Author(s): PHILIPPE, G., PAQUIN, J., PARENT-ROBERGE, H., GHACHEM, A., BOULAY, P. , Institution: UNIVERSITÉ DE SHERBROOKE, Country: CANADA, Abstract-ID: 1762

INTRODUCTION:
Heart rate variability (HRV) is a simple, non-invasive marker reflecting the influence of the autonomic nervous system on the cardiovascular system. Recent studies suggest its usefulness for monitoring training load in endurance athletes, where HRV trends may indicate physiological adaptation or maladaptation, such as overtraining. However, multiple factors beyond training volume, such as competitive season, stress and sleep, are integral part of a student-athlete’s life. The objective of this study was to investigate whether these factors modulate HRV changes measured by a Garmin wearable device (GWD) over a full year in varsity athletes.
METHODS:
This study included 18 varsity athletes (10 males, 8 females, age: 21.94 ± 2.3 years, weight: 70.09 ± 16.1 Kg, VO2max: 58.66 ± 9.7 mlO2.min-1.kg-1, body fat percentage: 15 ± 8%). Each athlete wore a GWD over a 12-month period to monitor nocturnal HRV. Subjective feeling was collected weekly with the Short Recovery and Stress Scale (SRSS) (1). ANOVA and linear mixed effect model were performed to measure interaction between HRV and GWD stress score, sleep score, sleep duration and competitive season. Spearman correlations were conducted to measure association between SRSS and GWD stress score.
RESULTS:
Individual HRV values were categorized into three groups relative to baseline: above (76.97 ± 28.97ms ), normal (73.63 ± 23.59ms) and below (62.68 ± 25.66ms). The effect of month on HRV was significant but small (η2 = 0.012, p < 0.05). November, December and March differed significantly from the preceding month (p < 0.05). Approximately 13% of the HRV variation was explained by the model (r2 marginal = 0.13). However, when the GWD stress score was excluded, the model explained only 6% of the variation (r2 conditional = 0, r2 marginal = 0.063). GWD stress score monitoring showed weak, non-significant, and scattered associations with self-reported recovery and stress (ρ range 0.00057-0.5; p range 0.009-0.99).
CONCLUSION:
These findings suggest that GWD HRV can capture context-dependent fluctuations across an academic year in varsity athletes. Although monthly HRV variations were statistically significant, their small magnitude implies that changes may relate to academic context, though multiple factors likely influence these results. In contrast, GWD stress score showed limited agreement with subjective stress and recovery, highlighting that they reflect distinct dimensions of athlete load. Collectively, theses findings suggest that GWD HRV monitoring alone has limited utility for interpreting physiological adaptation or stress in this population and should be used with caution, particularly without complementary contextual and subjective information.

[1]Kellmann, M., & Kölling, S. (2019). Recovery and stress in sport: A manual for testing and assessment. Routledge, Taylor & Francis Group.