Sex differences in performance-matched marathon runners

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

Investigations of exercise-induced increases in beta-endorphin, adrenocorticotropic hormone (ACTH) and cortisol concentration have been carried out mainly in men. Data concerning the female reaction are sparse and less clear. In a comparison between incremental exercise and marathon running 14 experienced female marathon runners volunteered to run to exhaustion according to an incremental treadmill protocol. They ran a marathon 4 weeks later. Blood was analysed for beta-endorphin, ACTH and cortisol concentration immediately prior to the laboratory treadmill test, 3, 30 and 60 min later, as well as prior to the marathon, after 60 min and 120 min of running and 3, 30 min, and 24 h after completion of the run. At each blood collection, lactate concentration, heart frequency and perceived exertion were determined. The mean marathon running time was 3.22 h. Baseline concentrations for beta-endorphin of 22 pmol.l-1 before the marathon and 19 pmol.l-1 before the treadmill exercise increased 1.4-fold 30 min after the marathon and 1.9-fold after the treadmill exercise; for ACTH the baseline of 4.7 and 4.0 pmol.l-1 was increased by 8.3- and 10.3-fold, respectively. Cortisol concentration rose exponentially from a baseline 17 micrograms.dl-1 and peaked at 2.2-fold 30 min after the run, when the maximal concentration also had been reached after the treadmill test, increasing 1.3-fold from a baseline of 21 micrograms.dl-1. The maximal values for cortisol concentration after both exercises differed from each other, while the maxima of ACTH and beta-endorphin concentrations were similar. The ACTH and beta-endorphin concentration declined more slowly during the recovery after the marathon than after the treadmill. Cortisol concentration was below baseline 24 h later. In comparison with men studied earlier, female marathon runners showed higher baseline concentrations and lesser increases in beta-endorphin and lower baseline concentrations and larger increases in ACTH concentration after both types of exercise. The delayed decrease in concentration of the hormones after the marathon was similar in male and female runners.

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.

Running economy (RE) determines the difference in distance running performance in highly-trained runners. Studies have highlighted RE importance during high-intensity running. Although RE is related to ground contact time (Tc), the relationship between RE at high-intensity running and Tc is not elucidated. It is also unclear whether the hamstrings muscle, which contribute to high-intensity running, affect RE and Tc. PURPOSE: We aimed to clarify the relationships among RE, Tc at intensity below and above the lactate threshold (LT) run, and hamstring muscle’s cross-sectional area in highly-trained long-distance runners. METHODS: We enrolled 11 highly trained male distance runners (IAAF score: 1038 ± 48). They underwent treadmill running test to assess the LT, and RE. RE was evaluated at 270 (RE270) and 360 m/min (RE360) as the intensity below and above the LT, respectively. Tc was also valuated at the same velocity (Tc270; Tc360). Both medial and lateral hamstring muscles’ cross-sectional areas (MHA and LHA, respectively) were calculated using MRI at 50% position of the right thigh. RESULTS: The subjects’ LT, RE270 and RE360 were 337 ± 17 m/min, 0.90 ± 0.08 kcal/ kg/km, and 1.00 ± 0.07 kcal/kg/km, respectively. The Tc270, Tc360, MHA and LHA values were 195 ± 17 ms, 164 ± 9 ms, 1.24 ± 0.18 cm2/kg0.67, and 0.99 ± 0.11 cm2/kg0.67, respectively. RE270 showed a significant relationship with Tc270, MHA, and LHA (r = -0.81, 0.75, and 0.60, respectively; P < 0.05), but no relationship was noted with IAAF score (r = -0.55; P = 0.08). Tc270 was related to MHA and LHA (r = -0.63 and -0.67; P < 0.05). Although RE360 showed a significant relationship with IAAF score, Tc360, and MHA (r = -0.73, -0.65, and 0.69, respectively; P < 0.05), no relationship was noted with LHA (r = 0.35; P = 0.30). Tc360 did not exhibit any significant relationship with MHA (r = -0.55; P = 0.08) and LHA (r = -0.52; P = 0.10). CONCLUSION: The findings of this study are as follows: (a) RE at high rather than low intensity is related to running performance; (b) longer Tc enhances RE; (c) superior RE and long Tc at low intensity are related to small hamstring muscles, but these associations disappear, except between RE and MHA, at high intensity. These results suggest that long Tc at high-intensity contributes to superior RE, but not to hamstring muscles, and leads to higher running performance.

While traditional resistance training has been widely used in distance running training, the effects of complex training combining heavy resistance and plyometric exercises on adolescent runners remain unclear. Understanding the impact of complex training on running economy and lower limb strength could provide valuable insights for optimizing training programs for young athletes. To examine the effects of an 8-week complex training program on lower limb strength and running economy in adolescent distance runners compared to traditional resistance training. Thirty-two male adolescent distance runners (age: 16.75 ± 0.68 years) were randomly assigned to either a complex training group (CT; n = 16) or a resistance training group (RT; n = 16). Both groups completed their respective training programs three times per week for 8 weeks, in addition to their regular endurance training. Running economy at three speeds (12, 14, and 16 km/h), maximal oxygen uptake (VO2max), blood lactate concentration, One-repetition maximum (1RM) squat strength, countermovement jump (CMJ), squat jump (SJ), drop jump (DJ), and reactive strength index (RSI) were assessed before and after the intervention. The CT group showed significantly greater improvements in running economy at all speeds (p < 0.001) compared to the RT group. Both groups demonstrated significant improvements in 1RM squat strength (CT: p < 0.001; RT: p < 0.001), CMJ (CT: p < 0.001; RT: p < 0.001), and SJ (CT: p = 0.005; RT: p < 0.001). The CT group exhibited superior improvements in CMJ peak power (p = 0.010), DJ performance (p = 0.017), and RSI (p < 0.001). Blood lactate concentrations at submaximal speeds decreased significantly more in the CT group compared to the RT group (p < 0.05). Complex training appears to be more effective than traditional resistance training for improving both running economy and power-related performance measures in adolescent distance runners. The combination of heavy resistance and plyometric exercises may provide superior neuromuscular adaptations that enhance both strength and running efficiency.

The purpose was to determine running economy and lactate threshold among a selection of male elite football players with high and low aerobic power. Forty male elite football players from the highest Swedish division (“Allsvenskan”) participated in the study. In a test of running economy (RE) and blood lactate accumulation the participants ran four minutes each at 10, 12, 14, and 16 km•h−1 at horizontal level with one minute rest in between each four minutes interval. After the last sub-maximal speed level the participants got two minutes of rest before test of maximal oxygen uptake (VO2max). Players that had a maximal oxygen uptake lower than the average for the total population of 57.0 mL O2•kg−1•minute−1 were assigned to the low aerobic power group (LAP) (n=17). The players that had a VO2max equal to or higher than 57.0 mL O2•kg−1•minute−1 were selected for the high aerobic power group (HAP) (n=23). The VO2max was significantly different between the HAP and LAP group. The average RE, measured as oxygen uptake at 12, 14 and 16km•h−1 was significantly lower but the blood lactate concentration was significantly higher at 14 and 16 km•h−1 for the LAP group compared with the HAP group.

Alterations in running shoe design have been studied and used in the prevention of injury and enhancement of performance allowing running shoe companies to market to a variety of runners based on skill level, foot-strike pattern, and even sex. These alterations have been shown to affect biomechanical and physiological variables associated with running. Some shoe companies have designed shoes specifically for biological female runners due to the morphological differences found between male and female feet. The purpose of this study is to determine if sex-specific running shoes can alter female runner biomechanics or physiology. Female runners were asked to run in the male and female models of the Altra Torin 4 Plush shoe to determine if there were differences in ground reaction forces (GRFs), sagittal plane joint angles and moments, oxygen consumption (VO2), respiratory exchange ratio (RER), and perceived level of comfort while running; There were no significant differences in GRFs, sagittal joint angles and moments, VO2, RER, or perceived comfort; There were no differences in measured biomechanical or physiological variables between the female and male version of the shoes suggesting that the alterations made to the female-specific shoe do not provide any additional benefit to female recreational runners.

The physiology of marathon running has been extensively studied both in the laboratory and in the field, but these investigations have been confined to elite competitors. In the present study 28 competitors who took part in a marathon race (42.2 km) have been studied; 18 male subjects recorded times from 2 h 19 min 58 s to 4 h 53 min 23 s; 10 female subjects recorded times between 2 h 53 min 4 s and 5 h 16 min 1 s. Subjects visited the laboratory 2-3 weeks after the race and ran on a motor driven treadmill at a series of speeds and inclines; oxygen uptake (VO2) was measured during running at average marathon racing pace. Maximum oxygen uptake (VO2 max) was measured during uphill running. For both males (r = 0.88) and females (r = 0.63), linear relationships were found to exist between marathon performance and aerobic capacity. Similarly, the fraction of VO2 max which was sustained throughout the race was significantly correlated with performance for both male (r = 0.74) and female (r = 0.73) runners. The fastest runners were running at a speed requiring approximately 75% of VO2 max; for the slowest runners, the work load corresponded to approximately 60% of VO2 max. Correction of these estimates for the additional effort involved in overcoming air resistance, and in running on uneven terrain will substantially increase the oxygen requirement for the faster runners, while having a much smaller effect on the work rate of the slowest competitors. Five minutes of treadmill running at average racing pace at zero gradient did not result in marked elevation of the blood lactate concentration in any of the subjects.

This study determined the effects of a 10-week strength training program on running economy in 12 female distance runners who were randomly assigned to either an endurance and strength training program (ES) or endurance training only (E). Training for both groups consisted of steady-state endurance running 4 to 5 days a week, 20 to 30 miles each week. The ES undertook additional weight training 3 days a week. Subjects were tested pre and post for O2, max, treadmill running economy, body composition, and strength. A repeated-measures ANOVA was used to determine significant differences between and within groups. The endurance and strength training program resulted in significant increases in strength (p < 0.05) for the ES in both upper (24.4%) and lower body (33.8%) lifts. There were no differences in treadmill O2, max and body composition in either group. Running economy improved significantly in the ES group, but no significant changes were observed in the E group. The findings suggest that strength training, when added to an endurance training program, improves running economy and has little or no impact on O2, max or body composition in trained female distance runners.

This systematic review evaluated the effects of high-intensity interval training (HIIT) and moderate-intensity continuous training (MICT) on running economy (RE) in endurance runners. The search was completed in March 2024 based on research databases, and the language of publication was restricted to English. The primary outcome measure was RE, and it was categorized into three subgroups: Zone 1 (Z1), Zone 2 (Z2), and Zone 3 (Z3). The secondary outcomes assessed were maximal oxygen uptake (VO2max) and blood lactate concentration. HIIT significantly improved RE compared to MICT (SMD = 0.44, 95% CI [0.15, 0.72], Z = 3.01, p < 0.05). MICT showed a greater effect on VO2max (MD = 2.48, 95% CI [1.61, 3.34], Z = 5.60, p < 0.05). HIIT was more effective at reducing blood lactate levels (MD = -0.15, 95% CI [-0.28, -0.02], Z = 2.20, p < 0.05). The results indicate that HIIT was more effective than MICT in enhancing RE and delaying lactate accumulation. HIIT can further improve RE and postpone blood lactate accumulation when performed at or below the lactate threshold (≤ Z2). VO2max was more pronounced with MICT. These findings suggest that endurance runners and coaches should choose appropriate methods to optimize physiological adaptations.

BackgroundAdvanced footwear technology (AFT) is reported to elicit an approximate 4% average improvement in running economy (RE). However, a large inter-individual variability remains unexplained, and limited research examined the impact of AFT during outdoor running. The aim was to compare the physiological, biomechanical and perceptual responses of 36 well-trained athletes to running outdoors using three different AFT and a traditional racing shoe.MethodsThirty-six well-trained athletes (19 males and 17 females) had their maximal aerobic capacity (O2max) and anaerobic threshold (AT) determined in laboratory conditions and were familiarised to the different shoe running conditions. Within 7 days, athletes ran 4 × 6 min running bouts, paced outdoors at 95% of their individual AT with 10 min recovery, in three different AFT conditions and a traditional flat. Oxygen uptake (O2), heart rate, rating of perceived exertion (RPE), lactate, shoe perception, and biomechanical responses were compared between the four running trials.ResultsNo differences were observed in the RE between shoe conditions, with a great inter-individual variability (range: 12% impairment to 14% improvement in RE). This variability was accompanied by a significant O2 order effect across exercise bouts (bout 2 lower than one [-1.1 mL/kg/min, p = 0.002]; bout 3 lower than 2 [-0.8 mL/kg/min, p = 0.027]; no differences between bouts 3 and 4). This variability was likely due to methodological issues such as one squared-wave RE measurement per shoe condition or the lack of a mirrored experimental design, among others. There was no order effect in other physiological or biomechanical variables. No significant differences were found in lactate, heart rate or rate of perceived exertion between running trials. Biomechanical responses to the different shoe conditions were also highly variable. One of the advanced AFT shoes showed a greater strike angle (+2.07°; p = 0.001), with no other significant differences between shoes conditions.ConclusionThe large variability in the physiological response to AFT may be explained by methodological considerations. A minimum of two-bout RE measurements, the use of a mirrored order, a sufficient familiarisation with shoes and experimental designs, among other considerations, seem crucial to enhance the ecological validity and reduce data variability.

Blood lactate accumulation is associated with development of muscle fatigue and negatively correlated to endurance performance. No research has quantified the effects of lactate presence at moderate levels of lactate accumulation. The purpose of this study was to test whether 2 moderate blood lactate concentration levels affect running economy (RE) when running at the individual lactate threshold (LT). Seven male world class endurance athletes with an average V[Combining Dot Above]O2max of 80.7 ± 2.7 ml·kg·min or 5.8 ± 0.5 L·min participated in this study. After the V[Combining Dot Above]O2max test, the subjects were resting or walking and in a random order tested for RE at their LT velocity when the blood lactate level reached either 3 mmol·L or 5 mmol·L. After a new 5-minute exercising period at maximal aerobic velocity, the crossover lactate value RE testing was performed. Running economy was significantly (p ≤ 0.05) deteriorated from 0.668 ± 0.044 to 0.705 ± 0.056 ml·kg·m or 5.5% (p ≤ 0.05) for blood lactate level of 3 mmol·L compared with 5 mmol·L, respectively. Increased lactate level from 3 to 5 mmol·L is thus accompanied by deteriorated RE at LT running velocity. The deteriorated RE at moderate levels of lactate concentration emphasizes the importance of avoiding intensities above LT in the early parts of a dominantly aerobic endurance competition. It also emphasizes the importance of a high V[Combining Dot Above]O2max for aerobic endurance athletes and may partly explain the V[Combining Dot Above]O2 slow component as impaired RE.

We sought to characterize the physical performance of older subjects in a community-based study of aging and fitness and to establish norms of oxygen consumption during exercise in this group. A community-based census identified 3057 age-eligible individuals of whom 2092 individuals (68.4%) agreed to participate in the overall study. Of these, 1101 subjects between 55 and 94 years of age who were free of known heart disease and able to perform treadmill exercise were tested using the Cornell protocol. Of these 1101 subjects, 73.8% of women and 89.5% of men (p < .001) exceeded what was considered a minimal threshold workload [i.e., > or =2 min of exercise and a respiratory exchange ratio (RER) > or = 1.00]. Moreover, 32.9% of women and 52.7% of men achieved a RER > or = 1.10 and were considered to have achieved maximal exercise. For all subjects who exceeded the minimal exercise workload, the mean exercise duration was 10.0 +/- 4.0 min for women and 13.2 +/- 4.6 min for men. Peak VO2/kg x min(-1) decreased linearly with age for both women (y = 38.33 - 0.27 x age + 1.12 H) and men (y = 41.78 - 0.26 x age + 12.65 H - 0.15 H x age), (p < .001 for sex difference), where H is defined as presence (=1) or absence (=0) of a disease condition. However, this sex-related difference disappeared when VO2/kg x min(-1) was adjusted for lean body mass. Thus, from a community-based sample of older persons, we have provided data (by 5-year age groups) for rates of participation in treadmill exercise testing and the success rate for achieving maximal exercise. Oxygen consumption and other exercise data are presented for older, healthy subjects as well as for those with various chronic diseases associated with aging (excluding those with cardiac or cerebrovascular disease). Exercise duration, peak VO2/kg x min(-1) (normalized for total and lean body mass), and peak exercise heart rate declined with age. Most of the sex difference in peak VO2/kg x min(-1) could be explained by the greater percent of body fat in women.

The aim of this study was to compare training volume and the distribution of training intensity of six of the best long-distance runners in Norway from the last decade. Three international-level long-distance runners (two males and one female) and three marathon runners (one male and two females) were included. The runners' training diaries for one of the seasons they competed in an international championship were analysed. The reported running volume (km/week) was used to estimate the distribution of training at the prescribed intensity zones in representative weeks in the preparation period and in the competition season. During the preparation period (November - February) the marathon runners ran an average of 186.6 ± 25.7 km/week and the track runners 161 ± 11 km/week. For all runners, 80 ± 5% of the weekly training distance (km/week) in this period was continuous running with a heart rate (HR) between 65–82% of maximum. The remaining 20% of total training volume (km/week) was performed at intensities near and above the anaerobic threshold (82–92% of HRmax). This was done in three to five weekly interval sessions or continuous running sessions. All athletes ran 11 – 13 sessions per week. The training volume (km/week) in the pre-competition period and the competition season did not differ much from the volume in the preparation period. The track runners increased the amount of high-intensity training at specific race pace in the pre-competition period (March and April), and in the track competition season (May - September).

This study compared oxygen uptake (V̇O2) and the respiratory exchange ratio (RER) between well-trained distance runners (DRs) and recreational runners (RRs) below, at, and above the lactate threshold (LT) during a four-minute run and clarified whether these variables reached steady state in DRs. Ten male well-trained DRs (maximal oxygen uptake [V̇O2max], 66.8±5.9 mL/kg/min; LT, 80.0±4.4% V̇O2max) and nine male RRs (V̇O2max, 53.9±3.7 mL/kg/min; LT, 76.6±8.0% V̇O2max) participated in this study. They performed four-minute runs at 70%, 80%, and 90% V̇O2max on a treadmill. The results illustrated that V̇O2 was higher in the fourth minute than in the third minute in RRs at 80% and 90% V̇O2max (Cohen's d=0.25 and 0.26, respectively), whereas, V̇O2 did not differ between the third and fourth minute in DRs at any intensity (Cohen's d=0.08, 0.03, and 0.04, respectively). The RER at each intensity differed between the third and fourth minutes in RRs (Cohen's d=0.25, 0.21, and 0.41, respectively); similarly, RER was only different between the third and fourth minutes at 90%V̇O2max (Cohen's d=0.39) in DRs. These results indicate that the slow component of V̇O2 is not observed in runners with good aerobic capacity even at running intensity exceeding the LT, whereas the RER does not reach steady state at this intensity.