Bio/neurofeedback: An effective tool for athlete psychological preparation leading up to and during the 2010 Winter Olympic Games
Bibliographic record
Abstract
Bio/neurofeedback training targets the development of an athlete's psychological skills, such as focus, management of anxiety, and recovery/relaxation ability in order to enhance overall performance. Focus training provides tools for the athlete to help them develop alertness and concentration and manage emotions, fears and distractions. Anxiety management training equips the athlete with skills to shift into a parasympathetic dominant state at will and regulate, or turn off, the stress response. Training to engage the body in deep relaxation serves to release stress from the nervous system. In the present study, funded byOwn The Podium (OTP), 16 athletes were trained for 30-45 hours, using bio/neurofeedback instrumentation to learn to control physiological and neurological function. Sensors were attached to the body for the purpose of acquiring biological and neurological signals such as those produced by muscles, sweat glands, body temperature, respiration, and heart rhythm (i.e. biofeedback modalities) and brainwaves (i.e. neurofeedback modality). Each of the 16 athletes who participated in the three year study improved their overall ability to self-regulate. Most improvement was in self-regulation of respiration rate, muscle tension and peripheral body temperature. The electrodermal response (arousal regulation) and heart rate variability ranges did not reach the required criteria consistently, indicating that work would need to be continued on those modalities.
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 | 0.001 |
| 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) | 0.004 | 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".