Sport Biofeedback: Exploring Implications and Limitations of Its Use
Bibliographic record
Abstract
Biofeedback is among the various self-regulation techniques that mental performance consultants can utilize in their practice with athletes. Biofeedback produces psychophysiological assessments in real time to enhance awareness of thoughts and emotions. Quantitatively, research shows that biofeedback can facilitate self-regulation, allowing an athlete to gain control over psychophysiological responses that could be detrimental to performance. With technology becoming a widespread tool in monitoring psychophysiological states, an exploration of consultants’ use of biofeedback, their perceptions of effectiveness, and limitations of their use was warranted to qualitatively evaluate efficiency of the tool. A qualitative descriptive approach was taken through semistructured interviews with 10 mental performance consultants. Inductive reasoning uncovered three themes: positive implications, practical limitations, and equipment options. With biofeedback, athletes have the ability to develop a deeper level of self-awareness and thereby facilitate the use of self-regulation strategies intended for optimal performance states and outcomes.
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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 | 0.096 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".