Background: Accurate assessment of cardiovascular parameters, including maximal heart rate (HRmax), resting heart rate (RHR), and heart rate reserve (HRR), is important for guiding training prescriptions in endurance athletes. Conventional age-based HRmax prediction formulas, particularly the widely used "220—age" equation, remain common despite substantial individual variation and documented underestimation in trained populations. Self-reported field data—defined as the highest heart rate recorded during maximal training or racing efforts—provide an ecologically valid alternative for examining HRmax in real-world settings. Methods: A cross-sectional study was conducted with 4,375 endurance athletes across multiple disciplines. Self-reported HRmax, RHR, weekly training load, and training history were collected via standardized web-based surveys administered globally from November 2022 to January 2023. Associations were evaluated using Pearson correlations, and the accuracy of age-based HRmax formulas was assessed via Bland-Altman analyses. Results: Age was strongly inversely correlated with HRmax (r = -0.60, p < 0.001) and HRR (r = -0.66, p < 0.001), but only weakly associated with RHR (r = 0.06, p < 0.001). Age-based formulas underestimated self-reported HRmax by 5-6 bpm (mean bias = -5.8 bpm for Fox; -4.8 bpm for Tanaka), with wide limits of agreement (Tanaka: -18.5 to +9.1 bpm; Fox: -20.2 to +8.6 bpm), indicating substantial individual variability. RHR was moderately inversely correlated with weekly training hours (r = -0.23, p < 0.001). No significant sex differences were observed. HRR exhibited high inter-individual variability. Conclusions: This exploratory analysis of self-reported field data demonstrates that age-based HRmax formulas show systematic underestimation and wide individual error in endurance athletes. These findings support the use of individualized, context-specific HRmax assessment, while highlighting the limitations of relying solely on age-based predictions.
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