Heart rate biofeedback attenuates effects of mental fatigue on exercise performance
Bibliographic record
Abstract
Objective: Cognitive control exertion increases mental fatigue and impairs subsequent physical performance. Few studies have investigated intervention strategies to attenuate the effects of mental fatigue on exercise behavior. This study examined heart rate (HR) biofeedback as a moderator of the effects of mental fatigue on vigorous-intensity exercise performance. Design: Within-subjects, crossover design. Methods: Participants (N=36) completed four 20-min sessions of self-paced, cycling exercise. Exercise was preceded by 10-min high or low cognitive control manipulations crossed with HR biofeedback or no feedback during exercise in a 2 (high vs. low cognitive control) X 2 (biofeedback vs. no feedback) factorial arrangement. Participants rated their intended rating of perceived exertion (RPE) and goal commitment prior to and following the cognitive control manipulations. HR and total work were recorded during each exercise session. Results: Mental fatigue was significantly greater following high cognitive control exertion, which corresponded with significant reductions in intended RPE and goal commitment. Participants exercised at a lower average HR and performed less work in the high cognitive control/no feedback condition, however, with HR biofeedback following high cognitive control exertion participants attained similar HRs and total work performed to the low cognitive control conditions, which did not differ. Conclusions: HR biofeedback improves self-regulation of exercise behavior in a mentally fatigued state. Without biofeedback, fatigued people may down-regulate exercise intensity. Findings have implications for the use of HR monitoring devices to improve intensity-based exercise prescription adherence when confronted with barriers such as mental fatigue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
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; both teacher heads agree on what is shown here.
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".