Investigating the use of pre‐training measures of autonomic regulation for assessing functional overreaching in endurance athletes
Bibliographic record
Abstract
The use of heart rate variability (HRV) to inform daily training prescription is becoming common in endurance sport. Few studies, however, have investigated the use of pre-training HRV to predict decreased performance or altered exercising autonomic response, typical of functional overreaching (FOR). Further, a new cardiac vagal tone (ProCVT) technology purports to eliminate some of the noise associated with daily HRV, and therefore may be better at predicting same-day performance. The purpose of this investigation was to examine if changes to resting HRV and ProCVT were associated with alterations in performance, maximal heart rate (HRmax), or heart rate recovery (HRrec) in FOR athletes. Twenty-eight recreational cyclists and triathletes were assigned to experimental/control conditions and underwent: 1 week of reduced training, 3 weeks of overload (OL) or regular training (CON), and 1 week of recovery. Testing occurred following the reduced training week (T1), post-3 weeks of training (T2), and following the recovery week (T3). Measures of resting HRV/ProCVT were collected each testing session, followed by maximal incremental exercise tests with HRrec taken 60 s post-exercise. Performance decreased from T1 to T2 in the OL group vs. CON (Δ-9 ± 12 vs. Δ9 ± 11 W, P < .001), as did HRmax (Δ-8 ± 4 vs. Δ-2 ± 4 bpm, P < .001). HRrec increased from T1 to T2 in the OL group vs. CON (Δ10 ± 9 vs. Δ2 ± 5 beats/min, P < .01). HRV and ProCVT did not change in either group. Same-day resting autonomic measures are insufficient in predicting alterations to performance or exercising HR measures following overload training.
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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 | 0.001 |
| 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 | 0.000 |
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".