Explore the vast range of titles available.

During the last nearly 50 years, the blood lactate curve and lactate thresholds (LTs) have become important in the diagnosis of endurance performance. An intense and ongoing debate emerged, which was mainly based on terminology and/or the physiological background of LT concepts. The present review aims at evaluating LTs with regard to their validity in assessing endurance capacity. Additionally, LT concepts shall be integrated within the ‘aerobic-anaerobic transition’ — a framework which has often been used for performance diagnosis and intensity prescriptions in endurance sports. Usually, graded incremental exercise tests, eliciting an exponential rise in blood lactate concentrations (bLa), are used to arrive at lactate curves. A shift of such lactate curves indicates changes in endurance capacity. This very global approach, however, is hindered by several factors that may influence overall lactate levels. In addition, the exclusive use of the entire curve leads to some uncertainty as to the magnitude of endurance gains, which cannot be precisely estimated. This deficiency might be eliminated by the use of LTs. The aerobic-anaerobic transition may serve as a basis for individually assessing endurance performance as well as for prescribing intensities in endurance training. Additionally, several LT approaches may be integrated in this framework. This model consists of two typical breakpoints that are passed during incremental exercise: the intensity at which bLa begin to rise above baseline levels and the highest intensity at which lactate production and elimination are in equilibrium (maximal lactate steady state [MLSS]). Within this review, LTs are considered valid performance indicators when there are strong linear correlations with (simulated) endurance performance. In addition, a close relationship between LT and MLSS indicates validity regarding the prescription of training intensities. A total of 25 different LT concepts were located. All concepts were divided into three categories. Several authors use fixed bLa during incremental exercise to assess endurance performance (category 1). Other LT concepts aim at detecting the first rise in bLa above baseline levels (category 2). The third category consists of threshold concepts that aim at detecting either the MLSS or a rapid/distinct change in the inclination of the blood lactate curve (category 3). Thirty-two studies evaluated the relationship of LTs with performance in (partly simulated) endurance events. The overwhelming majority of those studies reported strong linear correlations, particularly for running events, suggesting a high percentage of common variance between LT and endurance performance. In addition, there is evidence that some LTs can estimate the MLSS. However, from a practical and statistical point of view it would be of interest to know the variability of individual differences between the respective threshold and the MLSS, which is rarely reported. Although there has been frequent and controversial debate on the LT phenomenon during the last three decades, many scientific studies have dealt with LT concepts, their value in assessing endurance performance or in prescribing exercise intensities in endurance training. The presented framework may help to clarify some aspects of the controversy and may give a rationale for performance diagnosis and training prescription in future research as well as in sports practice.

Background: Active exercise therapy plays an essential role in tackling the global burden of obesity. Optimizing recommendations in individual training therapy requires that the essential parameters heart rate HR(IAT) and work load (W/kg(IAT) at individual anaerobic threshold (IAT) are known. Performance diagnostics with blood lactate is one of the most established methods for these kinds of diagnostics, yet it is also time consuming and expensive. Methods: To establish a regression model which allows HR(IAT) and (W/kg(IAT) to be predicted without measuring blood lactate, a total of 1234 performance protocols with blood lactate in cycle ergometry were analyzed. Multiple linear regression analyses were performed to predict the essential parameters (HR(IAT)) (W/kg(IAT)) by using routine parameters for ergometry without blood lactate. Results: HR(IAT) can be predicted with an RMSE of 8.77 bpm (p < 0.001), R2 = 0.799 (|R| = 0.798) without performing blood lactate diagnostics during cycle ergometry. In addition, it is possible to predict W/kg(IAT) with an RMSE (root mean square error) of 0.241 W/kg (p < 0.001), R2 = 0.897 (|R| = 0.897). Conclusions: It is possible to predict essential parameters for training management without measuring blood lactate. This model can easily be used in preventive medicine and results in an inexpensive yet better training management of the general population, which is essential for public health.

Nasal and oral exclusive breathing modes have benefits and drawbacks during submaximal exercise. It is less known whether these responses would extend to anaerobic work performed at high intensity. The purpose of this study is to find the most efficient mode of breathing during different phases of a maximal exercise at cardiopulmonary exercise test (CPET). Healthy subjects were recruited to perform 4 maximal CPETs (standard conditions (STD), exclusively nasal breathing (eNAS), exclusively oral breathing (eOR), partial nasal breathing (pNAS) with just one blocked nostril) using the same ramp protocol on an electronically braked cycle ergometer. Before the exercise a standard spirometry was executed in the same order. Twelve healthy subjects (28.6 ± 5.2 y, 50% males) performed the 4 CPETs within one month. Variables were analysed at rest, at anaerobic threshold (AT), at intermediate exercise steps, and at peak. Compared to STD, eOR, and pNAS conditions, eNAS was associated with a significant lower peakVO2, peakVCO2, peak ventilation, respiratory rate, VE/VCO2 slope, respiratory exchange ratio, and workload (p < 0.05 for all). Moreover, peak inspiration and peak expiration time were augmented, while forced expiratory volume and vital capacity at rest were reduced. Only minor differences were detected at rest or AT. eNAS breathing Borg scale was higher in all phases of the exercise. In young healthy subjects, an exclusively nasal respiration induces significant impairment on peak exercise capacity at CPET due to ventilatory limitation, with only minor effects on metabolic parameters at rest and in submaximal effort.

To provide an integrative framework of endurance performance, the anaerobic speed reserve gained increasing popularity in middle-distance running. The present study investigated athlete profiles and performance predications based on the anaerobic speed reserve framework in 400m athletes. Descriptive laboratory study. Maximal oxygen uptake, lactate threshold (vL4), maximal sprinting speed, maximal aerobic speed, and speed reserve ratio (speed reserve ratio; maximal sprinting speed:maximal aerobic speed) of national level and elite German 400m-sprinters (n = 13 females, age [yrs]: 20.8 ± 3.1, personal best (PB400) [s]: 55.1 ± 3.0 & n = 5 males, age: 22.8 ± 3.1, PB400: 46.7 ± 1.0) were assessed. A prediction model for 400m performance was computed via stepwise multiple regression. K-means clustering was calculated based on the speed reserve ratio. Maximal sprinting speed, maximal aerobic speed and vL4 showed moderate to large negative bivariate correlations with 400m performance (−0.61 < r < −0.94; p ≤ 0.008). Backward stepwise regression revealed maximal sprinting speed and maximal aerobic speed as strong predictors for 400m performance (adjusted R2 = 0.90, standard error of the estimate = 1.447 s [2.6 %]). K-means clustering revealed two distinct subgroups along the speed reserve ratio-continuum (sprint-type: speed reserve ratio ≥1.81; endurance-type: speed reserve ratio ≤1.77). Maximal sprinting speed and maximal aerobic speed are powerful predictors for 400m performance, with vL4 also being associated with 400m performance. Speed reserve ratio calculation enables a differentiation between 400m sprint-type and endurance-type athletes. The interplay of maximal sprinting speed and maximal aerobic speed enables a broader understanding of the contributing factors to 400m performance. These parameters may support coaches in programming training tailored to individual needs during different training periods.

Lactate analysis plays an important role in sports science and training decisions for optimising performance, endurance, and overall success in sports. Two parameters are widely used for these goals: aerobic (AeT) and anaerobic (AnT) thresholds. However, determining AeT proves more challenging than AnT threshold due to both physiological intricacies and practical considerations. Thus, the aim of this study was to determine AeT and AnT thresholds using machine learning modelling (ML) and to compare ML-obtained results with the parameters’ values determined using conventional methods. ML seems to be highly useful due to its ability to handle complex, personalised data, identify nonlinear relationships, and provide accurate predictions. The 183 results of CardioPulmonary Exercise Test (CPET) accompanied by lactate and heart ratio analyses from amateur athletes were enrolled to the study and ML models using the following algorithms: Random Forest, XGBoost (Extreme Gradient Boosting), and LightGBM (Light Gradient Boosting Machine) and metrics: R 2 , mean absolute error (MAE), mean squared error (MSE) and root mean square error (RMSE). The regressors used belong to the group of ensemble learning algorithms that combine the predictions of multiple base models to improve overall performance and counteract overfitting to training data. Based on evaluation metrics, the following models give the best predictions: for AeT: Random Forest has an R 2 value of 0.645, MAE of 4.630, MSE of 44.450, RMSE of 6.667; and for AnT: LightGBM has an R 2 of 0.803, the highest among the models, MAE of 3.439, the lowest among the models, MSE of 20.953, and RMSE of 4.577. Outlined research experiments, a comprehensive review of existing literature in the field, and obtained results suggest that ML models can be trained to make personalised predictions based on an individual athlete’s unique physiological response to exercise. Athletes exhibit significant variation in their AeT and AT, and ML can capture these individual differences, allowing for tailored training recommendations and performance optimization.

Cardiopulmonary exercise testing (CPET) is widely used within the United Kingdom for preoperative risk stratification. Despite this, CPET's performance in predicting adverse events has not been systematically evaluated within the framework of classifier performance. After prospective registration on PROSPERO (CRD42018095508) we systematically identified studies where CPET was used to aid in the prognostication of mortality, cardiorespiratory complications, and unplanned intensive care unit (ICU) admission in individuals undergoing non-cardiopulmonary surgery. For all included studies we extracted or calculated measures of predictive performance whilst identifying and critiquing predictive models encompassing CPET derived variables. We identified 36 studies for qualitative review, from 27 of which measures of classifier performance could be calculated. We found studies to be highly heterogeneous in methodology and quality with high potential for bias and confounding. We found seven studies that presented risk prediction models for outcomes of interest. Of these, only four studies outlined a clear process of model development; assessment of discrimination and calibration were performed in only two and only one study undertook internal validation. No scores were externally validated. Systematically identified and calculated measures of test performance for CPET demonstrated mixed performance. Data was most complete for anaerobic threshold (AT) based predictions: calculated sensitivities ranged from 20-100% when used for predicting risk of mortality with high negative predictive values (96-100%). In contrast, positive predictive value (PPV) was poor (2.9-42.1%). PPV appeared to be generally higher for cardiorespiratory complications, with similar sensitivities. Similar patterns were seen for the association of Peak VO2 (sensitivity 85.7-100%, PPV 2.7-5.9%) and VE/VCO2 (Sensitivity 27.8%-100%, PPV 3.4-7.1%) with mortality. In general CPET's 'rule-out' capability appears better than its ability to 'rule-in' complications. Poor PPV may reflect the frequency of complications in studied populations. Our calculated estimates of classifier performance suggest the need for a balanced interpretation of the pros and cons of CPET guided pre-operative risk stratification.

Lactate anaerobic threshold has been a commonly used metric in the field of training monitoring, however its invasiveness has been overwhelming. Therefore, this study utilized sweat sensors to monitor Na + and K + in sweat to investigate the possibility of using sweat for anaerobic threshold monitoring. Fifty-five subjects were asked to complete an incremental load riding test. The test started at 100 W and each level of load lasted 3 min with one minute of rest in increments of 25 W/3min until exhaustion. Sweat collection was performed on the left chest throughout the ride to test sweat Na + and K + concentrations at each level, and fingertip blood collection was performed to measure blood lactate concentrations. For high and middle level populations, sST and sPT showed higher correlation and agreement with bLT (HL: sST vs. bLT: r  = 0.559 p  < 0.05,sPT vs. bLT: r  = 0.667 p  < 0.05;ML: sST vs. bLT: r  = 0.802 , p  < 0.01, sPT vs. bLT: r  = 0.723 p  < 0.01), whereas for low level populations the method may not predict anaerobic threshold. The findings validates the possibility of sweat monitoring on anaerobic threshold testing, which suggests that sweat metrics are linked to energy metabolism metrics, which could help to advance sweat monitoring in the field of exercise practice. Further research is needed to advance exploration in this field.

PurposeThis study aimed to compare the effects of acute and multi-day low-dose sodium bicarbonate (SB) intake on high-intensity endurance exercise performance.MethodsIn a randomized, double-blind, cross-over design, twelve recreational male cyclists (age: 31.17 ± 4.91 years; V˙O2peak: 47.98 ± 7.68 ml·kg−1·min−1) completed three endurance performance tests following acute SB (ASB, 0.2 g·kg−1 SB), multi-day SB (MSB, 0.2 g·kg−1·day−1 SB for four days), and placebo (PLA) intake. The high-intensity endurance performance was assessed with a cycling exercise test, wherein participants cycled on a bicycle ergometer at 95% of the predetermined anaerobic threshold for 30 min, followed by a time-to-exhaustion test at 110% of the anaerobic threshold. Data were analyzed using one-way and two-way repeated-measures ANOVA.ResultsSignificant main effects of supplementation protocol were evident in pre-exercise bicarbonate concentrations (F = 27.93; p < 0.01; partial eta squared (η2) = 0.72; false discovery rate (FDR)-adjusted p value = 0.001). Prior to performance test, blood bicarbonate concentrations were significantly higher in MSB (25.78 ± 1.63 mmol·L−1 [95% CI 26.55–28.44] (p < 0.001; FDR-adjusted p value = 0.001)) and ASB (27.49 ± 1.49 mmol·L−1 [95% CI 24.75–26.81] (p < 0.001; FDR-adjusted p value = 0.007)) compared to PLA (23.75 ± 1.40 mmol·L−1 [95% CI 22.86 to 24.64]). Time-to-exhaustion increased in MSB (54.27 ± 9.20 min [95% CI 48.43–60.12]) compared to PLA (49.75 ± 10.80 min [95% CI 42.89–56.62]) (p = 0.048); however, this increase in MSB did not reach the significance threshold of 1% FDR (FDR-adjusted p value = 0.040). No significant difference was noted in exhaustion times between ASB (51.15 ± 8.39 min [95% CI 45.82–56.48]) and PLA (p > 0.05).ConclusionBoth acute and multi-day administration of low-dose SB improves buffering system in cyclists; nevertheless, neither intervention demonstrates sufficient efficacy in enhancing high-intensity endurance performance.

The aim of this study was the assessment of progressive low-dose sodium bicarbonate (NaHCO 3 ) supplementation on the anaerobic indices in two bouts of Wingate tests (WT) separated by wrestling-specific performance test and assessing the gender differences in response. Fifty-one (18 F) wrestlers completed a randomized trial of either a NaHCO 3 (up to 100 mg·kg −1 ) or a placebo for 10 days. Before and after treatment, athletes completed an exercise protocol that comprised, in sequence, the first WT 1 , dummy throw test (DT), and second WT 2 . The number of completed throws increased significantly in males from 19.3 ± 2.6 NaHCO 3pre to 21.7 ± 2.9 NaHCO 3post . ΔWT 2 -WT 1 improved particularly in the midsection of 30-s WT on NaHCO 3 . However, no significant differences were found in peak power (PP), power drop (PD) and average power (AP) (analyzed separately for each WT), and ΔWT 2 -WT 1 in PP and PD. Interaction with gender was significant for AP, PP and PD, every second of WT 1 and WT 2 , as well as DT test. In conclusion, our study suggests that the response to NaHCO 3 may be gender-specific and progressive low-dose NaHCO 3 supplementation allows the advantageous strengthening of wrestling-specific performance in males. It can also lead to maintenance of high anaerobic power mainly in the midsection of the 30-s Wingate test.

The aim of this study was to determine the extent to which the Tyme Wear smart shirt is as reliable and valid in detecting personalized ventilatory thresholds when compared to the Parvo Medics TrueOne 2400. In this validation study, 19 subjects were recruited to conduct two graded exercise test (GXT) trials. Each GXT trial was separated by 7 to 10 days of rest. During the GXT, gas exchange and heart rate data were collected by the TrueOne 2400 (TRUE) in addition to the ventilation data collected by the Tyme Wear smart shirt (S-PRED). Gas exchange data from TRUE were used to detect ventilatory threshold 1 (VT1) and ventilatory threshold 2 (VT2). TRUE and S-PRED VT1 and VT2 were compared to determine the reliability and validity of the smart shirt. Of the 19 subjects, data from 15 subjects were used during analysis. S-PRED exhibited excellent (intraclass correlation coefficient—CC > 0.90) reliability for detection of VT1 and VT2 utilizing time point and workload and moderate (0.90 > ICC > 0.75) reliability utilizing heart rate. TRUE exhibited excellent reliability for detection of VT1 and VT2 utilizing time point, workload, and heart rate. When compared to TRUE, S-PRED appears to underestimate the VT1 workload (p > 0.05) across both trials and heart rate (p < 0.05) for trial 1. However, S-PRED appears to underestimate VT2 workload (p < 0.05) and heart rate (p < 0.05) across both trials. The result from this study suggests that the Tyme Wear smart shirt is less valid but is comparable in reliability when compared to the gold standard. Moreover, despite the underestimation of S-PRED VT1 and VT2, the S-PRED-detected personalized ventilatory thresholds provide an adequate training workload for most individuals. In conclusion, the Tyme Wear smart shirt provides easily accessible testing to establish threshold-guided training zones but does not devalue the long-standing laboratory equivalent.