Increases in vagal-related indices of resting and post-exercise HRV, post-exercise HRR, and HR acceleration are evident when positive adaptation to training has occurred, allowing for increases in performance. However, increases in post-exercise HRV and HRR also occur in response to overreaching, demonstrating that additional measures of training tolerance may be required to determine whether training-induced changes in these parameters are related to positive or negative adaptations. Resting HRV is largely unaffected by overreaching, although this may be the result of methodological issues that warrant further investigation. HR acceleration appears to decrease in response to overreaching training, and thus may be a potential indicator of training-induced fatigue.

Certain models of footwear and footwear characteristics can improve running economy. Future research in footwear performance should include measures of running performance.

Vagally mediated HRV during standing increased in response to functional overreaching (indicating potential parasympathetic hyperactivity) and also to improvements in performance. Thus, additional measures such as training tolerance are required to interpret changes in vagally mediated HRV.

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When controlling for pre-exercise HR, rHRI assessment at 120 W most sensitively tracked performance. Increased RMSSD following HT indicated heightened parasympathetic modulation in fatigued athletes. HRR was only sensitive to changes in training status when assessed after maximal exercise, which may limit its practical applicability.

MAS may be predicted from the average speed during a TT for any distance between 1200 and 2200 m, with 2000 m being optimal. Performance of a TT may provide a simple alternative to traditional MAS testing.

Heart rate (HR) and HR variability (HRV) infer readiness to perform exercise in athletic populations. Technological advancements have facilitated HR and HRV quantification via photoplethysmography (PPG). This study evaluated the validity of WHOOP’s PPG-derived HR and HRV against electrocardiogram-derived (ECG) measures. HR and HRV were assessed via WHOOP and ECG over 15 opportunities. WHOOP-derived pulse-to-pulse (PP) intervals were edited with WHOOP’s proprietary filter, in addition to various filter strengths via Kubios HRV software. HR and HRV (Ln RMSSD) were quantified for each filter strength. Agreement was assessed via bias and limits of agreement (LOA), and contextualised using smallest worthwhile change (SWC) and coefficient of variation (CV). Regardless of filter strength, bias (≤0.39 ± 0.38%) and LOA (≤1.56%) in HR were lower than the CV (10–11%) and SWC (5–5.5%) for this parameter. For Ln RMSSD, bias (1.66 ± 1.80%) and LOA (±5.93%) were lowest for a 200 ms filter and WHOOP’s proprietary filter, which approached or exceeded the CV (3–13%) and SWC (1.5–6.5%) for this parameter. Acceptable agreement was found between WHOOP- and ECG-derived HR. Bias and LOA in Ln RMSSD approached or exceeded the SWC/CV for this variable and should be interpreted against its own level of bias precision.

Performance reductions were most sensitively tracked by rHRIrun following HT. This may be due to rHRIrun being assessed at a higher intensity than rHRIcyc, inferred from a higher steady-state HR and supported by a stronger within-subject relationship between rHRIrun and performance in individuals with a lower V˙O2peak, in whom the same exercise intensity would represent a greater physiological stress. rHRI assessed at relatively high exercise intensities may better track performance changes.