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Purpose: Very low intensity endurance training (LIT) does not seem to improve maximal oxygen uptake. The purpose of the present study was to investigate if very high volume of LIT could compensate the lack of intensity and is LIT affecting differently low and high intensity performances., Methods: Recreationally active untrained participants (n = 35; 21 females) cycled either LIT (mean training time 6.7 ± 0.7 h / week at 63% of maximal heart rate, n = 16) or high intensity training (HIT) (1.6 ± 0.2 h /week, n = 19) for 10 weeks. Two categories of variables were measured: Low (first lactate threshold, fat oxidation at low intensity exercise, post-exercise recovery) and high (aerobic capacity, second lactate threshold, sprinting power, maximal stroke volume) intensity performance., Results: Only LIT enhanced pooled low intensity performance (LIT: p = 0.01, ES = 0.49, HIT: p = 0.20, ES = 0.20) and HIT pooled high intensity performance (LIT: p = 0.34, ES = 0.05, HIT: p = 0.007, ES = 0.48)., Conclusions: Overall, very low endurance training intensity cannot fully be compensated by high training volume in adaptations to high intensity performance, but it nevertheless improved low intensity performance. Therefore, the intensity threshold for improving low intensity performance is lower than that for improving high intensity performance. Consequently, evaluating the effectiveness of LIT on endurance performance cannot be solely determined by high intensity performance tests., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Matomäki et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: Without appropriate standardization of exercise doses, comparing high- (HI) and low-intensity (LI) training outcomes might become a matter of speculation. In athletic preparation, proper quantification ensures an optimized stress-to-recovery ratio. This review aims to compare HI and LI doses by estimating theoretically the conversion ratio, 1:x, between HI and LI: How many minutes, x, of LI are equivalent to 1 minute of HI using various quantification methods? A scrutinized analysis on how the dose increases in relation to duration and intensity was also made., Analysis: An estimation was conducted across 4 categories encompassing 10 different approaches: (1) "arbitrary" methods, (2) physiological and perceptual measurements during exercise, (3) postexercise measurements, and comparison to (4a) acute and (4b) chronic intensity-related maximum dose. The first 2 categories provide the most conservative estimation for the HI:LI ratio (1:1.5-1:10), and the third, slightly higher (1:4-1:11). The category (4a) provides the highest estimation (1:52+) and (4b) suggests 1:10 to 1:20. The exercise dose in the majority of the approaches increase linearly in relation to duration and exponentially in relation to intensity., Conclusions: As dose estimations provide divergent evaluations of the HI:LI ratio, the choice of metric will have a large impact on the research designs, results, and interpretations. Therefore, researchers should familiarize themselves with the foundations and weaknesses of their metrics and justify their choice. Last, the linear relationship between duration and exercise dose is in many cases assumed rather than thoroughly tested, and its use should be subjected to closer scrutiny.

This study examined the predictive quality of intervals performed at maximal sustainable effort to predict 3-km and 10-km running times. In addition, changes in interval performance and associated changes in running performance were investigated. Either 6-week (10-km group, n=29) or 2-week (3-km group, n=16) interval training periods were performed by recreational runners. A linear model was created for both groups based on the running speed of the first 6×3-min interval session and the test run of the preceding week (T1). The accuracy of the model was tested with the running speed of the last interval session and the test run after the training period (T2). Pearson correlation was used to analyze relationships between changes in running speeds during the tests and interval sessions. At T2, the mean absolute percentage error of estimate for 3-km and 10-km test times were 2.3% and 3.4%, respectively. The change in running speed of intervals and test runs from T1 to T2 correlated (r=0.75, p<0.001) in both datasets. Thus, the maximal sustainable effort intervals were able to predict 3-km and 10-km running performance and training adaptations with good accuracy, and current results demonstrate the potential usefulness of intervals as part of the monitoring process., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Introduction: This is one of the first intervention studies to examine how low- (LIT) and high-intensity endurance training (HIT) affect durability, defined as 'time of onset and magnitude of deterioration in physiological-profiling characteristics over time during prolonged exercise'. Methods: Sedentary and recreationally active men (n = 16) and women (n = 19) completed either LIT (average weekly training time 6.8 ± 0.7 h) or HIT (1.6 ± 0.2 h) cycling for 10 weeks. Durability was analyzed before and after the training period from three factors during 3-h cycling at 48% of pretraining maximal oxygen uptake (VO 2max ): 1) by the magnitude and 2) onset of drifts (i.e. gradual change in energy expenditure, heart rate, rate of perceived exertion, ventilation, left ventricular ejection time, and stroke volume), 3) by the 'physiological strain', defined to be the absolute responses of heart rate and its variability, lactate, and rate of perceived exertion. Results: When all three factors were averaged the durability was improved similarly (time x group p = 0.42) in both groups (LIT: p = 0.03, g = 0.49; HIT: p = 0.01, g = 0.62). In the LIT group, magnitude of average of drifts and their onset did not reach statistically significance level of p < 0.05 (magnitude: 7.7 ± 6.8% vs. 6.3 ± 6.0%, p = 0.09, g = 0.27; onset: 106 ± 57 min vs. 131 ± 59 min, p = 0.08, g = 0.58), while averaged physiological strain improved ( p = 0.01, g = 0.60). In HIT, both magnitude and onset decreased (magnitude: 8.8 ± 7.9% vs. 5.4 ± 6.7%, p = 0.03, g = 0.49; onset: 108 ± 54 min vs. 137 ± 57 min, p = 0.03, g = 0.61), and physiological strain improved ( p = 0.005, g = 0.78). VO 2max increased only after HIT (time x group p < 0.001, g = 1.51). Conclusion: Durability improved similarly by both LIT and HIT based on reduced physiological drifts, their postponed onsets, and changes in physiological strain. Despite durability enhanced among untrained people, a 10-week intervention did not alter drifts and their onsets in a large amount, even though it attenuated physiological strain., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Matomäki, Heinonen, Nummela, Laukkanen, Auvinen, Pirkola and Kyröläinen.)

Background: Much is known about theoretical bases of different mechanical efficiency indices and effects of physiological and biomechanical factors to them. However, there are only a few studies available about practical bases and interactions between these efficiency indices, which were the aims of the present study., Methods: Fourteen physically active men (n = 12) and women (n = 2) participated in this study. From the incremental test, six different mechanical efficiency indices were calculated for cycling work: gross (GE) and net (NE) efficiencies, two work efficiencies (WE), and economy (T) at 150 W, and in addition delta efficiency (DE) using 3-5 observation points., Results: It was found that the efficiency indices can be divided into three groups by Spearman's rank correlation: GE, T, and NE in group I; DE and extrapolated WE in group II; and measured WE in group III. Furthermore, group II appeared to have poor reliability due to its dependence on a work-expended energy regression line, which accuracy is poorly measured by confidence interval., Conclusion: As efficiency indices fall naturally into three classes that do not interact with each other, it means that they measure fundamentally different aspects of mechanical efficiency. Based on problems and imprecisions with other efficiency indices, GE, or group I, seems to be the best indicator for mechanical efficiency because of its consistency and unambiguity. Based on this methodological analysis, the baseline subtractions in efficiency indices are not encouraged.

An exercise bout or a dehydration often causes a reduction in plasma volume, which should be acknowledged when considering the change in biomarkers before and after the plasma changing event. The classic equation from Dill and Costill (1974, J. Appl. Physiol., 37, 247-248) for plasma volume shift is usually utilized in such a case. Although this works well with plasma and serum biomarkers, we argue in this note that this traditional approach gives misleading results in the context of whole blood biomarkers, such as lactate, white cells, and thrombocytes. In this study, we demonstrate that to calculate the change in the total amount of circulating whole blood biomarker, one should utilize a formula [Formula: see text] Here Hb and BM are, respectively, the concentrations for the hemoglobin and for the inspected whole blood biomarker before (pre) and after (post) the plasma changing incident., (© 2018 The Authors. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society.)