Parpa, Koulla
ORCID: 0000-0002-1139-7731, Georgiadis, Antreas, Intziegianni, Konstantina
ORCID: 0000-0002-7546-6767, Govindasamy, Karuppasamy and Michaelides, Marcos
ORCID: 0000-0002-9226-4657
(2025)
Heart rate variability and subjective indicators of recovery in U19 soccer players.
Journal of Sport Science & Innovation, 1
(1).
pp. 30-40.
ISSN 3108-6012
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Official URL: https://doi.org/10.65668/d1x61b41
Abstract
The purpose of this study was to investigate the relationship between HRV parameters and subjective measures of recovery in U19 elite soccer players. Eleven soccer players (age 17.64 ± 1.03 years, weight 72.73 ± 6.05 kg, height 177.27 ± 5.83 cm) from the same team volunteered to participate in the study. Morning HRV measures were collected using photoplethysmography via the HRV4 training smartphone application, along with self-reported data (fatigue levels, sleep quality from the previous night, morning mental energy, soreness, and the previous day’s RPE). Means and standard deviations were calculated for HR, RMSSD and LnRMSSD, subjective responses and anthropometric measurements. Pearson product-moment correlation coefficients were used to assess the associations between HRV parameters and self-reported measures. A total of 184 HRV measurements and self-reported parameters were evaluated for the 11 players. Based on the analysis, both LnRMSSD and RMSSD showed strong positive correlations (p<0.01) with sleep quality and mental energy. In contrast, they were negatively correlated (p<0.01) with fatigue and RPE. Self-reported variables indicated that better sleep was significantly associated with higher energy and lower fatigue, while greater mental energy was also linked with reduced fatigue. Our findings suggest that daily monitoring of HRV using accessible smartphone technology may provide coaches and practitioners with a practical, non-invasive tool to track autonomic function, recovery status and training adaptations in young athletes. Integrating HRV data with subjective wellness measures could enable more individualized prescriptions, support early detection of fatigue or overload, and ultimately optimize performance.
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