Is heart rate a convenient tool to monitor over-reaching? A systematic review of the literature
Bibliographic record
Abstract
OBJECTIVE: A meta-analysis was conducted on the effect of overload training on resting HR, submaximal and maximal exercise HR (HR), and heart rate variability (HRV), to determine whether these measures can be used as valid markers of over-reaching. METHODS: Six databases were searched using relevant terms and strategies. Criteria for study inclusion were: participants had to be competitive athletes, an increased training load intervention had to be used, and all necessary data to calculate effect sizes had to be available. An arbitrary limit of 2 weeks was chosen to make the distinction between short-term and long-term interventions. Dependent variables were HR and HRV (during supine rest). Standardised mean differences (SMD) in HR or HRV before and after interventions were calculated, and weighted according to the within-group heterogeneity to develop an overall effect. RESULTS: In these competitive athletes, short-term interventions resulted in a moderate increase in both resting HR (SMD = 0.55; p = 0.01) and low frequency/high frequency ratio (SMD = 0.52; p = 0.02), and a moderate decrease in maximal HR (SMD = -0.75; p = 0.01). Long-term interventions resulted in a small decrease in HR during submaximal (SMD = -0.38; p = 0.006) and maximal exercise (SMD = -0.33; p = 0.007), without alteration of resting values. CONCLUSION: The small to moderate amplitude of these alterations limits their clinical usefulness, as expected differences may fall within the day-to-day variability of these markers. Consequently, correct interpretation of HR or HRV fluctuations during the training process requires the comparison with other signs and symptoms of over-reaching to be meaningful.
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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 | 0.012 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.015 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".