Introduction
Accurate physiological performance diagnostics traditionally require laboratory-based assessments such as lactate threshold tests and cardiopulmonary exercise testing (Petek et. al., 2021; Cerezuela-Espejo et al., 2018). While these methods are well established, translating threshold determination into natural environments remains challenging. Trail running and cross-country (XC) skiing, in particular, involve changing terrain, which limits the applicability of conventional indoor protocols (de Waal et al., 2021). In XC skiing, upper body power and technical efficiency further contribute to the changing performance demands across terrain (Mahood et al., 2001).
Although recent research has compared laboratory and field testing in endurance sports (Giovanelli et al., 2020), reliable field-based methods for threshold determination without portable respiratory gas analysis are still lacking. To address this gap, we implemented a standardized protocol applicable to both trail running and XC skiing. The aim was to examine how heart rate (HR) measured during an outdoor protocol corresponds to laboratory-derived threshold HR, and to assess whether physiological thresholds can be predicted solely from outdoor data, enabling laboratory-free performance diagnostics.
Methods
Sixteen trained runners (9 male, 7 female; age 33.8 ± 8.3 years) and 20 XC skiers (11 male, 9 female; age 40.55 ± 11.7 years; self-selected classic or skating technique) completed two comparable study protocols: (1) Outdoor test consisting of three laps performed at progressively increasing intensities on a standardized sport-specific course, with a common XC skiing course for both techniques and (2) Incremental laboratory treadmill lactate-threshold test, separated by a standardized recovery period. During both tests HR, capillary blood lactate, and rating of perceived exertion were collected; respiratory gas exchange was only measured indoors. Laboratory thresholds were determined using the modified D-Max method and subsequently cross-checked against ventilatory threshold criteria (Marcin et al., 2020). Both the trail running and XC skiing courses were segmented into physiologically meaningful sectors with homogeneous gradients. High-resolution GNSS-IMU data were collected, and HR was analyzed on a sector-wise basis. Pearson correlation and Bias analyses were performed to evaluate the relationship between HR at the laboratory thresholds and outdoor sector-based HR across laps. A multiple linear regression (MLR) model with leave-one-out cross-validation (LOOCV) was applied to predict threshold HR using outdoor parameters solely.
Results
Laboratory-derived anaerobic threshold HR showed strong correlations with mean sector HR of the flat and uphill sector of lap 2 for trail running (r = 0.89–0.94, p < .001) and the flat and uphill sector of lap 3 for XC skiing (r = 0.85-0.86, p < .001). Bias analyses confirmed these findings. The MLR-LOOCV model predicted anaerobic threshold HR with a mean absolute error of 3.58 bpm in trail running and 4.76 bpm in XC skiing, with higher prediction accuracy in trail running. Aerobic threshold HR showed weaker and more variable relationships in both disciplines.
Discussion/Conclusion
The protocol demonstrated ecological validity for field-based anaerobic threshold assessment in both sports. The accurate prediction of anaerobic threshold HR from outdoor data alone, with acceptable errors in both disciplines, supports this approach as a practical, laboratory-free alternative for endurance performance diagnostics. The slightly higher prediction error in XC skiing may reflect greater inter-individual variability in technique and pacing strategy, but also the influence of varying snow conditions, which represent an additional source of external variability not present in trail running. Overall, these findings support the protocol’s applicability across endurance sports, while highlighting the need for future work to improve prediction accuracy and more thoroughly address sport-specific confounders.
References
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