Examining the Effects of an Interspersed Biofeedback Training Intervention on Physiological Indices
Bibliographic record
Abstract
The study aimed to determine whether athletes who practice biofeedback are able to self-regulate by reaching resonance frequency and gaining physiological control quicker than if practice time integrates imagery or a rest period. Intervention effectiveness (e.g., intervention length, time spent training) was also explored. Twenty-seven university athletes were assigned to one of three groups: (a) biofeedback (i.e., continuous training), (b) biofeedback/imagery (i.e., interspersed with imagery), and (c) biofeedback/rest (i.e., interspersed with a rest period). Five biofeedback sessions training respiration rate, heart rate variability, and skin conductance were conducted. A repeated-measure analysis of variance showed a significant interaction between groups over time (p ≤ .05) for respiration rate, heart rate variability, and skin conductance, indicating that resonance frequency and physiological control was regained following imagery or a rest period. Postmanipulation check data found intervention length and training time to be sufficient. Combining imagery with biofeedback may optimize management of psychophysiological processes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".