Heart rate variability mediates motivation and fatigue throughout a high-intensity exercise program
Bibliographic record
Abstract
High-intensity exercise interventions are often promoted as a time-efficient public health intervention to combat chronic disease. However, increased physical effort and subsequent fatigue can be barriers to long-term maintenance of high-intensity exercise programs. The purpose of the present study was to determine if heart rate variability (HRV) mediated state traits related to exercise program adherence. Fifty-five healthy men and women (ages 19–35 years) used a commercially available smartphone application to monitor daily HRV status throughout a 6-week high-intensity exercise intervention. Participants reported state motivation to exercise and global physical fatigue immediately prior to each exercise session. Temporary shifts toward increased parasympathetic reactivation (p = 0.030) resulted in significant increases in daily fatigue (p < 0.001) and decreases in motivation to exercise (p = 0.028). Through modulation of exercise volume, in response to these temporary shifts in HRV, these effects were reversed (p < 0.001) via increased parasympathetic withdrawal (p = 0.018). For the first time, these data demonstrate a mediating effect of HRV on adherence-related trait states throughout a high-intensity exercise program. Applied strategies, such as appropriately timed exercise volume moderation, may be able to leverage this effect and help facilitate long-term exercise program maintenance. Novelty These data establish a link between expected shifts in HRV throughout high-intensity exercise programs with motivation to participate and physical fatigue. Modulation of training volume, in response to these shifts, can optimize adherence-related behavioral responses during high-exercise programs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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) | 0.002 | 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".