Is heart rate a convenient tool to monitor over-reaching? A systematic review of the literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it