Impact of Biofeedback Interventions on Driving Performance in Individuals with Persistent Post-Concussive Symptoms
Bibliographic record
Abstract
Low resolution electromagnetic tomography (LoRETA) neurofeedback and heart rate variability (HRV) biofeedback may improve driving ability by enhancing attention, impulse control, and peripheral vision, and reducing stress. However, it is unclear whether combined LoRETA neurofeedback and HRV biofeedback can improve driving performance for individuals experiencing persistent post-concussive symptoms (PPCS). In this study, seven individuals with PPCS completed an eight-week LoRETA neurofeedback and HRV biofeedback intervention. Changes in participants’ simulated driving performance and self-reported symptoms were measured and compared to two control groups: individuals with PPCS (n = 9), and healthy control participants (n = 8). Individuals in the intervention and PPCS control groups reported reduced PPCS severity (p < .05) compared to healthy control participants. Interestingly, individuals in the intervention group responded variably. These results indicate that more research is necessary to identify the subgroup of individuals that respond to LoRETA neurofeedback and HRV biofeedback and confirm these preliminary results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".