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Training adaptation and heart rate variability in elite endurance athletes: opening the door to effective monitoring.
Abstract
The measurement of heart rate variability (HRV) is often considered a convenient non-invasive assessment tool for monitoring individual adaptation to training. Decreases and increases in vagal-derived indices of HRV have been suggested to indicate negative and positive adaptations, respectively, to endurance training regimens. However, much of the research in this area has involved recreational and well-trained athletes, with the small number of studies conducted in elite athletes revealing equivocal outcomes. For example, in elite athletes, studies have revealed both increases and decreases in HRV to be associated with negative adaptation. Additionally, signs of positive adaptation, such as increases in cardiorespiratory fitness, have been observed with atypical concomitant decreases in HRV. As such, practical ways by which HRV can be used to monitor training status in elites are yet to be established. This article addresses the current literature that has assessed changes in HRV in response to training loads and the likely positive and negative adaptations shown. We reveal limitations with respect to how the measurement of HRV has been interpreted to assess positive and negative adaptation to endurance training regimens and subsequent physical performance. We offer solutions to some of the methodological issues associated with using HRV as a day-to-day monitoring tool. These include the use of appropriate averaging techniques, and the use of specific HRV indices to overcome the issue of HRV saturation in elite athletes (i.e., reductions in HRV despite decreases in resting heart rate). Finally, we provide examples in Olympic and World Champion athletes showing how these indices can be practically applied to assess training status and readiness to perform in the period leading up to a pinnacle event. The paper reveals how longitudinal HRV monitoring in elites is required to understand their unique individual HRV fingerprint. For the first time, we demonstrate how increases and decreases in HRV relate to changes in fitness and freshness, respectively, in elite athletes.
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“HRV, i.e., the beat-to-beat variation over a distinct time period, under rest conditions or while exercising is an interesting marker of internal load because the internal load quantification by HRV indices is progressive and takes the individual fitness level as well as daily readiness and actual health state into account”
From Dose-Response Matters! - A Perspective on the Exercise Prescription in Exercise-Cognition Research (2019) · cites this paper as Plews et al., 2013
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Metadata
- Authors
- Plews, D. J., Laursen, P. B., Stanley, J., Kilding, A. E., & Buchheit, M.
- Affiliations
- Auckland University of Technology (NZ)
- The University of Queensland (AU)
- Aspire Academy (QA)
- Corresponding author
- Plews, Daniel J.
- Venue
- Sports Medicine
- Published
- 2013-07-13
- Volume
- 43
- Issue
- 9
- Pages
- 773-781
- ISSN
- 0112-1642 (ISSN-L)
- 1179-2035
- OpenAlex
- W2082001224
- Cited by
- 617
- References
- 67
- Citations by year
- 2026: 84 · 2025: 61 · 2024: 40 · 2023: 30 · 2022: 53 · 2021: 60 · 2020: 63 · 2019: 47 · 2018: 47 · 2017: 42 · 2016: 42 · 2015: 22 · 2014: 22 · 2013: 3
- FWCI
- 5.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Sports Medicine (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 effects, endurance athletes, elite athletes, training load monitoring, vagal tone, HRV indices, cardiorespiratory fitness, athlete readiness, Olympic athletes, world champions
- MeSH terms
- Adaptation, Physiological, Physical Exertion, Heart Rate, Humans, Sports, Time Factors, Athletic Performance, Physical Conditioning, Human
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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