Biofeedback as an intervention for persistent post-concussive symptoms: A randomized feasibility trial
Bibliographic record
Abstract
Background Case reports indicate that low-resolution electromagnetic tomography neurofeedback and heart rate variability biofeedback may improve physiological functioning in individuals with persistent post-concussive symptoms. However, it is unclear whether larger-scale studies are feasible. Purpose To evaluate the feasibility of a combined low-resolution electromagnetic tomography neurofeedback and heart rate variability biofeedback intervention for individuals with persistent post-concussive symptoms. Methods Individuals with persistent post-concussive symptoms were randomized into intervention and control groups, and their baseline and post-test assessments were compared to a healthy control group. Outcomes included self-report questionnaires, resting electroencephalograph and electrocardiograph recordings, and a driving simulation task. Participants in the intervention group completed three 20 min low-resolution electromagnetic tomography neurofeedback sessions per week and at-home heart rate variability biofeedback training every morning and night for 8 weeks. Feasibility was evaluated according to recruitment capability and sample characteristics, data collection procedures, suitability of the intervention and study procedures, management and implementation of the study intervention, and preliminary participant responses to the intervention. Results Thirty-three individuals were recruited and 24 completed this study (seven intervention participants, nine persistent post-concussive symptoms control participants, and eight healthy control participants). One-quarter of participants (four intervention participants and three persistent post-concussive symptoms control participants) experienced simulator sickness during the driving simulator task and had to withdraw from the study. Intervention participants had an 88% and 86% compliance rate for the low-resolution electromagnetic tomography neurofeedback and heart rate variability biofeedback sessions, respectively. Low-resolution electromagnetic tomography neurofeedback sessions took approximately 1 h to complete per participant. Preliminary analysis indicated that the intervention reduced electroencephalograph z -score deviation with a very large effect size ( d = 1.36) compared to the other study groups. Conclusions Pilot studies evaluating the efficacy of low-resolution electromagnetic tomography neurofeedback and heart rate variability biofeedback should be performed to confirm these preliminary findings. However, the protocol should be modified to reduce participant fatigue and withdrawal. This trial was registered with Clinicialtrials.gov (NCT03338036; https://clinicaltrials.gov/ct2/show/NCT03338036?term=03338036&draw=2&rank=1 ).
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".