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New Findings: What is the topic of this review? Biological and methodological factors associated with the variable changes in cardiorespiratory fitness in response to endurance training. What advances does it highlight? Several biological and methodological factors exist that each contribute, to a given extent, to response variability. Notably, prescribing exercise intensity relative to physiological thresholds reportedly increases cardiorespiratory fitness response rates compared to when prescribed relative to maximum physiological values. As threshold-based approaches elicit more homogeneous acute physiological responses among individuals, when repeated over time, these uniform responses may manifest as more homogeneous chronic adaptations thereby reducing response variability., Abstract: Changes in cardiorespiratory fitness (CRF) in response to endurance training (ET) exhibit large variations, possibly due to a multitude of biological and methodological factors. It is acknowledged that ∼20% of individuals may not achieve meaningful increases in CRF in response to ET. Genetics, the most potent biological contributor, has been shown to explain ∼50% of response variability, whilst age, sex and baseline CRF appear to explain a smaller proportion. Methodological factors represent the characteristics of the ET itself, including the type, volume and intensity of exercise, as well as the method used to prescribe and control exercise intensity. Notably, methodological factors are modifiable and, upon manipulation, alter response rates to ET, eliciting increases in CRF regardless of an individual's biological predisposition. Particularly, prescribing exercise intensity relative to a physiological threshold (e.g., ventilatory threshold) is shown to increase CRF response rates compared to when intensity is anchored relative to a maximum physiological value (e.g., maximum heart rate). It is, however, uncertain whether the increased response rates are primarily attributable to reduced response variability, greater mean changes in CRF or both. Future research is warranted to elucidate whether more homogeneous chronic adaptations manifest over time among individuals, as a result of exposure to more homogeneous exercise stimuli elicited by threshold-based practices., (© 2021 The Authors. Experimental Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.)

Introduction: Critical speed (CS) represents the highest intensity at which a physiological steady state may be reached. The aim of this study was to evaluate whether estimations of CS obtained from raw training data can predict performance and pacing in marathons., Methods: We investigated running activities logged into an online fitness platform by >25,000 recreational athletes before big-city marathons. Each activity contained time, distance, and elevation every 100 m. We computed grade-adjusted pacing and the fastest pace recorded for a set of target distances (400, 800, 1000, 1500, 3000, and 5000 m). CS was determined as the slope of the distance-time relationship using all combinations of, at least, three target distances., Results: The relationship between distance and time was linear, irrespective of the target distances used (pooled mean ± SD: R = 0.9999 ± 0.0001). The estimated values of CS from all models were not different (3.74 ± 0.08 m·s), and all models correlated with marathon performance (R = 0.672 ± 0.036, error = 8.01% ± 0.51%). CS from the model including 400, 800, and 5000 m best predicted performance (R = 0.695, error = 7.67%) and was used in further analysis. Runners completed the marathon at 84.8% ± 13.6% CS, with faster runners competing at speeds closer to CS (93.0% CS for 150 min marathon times vs 78.9% CS for 360 min marathon times). Runners who completed the first half of the marathon at >94% of their CS, and particularly faster than CS, were more likely to slowdown by more than 25% in the second half of race., Conclusion: This study suggests that estimations of CS from raw training data can successfully predict marathon performance and provide useful pacing information.

Objectives: The aim of this study was to evaluate the effects of caffeine on physiological responses to submaximal exercise, with a focus on blood lactate concentration ([BLa])., Methods: Using a randomised, single-blind, crossover design; 16 endurance-trained, male cyclists (age: 38 ± 8 years; height: 1.80 ± 0.05 m; body mass: 76.6 ± 7.8 kg; [Formula: see text]: 4.3 ± 0.6 L∙min-1) completed four trials on an electromagnetically-braked cycle ergometer. Each trial consisted of a six-stage incremental test (3 minute stages) followed by 30 minutes of passive recovery. One hour before trials 2-4, participants ingested a capsule containing 5 mg∙kg-1 of either caffeine or placebo (maltodextrin). Trials 2 and 3 were designed to evaluate the effects of caffeine on various physiological responses during exercise and recovery. In contrast, Trial 4 was designed to evaluate the effects of caffeine on [BLa] during passive recovery from an end-exercise concentration of 4 mmol∙L-1., Results: Relative to placebo, caffeine increased [BLa] during exercise, independent of exercise intensity (mean difference: 0.33 ± 0.41 mmol∙L-1; 95% likely range: 0.11 to 0.55 mmol∙L-1), but did not affect the time-course of [BLa] during recovery (p = 0.604). Caffeine reduced ratings of perceived exertion (mean difference: 0.5 ± 0.7; 95% likely range: 0.1 to 0.9) and heart rate (mean difference: 3.6 ± 4.2 b∙min-1; 95% likely range: 1.3 to 5.8 b∙min-1) during exercise, with the effect on the latter dissipating as exercise intensity increased. Supplement × exercise intensity interactions were observed for respiratory exchange ratio (p = 0.004) and minute ventilation (p = 0.034)., Conclusions: The results of the present study illustrate the clear, though often subtle, effects of caffeine on physiological responses to submaximal exercise. Researchers should be aware of these responses, particularly when evaluating the physiological effects of various experimental interventions.