Dynamic heart rate and power output modeling to predict lactate threshold in recreational cyclists

The aim of this study was to determine if heart rate responses to training intensity during road cycling could be modelled with compact time-variant mathematical model structures. The model performance was evaluated in terms of model order (complexity), number of inputs and parameter estimation methods used (time-invariant vs. time-variant). Thirteen male cyclists performed two identical cycling tests of 27 km on the road. Uphill sections were introduced to induce dynamic variations in heart rate. The heart rate and training intensity, represented by power output and road inclination, were measured in real-time. Taking only power as system input allowed to explain the variations in heart rate in an accurate way , since adding the road inclination as an additional input did not significantly improve the modelling performance . Furthermore, we demonstrated that models with first-order dynamics accurately describes the heart rate responses to power variations , but that more complex second-order model structures were significantly better than the first-order model structures (P=0.028). Finally, the heart rate dynamics appeared to be time-variant, since the time-variant model structures were significantly better than the time-invariant model structures . So, compact time-variant second-order model structures could be used to model the heart rate response to training intensity as a basis for training optimisation.

Vermeire, KM, Vandewiele, G, Caen, K, Lievens, M, Bourgois, JG, and Boone, J. Training progression in recreational cyclists: no linear dose-response relationship with training load. J Strength Cond Res 35(12): 3500-3505, 2021-The purpose of the study was to assess the relationship between training load (TL) and performance improvement in a homogeneous group of recreational cyclists, training with a self-oriented training plan. Training data from 11 recreational cyclists were collected over a 12-week period. Before and after the training period, subjects underwent a laboratory incremental exercise test with blood lactate measurements to determine the power output associated with the aerobic threshold (PAT) and the anaerobic threshold (PANT), and the maximal power output (PMAX) was also determined. Mean weekly TL (calculated using the training impulse (TRIMP) of Banister, Edwards TRIMP, Lucia TRIMP and the individualized TRIMP) were correlated to the progression in fitness parameters using Pearson Correlation. Training intensity distribution (TID) was also determined (% in zone 1 as <AT; % in zone 2 as between AT and ANT; % in zone 3 as >ANT). No significant correlations between mean weekly TRIMP values and the improvement on PMAX (r = -0.22 to 0.08), PANT (r = -0.56 to -0.31) and PAT (r = -0.08 to 0.41) were found. The TID was significant in a multiple regression with PANT as dependent variable (y = 0.0088 + 0.1094 × Z1 - 0.2704 × Z2 + 1.0416 × Z3; p = 0.02; R2 = 0.62). In conclusion, this study shows that the commonly used TRIMP methods to quantify TL do not show a linear dose-response relationship with performance improvement in recreational cyclists. Furthermore, the study shows that TID might be a key factor to establish a relationship with performance improvement.

This study investigated the effects of caffeine on subsequent performance and recovery from a 2000-m rowing ergometer. Nine trained male rowers (18.3 ± 1.1 years) consumed 3 or 6 mg kg−1 of caffeine or placebo. In three sequential time trials (TT), 30-min after (Trial1) and 6.5 h after (Trial2) caffeine intervention, participants performed a 2000-m ergometer effort. Power output (PO), stroke rate (SR), and time were monitored during the TTs. Ventilatory variables, serum creatine kinase (CK), blood lactate (BLa), and heart rate (HR) were recorded in Trial1 and Trial2, 30 min after Trial1 (1st recovery) and 30-min before Trial2 (2nd recovery). After ingesting 6 mg kg−1 of caffeine, PO was significantly greater in Trial1 (8.2%) compared to the placebo. Also significant increases in $$\dot{V}{\text{O}}_{{ 2 {\text{peak}}}}$$ during Trial1 (10.1%) and 2 (13.9%), HR at $$\dot{V}{\text{O}}_{{ 2 {\text{peak}}}}$$ (5.5%), $${\text{HR}}_{{V{\text{O}}_{{ 2 {\text{peak}}}} }}$$ during Trial1 (10.0%) and 2 (11.3%), $$\dot{V}{\text{O}}_{ 2}$$ /HRpeak in Trial1 (19.6%) and 2 (17.3%), $$\dot{V}{\text{O}}_{ 2}$$ at lactate threshold (LT) in Trial1 (24.6%), and significant decrease in ventilatory evaluation ( $$\dot{V}_{\text{E}}$$ ) (9.0%) were shown in 6 mg kg−1 compared to placebo. In recovery phase, minute ventilation (MV) was significantly higher in 1st (17.1%) and 2nd recovery (28.7%) and RER increased in 1st recovery (9.9%, P = 0.04) in 6 mg kg−1 compared to placebo. CK increased significantly in 2nd recovery (14.1%). BLa in 6 mg kg−1 was higher than placebo in Trial1 (18.6%) and Trial2 (24.4%). Based on improvements in ventilatory variables, and decreased CK and BLa levels after 6 mg kg−1 caffeine ingestion in performance and recovery, this study provides novel data by demonstrating that pre-exercise caffeine ingestion improves performance and subsequent recovery from rowing effort.

In this study, we evaluated the validity of a sharp increase in pulse deficit (PD) as a noninvasive index for estimation of the first lactate threshold (LT (1)) in healthy individuals with various levels of aerobic fitness. Three groups of healthy male subjects participated in the study: 15 sedentary individuals, 14 students of physical education, and 13 competitive athletes. Each subject performed a maximal incremental exercise test on the cycle ergometer for the determination of the LT (1), the second lactate threshold, and peak power output. On different days, subjects performed several 8-min bouts of constant-load exercise on the cycle ergometer, corresponding to each of the power outputs of the maximal test, to evaluate PD, which was calculated as the total number of heart beats of the last 4 min minus the total number of heart beats in the first 4 min of exercise. The three groups presented similar blood lactate, heart rate and pulse deficit responses to exercise. For the first power output up to the LT (1), PD showed no significant changes. For the three groups, a sharp increase in PD was seen at the intensity immediately above LT (1). There was a significant correlation between PD and blood lactate changes from the rest to 4th min of submaximal exercise (r = 0.83, p < 0.05). The power output before a sharp increase in PD detected during constant-load exercise (112 +/- 38 W) and the power output corresponding to the LT (1) detected during the incremental test (111 +/- 37 W, p = 0.323) were similar and strongly correlated (r = 0.99, p = 0.0001). The absolute cut-point value of 25 beats for PD had a sensitivity of 100 %, a specificity of 95 %, and a positive predictive value of 90 % for the detection of LT (1). The determination of PD provides an accurate noninvasive estimate of the LT (1) in healthy young men with different levels of fitness. One 8-min submaximal exercise bout can establish if an individual is exercising above or below the LT (1).

Physiological work refers to the amount of energy that is expended during exercise and it is related to exercise intensity. In general, exercise intensity assessment includes heart rate (HR), rate of perceived exertion (RPE), power output (PO), oxygen consumption (VO2), and blood lactate (BL) levels. These measurements are used for both males and females to evaluate exercise intensity despite their physiological differences. The purpose of this study was to determine if there are differences between sexes in different exercise intensity models when using the same physiological work. Thirty young, healthy individuals (15 males) were recruited with ages ranging from 19 to 33 y. The individuals were scheduled for two (2) lab visits within 48–72 hours of each other. A graded exercise test (GXT) on a stationary bike was conducted during the first visit. At the end of each stage, the RPE, PO, VO2, and BL were taken. After the GXT was completed, peak PO and VO2max were recorded. BL during the GXT was used to determine 3 5‐min steady‐state workloads (low: 0–2 mmol/l; medium: 2–4 mmol/l; and high: &gt;4 mmol/l) as physiological work for the second test. The measures obtained at the end of each steady‐state physiological workload were HR, %HRmax (HR/(220‐age)), RPE, PO, %POmax, VO2, %VO2max, and BL. A two‐way repeated measurements ANOVA was performed to compare all exercise intensity variables obtained during the second test between males and females (α=0.05). Females had higher HR and %HR than males in all 3 physiological workloads (116±14 vs. 97±14 bpm and 68.7±7.5 vs. 59.4±8.8%, 143±16 vs. 128±20 bpm and 84.9±6.2 vs. 75.4±6.7%, and 164±16 vs. 149±17 bpm and 97.0±3.7 vs. 91.1±7.3%, respectively, all p&lt;0.05). Interestingly, females had a higher %VO2max only during the lower physiological workload (554.±9.8 vs. 45.6±8.4%, p&lt;0.05). Throughout the stages of Low (0–2mmol/l), Medium (2–4mmol/l), and High (&gt;4mmol/l) physiological workloads %PO, RPE, and BL did not differ between sexes. Unlike previous reports, our study found that RPE was similar for both sexes within the same %PO. Our data demonstrates that HR and %HR should not be used interchangeable in males and females, especially when physiological work rate is the critical factor for increased performance.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Most of the bodily functions are regulated by multiple interactions between the parasympathetic (PNS) and sympathetic (SNS) nervous system. In this study, we propose a novel framework to quantify the causal flow of information between PNS and SNS through the analysis of heart rate variability (HRV) and electrodermal activity (EDA) signals. Our method is based on a time-varying (TV) multivariate autoregressive model of EDA and HRV time-series and incorporates physiologically inspired assumptions by estimating the Directed Coherence in a specific frequency range. The statistical significance of the observed interactions is assessed by a bootstrap procedure purposely developed to infer causalities in the presence of both TV model coefficients and TV model residuals (i.e., heteroskedasticity). We tested our method on two different experiments designed to trigger a sympathetic response, i.e., a hand-grip task (HG) and a mental-computation task (MC). Our results show a parasympathetic driven interaction in the resting state, which is consistent across different studies. The onset of the stressful stimulation triggers a cascade of events characterized by the presence or absence of the PNS-SNS interaction and changes in the directionality. Despite similarities between the results related to the two tasks, we reveal differences in the dynamics of the PNS-SNS interaction, which might reflect different regulatory mechanisms associated with different stressors. We estimate causal coupling between PNS and SNS through MVAR modeling of EDA and HRV time-series. Our results suggest promising future applicability to investigate more complex contexts such as affective and pathological scenarios.

The purposes of this study were to determine anaerobic threshold (AT) during discontinuous dynamic and resistive exercise protocols by analysing of heart rate variability (HRV) and blood lactate (BL) in healthy elderly subjects and compare the cardiovascular, metabolic and autonomic variables obtained from these two forms of exercise. Fourteen elderly (70 ± 4 years) apparently healthy males underwent the following tests: (i) incremental ramp test on cycle ergometer, (ii) one repetition maximum (1RM) leg press at 45°, (iii) a discontinuous exercise test on a cycle ergometer (DET-C) protocol and (iv) a resistance exercise leg press (DET-L) protocol. Heart rate, blood pressure and BL were obtained during each increment of exercise intensity. No significant differences (P>0·05) were found between methods of AT determination (BL and HRV) nor the relative intensity corresponding to AT (30% of maximum intensity) between the types of exercise (DET-C and DET-L). Furthermore, no significant differences (P>0·05) were found between the DET-C and DET-L in relation to HRV, however, the DET-L provided higher values of systolic blood pressure and BL (P<0·05) from the intensity corresponding to AT. We conclude that HRV was effective in determination of AT, and the parasympathetic modulation responses obtained during dynamic and resistive exercise protocols were similar when compared at the same relative intensity. However, DET-L resulted in higher values of blood pressure and BL at workloads beyond AT.

Full text Figures and data Side by side Abstract Editor's evaluation Introduction Materials and methods Results Discussion Data availability References Decision letter Author response Article and author information Metrics Abstract Background: Oxygen uptake (VO2) is one of the most important measures of fitness and critical vital sign. Cardiopulmonary exercise testing (CPET) is a valuable method of assessing fitness in sport and clinical settings. There is a lack of large studies on athletic populations to predict VO2max using somatic or submaximal CPET variables. Thus, this study aimed to: (1) derive prediction models for maximal VO2 (VO2max) based on submaximal exercise variables at anaerobic threshold (AT) or respiratory compensation point (RCP) or only somatic and (2) internally validate provided equations. Methods: Four thousand four hundred twenty-four male endurance athletes (EA) underwent maximal symptom-limited CPET on a treadmill (n=3330) or cycle ergometer (n=1094). The cohort was randomly divided between: variables selection (nrunners = 1998; ncyclist = 656), model building (nrunners = 666; ncyclist = 219), and validation (nrunners = 666; ncyclist = 219). Random forest was used to select the most significant variables. Models were derived and internally validated with multiple linear regression. Results: Runners were 36.24±8.45 years; BMI = 23.94 ± 2.43 kg·m−2; VO2max=53.81±6.67 mL·min−1·kg−1. Cyclists were 37.33±9.13 years; BMI = 24.34 ± 2.63 kg·m−2; VO2max=51.74±7.99 mL·min−1·kg−1. VO2 at AT and RCP were the most contributing variables to exercise equations. Body mass and body fat had the highest impact on the somatic equation. Model performance for VO2max based on variables at AT was R2=0.81, at RCP was R2=0.91, at AT and RCP was R2=0.91 and for somatic-only was R2=0.43. Conclusions: Derived prediction models were highly accurate and fairly replicable. Formulae allow for precise estimation of VO2max based on submaximal exercise performance or somatic variables. Presented models are applicable for sport and clinical settling. They are a valuable supplementary method for fitness practitioners to adjust individualised training recommendations. Funding: No external funding was received for this work. Editor's evaluation The authors have established new formulas to predict maximum oxygen uptake for cyclists and runners based on submaximal exercise testing and anthropometric characteristics. This is an important study with a large and comprehensive dataset, which may be helpful for many exercise labs. The work is convincing, using appropriate and validated methodology in line with the current state-of-the-art, as shown by references to common exercise books. https://doi.org/10.7554/eLife.86291.sa0 Decision letter Reviews on Sciety eLife's review process Introduction The oxygen uptake (VO2) is considered an important metric in assessing cardiorespiratory fitness, health status, or endurance performance potential (Guazzi et al., 2012). With the application of standardised procedures and interpretation protocols, during graded exercise tests (GXT), the (maximal oxygen uptake) VO2max can be established (Bentley et al., 2007). GXT is the most widely used assessment to examine the dynamic relationship between exercise and integrated physiological systems (Albouaini et al., 2007; Bentley et al., 2007). The information from GXT during cardiopulmonary exercise testing (CPET) can be applied across the spectrum of sport performance, occupational safety screening, research, and clinical diagnostics (Guazzi et al., 2017). VO2 max is often used as a boundary between severe and extreme intensity domains and by definition requires maximal effort from the tested subject (Gaesser and Poole, 1996). However, it is not always recommended or possible to undertake a test to exhaustion (Guazzi et al., 2012). For the athletes, the proximity of competition or injury history can allow submaximal testing, but not testing to exhaustion (Sassi et al., 2006). Testing that requires maximal effort may be disruptive to the training process or interfere with race performance (Coutts et al., 2007; Lamberts et al., 2011). Due to practical constraints, tests to exhaustion or peak-power-output tests are often performed only two or three times a year (Coutts et al., 2007). However, VO2 values are widely used in sport science and the decision-making process (Mann et al., 2013). VO2 is widely considered one of the major endurance performance determinants (Joyner and Coyle, 2008). Using VO2max to guide the selection process, prescribing training intensity, assessing training adaptations, or predicting race times is a common practice in high-performance sports (Bassett and Howley, 2000; Bentley et al., 2007; Hawley and Noakes, 1992; Noakes et al., 1990). VO2max is also one of the critical vital signs coordinating the function of the cardiovascular, respiratory, and muscular systems, it is an indicator of overall body health status (Kaminsky et al., 2017). Quantifying VO2max provides additional input regarding clinical decision-making, risk stratification, evaluation of therapy, and physical activity guidelines (Guazzi et al., 2012). For patients undertaking a test to exhaustion is rarely needed or possible due to health restraints or cardiac risk (Guazzi et al., 2016). For many years researchers have studied indirect methods of estimating VO2max(Sartor et al., 2013). Protocols such as the Astrand-Ryhming Test, Six-Minute Walk Test, or YMCA Step Test have been established and validated (Astrand and Ryhming, 1954; Beutner et al., 2015; Carey, 2022; Jalili et al., 2018). Moreover, estimation of the VO2 and heart rate (HR) values below the ventilatory threshold can be based on cardiorespiratory kinetics assessment using randomised changes in the work rate known as a pseudo-random binary sequences testing (Hoffmann et al., 2022). However, with the development of technology, the accessibility of laboratory testing and mobile testing improved (Montoye et al., 2020; Pritchard et al., 2021). Therefore, new opportunities to develop more precise yet simple and accessible methods and models to assess VO2max occur (Jurov et al., 2023). This appears to be especially important considering the low prediction accuracy of most of the VO2max formulae that were validated in our previous study (Wiecha et al., 2023). Recently, we have been observing the development of prediction methods with the usage of machine learning (ML) and artificial intelligence (AI) (Ashfaq et al., 2022). Both ML and AI are used in sport science as forecasting and decision-making support tools (Abut and Akay, 2015; Bobowik and Wiszomirska, 2022; Chmait and Westerbeek, 2021; Hammes et al., 2022; Rossi et al., 2021). There is growing evidence that VO2max prediction based on ML models, especially support vector ML and artificial neural network models, exhibits more robust and accurate results compared to MLR only (Abut and Akay, 2015; Ashfaq et al., 2022). Therefore, in this research, with the support of ML, we look for algorithms and prediction patterns that allow us to use values obtained during submaximal CPET and somatic measurements to estimate maximal VO2max values in male runners and cyclists. We stipulate that prediction models allow for accurate calculation of VO2max based on somatic or submaximal CPET variables. Materials and methods We have applied the development and validation of the prediction TRIPOD guidelines to conduct the study (see Supplementary Material 1TRIPOD Checklist for Prediction Model Development and Validation) (Collins et al., 2015). The study is based on retrospective data analysis from the CPET registry collected from 2013 to 2021 at the medical clinic (Sportslab, Warsaw, Poland). All CPET have been performed at the individual request of participants, as a part of regular training monitoring or performance assessment. Ethical approval Request a detailed protocol The Institutional Review Board of the Bioethical Committee at the Medical University of Warsaw (AKBE/32/2021) has approved the study protocol. The regulations of the Declaration of Helsinki were met during all parts of the study. Each study participant delivered written consent to undergo CPET and participate in the study. Derivation cohort Request a detailed protocol We selected the cohort with the use of rigorous exclusion/inclusion criteria. Due to the insufficient number of women in our database and the number of potential variables in the regression models for adequate power, we had to limit ourselves to conduct analysis in the male population only (Martens and Logan, 2021). Out of 6439 healthy, adult male cyclists and long-distance runners that undergone CPET, 4423 met the criteria as further: (1) age ≥18 years, (2) declared regular cycling or running training for ≥3 months, (3) had no extreme outliers ≤ or ≥±3 standard deviations (SD) from mean for all of the testing variables (beyond ≥±3 SD in VO2max), (4) lack of any injury, medical condition, or addiction in medical history that may affect exercise capacity, (5) not taking any medications with a modifying effect on exercise capacity, (6) maximum exertion achieved during CPET. We defined the maximum exertion in CPET as the fulfilment of the minimum six of the following criteria: (1) respiratory exchange ratio (RER) ≥1.10, (2) present VO2 plateau (growth <100 mL·min–1 in VO2 despite increased running speed or cycling power), (3) respiratory frequency (fR) ≥45 breaths·min–1, (4) declared subjective exertion intensity during CPET ≥18 in the Borg scale (Borg, 1970), (5) blood lactate concentration [La-]b ≥8 mmol·L–1, (6) growth in speed/power ≥10% of respiratory compensation point (RCP) values after exceeding the RCP, (7) peak heart rate (HRpeak) ≥15 beats·min–1 below predicted maximal heart rate (HRmax) (Lach et al., 2021). Participants’ selection procedure has been shown in Figure 1. Figure 1 Download asset Open asset Flowchart of the preliminary inclusion and exclusion process. Abbreviations: EA, endurance athlete; CPET, cardiopulmonary exercise testing; SD, standard deviation; TE, treadmill; RER, respiratory exchange ratio; VO2, oxygen uptake (mL·min−1·kg−1); [La−]b, lactate concentration (mmol·L−1); fR, breathing frequency (breaths·min−1); RCP, respiratory compensation point; HRpeak, peak heart rate (beats·min−1); HRmax, maximal heart rate (bpm). At both stages of the selection, some participants met several (>1) exclusion criteria. Somatic measurements and CPET protocols Request a detailed protocol Body mass was measured with a body composition (BC) analyser (Tanita, MC 718, Japan) with the multifrequency of 5 kHz/50 kHz/250 kHz via the bioimpedance analysis and normal testing mode. The participants’ skin was cleaned with alcohol before placing the electrodes on the skin. Prior to the test, the participants received instructions to refrain from exercising for 2 hr, consume a light meal rich in carbohydrates 2–3 hr beforehand, and maintain hydration by drinking isotonic beverages. Additionally, they were advised to abstain from medications, caffeine, and cigarettes on the day of the test. Running CPET (TE) was performed on a mechanical treadmill (h/p/Cosmos Quasar, Germany). Cycling CPET (CE) was performed on Cyclus-2 (RBM elektronik-automation GmbH, Leipzig, Germany). Hans Rudolph V2 mask (Hans Rudolph, Inc, Shawnee, KS, USA), breath-by-breath method with Cosmed Quark CPET gas exchange analysing device (Cosmed Srl, Rome, Italy), and Quark PFT Suite to Omnia 1.6 software were utilised. The gas analyser device was regularly calibrated with the reference gas (16% O2; 5% CO2) in accordance with the manufacturer’s instructions (Airgas USA, LLC, Plumsteadville, PA, USA). From 2013 to 2021, three Cosmed Quark CPET units were used. HR was measured with the Cosmed torso belt (Cosmed srl, Rome, Italy). [La-]b was measured via enzymatic-amperometric electrochemical technique with Super GL2 analyser (Müller Gerätebau GmbH, Freital, Germany). The [La-]b analyser was regularly calibrated before each measurement series. The 40 m2 indoor, air-conditioned laboratory with 20–22°C temperature and 40–60% humidity, and 100 m ASL provided the same conditions for all BC and CPET. Each CPET began with a 5 min personalised warm-up (walk or easy jog with ‘conversational’ intensity for running, easy pedalling with ‘conversational’ intensity for cycling). Then after the preparation (about 5 min), the continuous progressive step test was conducted. Due to the population diversity (training status), the running test speed started from 7 to 12 km·hr–1 with a 1% treadmill incline. The choice of initial starting speed was determined by the interview and sports results achieved. For example, those running less than 60 min at a distance of 10 km started the test at 7 km/hr, while those running 10 km for less than 35 min started the test at an initial speed of 12 km/hr. The pace increased by 1 km·hr–1 every 2 min with no change in incline. The cycling test began at 60–150 W, depending on the athletes training status. The power increased by 20–30 W every 2 min. It was recommended to maintain a constant cadence of 80–90 (repetition·min–1) during the test. The tests were terminated due to exhaustion: volitional inability to continue the activity or/and VO2 and HR plateau with increasing load or/and observed disturbance of coordination in running or/and inability to maintain the set cadence. Due to the graded protocol used, the cycling power and running speed values have been calculated as a function of time to better reflect the actual level for the test moment being determined (Kuipers et al., 1985). Before the test, after every step, and 3 min after the termination of the effort technician took a 20 µL blood sample from a fingertip. Samples were collected during the test without interrupting the effort. The samples were taken from the initial puncture. The first blood drop was collected into the swab and the second blood drop was drawn for further analysis into the capillary. VO2max was recorded as the highest value (15 s intervals) before the termination of the test. HRmax was recorded as the highest value obtained at the end of the test, without averaging. The anaerobic threshold (AT) was established with the following criteria: (1) common start of VE/VO2 and VE/VCO2 curves, (2) end-tidal partial pressure of oxygen raised constantly with the end-tidal partial pressure of carbon dioxide (Beaver et al., 1986). The was established with the following criteria: (1) PetCO2 must decrease after reaching maximal amount, (2) the presence of fast nonlinear growth in VE (second deflection), (3) the VE/VCO2 ratio achieved minimum and started to rise, and (4) a nonlinear increase in VCO2 versus VO2 (lack of linearity) (Beaver et al., 1986). The [La-]b was estimated for AT and RCP in relation to power or speed (Wiecha et al., 2022). Data analysis Request a detailed protocol Our comprehensive ML approach enables the evaluation of each formula by preliminary variables precision (at the stage of selection), then accuracy (during the model’s building) and recall (in internal validation). Individual CPET results were saved into the Excel file (Microsoft Corporation, Redmond, WA, USA) and a custom-made script was used to generate the database in Excel (Python programming). Further, mean, SD, and 95% confidence intervals (CI) were calculated. The normality of the distribution of the data was examined using the Shapiro-Wilk test and intergroup differences were calculated using the Student’s t-test for independent variables. Three-step variable selection procedures based on random forests were applied using the R package VSURF in RStudio software (R Core Team, Vienna, Austria; version 3.6.4) (Genuer et al., 2016). For each level of measurement (AT, RCP) and their combination (AT+RCP), significant variables were identified separately. The first step was dedicated to eliminate irrelevant variables from the dataset. Second step aimed to select all variables related to the response for interpretation purposes. The third step refined the selection by eliminating redundancy in the set of variables selected by the second step, for prediction purposes (Genuer et al., 2017). Each time for variables selection, the anthropometric variables as in Tables 1–2 and the CPET parameters given in Tables 3–4 from a specific level of measurement (AT; RCP) and their combinations were visible. Table 1 Basic anthropometric characteristics for runners. Variable (unit)Derivation group n=1998Testing group n=666Validation group n=666MeanCISDMeanCISDMeanCISDAge (years)36.235.6–36.98.4535.935.5–36.38.0535.534.9–36.28.14Height (cm)180.0179.6–180.56.04179.4179.1–179.76.13179.7179.2–180.26.61BM (kg)77.777.0–78.49.3577.777.3–78.19.2977.977.1–78.610.1BMI (kg·m–2)23.923.8–24.12.4324.124.0–24.22.4124.123.9–24.32.56BF (%)15.415.1–15.74.5515.515.3–15.74.5215.415.1–15.84.55FM (kg)12.211.9–12.64.6812.312.1–12.54.6512.311.9–12.74.92FFM (kg)65.565.0–66.06.4365.465.1–65.76.3165.665.1–66.16.86 BM, body mass; BMI, body mass index; BF, body fat; FM, fat mass; FFM, fat-free mass; CI, 95% confidence interval; SD, standard deviation. Table 2 Basic anthropometric characteristics for cyclists. Variable (unit)Derivation group n=656Testing group n=219Validation group n=219MeanCISDMeanCISDMeanCISDAge (years)37.336.6–38.09.1337.135.9–38.49.5037.636.5–38.88.46Height (cm)179.9179.4–180.46.27180.1179.2–181.06.96180.2179.4–181.06.13BM (kg)78.878.1–79.69.8079.177.7–80.510.479.878.4–81.310.9BMI (kg·m–2)24.324.1–24.62.6324.424.0–24.72.8024.624.2–25.02.96BF (%)16.415.7–17.14.9916.115.7–16.54.8116.215.5–16.84.87FM (kg)13.312.6–14.15.6613.012.6–13.45.2713.312.5–14.05.85FFM (kg)65.864.9–66.66.2565.865.4–66.36.0666.665.7–67.46.58 BM, body mass; BMI, body mass index; BF, body fat; FM, fat mass; FFM, fat-free mass; CI, 95% confidence interval; SD, standard deviation. Table 3 Cardiopulmonary exercise testing (CPET) characteristics for runners. Variable (unit)Derivation group n=1998Testing group n=666Validation group n=666MeanCISDMeanCISDMeanCISDrVO2AT (mL·min–1·kg–1)38.438.1–38.85.0138.538.3–38.74.8838.137.7–38.55.16RERAT0.870.86–0.870.040.870.86–0.870.040.870.86–0.870.04HRAT (beats·min–1)151.5150.8–152.310.3151.0150.6–151.510.8152.0151.2–152.810.8VEAT (L·min–1)79.178.1–80.012.278.377.8–78.912.077.276.3–78.212.0SPEEDAT (km·h–1)11.010.9–11.11.4511.011.0–11.11.3610.910.8–11.01.42LAAT (mmol·L–1)2.082.02–2.140.631.801.76–1.830.622.352.27–2.420.72rVO2RCP (mL·min–1·kg–1)47.547.0–48.05.8847.747.4–48.06.1547.346.8–47.86.16RERRCP1.001.00–1.000.041.001.00–1.000.041.001.00–1.000.03HRRCP (beats·min–1)173.4172.7–174.19.21173.2172.8–173.69.30174.3173.5–175.09.50VERCP (L·min–1)114.7113.5–116.015.9113.9113.1–114.616.7112.7111.4–114.016.2SPEEDRCP (km·h–1)14.013.9–14.11.7714.114.0–14.11.7013.913.8–14.11.75LARCP (mmol·L–1)4.724.63–4.821.044.404.34–4.451.044.814.69–4.931.14rVO2max (mL·min–1·kg–1)53.853.3–54.36.6754.354.0–54.66.9553.853.3–54.37.09 CI, 95% confidence interval; SD, standard deviation; rVO2AT, oxygen uptake at anaerobic threshold relative to body mass; RERAT, respiratory exchange ratio at anaerobic threshold; HRAT, heart rate at anaerobic threshold; VEAT, pulmonary ventilation at anaerobic threshold; SPEEDAT, velocity at anaerobic threshold; LAAT, blood lactate concentration at anaerobic threshold; rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass; RERRCP, respiratory exchange ratio at respiratory compensation point; HRRCP, heart rate at respiratory compensation point; VERCP, pulmonary ventilation at respiratory compensation point; SPEEDRCP, velocity at respiratory compensation point; LARCP, blood lactate concentration at respiratory compensation point; rVO2max, maximal oxygen uptake relative to body mass. Table 4 Cardiopulmonary exercise testing (CPET) characteristics for cyclists. Variable (unit)Derivation group n=656Testing group n=219Validation group n=219MeanCISDMeanCISDMeanCISDrVO2AT (mL·min–1·kg–1)33.032.5–33.45.8433.232.4–33.95.6833.732.9–34.55.89RERAT0.870.87–0.870.040.870.87–0.880.040.870.87–0.880.04HRAT CI, 95% confidence interval; SD, standard deviation; rVO2AT, oxygen uptake at anaerobic threshold relative to body mass; RERAT, respiratory exchange ratio at anaerobic threshold; HRAT, heart rate at anaerobic threshold; VEAT, pulmonary ventilation at anaerobic threshold; power at anaerobic threshold relative to body mass; LAAT, blood lactate concentration at anaerobic threshold; rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass; RERRCP, respiratory exchange ratio at respiratory compensation point; HRRCP, heart rate at respiratory compensation point; VERCP, pulmonary ventilation at respiratory compensation point; LARCP, blood lactate concentration at respiratory compensation point; power at respiratory compensation point relative to body mass; rVO2max, maximal oxygen uptake relative to body mass. selection variables were in the further only selected parameters were into multiple linear regression The data for MLR model building were randomly into that is testing, validation and of the a only significant were in the Derived are by the of mean and mean analysis was used to the model’s precision and accuracy during validation and tests to the fulfilment of MLR test the of in MLR test assessment between and test of Each model was examined the and any have not been 2 package in RStudio (R Core Team, Vienna, Austria; version version for and software version were used in was considered as the Results Somatic measurements and CPET results data of the runners models for testing, and validation are in Table while cyclists are in Table The runners of and for testing, and validation the cyclists and differences between of runners and cyclists were in BMI and between testing in all between validation only in CPET results for runners models are in Table 3 and for cyclists in Table Runners in the cohort achieved relative to body mass VO2max of in testing group and in validation group cyclists mean was and for testing, and validation to body mass oxygen uptake at anaerobic threshold in runners for ± ± and ± of in testing, and validation it was ± ± and ± of rVO2max, relative to body mass oxygen uptake at respiratory compensation point in runners for ± ± and ± of for testing, and validation while in cyclists for ± ± and ± of rVO2max, There were no significant differences in values between testing, and validation the runners and cyclists between runners and cyclists results were all significant Prediction models based on AT and RCP Full of MLR prediction models for cyclists are in Table for runners in Table The models prediction performance is as with and for cyclists from for somatic parameters to for RCP equations. For runners from for to for AT and equations. for cyclists models was the for RCP and the highest for For from for AT and to for equation. observed for cyclists was the for RCP in the validation group and the highest for while in runners the for AT and and the highest for The performance of prediction is in Figure Figure 2 Download asset Open asset of prediction for Abbreviations: maximal oxygen anaerobic threshold; RCP, respiratory compensation point; All values are in performance for running while the performance for cycling equations. performance of the prediction model for for for AT and for somatic-only equation. Table 5 VO2max prediction for cyclists. linear regression group group = = = = based on anaerobic threshold; RCP, based on respiratory compensation point; based on somatic variables mean mean maximal oxygen uptake relative to body mass rVO2AT, oxygen uptake at anaerobic threshold relative to body mass power at anaerobic threshold relative to body mass rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass VERCP, pulmonary ventilation at respiratory compensation point BF, body fat BM, body mass Table VO2max prediction for runners. linear regression group group = = = = based on anaerobic threshold; RCP, based on respiratory compensation point; based on somatic variables mean mean maximal oxygen uptake relative to body mass rVO2AT, oxygen uptake at anaerobic threshold relative to body mass SPEEDAT, velocity at anaerobic threshold FFM, fat mass VEAT, pulmonary ventilation at anaerobic threshold HRAT, heart rate at anaerobic threshold BF, body fat rVO2RCP, oxygen uptake at respiratory compensation point relative to body mass SPEEDRCP, velocity at respiratory compensation point Models validation of each model for cyclists is in Table while for runners in Table the performance of our prediction was to that observed in the

Physiological demands of water polo goalkeeping

The aims of this work were: 1) to determine the correlation between the Anaerobic Threshold (AT) and the performance on the triathlon of Half lronman; 2) to describe and to compare the Heart Rate (HR) and the correspondent speed at AT in swimming. cycling and running. with the HR and the speed of these events during the triathlon; 3) to describe the behavior of the lactate (LAC) and glucemia (GLU) during the events. Six males triathletes between the ages of 18 to 24 participated in this study. AT was defined as the speed corresponding to a lactate concentration of 4 mM and was determined by measuring plasma lactate concentration after each bout of swimming (3x200 m progressive), cycling (3x2400 m progressive) and running (3x1200 m progressive) tests. The speed at 4 mM lactate concentration was calculated by linear interpolation. The data were obtained during a Half Ironman (1.9 km swimming, 90 km cycling and 21 km running) competition. HR, LAC and GLU were determinated during the transition of the events and the end of the competition. HR was also determinated in three others points of control in the cycling and running course. Swimming and cycli1'!g AT were significantly correlated with the competition speed in these events (r = 0.92 and r = 0.92, respectively). However, the same did not occur in the running (r = 0.63). The swimming event speed (64.8 m/min) was not different from the speed at AT (65.5 m/min). On the other hand. the speed of cycling (538.0 m/min) and running (201.0 m/min) were significantly lower than the speed at AT (639.0 and 264.0 m/min respectively). The HR at swimming was higher (p > 0.05) than HR of AT. At running, only HR in the last two parts of the event, was significantly lower than HR of AT. During cycling , there were not significant differences between HR of the event and HR of AT. as well as HR among the four parts of the competition. The concentration of LAC in swimming was higher than cycling and running, but in these last two events there were no differences. GLU was lower just after the running, when compared to the figure obtained after swimming. These results suggest that: 1) AT is able to predict the performance of swimming and cycling during the triathlon competition of Half Ironman, but the same does not occur at running; 2) The use of HR to control the competition intensity. mainly during cycling and running, might present limitations.

Background: High-intensity interval training (HIIT) is a well-established training modality to improve aerobic and anaerobic capacity. However, sex-specific aspects of different HIIT protocols are incompletely understood. This study aimed to compare two HIIT protocols with different recovery periods in moderately trained females and males and to investigate whether sex affects high-intensity running speed and speed decrement.Methods: Fifty moderately trained participants (30 females and 20 males) performed an exercise field test and were randomized by lactate threshold (LT) to one of two time- and workload-matched training groups. Participants performed a 4-week HIIT intervention with two exercise sessions/week: Group 1 (4 × 30,180 HIIT), 30-s all-out runs, 180-s active recovery and Group 2 (4 × 30,30 HIIT), 30-s all-out runs, 30-s active recovery. High-intensity runs were recorded, and speed per running bout, average speed per session, and speed decrement were determined. Blood lactate measurements were performed at baseline and follow-up at rest and immediately post-exercise.Results: Females and males differed in running speed at LT and maximal running speed determined during exercise field test (speed at LT, females: 10.65 ± 0.84 km h−1, males: 12.41 ± 0.98 km h−1, p < 0.0001; maximal speed, females: 14.55 ± 1.05 km h−1, males: 17.41 ± 0.68 km h−1, p < 0.0001). Estimated maximal oxygen uptake was ~52.5 ml kg−1 min−1 for females and 62.6 ml kg−1 min−1 for males (p < 0.0001). Analysis of HIIT protocols revealed an effect of sex on change in speed decrement (baseline vs. follow-up) in that females showed significant improvements only in the 4 × 30:30 HIIT group (p = 0.0038). Moreover, females performing the 4 × 30:30 protocol presented increased speed per bout and average speed per session at follow-up (all p ≤ 0.0204), while no effect was detected for females performing the 4 × 30:180 protocol. Peak blood lactate levels increased in all HIIT groups (all p < 0.05, baseline vs. follow-up), but males performing the 4 × 30:180 protocol showed no difference in lactate levels.Conclusions: If not matched for physical performance, females, but not males, performing a 4 × 30 HIIT protocol with shorter recovery periods (30 s) present increased average high-intensity running speed and reduced speed decrement compared to longer recovery periods (180 s). We conclude that female- and male-specific HIIT protocols should be established since anthropometric and physiological differences across sexes may affect training performance in real-world settings.

Most cycling studies involve professional cyclists. Because training may affect riding style, it is of interest to determine the physiological basis for the personal choice of cycling cadence in nonprofessional cyclists. Eleven nonprofessional (5.2±1.7-year-riding experience) male road cyclists, aged 35.0±11.0 years, underwent four separate laboratory test sessions. The first two sessions included habituation, anthropometry, V˙O2max,$\dot V{{\text{O}}_{\text{2}}}{\text{max}},$ and lactate threshold (LaTH) measurements. Freely preferred cadence at LaTH was determined during the second session (mean±SD=94.7±2.9 rev·min-1). During the third and fourth sessions participants performed LaTH tests at 60 and 95 rev·min-1 in a randomized order, with power output (PO) increments of 25 W every 4 min, up to ~90% of V˙O2max.$\dot V{{\text{O}}_{\text{2}}}{\text{max}}{\text{.}}$ Results: V˙O2,$\dot V{{\text{O}}_{\text{2}}},$ expired ventilation (V˙E),$({\dot V_E}),$ blood lactate (La), and calculated net mechanical efficiency (MEnet) rose with increased PO. At 95 rev·min-1, V˙O2, V˙E,$\dot V{{\text{O}}_2},{\text{ }}{\dot V_{\text{E}}},$ and La were significantly higher than at 60 rev·min-1 at all POs. MEnet at 95 rev·min-1 was lower than at 60 rev·min-1. Mean PO attained at LaTh did not differ significantly between 60 and 95 rev·min-1 (220.9±29.0 and 214.5±9.2 W, respectively). La values at LaTH were higher at 95 rev·min-1 than at 60 rev·min-1 (3.01±0.17 vs. 2.10±0.13 mM, p<0.05, respectively). Our findings indicate that mechanical and physiological efficiencies may not determine the choice of cycling cadence by nonprofessional cyclists. This choice may reflect the need to maintain endurance at the expense of riding at a lower than optimal riding efficiency.

To examine the fatigue response during an exhaustive heavy exercise performed under control of oxygen uptake (SS@V.O (2)Delta50) or power output (SS@pDelta50), eleven trained male subjects performed an incremental test to determine the peak of the oxygen uptake value (V.O (2peak)) and lactate threshold and two exhaustive steady-state cycling exercises at the intermediate value between the lactate threshold and V.O (2peak) (SS@V.O (2)Delta50 and SS@pDelta50). The control of V.O (2) induced an oscillation of the power output, which lowered the average power output (276 +/- 47 vs. 315 +/- 40 W, p = 0.004) and cancelled the slow component of oxygen kinetics. However, all subjects reached maximal cardiac output (CO) and heart rate (HR) values which were sustained almost two times longer in SS@V.O (2)Delta50 compared to SS@pDelta50 (979 +/- 854 vs. 475 +/- 236 s, p = 0.046 for CO and 1050 +/- 890 vs. 513 +/- 288 s, p = 0.037 for HR). Furthermore, SS@pDelta50 elicited V.O (2peak) but not SS@V.O (2)Delta50 (4963 +/- 434 vs. 4723 +/- 460 mL . min (-1), p = 0.026). Finally, the time spent at the maximal CO and HR values is correlated with time to exhaustion at V.O (2)Delta50. In conclusion, the cause of fatigue does not seem to have the same origin during exhaustive supra-lactate threshold exercise under control of V.O (2) (V.O (2)Delta50) compared to constant power output (pDelta50), while both elicit the maximal HR and CO values.

A group of ten young experienced oarsmen [mean age 17.5 (SD 1.7) years, height 182.5 (SD 5.9) cm, body mass 77.0 (SEM 10.6) kg] exercised in a progressive incremental test (PIE: 50W x 3 min[-1]) on a rowing ergometer to determine the power output corresponding to the 4 mmol lactate x l(-1) anaerobic threshold (AT4) and the individual anaerobic threshold (IAT). Within 10 days they performed at random two 30-min prolonged exercise tests (PET) at power outputs corresponding to AT4 and IAT to demonstrate whether or not a steady-state blood lactate concentration [La-]b could be maintained. Oxygen uptake (VO2), heart rate (HR) and [La-]b were measured. The PIE revealed significant lower values at IAT compared to AT4 for power output (P < 0.01), HR (P < 0.01) and VO2 (P < 0.01). Coefficients of correlation between AT4 and IAT were 0.68 for power output, 0.79 for HR and 0.70 for VO2. All the subjects could complete the PET at IAT and only six out of the ten at AT4. The [La-]b at the end of PET was significantly higher at AT4 compared to IAT (P < 0.01). Similar results were found for VO2 (P < 0.01) and for HR (P < 0.01). A steady state of mean blood lactate concentration ([La-]ss was found only for the power output at IAT. From the results of this study, we concluded that both concepts of anaerobic threshold gave different information about submaximal endurance capacity. Only IAT represented on average [La-]ss. It is suggested that more research is needed to elicit optimal guidelines for the intensity of endurance training.

Evaluation and use of the Lactate Pro, a portable lactate meter, in monitoring the physiological well-being of farmed Atlantic cod ( Gadus morhua)