Validating subjective ratings with wearable data for a nuanced understanding of load-recovery status in elite endurance athletes

Background The emergence of wearable technology offers enhanced real-time health management, including sleep, recovery, and exercise optimization. Despite their potential to monitor load-recovery parameters in elite athletes, the selection, combination, and interpretation of reliable metrics remain unclear. Objective This study assessed data from three wearables measuring sleep, continuous glucose, and exercise, together with the Profile of Mood State dimensions alongside subjective ratings via the Readiness Advisor application to evaluate their association and value in load-recovery monitoring. Methods Twenty national team endurance athletes, competing at the highest international level, were monitored during one year of training, recovery, and competitions. Data collections were made with GPS watches and heart rates monitors, Ōura rings, continuous glucose monitors, POMS questionnaires and subjective ratings in the RA app. Results Significant correlations were found between each RA question and its counterpart in a linear mixed model (r values = 0.39–0.81). However, time series analysis through autoregressive integrated moving average (ARIMA) revealed individual variability. Conclusions These findings indicate an influence of external aspects and advocate for a multifaceted approach to assessment of load-recovery balance, well-being and performance. Moreover, personalized analyses proved more accurate than group averages, emphasizing the need for individualized monitoring. Integrating subjective and objective data appears essential for nuanced athlete status understanding that could be developed into future athletic digital twins, advancing high-performance monitoring and the precision and effectiveness of athletic health management.