Bridging Lab and Field: Predicting Laboratory Thresholds from Outdoor Trail-Running Data for Field-Based Performance Diagnostics

Abstract

Introduction: Accurate physiological performance diagnostics traditionally require laboratory-based assessments such as lactate testing 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 in particular involves fluctuating gradients and variable surfaces that limit the applicability of conventional indoor protocols (de Waal et. al., 2021). 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, we implemented a standardized trail-running protocol and aimed to examine how heart rates (HR) measured during the outdoor protocol correspond 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) completed (1) a 3×1.2 km trail-running protocol performed at progressively increasing intensities across laps on a standardized elevation profile, and (2) an 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. The laboratory thresholds were determined using the modified D-Max method and subsequently cross-checked against ventilatory threshold criteria (Marcin et. al., 2020). The outdoor course was segmented into physiologically meaningful sectors with homogeneous gradients. Relative sector times were extracted from high-resolution GNSS–IMU data, while mean sector heart rates were obtained from Polar chest belt measurements. 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 very strong correlations with mean HR in the second lap of the field test, especially in Sector 1 (Uphill) and Sector 2 (Flat) (r = 0.89–0.94, p < .001). Bias analysis confirmed the closest agreement with indoor anaerobic threshold HR in Lap 2, Sector 2. The MLR-LOOCV model predicted anaerobic threshold HR with high accuracy (RMSE: 4.343 bpm; CCC: 0.940). Aerobic threshold HR showed weaker relationships with mean-sector HR (highest r ≈ 0.76), meaning participants exceeded aerobic threshold even during the first lap in the field test, limiting mapping resolution at lower intensities.

Discussion/Conclusion: The specific trail-running protocol applied in this study could reliably identify HR at anaerobic threshold, with Lap 2 representing the primary threshold-relevant intensity zone. Moreover, threshold HR was predicted solely from outdoor performance and physiological data, demonstrating the feasibility of field-based performance diagnostics in trail running. This approach offers a practical, sports-oriented, and ecologically valid method for threshold assessment, eventually expanding performance testing beyond laboratory settings. Future work will refine prediction algorithms, extend to other sports and derive full training-zone prescriptions directly from outdoor data.

References

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Marcin, T., Eser, P., Prescott, E., Prins, L. F., Kolkman, E., Brouwers, R. W. M., Beckers, P., Cornelissen, V., Van der Velde, A. E., Van der Velde, E. E., Wilhelm, M., & EU-CaRE Study Group. (2020). Training intensity and improvements in exercise capacity in elderly patients undergoing European cardiac rehabilitation – the EU-CaRE multicenter cohort study. PLOS ONE, 15(11), Article e0242503. https://doi.org/10.1371/journal.pone.0242503

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