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Relation between heart rate variability and training load in middle-distance runners
Abstract
PURPOSE: Monitoring physical performance is of major importance in competitive sports. Indices commonly used, like resting heart rate, VO2max, and hormones, cannot be easily used because of difficulties in routine use, of variations too small to be reliable, or of technical challenges in acquiring the data. METHODS: We chose to assess autonomic nervous system activity using heart rate variability in seven middle-distance runners, aged 24.6 +/- 4.8 yr, during their usual training cycle composed of 3 wk of heavy training periods, followed by a relative resting week. The electrocardiogram was recorded overnight twice a week and temporal and frequency indices of heart rate variability, using Fourier and Wavelet transforms, were calculated. Daily training loads and fatigue sensations were estimated with a questionnaire. Similar recordings were performed in a sedentary control group. RESULTS: The results demonstrated a significant and progressive decrease in parasympathetic indices of up to -41% (P < 0.05) during the 3 wk of heavy training, followed by a significant increase during the relative resting week of up to +46% (P < 0.05). The indices of sympathetic activity followed the opposite trend, first up to +31% and then -24% (P < 0.05), respectively. The percentage increasing mean nocturnal heart rate variation remained below 12% (P < 0.05). There was no significant variation in the control group. CONCLUSION: This study confirmed that heavy training shifted the cardiac autonomic balance toward a predominance of the sympathetic over the parasympathetic drive. When recorded during the night, heart rate variability appeared to be a better tool than resting heart rate to evaluate cumulated physical fatigue, as it magnified the induced changes in autonomic nervous system activity. These results could be of interest for optimizing individual training profiles.
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“The training status of athletes may affect HRV.83,84”
From Wearable sensors for monitoring the physiological and biochemical profile of the athlete (2019) · cites this paper as [83]
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Metadata
- Authors
- PICHOT, V., ROCHE, F., GASPOZ, J.-M., ENJOLRAS, F., ANTONIADIS, A., MININI, P., COSTES, F., BUSSO, T., LACOUR, J.-R., & BARTH??L??MY, J. C.
- Affiliations
- Université Jean Monnet (FR)
- Lyon 1 Université (FR)
- Venue
- Medicine & Science in Sports & Exercise
- Published
- 2000-10
- Volume
- 32
- Issue
- 10
- Pages
- 1729-1736
- ISSN
- 0195-9131 (ISSN-L)
- OpenAlex
- W2135284591
- Cited by
- 315
- References
- 36
- Citations by year
- 2026: 7 · 2025: 11 · 2024: 7 · 2023: 11 · 2022: 19 · 2021: 12 · 2020: 20 · 2019: 17 · 2018: 21 · 2017: 15 · 2016: 19 · 2015: 11 · 2014: 21 · 2013: 23 · 2012: 18
- FWCI
- 4.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medicine & Science in Sports & Exercise (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Heart Rate Variability and Autonomic Control
- Health Sciences › Medicine › Orthopedics and Sports Medicine › Sports Performance and Training
- Health Sciences › Medicine › Physiology (medical) › Cardiovascular and exercise physiology
- Keywords
- heart rate variability, training load, middle-distance runners, autonomic nervous system, sympathetic activity, parasympathetic activity, nocturnal heart rate variability, fatigue, training load monitoring, wavelet transform, Fourier transform
- MeSH terms
- Adult, Autonomic Nervous System, Electrocardiography, Fatigue, Fourier Analysis, Heart Rate, Humans, Longitudinal Studies, Male, Prospective Studies, Surveys and Questionnaires, Running, Case-Control Studies, Exercise Tolerance
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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