Modeling Stress-Recovery Status Through Heart Rate Changes Along a Cycling Grand Tour

BACKGROUND: Heart rate (HR) and HR variability (HRV) indices are established tools to detect abnormal recovery status in athletes. A low HR and vagally mediated HRV index change between supine and standing positions reflected a maladaptive training stress-recovery status. OBJECTIVES: Our study was focused on a female multistage cycling event. Its overall aim was twofold: (1) quantify the correlation between (a) the change in HR and HRV indices during an active orthostatic test and (b) subjective/objective fatigue, physical load, and training level indicators; and (2) formulate a model predicting the stress-recovery status as indexed by [Formula: see text] and ΔLnRMSSD (defined as the difference between standing and supine mean RR intervals and LnRMSSD, respectively), based on subjective/objective fatigue indicators, physical load, and training levels. METHODS: Ten female cyclists traveled the route of the 2017 Tour de France, comprising 21 stages of 200 km on average. From 4 days before the beginning of the event itself, and until 1 day after its completion, every morning, each cyclist was subjected to HR and HRV measurements, first at rest in a supine position and then in a standing position. The correlation between HR and HRV indices and subjective/objective fatigue, physical load, and training level indicators was then computed. Finally, several multivariable linear models were tested to analyze the relationships between HR and HRV indices, fatigue, workload, and training level indicators. RESULTS: HR changes appeared as a reliable indicator of stress-recovery status. Fatigue, training level, and [Formula: see text] displayed a linear relationship. Among a large number of linear models tested, the best one to predict stress-recovery status was the following: [Formula: see text] 1,249.37+12.32V̇O(2)(max) + 0.36 km⋅week(–1)−8.83 HR(max)−5.8 RPE−28.41 perceived fatigue with an adjusted R(2) = 0.322. CONCLUSION: The proposed model can help to directly assess the adaptation status of an athlete from RR measurements and thus to anticipate a decrease in performance due to fatigue, particularly during a multistage endurance event.