Modelling complex sports adaptation with electrocardiography: analysis of standard ECG, heart rate variability and QRST integral changes in elite athletes.
6 affiliations
- 1Heart and Vascular Center, Semmelweis University, Városmajor street 68, Budapest, 1122, Hungary.
- 2Department of Sports Medicine, Semmelweis University, Városmajor street 68, Budapest, 1122, Hungary.
- 3State Heart Hospital, Gyógy square 2, Balatonfüred, 8230, Hungary.
- 4Department of Electrical Engineering and Information Systems, University of Pannonia, Egyetem street 10, Veszprém, 8200, Hungary.
- 5Heart and Vascular Center, Semmelweis University, Városmajor street 68, Budapest, 1122, Hungary. [email protected].
- 6Department of Sports Medicine, Semmelweis University, Városmajor street 68, Budapest, 1122, Hungary. [email protected].
Abstract
Background Basic resting electrocardiographic changes caused by regular intense exercise are known characteristics of the athlete’s heart. Through detailed analysis of short-term ECG recordings, our aim was to study electrocardiographic, heart rate variability, and ventricular heterogeneity changes of the athlete’s heart as a function of factors influencing sports adaptation. Methods Analysis of 1-minute resting lead I ECG recordings was used to determine standard ECG parameters, time-domain indicators of heart rate variability, and mean and standard deviation of QRST integrals. Statistical analysis was performed in Python development environment. Results 500 healthy athletes (age: 19[16–24]y, male: 55%, training: 16.7 ± 6.5 h/w) were compared with 100 non-athlete controls (age: 21[17–23]y, male: 51%). Athletes had lower heart rate (68[61–78]vs.82[73–91]BPM, p < 0.001), increased p-wave amplitude (89[71–108]vs.80[62–96]µV, p < 0.001), PQ interval (152[140–168]vs.142[132–152]ms, p < 0.001), QRS width (90[82–100]vs.82[78–90]ms, p < 0.001), T-wave amplitude (257[198–311]vs.195[146–248]µV, p < 0.001), SDNN (64.7[47.2–87.2]vs.50.6[36.5–66.3]ms, p < 0.001), RMSSD (52.6[34.8–78.3]vs.36.4[26.8–52.6]ms, p < 0.001), pNN50 (31.1[12.7–50.9]vs.14.7[4.9–31.6]%, p < 0.001) and mean QRST integral (34.9[28.3–43.4]vs.26.1[19.7–33.9]mV*ms, p < 0.001) compared to controls. Mixed athletes had wider QRS compared to power athletes (94[90–102]vs.88[84–96]ms, p < 0.005). Male athletes had lower QTc, increased PQ interval, QRS width, T-wave amplitude, mean QRST integral, and its deviation. In the control group, females had higher resting heart rate and QTc duration, while AVNN and the relative deviation of QRST integral were lower. Many of the above parameters also showed weak correlation with age and weekly exercise hours. Conclusions Our findings indicate that a 1-minute lead I ECG recording can detect electrical differences between athletes and non-athlete controls and may provide a practical framework for future longitudinal studies of athletic cardiac adaptation. Graphical Abstract Abbreviations: AVNN: average N-N interval, SDNN: standard deviation of N-N intervals, RMSSD: root mean square of successive N-N differences, pNN50: proportion of N-N intervals that differ by at least 50 ms
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BibTeX
@article{boroncsok2026modelling,
title = {{Modelling complex sports adaptation with electrocardiography: analysis of standard ECG, heart rate variability and QRST integral changes in elite athletes.}},
author = {Dóra Boroncsok and Máté Babity and Márk Zámodics and Anna Menyhárt-Hetényi and Gergely Tuboly and György Kozmann and Regina Benkő and Alexandra Fábián and Zsuzsanna Ladányi and Dorottya Balla and Hajnalka Vágó and Attila Kovács and Béla Merkely and Orsolya Kiss},
journal = {BMC sports science, medicine \& rehabilitation},
year = {2026},
month = jul,
doi = {10.1186/s13102-026-01901-8},
url = {https://europepmc.org/article/MED/42907907},
note = {CC BY},
}
RIS
TY - JOUR
TI - Modelling complex sports adaptation with electrocardiography: analysis of standard ECG, heart rate variability and QRST integral changes in elite athletes.
AU - Dóra Boroncsok
AU - Máté Babity
AU - Márk Zámodics
AU - Anna Menyhárt-Hetényi
AU - Gergely Tuboly
AU - György Kozmann
AU - Regina Benkő
AU - Alexandra Fábián
AU - Zsuzsanna Ladányi
AU - Dorottya Balla
AU - Hajnalka Vágó
AU - Attila Kovács
AU - Béla Merkely
AU - Orsolya Kiss
PY - 2026
DA - 2026/07/01
JO - BMC sports science, medicine & rehabilitation
DO - 10.1186/s13102-026-01901-8
UR - https://europepmc.org/article/MED/42907907
AB - Background Basic resting electrocardiographic changes caused by regular intense exercise are known characteristics of the athlete’s heart. Through detailed analysis of short-term ECG recordings, our aim was to study electrocardiographic, heart rate variability, and ventricular heterogeneity changes of the athlete’s heart as a function of factors influencing sports adaptation. Methods Analysis of 1-minute resting lead I ECG recordings was used to determine standard ECG parameters, time-domain indicators of heart rate variability, and mean and standard deviation of QRST integrals. Statistical analysis was performed in Python development environment. Results 500 healthy athletes (age: 19[16–24]y, male: 55%, training: 16.7 ± 6.5 h/w) were compared with 100 non-athlete controls (age: 21[17–23]y, male: 51%). Athletes had lower heart rate (68[61–78]vs.82[73–91]BPM, p < 0.001), increased p-wave amplitude (89[71–108]vs.80[62–96]µV, p < 0.001), PQ interval (152[140–168]vs.142[132–152]ms, p < 0.001), QRS width (90[82–100]vs.82[78–90]ms, p < 0.001), T-wave amplitude (257[198–311]vs.195[146–248]µV, p < 0.001), SDNN (64.7[47.2–87.2]vs.50.6[36.5–66.3]ms, p < 0.001), RMSSD (52.6[34.8–78.3]vs.36.4[26.8–52.6]ms, p < 0.001), pNN50 (31.1[12.7–50.9]vs.14.7[4.9–31.6]%, p < 0.001) and mean QRST integral (34.9[28.3–43.4]vs.26.1[19.7–33.9]mV*ms, p < 0.001) compared to controls. Mixed athletes had wider QRS compared to power athletes (94[90–102]vs.88[84–96]ms, p < 0.005). Male athletes had lower QTc, increased PQ interval, QRS width, T-wave amplitude, mean QRST integral, and its deviation. In the control group, females had higher resting heart rate and QTc duration, while AVNN and the relative deviation of QRST integral were lower. Many of the above parameters also showed weak correlation with age and weekly exercise hours. Conclusions Our findings indicate that a 1-minute lead I ECG recording can detect electrical differences between athletes and non-athlete controls and may provide a practical framework for future longitudinal studies of athletic cardiac adaptation. Graphical Abstract Abbreviations: AVNN: average N-N interval, SDNN: standard deviation of N-N intervals, RMSSD: root mean square of successive N-N differences, pNN50: proportion of N-N intervals that differ by at least 50 ms
SN - 2052-1847
ER -