Biofeedback and physiological reactivity in high-performance athletes
Bibliographic record
Abstract
Pressure and stress are salient experiences for high-performance athletes. Consequently, many of these athletes struggle to manage the demands of sport. High-performance athletes must adapt to changing performance demands, pressure and heightened psychophysiological activity; failure to do so reduces performance. Failure for athletes to self-regulate their psychophysiological responses impairs concentration, attention, motor functioning and coordination, and increases distraction. Sustained sympathetic nervous system activation associated with arousal responses leads to deteriorating health outcomes including depression, illness and burnout. This further emphasizes the importance of athletes having effective coping and self-regulatory strategies to optimize recovery in sport. Researchers who study biofeedback (BF) training assert that it is an effective method for athletes to achieve superior performance under pressure and optimal recovery. In this study, I explored the effects of BF training on psychophysiological recovery (skin conductance and respiration rate) and reaction time in university-level athletes (N = nine). Participants were randomized into experimental and control conditions and underwent a baseline assessment, a training phase and follow-up assessment. Experimental participants received five sessions of BF respiration training and the control participants underwent five sessions of guided breathing training, without the use of BF equipment. Using nonparametric analyses, no significant differences were found in the variables of interest between the groups. However, interesting trends were observed for changes in the skin conductance response and respiration rate. Implications for athletes' performance, coping and recovery and limitations to this study will be discussed. Keywords: biofeedback, athletes, psychophysiological arousal, self-regulation, recoveryAcknowledgments: Dr. Adrienne Leslie-Toogood, Dr. Melanie Gregg, Dr. Lorna Jakobson
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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 | 0.000 | 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) | 0.001 | 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".