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 or reliance of metrics in relation to perceived impact remain unclear. Objective This study assessed data from three wearables measuring sleep, continuous glucose, and exercise, together with the Profile of Mood State (POMS) dimensions alongside subjective ratings via the Readiness Advisor application (RA app) (Silicon Valley Exercise Analytics, svexa, Menlo Park, California, USA) 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 Global Positioning System (GPS) watches and heart rate monitors, Oura rings (Oura Health OY, Oulu, Finland), 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 analysis) revealed individual variability. Conclusions These findings indicate an influence of external aspects and advocate for a multifaceted approach to the 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 understanding of the athlete status, advancing high-performance monitoring and athletic health management. Key Points - This study examines the correlation between nine subjectively rated and objectively measured data parameters collected from 20 national team endurance athletes during one year of regular training and competitions. - Group level analyses only showed moderate significant correlations between the objective and subjective counterparts in three cases. When accounting for individual differences, all subjective parameters presented moderate to strong associations with their objective counterpart (r values = 0.39-0.81). Partial regression analysis suggests that external factors contribute to the observed variability and limit their interchangeability. This advocates for monitoring regimens to include a combination of markers of both objective and subjective origin, to provide a reliable and nuanced understanding of the athlete situation for informed decision-making. - Individual-level analyses showed large variability, with athletes exhibiting non-, positive, and negative correlations. These results indicate the requirement of individually adapting both monitoring regimes, assessments and load-recovery management.
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