Developing negative split pacing in endurance athletes: practical guidelines and training models

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.

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.

Race walking can be considered as a long-distance performance and it can be described as the technical and athletic expression of fast walking. The physiological determinants of these performances have been well documented; moreover, several recent studies demonstrated that concurrent strength and endurance training can improve performance in endurance athletes. Thus, the purpose of this report was to monitor the adaptations of a combined strength, performed by circuit resistance training (CRT), and endurance programme in two top level female race walkers. The subjects were examined before and after 12 weeks of CRT and endurance training and performed an incremental field test to determine maximum oxygen uptake (.VO2max), running economy (RE) and lactate threshold (LT). The results showed that 12 weeks of combined CRT and endurance programme did not correspond to an alteration in.VO2max and RE, while improvements in LT and 5-km performance were seen.

This case study examines the impact of low serum ferritin (sFe) on physiological assessment measures and performance in a young female 1500-m runner undertaking approximately 95-130 km/wk training. The study spans 4 race seasons and an Olympic Games. During this period, 25 venous blood samples were analyzed for sFe and hemoglobin (Hb); running economy, VO(2max), and lactate threshold were measured on 6 occasions separated by 8-10 mo. Training was carefully monitored including 65 monitored treadmill training runs (targeting an intensity associated with the onset of blood lactate accumulation) using blood lactate and heart rate. Performances at competitive track events were recorded. All data were compared longitudinally. Mean sFe was 24.5 ± 7.6 μg/L (range 10-47), appearing to be in gradual decline with the exception of 2 data points (37 and 47 μg/L) after parenteral iron injections before championships, when the lowest values tended to occur, coinciding with peak training volumes. Each season, 1500-m performance improved, from 4:12.8 in year 1 to 4:03.5 in year 4. VO(2max) (69.8 ± 2.0 mL · kg(-1) · min(-1)) and running economy (%VO(2max) at a fixed speed of 16 km/h; max 87.8%, min 80.3%) were stable across time and lactate threshold improved (from 14 to 15.5 km/h). Evidence of anemia (Hb <12 g/dL) was absent. These unique data demonstrate that in 1 endurance athlete, performance can continue to improve despite an apparent iron deficiency. Raising training volume may have caused increased iron utilization; however, the effect of this on performance is unknown. Iron injections were effective in raising sFe in the short term but did not appear to affect the long-term pattern.

Recent studies have demonstrated that on-transient oxygen uptake (VO2) kinetics are slowed when constant-load exercise is initiated from the upper compared to lower region of the moderate intensity domain (below the lactate threshold, LT). These studies, focusing exclusively on lower limb exercise, attributed this intensity-dependent slowing of VO2 kinetics during moderate-intensity exercise to different muscle fiber recruitment patterns or altered oxygen transport dynamics. PURPOSE: To determine if pulmonary VO2 and heart rate (HR) kinetics are slowed during constant-load arm exercise (arm-cranking) performed in the upper (90%LT) compared to lower (45%LT) regions of the moderate intensity domain. METHODS: Seven healthy male subjects (26 ± 3 yr) performed 4-8 repetitions of arm-cranking exercise involving: 1) a single, 6-min step transition from 9 W to 90% of the estimated LT (AC90%); two continuous, equal, 6-min step transitions from 9 W to 45% LT (AC45%), and then to 90% LT (AC45-90%); and 3) the AC90% protocol superimposed upon low-intensity leg-cycling (AC90%+LC), where baseline HR >100 beats·min-1 (range: 100-120 beats·min-1). RESULTS: The time constants (t) of the VO2 and HR responses were longer (P<0.05) during AC45-90% (tVO2: 70 ± 5 s; tHR: 59 ± 4 s) compared to AC45% (tVO2: 54 ± 6 s; tHR: 47 ± 5 s) and AC90% (tVO2: 53 ± 4 s; tHR: 46 ± 3 s). The gain (DVO2/DW) and blood lactate concentration were also significantly greater (P<0.05) for AC45-90% than AC45%. During arm-cranking superimposed upon leg-cycling (AC90%+LC), HR kinetics were significantly slowed when compared to AC90% (mean difference for tHR: 9 ± 4 s, P<0.05) and AC45% (mean difference for tHR: 9 ± 5 s, P<0.05). Similar comparisons between conditions for tVO2 yielded no significant differences. CONCLUSION: During arm exercise performed within the moderate intensity domain, VO2 and HR kinetics do not conform to a dynamically linear model. Since HR, but not VO2 kinetics were slowed during AC90%+LC, factors other than convective oxygen delivery (e.g. those intrinsic to the exercising muscle) may explain the slower VO2 response in the upper reaches of the moderate intensity domain for arm exercise.

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

Strength training has been shown to cause acute detrimental effects on running performance. However, there is limited investigation on the effect of various strength training variables, whilst controlling eccentric contraction velocity, on running performance. The present study examined the effects of intensity and volume (i.e. whole body vs. lower body only) of strength training with slow eccentric contractions on running economy (RE) [i.e. below anaerobic threshold (AT)] and time-to-exhaustion (TTE) (i.e. above AT) 6 hours post. Fifteen trained and moderately endurance trained male runners undertook high-intensity whole body (HW), high-intensity lower body only (HL) and low-intensity whole body (LW) strength training sessions with slow eccentric contractions (i.e. 1:4 second concentric-to-eccentric contraction) in random order. Six hours following each strength training session, a RE test with TTE was conducted. The results showed that HW, HL and LW sessions had no effect on RE and that LW session had no effect on TTE (P≥0.05). However, HW and HL sessions significantly reduced TTE (P<0.05). These findings demonstrate that a 6-hour recovery period following HW, HL and LW sessions may minimize attenuation in endurance training performance below AT, although caution should be taken for endurance training sessions above AT amongst trained and moderately endurance trained runners.

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.

Background: Physiological markers such as maximal oxygen uptake (VO2 Max), lactate threshold (LT), and peak blood lactate (PBL) are critical for understanding the aerobic and anaerobic adaptations that influence athletic performance. However, their combined assessment across different sporting disciplines is limited. Objectives: This study aimed to compare VO2 Max, LT, and PBL between power and endurance athletes and to explore their interrelationships among these variables within each group. Methods: A cross-sectional comparative design was employed involving 60 athletes aged 18-21 years (30 power athletes and 30 endurance athletes) with a minimum of three years of competitive experience. VO2 Max was measured via graded exercise testing, Lactate Threshold through serial capillary blood sampling, and Peak Blood Lactate with three minutes post-exercise. Independent t-tests and Pearson’s correlation coefficient were used for data analysis (p &lt; 0.05). Results: Endurance athletes exhibited significantly higher VO2 Max and LT values, reflecting superior aerobic capacity and delayed lactate accumulation, whereas power athletes showed significantly greater PBL, indicating enhanced anaerobic glycolysis and metabolic acidosis tolerance. Strong positive correlations were found between VO2 Max and LT in both groups, where LT and PBL were not significantly correlated. Conclusion: Distinct metabolic profiles characterize power and endurance athletes, underscoring the importance of sport-specific training interventions and comprehensive physiological assessments to optimize performance and conditioning strategies.

Six male and six female runners were chosen on the basis of age (20-30 years) and their performance over the marathon distance (mean time = 199.4, SEM 2.3 min for men and 201.8, SEM 1.8 min for women). The purpose was to find possible sex differences in maximal aerobic power (VO2max), anaerobic threshold, running economy, degree and utilization of VO2max (when running a marathon) and amount of training. The results showed that performance-matched male and female marathon runners had approximately the same VO2max (about 60 ml.kg-1.min-1). For both sexes the anaerobic threshold was reached at an exercise intensity of about 83% of VO2max, or 88%-90% of maximal heart rate. The females' running economy was poorer, i.e. their oxygen uptake during running at a standard submaximal speed was higher (P less than 0.05). The heart rate, respiratory exchange ratio and blood lactate concentration also confirmed that a given running speed resulted in higher physiological strain for the females. The percentage utilization of VO2max at the average marathon running speed was somewhat higher for the females, but the difference was not significant. For both sexes the oxygen uptake at average speed was 93%-94% of the oxygen uptake corresponding to the anaerobic threshold. Answers to a questionnaire showed that the females' training programme over the last 2 months prior to running the actual marathon comprised almost twice as many kilometers of running per week compared to the males (60 and 33 km, respectively).(ABSTRACT TRUNCATED AT 250 WORDS)

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.

Previous research has reported that plyometric training improves running economy (RE) and ultimately distance-running performance, although the exact mechanism by which this occurs remains unclear. This study examined whether changes in running performance resulting from plyometric training were related to alterations in lower leg musculotendinous stiffness (MTS). Seventeen male runners were pre- and post-tested for lower leg MTS, maximum isometric force, rate of force development, 5-bound distance test (5BT), counter movement jump (CMJ) height, RE, VO(2max), lactate threshold (Th(la)), and 3-km time. Subjects were randomly split into an experimental (E) group which completed 6 weeks of plyometric training in conjunction with their normal running training, and a control (C) group which trained as normal. Following the training period, the E group significantly improved 3-km performance (2.7%) and RE at each of the tested velocities, while no changes in VO(2max) or Th(la) were recorded. CMJ height, 5BT, and MTS also increased significantly. No significant changes were observed in any measures for the C group. The results clearly demonstrated that a 6-week plyometric programme led to improvements in 3-km running performance. It is postulated that the increase in MTS resulted in improved RE. We speculate that the improved RE led to changes in 3-km running performance, as there were no corresponding alterations in VO(2max) or Th(la).

In this study, a comparison was made between muscle strength, power and muscle and tendon (km and kt respectively) stiffness of the triceps surae muscle group and running economy (RE) in trained male runners. Twelve well-trained male runners (age = 21 +/- 2.7 y, height = 178.1 +/- 7.1 cm, body mass = 66.7 +/- 3.2 kg, VO2max = 68.3 +/- 4.3 mL x kg(-1) x min(-1), 5000-m time = 15:04 min:s) underwent passive stiffness testing using a free oscillation method. Muscle strength was determined via a maximal isometric squat test and power determined via a maximal countermovement jump (CMJ). On a separate day, subjects performed an incremental treadmill test and their RE, lactate threshold, and VO2max were determined. Fingertip blood lactate was determined at the end of each 3-min stage. Lactate threshold was defined as a nonlinear increase in lactate accumulation. A statistically significant correlation was found between km and VO2 at stage 6 (r = -0.69, P = .01). In addition, statistically significant correlations were observed between CMJ peak force production and VO2 at stage 2 (r = .66, P = .02), stage 3 (r = .70, P = .01), and stage 4 (r = .58, P = .04). No other statistically significant correlations were observed. These data suggest that greater muscle stiffness and less power are associated with greater RE. Future study in this area should focus on determining the mechanisms behind this relationship and how to best apply them to a running population through training techniques.

Resistance training and exercise tolerance during high-intensity exercise: moving beyond just running economy and muscle strength.

A number of studies have illustrated that blood flow restricted (BFR) resistance training increasing muscle function even when low-intensity training is utilized. Recently BFR has also applied to aerobic training; however less is known about the effect of aerobic training with BFR on the oxygen uptake (VO2) kinetic responses during exercise with or without BFR. PURPOSE: To elucidate the effects of 8-weeks cycle training with BFR on VO2 kinetics at the onset of constant-load cycle exercise with or without BFR. METHODS: Eighteen healthy subjects were randomly assigned to BFR (n = 9; 22±5 yrs) or no BFR group serving as the control (CON; n = 9; 21±2 yrs). Both groups trained for 30 minutes, 3 days/week for 8 weeks. BFR was performed for 5 minutes every 10 minutes by applying cuffs to the upper thighs. Breath-by-breath VO2 was measured during the performance of 5-min moderate intensity (below the lactate threshold) or 6-min heavy intensity exercise (above the lactate threshold) without BFR at Pre-, 4 weeks after (Mid-), and Post-training. At Post, we also measured VO2 kinetics during exercise with BFR (Post+BFR). Two-way repeated ANOVAs were used (BFR vs CON and Pre vs Mid vs Post vs Post+BFR) with significance accepted as p<0.05; and Tukey’s post hoc tests utilized as needed. RESULTS: Although time constant (τ) of VO2 kinetics at the fast component during moderate exercise fitted the exponential model was significantly faster in Mid and Post than that of Pre (26.8 to 16.3 and 15.5 s and 27.5 to 22.1 and 18.5 s in BFR and CON, p<0.05, respectively), significant group-by-time interaction was not detected. The amplitude of the VO2 slow component during heavy exercise significantly decreased in Mid and Post than that of Pre (281 to 103 and 128 mL/min and 300 to 154 and 160 mL/min in BFR and CON, p<0.05, respectively), while a significant group-by-time interaction was not detected. Both τ of VO2 kinetics during moderate exercise and the amplitude of the VO2 slow component during heavy exercise significantly increased from Post to Post+BFR (19.1 and 28.0 s, and 266 and 256 mL/min in BFR and CON, p<0.05, respectively); however significant group-by-time interactions were not detected either. CONCLUSION: Appling the BFR to eight-weeks cycle training showed no further change in VO2 kinetics at the onset of exercise. Supported by KAKENHI (23700788)