial could be considered as a therapeutic strategy. Clinical trial registration ClinicalTrials.gov CTRI, CTRI/2023/03/050430.
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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 | 0.024 | 0.021 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.052 | 0.008 |
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".