Effects of a heart rate variability biofeedback intervention on athletes' psychological responses following injury
Bibliographic record
Abstract
The purpose of this study was to examine the effects of a heart rate variability biofeedback (HRV BFB) intervention on pain catastrophizing and the psychological response variables of injured athletes. HRV BFB effects on athletes' physiological indices including heart rate variability (HRV), heart rate, and respiration were also assessed. Participants were 28 athletes who had sustained a moderate to severe musculoskeletal sports injury, ranging in age from 18 to 36 years (Mage = 20.82, SD = 3.41). All participants were out of training and competition and engaged in a rehabilitation program. This investigation was experimental in nature with a randomized, single-blinded study design. Participants were randomized into one of three conditions: HRV BFB intervention, HRV BFB placebo, or control conditions. Assessments of psychological outcomes and physiological indices were assessed at Baseline, Week 1, Week 2, and Week 3. Compared to placebo or control groups, athletes who received the HRV BFB intervention reported significantly greater reductions in devastation (F(6, 75) = 5.84, p < .001, η2 = .32), isolation (F(5.53, 69.07) = 2.69, p = .024, η2 = .18), pain magnification, and resting respiration rate (F(6, 72) = 7.37, p < .001, η2 = .38); these athletes also reported significantly greater improvements in reorganization (F(6, 75) = 2.73, p = .019, η2 = .18) and low-frequency HRV (F(6, 72) = 2.72, p = .019, η2 = .19). The findings suggest that HRV BFB may hold potential to improve athletes' psychological responses and pain magnification after sustaining an injury and that it shows promise as a useful psychological intervention for injury rehabilitation.
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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) | 0.001 | 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 | 0.000 | 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".