Exercise Modality Influences Lactate Production and RPE: Running vs. Cycling, Intervals vs. Continuous

Exercise Modality Influences Lactate Production and RPE: Running vs. Cycling, Intervals vs. Continuous | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Exercise Modality Influences Lactate Production and RPE: Running vs. Cycling, Intervals vs. Continuous Cheng Zhang, Zhenhe Dong, Ran Feng, Linxiao Wu, Yan Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6901017/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract PURPOSE: Lactate has been redefined from a metabolic waste product to a key signaling molecule regulating energy metabolism, gene expression, and disease progression. While it confers benefits like mitochondrial biogenesis and cognitive enhancement, it poses risks such as tumor microenvironment exacerbation. Precise regulation of exercise-induced lactate exposure is thus critical for population-specific prescriptions (e.g., elderly, cancer patients). This study investigated the coupling between lactate accumulation and subjective fatigue by comparing whole-body (running) vs. localized (cycling) and intermittent vs. continuous modalities, using blood lactate area-under-curve (AUC) and Rating of Perceived Exertion (RPE). Methods: Twelve healthy adults (6M/6F) completed 3 intensities×2 modalities (MIIT/MICT/HIIT×running/cycling ergometry). Lactate AUC was calculated via trapezoidal rule. We innovatively proposed Lactate Production Efficiency (LPE = AUC/RPE) to quantify lactate exposure per unit RPE. Results: 1.Cycling induced 59% (MIIT, P<0.01) and 67% (HIIT, P<0.05) higher lactate AUC than running, irrespective of intensity/intermittency. HIIT cycling yielded 52% higher LPE than running (20.48 vs. 13.47 mmol·min/L·scale, P<0.01), indicating superior lactate stress per fatigue unit. 2.Males showed 36-43% higher running AUC than females (P<0.05), suggesting heightened metabolic sensitivity. HIIT increased lactate AUC by 44-87% versus MICT (P<0.05), confirming interval efficacy. 3.Cycling HIIT/MIIT optimized lactate elevation; running MIIT minimized lactate exposure. Conclusion: We introduce LPE as the first metric quantifying exercise-modality effects on fatigue-lactate decoupling. Key findings include: (1) metabolic stress concentration in localized exercise (cycling), (2) male-specific lactate sensitivity during whole-body running, and (3) RPE-based strategies for precision exercise prescription. This advances personalized interventions in sports medicine. Biological sciences/Physiology Health sciences/Health care/Health services Lactate Accumulation Exercise Modality Rating of Perceived Exertion (RPE) Gender Differences Exercise Prescription Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction For decades, lactate produced during exercise was predominantly viewed as the end-product of glycolysis and a marker of muscular fatigue, its role in the human body simplistically reduced to that of a "metabolic waste product" [1] . However, with the advent of the "lactate shuttle" theory [2] and the discovery of "histone lactylation" mechanisms [3] , the physiological functions of lactate have been fundamentally redefined. Lactate is no longer considered merely a metabolic by-product; instead, it has emerged as a key signaling molecule playing multifaceted roles in energy metabolism, gene regulation, and cellular signaling. In the context of health promotion, the beneficial effects of lactate are gaining increasing recognition [4] . As a cross-tissue energy substrate, lactate can be transported via the "lactate shuttle" to organs such as the liver, heart, and brain, supporting their function. For instance, lactate promotes skeletal muscle mitochondrial biogenesis [5] , mediates myocyte proliferation and differentiation [6] , and serves as a significant energy source for the brain, enhancing cognitive function [7,8] . Studies indicate that lactate can augment lactylation levels at the K1897 site of myosin α-MHC, counteracting myocardial injury and aiding in heart failure treatment [9] . Furthermore, lactylation modification has been shown to reduce appetite, contributing to weight management [10] . Consequently, elevating lactate levels through exercise may represent a viable strategy for optimizing health in populations seeking metabolic enhancement, muscle function preservation, brain health improvement, cardiac rehabilitation, or weight loss. Nevertheless, the role of lactate exhibits significant duality. Under pathological conditions, lactate accumulated within the tumor microenvironment can activate cancer cell survival signaling pathways, promoting invasion, immune evasion, and angiogenesis [11,12,13] . The mechanisms of radiotherapy and most chemotherapeutic agents involve direct or indirect induction of DNA damage to trigger cell death. Lactate-driven lactylation of NBS1 (Nijmegen breakage syndrome protein 1) promotes homologous recombination (HR)-mediated DNA repair, thereby fostering chemoresistance in tumor cells [14] . Thus, cancer patients may require controlled exercise intensity to avoid excessive lactate production [15,16,17] . Elevated lactate levels can also be sensed by alanyl-tRNA synthetases 1 and 2 (AARS1/2) within cells. Subsequently, AARS2 utilizes lactate to lactylate cGAS (cyclic GMP-AMP synthase), leading to its inactivation. This drastically reduces cGAS's affinity for DNA. Given cGAS's critical role in antiviral innate immune responses, hyperlactatemia may potentially suppress antiviral immunity [14] ,increasing susceptibility to infections like the common cold among exercising individuals. This paradoxical nature of lactate underscores the critical importance of precisely regulating exercise-induced lactate levels: distinct populations exhibit significantly divergent requirements for lactate during exercise rehabilitation—some individuals necessitate efficient elevation of lactate through exercise to gain health benefits, while others require avoidance of lactate accumulation to mitigate pathological risks. Exercise physiology mechanisms indicate that lactate production is closely linked to muscle recruitment patterns and energy metabolism pathways. Exercises predominantly engaging localized large muscle groups (e.g., cycling ergometry) often impose concentrated load per muscle unit, predisposing to local hypoxia and activating the glycolytic pathway, potentially yielding higher lactate levels. Conversely, exercises involving multiple muscle groups across the whole body (e.g., running) may exhibit relatively lower lactate production efficiency due to dispersed energy expenditure. Furthermore, the periodic superimposition of high-intensity bouts during intermittent exercise may exacerbate anaerobic metabolism, theoretically inducing greater lactate accumulation compared to continuous exercise [18,19] . However, the quantitative relationship between lactate production and subjective fatigue perception (Rating of Perceived Exertion, RPE) across different exercise modalities (localized vs. whole-body, intermittent vs. continuous) remains inadequately characterized, limiting the precision of exercise prescription design [20] . To address this gap, the present study systematically compared lactate production and RPE responses by employing two representative exercise modalities (Running: whole-body, multi-muscle group exercise; Cycling Ergometry: localized large lower-limb muscle group exercise) combined with three distinct protocols: Moderate-Intensity Continuous Training (MICT), Moderate-Intensity Interval Training (MIIT), and High-Intensity Interval Training (HIIT). By analyzing Lactate Production Efficiency (LPE, calculated as lactate AUC per unit RPE), this study aims to elucidate the influence of exercise modality on the coupling between physiological stress and subjective perception. The findings are intended to provide a scientific basis for designing personalized exercise prescriptions targeting health promotion, disease prevention, and rehabilitation. Methods 2.1 Research Methods 2.1.1 Study Design This study employed a randomized controlled crossover design. Twelve healthy graduate students (6 females, 6 males) were recruited. Ethical approval was granted by the Capital University of Physical Education and Sports Ethical Committee.（Approval Review Number：2024A023）This study followed CONSORT guidelines for a randomized controlled trial, and informed consent has been obtained from all subjects. Prior to the experiment, all participants underwent cardiopulmonary exercise testing (CPET) to determine their maximal oxygen uptake (VO₂max), which served as the basis for setting subsequent exercise intensities (see Table 1). Participants were required to complete three exercise protocols: Moderate-Intensity Interval Training (MIIT), Moderate-Intensity Continuous Training (MICT), and High-Intensity Interval Training (HIIT). Each protocol was performed under two exercise modalities: cycling ergometry and treadmill running (see Figure 1). The experimental procedure consisted of a warm-up, the main exercise phase, and post-exercise measurements. The warm-up phase included 5 minutes of dynamic stretching followed by 3 minutes of cycling at 50W, with a 1-minute rest before commencing the main exercise. During exercise, capillary blood samples were collected from the fingertip every 5 minutes for blood lactate measurement. Heart rate (HR) was monitored in real-time using a Polar HR monitor (H10, China) to ensure exercise intensity compliance. Immediately post-exercise, participants' average HR and Rating of Perceived Exertion (RPE) were recorded. Specific exercise intensities and durations are detailed in Table 2. 2.1.2 Cardiopulmonary Exercise Testing (CPET) CPET was performed using a Schiller exercise testing system (AT-104HS-Ergo, Switzerland) following the BRUCE protocol. Participants began walking on a treadmill at 2.7 km/h with zero inclination. The treadmill gradient was increased incrementally every 3 minutes, with speed adjustments introduced at the 9-minute mark. Testing continued until volitional exhaustion (see Table 3). Expired gases were analyzed in real-time using a metabolic cart (Ganshorn, Germany), and electrocardiography monitored HR. VO₂max was considered achieved if at least three of the following four criteria were met: plateau in VO₂ (change ≤ 2.1 ml/kg/min); respiratory exchange ratio (RER) ≥ 1.1; attainment of age-predicted maximum HR (210 - [0.65 × age] ± 10 bpm); RPE score ≥ 18. The final VO 2max was calculated as the average of the three highest consecutive values. 2.1.3 Heart Rate Monitoring Heart rate was recorded continuously throughout exercise and rest periods using a Polar HR monitor (H10, China). Average HR during each session was analyzed post-experiment (see Table 5). For moderate-intensity exercise, average HR was maintained between 64-76% HRmax. For high-intensity exercise, average HR was maintained between 77-95% HRmax (see Table 2). 2.1.4 Blood Lactate Measurement Baseline blood lactate samples were collected 10 minutes before each exercise session. Capillary blood samples were obtained from the fingertip at minutes 5, 10, 15, 20, 25, and 30 during the exercise phase, and additionally at minute 5 post-exercise. Blood lactate concentration was measured immediately using a portable lactate analyzer (EKF Biosen, Lactate Scout 4, Germany). 2.1.5 Area Under the Blood Lactate-Time Curve Analysis (AUC) The Area Under the Blood Lactate-Time Curve (AUC) served as the key metric for quantifying the total lactate exposure throughout the exercise session. By integrating changes in blood lactate concentration over the entire exercise duration, AUC overcomes the limitation of single time-point measurements and facilitates comparison of the overall lactate accumulation burden across different exercise modalities. AUC was calculated using the trapezoidal method according to the following formula: where Cᵢ represents the blood lactate concentration (mmol/L) at the *i*-th time point, and tᵢ represents the sampling time (min). This method has been validated as an effective means of quantifying exercise metabolic load and is particularly suitable for comparing lactate accumulation profiles across different exercise modalities. Compared to single time-point measurements, AUC provides a more comprehensive characterization of the metabolic response to exercise. For instance, Valborg Land et al demonstrated that lactate AUC is twice as sensitive as peak VO₂(VO2peak) in detecting the effects of exercise interventions and effectively differentiates the efficacy between distinct exercise types [21] . 2.1.6 RPE Measurement The Rating of Perceived Exertion (RPE) was assessed using the Borg RPE scale [22] (see Table 4). Participants were asked to report their RPE value immediately following the completion of each exercise session (see Table 5). 2.2 Statistica Analysis Bar graphs, box plots, and raincloud plots presented in the figures were generated using GraphPad Prism software (Version 10.4.1, GraphPad Software, USA). Other experimental data were analyzed using Python software (Version 3.12.1, Python Software Foundation, Netherlands). Continuous data were assessed for normality using the Shapiro-Wilk test. Data conforming to a normal distribution are presented as mean ± standard deviation (SD). Non-normally distributed data are presented as median (interquartile range, IQR). In accordance with our hypotheses and study design: 1. For comparisons of data obtained within participants across different exercise conditions (e.g., running vs. cycling, MIIT vs. MICT vs. HIIT), paired statistical tests were used. If data met the assumption of normality (confirmed by Shapiro-Wilk test), paired *t*-tests were employed. For non-normally distributed data, Wilcoxon signed-rank tests were used. 2. For comparisons of data between genders (male vs. female) within the same exercise condition, independent samples *t*-tests were used (assuming normality). 3. All calculated AUC values were confirmed to be normally distributed; therefore, paired *t*-tests were used for their analysis across exercise conditions.Statistical significance was set at P < 0.05. Results 3.1 Significantly Higher Lactate Accumulation in Localized Large-Muscle Group Exercise (Cycling) Compared to Whole-Body Exercise (Running) Analysis of the Area Under the Blood Lactate-Time Curve (AUC) revealed that lactate accumulation was significantly higher during cycling ergometry compared to treadmill running, regardless of intensity (moderate: MIIT/MICT or high: HIIT) (Figure 2): During Moderate-Intensity Interval Training (MIIT), the lactate AUC for cycling (183.19 ± 47.05 mmol·min/L) was 59% higher than for running (115.19 ± 26.50 mmol·min/L) (P < 0.01). During High-Intensity Interval Training (HIIT), the lactate AUC for cycling (329.33 ± 60.38 mmol·min/L) was 67% higher than for running (197.29 ± 57.85 mmol·min/L) (P < 0.05). This indicates that cycling, predominantly engaging localized lower-limb muscle groups, induces greater lactate accumulation at equivalent intensities compared to running, which utilizes a whole-body, multi-muscle group synergistic metabolic pattern. 3.2 No Significant Difference in Lactate Accumulation or Subjective Fatigue Between Intermittent and Continuous Exercise at Moderate Intensity Comparison between Moderate-Intensity Interval Training (MIIT) and Moderate-Intensity Continuous Training (MICT) showed: •Lactate Accumulation: No significant difference was observed between cycling MIIT (183.19 ± 47.05 mmol·min/L) and cycling MICT (175.90 ± 48.84 mmol·min/L) (P > 0.05). Similarly, no significant difference existed between running MIIT (115.19 ± 26.50 mmol·min/L) and running MICT (136.58 ± 46.25 mmol·min/L) (P > 0.05; Figure 3A-B). •Subjective Fatigue (RPE): No significant differences in RPE values were found between the intermittent and continuous protocols for either exercise modality (P > 0.05; Figure 3C). These results suggest that the specific intermittent protocol employed in this study (4 minutes of exercise + 1 minute of rest) did not significantly alter lactate accumulation load or subjective fatigue perception at moderate intensity compared to continuous exercise. This may be related to the achievement of an energy metabolic steady state during the intermittent cycles that approximated that of continuous exercise. The 4-min exercise / 1-min rest pattern may have allowed a balance between lactate production during exercise bouts and mitochondrial oxidation during recovery periods, resulting in no significant difference in overall AUC. 3.3 Highest Lactate Production Efficiency in Intermittent Cycling, Lowest in Intermittent Running Analysis using the Lactate Production Efficiency (LPE, calculated as AUC/RPE) metric revealed significant modality-specific differences: •High-Intensity Interval Training (HIIT): Cycling efficiency (20.48 ± 4.00 mmol·min/L·scale) was 52% higher than running efficiency (13.47 ± 3.51 mmol·min/L·scale) (P < 0.01). •Moderate-Intensity Interval Training (MIIT): Cycling efficiency (15.03 ± 4.76 mmol·min/L·scale) was 62% higher than running efficiency (9.25 ± 2.06 mmol·min/L·scale) (P 0.05; Figure 4). These findings demonstrate that for an equivalent level of subjective fatigue perception, intermittent cycling induces a substantially stronger lactate stress response. Conversely, intermittent running exhibits the lowest lactate production efficiency among the protocols tested. This quantification provides a critical basis for the precise control of lactate exposure. 3.4 Significantly Higher Lactate Accumulation in Males During Running, with Gender Differences Amplifying at Higher Intensities Gender-stratified analysis revealed significant differences in lactate AUC specifically during treadmill running (Figure 5): •During Moderate-Intensity Interval Training (MIIT), Moderate-Intensity Continuous Training (MICT), and High-Intensity Interval Training (HIIT) running protocols, male participants exhibited significantly higher lactate AUC compared to females: 36% higher in MIIT, 57% higher in MICT, and 43% higher in HIIT (All P < 0.05). •For cycling ergometry, a significant gender difference favoring higher lactate AUC in males was observed only during Moderate-Intensity Interval Training (MIIT) (Male: 217.33 ± 21.36 mmol·min/L vs. Female: 149.04 ± 40.48 mmol·min/L; P 0.05). These results suggest that males exhibit greater sensitivity to the metabolic stress induced by whole-body running exercise involving multi-muscle group recruitment. This heightened sensitivity in males may be associated with gender-related differences in glycolytic enzyme activity or muscle mass. Discussion 4.1 Mechanistic Differences in Lactate Accumulation by Exercise Type: The Metabolic Advantage of Localized Muscle Groups The present study found significantly higher lactate accumulation (AUC) during cycling ergometry compared to treadmill running. This finding is closely linked to the distinct muscle recruitment patterns inherent to these exercise modalities. Cycling primarily engages large lower-limb muscle groups, such as the quadriceps femoris and hamstrings, whereas running involves synergistic activation of multiple muscle groups across the whole body. At equivalent heart rates, cycling imposes a higher relative load per contracting muscle unit within these large lower-limb groups. This concentrated effort may create an amplification effect of local metabolic stress. The high-load contractions in large lower-limb muscles rapidly deplete muscle glycogen and activate the glycolytic pathway, leading to a rate of pyruvate production that exceeds mitochondrial oxidative capacity, thereby promoting lactate accumulation. 4.2 Decoupling of Subjective Fatigue and Lactate Production: Lactate Production Efficiency per Unit RPE While lactate AUC was significantly higher during cycling than running (Figure 2), no significant difference in RPE was observed between these modalities (Figure 3C). This suggests that equivalent levels of subjective fatigue can correspond to markedly different physiological lactate loads, indicating a decoupling phenomenon between RPE and lactate production. The Rating of Perceived Exertion (RPE) represents the central nervous system's (CNS) integrated subjective assessment of exercise stress. Its genesis involves complex interactions between afferent physiological signals and central neural processing, rather than being solely or directly stimulated by rising lactate levels. Elevations in RPE are strongly associated with cardiovascular load afferent feedback. Increases in heart rate and stroke volume, mediated via vagal and sympathetic nerve afferents to the CNS, show a significant positive correlation with RPE [18] . For instance, the significantly higher RPE observed during HIIT compared to MIIT in this study directly corresponds to the difference in heart rate zones (77%-95% vs. 64%-76% HRmax, Table 5). RPE arises from the CNS's integration of multiple inputs: peripheral metabolic stress signals (including:lactate,pH,HR), muscle mechanoreceptor feedback, and cognitive/emotional factors. Lactate, as a peripheral metabolic marker, indirectly modulates RPE by activating muscle metaboreceptors and influencing central neurotransmitter balance, but it does not directly enter the brain to elicit the sensation of fatigue. This study introduces, for the first time, the novel metric "Lactate Production Efficiency per Unit RPE" (LPE, AUC/RPE), revealing the superior efficiency of cycling in generating lactate for a given perception of effort. Exercise prescription design must therefore consider the dynamic relationship between RPE and lactate response. For example, cycling can elicit higher lactate exposure at equivalent RPE levels, making it suitable for populations requiring highly efficient lactate elevation (e.g., for metabolic or cognitive benefits). Conversely, the "low RPE - low lactate" characteristic of running may be more appropriate for scenarios where fatigue perception needs to be carefully managed, such as in elderly individuals or rehabilitation patients. 4.3 Exercise-Specific Nature of Gender Differences The significantly higher lactate accumulation observed in males compared to females during treadmill running aligns with previous studies reporting greater lactate production in males during high-intensity exercise when relative intensity is controlled [23] . However, a significant gender difference in cycling was only observed during moderate-intensity interval training (MIIT). This modality-specific pattern may be related to inherent gender differences in muscle fiber type composition: males typically possess a higher proportion of type II (fast-twitch, glycolytic) muscle fibers and exhibit greater glycolytic enzyme activity, while females often demonstrate a higher proportion of type I (slow-twitch, oxidative) fibers and superior oxidative metabolic capacity. For instance, citrate synthase activity, a key mitochondrial enzyme, is approximately 15% higher in females, promoting enhanced mitochondrial oxidation of pyruvate. Notably, during cycling HIIT, although lactate levels tended to be numerically higher in males, the gender difference did not reach statistical significance. The localized muscle group metabolic pattern of cycling may mask underlying gender-related metabolic differences, whereas the whole-body metabolic stress imposed by running amplifies gender-specific metabolic sensitivity. As this study did not directly measure muscle fiber types or hormone levels, future research employing molecular biological techniques is warranted to validate this hypothesis. Conclusion This study, by comparing lactate accumulation (AUC) and Rating of Perceived Exertion (RPE) across running vs. cycling and intermittent vs. continuous exercise modalities, has elucidated the differential impact of exercise mode on the coupling between lactate production and subjective perception. This provides novel evidence for the design of precise exercise prescriptions. 5.1 Key Findings: Exercise Mode Determines Lactate Production Efficiency and Fatigue Perception •Localized vs. Whole-Body Exercise: Cycling, predominantly engaging the lower limbs, generated significantly higher lactate accumulation (AUC) at equivalent intensities. Crucially, its Lactate Production Efficiency per Unit RPE (LPE, AUC/RPE) was significantly superior to running during intermittent protocols (52% higher in HIIT, 62% higher in MIIT). This indicates that cycling induces lactate stress more efficiently for the same level of subjective fatigue. •Intermittent vs. Continuous Exercise: While no significant difference in lactate accumulation was found between Moderate-Intensity Interval Training (MIIT) and Moderate-Intensity Continuous Training (MICT), High-Intensity Interval Training (HIIT) elicited substantially higher lactate AUC compared to MICT (+87% for cycling, +44% for running). This confirms high-intensity intervals as an effective strategy for augmenting lactate exposure. •Gender Differences: Males exhibited significantly higher lactate AUC during running compared to females (+36% in MIIT, +43% in HIIT). In contrast, a gender difference in cycling was only evident during moderate-intensity intervals. This demonstrates heightened male sensitivity to the whole-body metabolic stress of running. 5.2 Exercise Prescription Optimization Strategies •Populations Requiring High Lactate Exposure (e.g., metabolic regulation, muscle maintenance, cardiac rehabilitation, appetite suppression): Based on acute exercise data from healthy individuals, cycling MIIT or HIIT may be effective protocols for efficiently elevating lactate [24] . These modalities achieve potent lactate stimulation within a manageable perception of fatigue. Safety and efficacy must be validated within specific target populations. •Populations Requiring Controlled Lactate Exposure (e.g., cancer patients, immunocompromised individuals): Running MIIT, characterized by the lowest LPE (lactate per unit RPE), should be prioritized to minimize the risk of excessive lactate accumulation. •Gender-Specific Considerations: For males, careful control of lactate peaks during running HIIT is advised (e.g., employing phased intensity progression) [25,26] . Females can leverage cycling exercise to achieve higher lactate stimulation benefits at equivalent RPE levels. 5.3 Scientific Value and Practical Significance This study is the first to propose the quantitative metric "Lactate Production Efficiency per Unit RPE" (LPE), revealing the critical role of exercise modality in decoupling lactate production from subjective perception. For instance, the "high lactate - moderate RPE" characteristic of cycling offers a novel pathway for enhancing metabolic activity in elderly populations through low-perceived-exertion exercise. Conversely, the "low lactate - low RPE" profile of running is better suited for special populations needing to avoid excessive stress [27] . The discovery of gender differences further underscores the necessity of prescribing exercise based on relative intensity (e.g., %HRmax) rather than absolute intensity, ensuring both safety and effectiveness. 5.4 Study Limitations and Future Directions •Sample Size and Protocol Limitations: The study involved a relatively small sample size (n=12), and the exercise protocols were laboratory-standardized, potentially limiting generalizability to real-world exercise settings. Future research should expand the sample size and include participants with varying training statuses. While preliminary conclusions are drawn from healthy individuals, factors like age-related muscle decline (sarcopenia) in the elderly or metabolic abnormalities in cancer patients may alter exercise responses, warranting further investigation. •Depth of Mechanistic Insight: Direct measurement of metabolic enzyme activity or mitochondrial function in muscle biopsy samples was not performed. Future studies should integrate omics technologies (e.g., proteomics, metabolomics) to further elucidate the molecular mechanisms underlying exercise modality and gender differences. •Clinical Application Extension: Lactate Production Efficiency per Unit RPE (LPE) holds promise as a potential biomarker for personalized exercise prescription. Its clinical utility, particularly for populations like individuals with diabetes or obesity, requires validation in larger cohorts. Declarations 6 AUTHOR CONTRIBUTIONS CZ and YZ conceptualized the study. Z-HD and L-XW contributed to methodology. YZ provided resources. CZ and RF handled visualization. YZ supervised the study. YZ funded the acquisition. All authors have read and agreed to the published version of the manuscript. 7 Data Availability The data underlying this paper, which includes the privacy of the individuals involved, cannot be made public for the following reasons. These data will be shared with the respective authors upon reasonable request. If you need the data, you can contact me via my e-mail: [email protected] . References Sun,Shiren,et al.Lactic acid: no longer an inert and end-product of glycolysis.Physiology 32.6 (2017): 453-463. Brooks,George A.The science and translation of lactate shuttle theory.Cell metabolism 27.4 (2018): 757-785. Zhang, Di, et al.Metabolic regulation of gene expression by histone lactylation.Nature 574.7779 (2019): 575-580. Li, Veronica L., et al. "An exercise-inducible metabolite that suppresses feeding and obesity." Nature 606.7915 (2022): 785-790. 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Sports Medicine 53.5 (2023): 977-991. Coco M, Buscemi A, Cavallari P, Massimino S, Rinella S, Tortorici MM, Maci T, Perciavalle V, Tusak M, Di Corrado D, Perciavalle V and Zappalà A (2020) Executive Functions During Submaximal Exercises in Male Athletes: Role of Blood Lactate. Front. Psychol. 11:537922. Bergerot, Cristiane Decat, et al. Enhancing cancer supportive care: integrating psychosocial support, nutrition, and physical activity using telehealth solutions. JCO Global Oncology 10 (2024): e2400333. Tables Table 1: Basic Physical Data of Participants in the Experiment Male（n=6） Female(n=6) P η 2 Age(yr) 27.2±5.8 23.3±2.8 0.214 0.15 Height(cm) 179.8±7.3 163.5±5.3 0.002 ﹡ 0.62 Weight(kg) 80.3±9 53.3±5.6 <0.001 ﹡ 0.765 BMI(kg∙m-2) 24.4±1.6 19.7±1.3 <0.001 ﹡ 0.716 VO2max(ml∙kg-1∙min-1) 48.1±5.2 43.2±4.4 0.134 0.21 Table 1. Basic physical data of participants. ﹡ Signifcantly diferent between the groups.Abbreviations:BMI,body mass index;VO 2 max,maximal oxygen uptake. Table 2: Experimental Condition Settings Form of Test Intensity Mean Duration Rest Rep Set Rest type MIIT 64-76%HRmax 70% HRmax 4min 1min 6 1 Negative MICT 64-76%HRmax 70% HRmax 30min NO 1 1 NO HIIT 77-95%HRmax 85% 4min 1min 6 1 Negative Table 3: Cardiopulmonary Exercise Test Conditions Grade Speed（km/h） Incline% Time（min） METs 0 2.7 0 3 2.0 1/2 2.7 5 3 3.5 1 2.7 10 3 5.0 2 4.0 12 3 7 3 5.5 14 3 10 4 6.8 16 3 13 5 8.0 18 3 16 6 8.9 20 3 19 7 9.7 22 3 22 Note: Incline 1 = 1.75% Table 4: Perceived Exertion（Rating on 6-20 RPE Scale） Intensity Very light Light <<Moderate Vigorous Near-maximal to maximal Perceived Exertion(Rating on 6-20 RPE Scale) <9 9-11 12-13 14-17 ≥18 Table 5: Heart Rate (HR) and Rate of Perceived Exertion (RPE) During Moderate and High-Intensity Interval Training on Cycling and Treadmills Cycling（MIIT） Treadmills（MIIT） Cycling（HIIT） Treadmills（HIIT） HR，bpm 132.5±4.8 135.2±6.83 155.6±9.82 158.8±6.39 RPE,6-20scale 12.50±1.89 12.58±1.66 16.17±1.07 14.58±1.75 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6901017","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":522560590,"identity":"1c772b70-6c13-4291-abd9-391b81e73fd5","order_by":0,"name":"Cheng Zhang","email":"","orcid":"","institution":"Capital University of Physical Education and Sports","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Zhang","suffix":""},{"id":522560591,"identity":"c5a61388-33ee-4d8f-b233-4edc6347c6cf","order_by":1,"name":"Zhenhe Dong","email":"","orcid":"","institution":"Capital University of Physical Education and 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1","display":"","copyAsset":false,"role":"figure","size":63994,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic Representation of the Experimental Design\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6901017/v1/d9100ac45718ceade7218249.png"},{"id":92681255,"identity":"bef97479-d14e-40b3-9213-fd9ecc8567b3","added_by":"auto","created_at":"2025-10-03 01:08:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":26898,"visible":true,"origin":"","legend":"\u003cp\u003eLactic Acid and Fatigue Levels in Treadmills and Cycling Exercises\u003c/p\u003e\n\u003cp\u003eA: Blood Lactic Acid Levels During Moderate Intensity Interval Training in Cycling and Treadmill Exercises\u003c/p\u003e\n\u003cp\u003eB: Blood Lactic Acid Levels During High-Intensity Interval Training on a Cycling and a Motorized Treadmill\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6901017/v1/d2e6b5892a39a831dce39727.png"},{"id":92679739,"identity":"ba61b5bf-0987-4c1d-80f4-a862bb5079f1","added_by":"auto","created_at":"2025-10-03 01:00:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43778,"visible":true,"origin":"","legend":"\u003cp\u003eBlood Lactic Acid and Perceived Exertion (RPE) Levels in Moderate-Intensity Continuous and Interval Training\u003c/p\u003e\n\u003cp\u003eA: Blood Lactic Acid Levels in Moderate-Intensity Continuous and Interval Cycling\u003c/p\u003e\n\u003cp\u003eB: Blood Lactic Acid Levels in Moderate-Intensity Continuous and Interval Treadmill Training\u003c/p\u003e\n\u003cp\u003eC: Perceived Exertion (RPE) Levels Across Four Different Exercise Modes\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6901017/v1/fbe431f286f9e64c8b8ab711.png"},{"id":92681256,"identity":"25fbbaa0-4e44-4c6d-bedf-353a9934eae9","added_by":"auto","created_at":"2025-10-03 01:08:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":24476,"visible":true,"origin":"","legend":"\u003cp\u003eAUC lactate/RPE Levels Across six Different Exercise Modes\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6901017/v1/6a07b5a87820703f864b2034.png"},{"id":92679761,"identity":"8d937441-abc8-4952-a3bc-e548ec0b1879","added_by":"auto","created_at":"2025-10-03 01:00:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":70749,"visible":true,"origin":"","legend":"\u003cp\u003eBlood Lactic Acid Levels and Gender Differences in Moderate and High-Intensity Interval Training\u003c/p\u003e\n\u003cp\u003eA: Blood Lactic Acid Levels in Men and Women During Moderate-Intensity Interval Cycling\u003c/p\u003e\n\u003cp\u003eB: Blood Lactic Acid Levels in Men and Women During Moderate-Intensity Interval Treadmill Training\u003c/p\u003e\n\u003cp\u003eC: Blood Lactic Acid Levels in Men and Women During Moderate-Intensity Continuous Cycling\u003c/p\u003e\n\u003cp\u003eD: Blood Lactic Acid Levels in Men and Women During Moderate-Intensity Continuous Treadmill Training\u003c/p\u003e\n\u003cp\u003eE: Blood Lactic Acid Levels in Men and Women During High-Intensity Interval Cycling\u003c/p\u003e\n\u003cp\u003eF: Blood Lactic Acid Levels in Men and Women During High-Intensity Interval Treadmill Training\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6901017/v1/53b6d2f761dcf39b0b6dad8d.png"},{"id":102747405,"identity":"7f7c3ae1-85ab-417c-8a9c-e4f6265bad6f","added_by":"auto","created_at":"2026-02-16 09:04:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1132134,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6901017/v1/a3c9d44d-f954-4179-956c-1ddbcff8cb55.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exercise Modality Influences Lactate Production and RPE: Running vs. Cycling, Intervals vs. Continuous","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFor decades, lactate produced during exercise was predominantly viewed as the end-product of glycolysis and a marker of muscular fatigue, its role in the human body simplistically reduced to that of a \"metabolic waste product\"\u003csup\u003e[1]\u003c/sup\u003e. However, with the advent of the \"lactate shuttle\" theory\u003csup\u003e[2]\u003c/sup\u003e and the discovery of \"histone lactylation\" mechanisms\u003csup\u003e[3]\u003c/sup\u003e, the physiological functions of lactate have been fundamentally redefined. Lactate is no longer considered merely a metabolic by-product; instead, it has emerged as a key signaling molecule playing multifaceted roles in energy metabolism, gene regulation, and cellular signaling.\u003c/p\u003e\n\u003cp\u003eIn the context of health promotion, the beneficial effects of lactate are gaining increasing recognition\u003csup\u003e[4]\u003c/sup\u003e. As a cross-tissue energy substrate, lactate can be transported via the \"lactate shuttle\" to organs such as the liver, heart, and brain, supporting their function. For instance, lactate promotes skeletal muscle mitochondrial biogenesis\u003csup\u003e[5]\u003c/sup\u003e, mediates myocyte proliferation and differentiation\u003csup\u003e[6]\u003c/sup\u003e, and serves as a significant energy source for the brain, enhancing cognitive function\u003csup\u003e[7,8]\u003c/sup\u003e. Studies indicate that lactate can augment lactylation levels at the K1897 site of myosin α-MHC, counteracting myocardial injury and aiding in heart failure treatment\u003csup\u003e[9]\u003c/sup\u003e. Furthermore, lactylation modification has been shown to reduce appetite, contributing to weight management\u003csup\u003e[10]\u003c/sup\u003e. Consequently, elevating lactate levels through exercise may represent a viable strategy for optimizing health in populations seeking metabolic enhancement, muscle function preservation, brain health improvement, cardiac rehabilitation, or weight loss.\u003c/p\u003e\n\u003cp\u003eNevertheless, the role of lactate exhibits significant duality. Under pathological conditions, lactate accumulated within the tumor microenvironment can activate cancer cell survival signaling pathways, promoting invasion, immune evasion, and angiogenesis\u003csup\u003e[11,12,13]\u003c/sup\u003e. The mechanisms of radiotherapy and most chemotherapeutic agents involve direct or indirect induction of DNA damage to trigger cell death. Lactate-driven lactylation of NBS1 (Nijmegen breakage syndrome protein 1) promotes homologous recombination (HR)-mediated DNA repair, thereby fostering chemoresistance in tumor cells\u003csup\u003e[14]\u003c/sup\u003e. Thus, cancer patients may require controlled exercise intensity to avoid excessive lactate production\u003csup\u003e[15,16,17]\u003c/sup\u003e. Elevated lactate levels can also be sensed by alanyl-tRNA synthetases 1 and 2 (AARS1/2) within cells. Subsequently, AARS2 utilizes lactate to lactylate cGAS (cyclic GMP-AMP synthase), leading to its inactivation. This drastically reduces cGAS's affinity for DNA. Given cGAS's critical role in antiviral innate immune responses, hyperlactatemia may potentially suppress antiviral immunity\u003csup\u003e[14]\u003c/sup\u003e,increasing susceptibility to infections like the common cold among exercising individuals.\u003c/p\u003e\n\u003cp\u003eThis paradoxical nature of lactate underscores the critical importance of precisely regulating exercise-induced lactate levels: distinct populations exhibit significantly divergent requirements for lactate during exercise rehabilitation—some individuals necessitate efficient elevation of lactate through exercise to gain health benefits, while others require avoidance of lactate accumulation to mitigate pathological risks.\u003c/p\u003e\n\u003cp\u003eExercise physiology mechanisms indicate that lactate production is closely linked to muscle recruitment patterns and energy metabolism pathways. Exercises predominantly engaging localized large muscle groups (e.g., cycling ergometry) often impose concentrated load per muscle unit, predisposing to local hypoxia and activating the glycolytic pathway, potentially yielding higher lactate levels. Conversely, exercises involving multiple muscle groups across the whole body (e.g., running) may exhibit relatively lower lactate production efficiency due to dispersed energy expenditure. Furthermore, the periodic superimposition of high-intensity bouts during intermittent exercise may exacerbate anaerobic metabolism, theoretically inducing greater lactate accumulation compared to continuous exercise\u003csup\u003e[18,19]\u003c/sup\u003e. However, the quantitative relationship between lactate production and subjective fatigue perception (Rating of Perceived Exertion, RPE) across different exercise modalities (localized vs. whole-body, intermittent vs. continuous) remains inadequately characterized, limiting the precision of exercise prescription design\u003csup\u003e[20]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo address this gap, the present study systematically compared lactate production and RPE responses by employing two representative exercise modalities (Running: whole-body, multi-muscle group exercise; Cycling Ergometry: localized large lower-limb muscle group exercise) combined with three distinct protocols: Moderate-Intensity Continuous Training (MICT), Moderate-Intensity Interval Training (MIIT), and High-Intensity Interval Training (HIIT). By analyzing Lactate Production Efficiency (LPE, calculated as lactate AUC per unit RPE), this study aims to elucidate the influence of exercise modality on the coupling between physiological stress and subjective perception. The findings are intended to provide a scientific basis for designing personalized exercise prescriptions targeting health promotion, disease prevention, and rehabilitation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Research Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.1 Study Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study employed a randomized controlled crossover design. Twelve healthy graduate students (6 females, 6 males) were recruited. Ethical approval was granted by the Capital University of Physical Education and Sports Ethical Committee.（Approval Review Number：2024A023）This study followed CONSORT guidelines for a randomized controlled trial, and informed consent has been obtained from all subjects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrior to the experiment, all participants underwent cardiopulmonary exercise testing (CPET) to determine their maximal oxygen uptake (VO₂max), which served as the basis for setting subsequent exercise intensities (see Table 1). Participants were required to complete three exercise protocols: Moderate-Intensity Interval Training (MIIT), Moderate-Intensity Continuous Training (MICT), and High-Intensity Interval Training (HIIT). Each protocol was performed under two exercise modalities: cycling ergometry and treadmill running (see Figure 1).\u003c/p\u003e\n\u003cp\u003eThe experimental procedure consisted of a warm-up, the main exercise phase, and post-exercise measurements. The warm-up phase included 5 minutes of dynamic stretching followed by 3 minutes of cycling at 50W, with a 1-minute rest before commencing the main exercise. During exercise, capillary blood samples were collected from the fingertip every 5 minutes for blood lactate measurement. Heart rate (HR) was monitored in real-time using a Polar HR monitor (H10, China) to ensure exercise intensity compliance. Immediately post-exercise, participants\u0026apos; average HR and Rating of Perceived Exertion (RPE) were recorded. Specific exercise intensities and durations are detailed in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.2 Cardiopulmonary Exercise Testing (CPET)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCPET was performed using a Schiller exercise testing system (AT-104HS-Ergo, Switzerland) following the BRUCE protocol. Participants began walking on a treadmill at 2.7 km/h with zero inclination. The treadmill gradient was increased incrementally every 3 minutes, with speed adjustments introduced at the 9-minute mark. Testing continued until volitional exhaustion (see Table 3). Expired gases were analyzed in real-time using a metabolic cart (Ganshorn, Germany), and electrocardiography monitored HR. VO₂max was considered achieved if at least three of the following four criteria were met: plateau in VO₂ (change \u0026le; 2.1 ml/kg/min); respiratory exchange ratio (RER) \u0026ge; 1.1; attainment of age-predicted maximum HR (210 - [0.65 \u0026times; age] \u0026plusmn; 10 bpm); RPE score \u0026ge; 18. The final VO\u003csub\u003e2max\u003c/sub\u003e was calculated as the average of the three highest consecutive values.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.3 Heart Rate Monitoring\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeart rate was recorded continuously throughout exercise and rest periods using a Polar HR monitor (H10, China). Average HR during each session was analyzed post-experiment (see Table 5). For moderate-intensity exercise, average HR was maintained between 64-76% HRmax. For high-intensity exercise, average HR was maintained between 77-95% HRmax (see Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.4 Blood Lactate Measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline blood lactate samples were collected 10 minutes before each exercise session. Capillary blood samples were obtained from the fingertip at minutes 5, 10, 15, 20, 25, and 30 during the exercise phase, and additionally at minute 5 post-exercise. Blood lactate concentration was measured immediately using a portable lactate analyzer (EKF Biosen, Lactate Scout 4, Germany).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.5 Area Under the Blood Lactate-Time Curve Analysis (AUC)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Area Under the Blood Lactate-Time Curve (AUC) served as the key metric for quantifying the total lactate exposure throughout the exercise session. By integrating changes in blood lactate concentration over the entire exercise duration, AUC overcomes the limitation of single time-point measurements and facilitates comparison of the overall lactate accumulation burden across different exercise modalities. AUC was calculated using the trapezoidal method according to the following formula:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003ewhere Cᵢ represents the blood lactate concentration (mmol/L) at the *i*-th time point, and tᵢ represents the sampling time (min). This method has been validated as an effective means of quantifying exercise metabolic load and is particularly suitable for comparing lactate accumulation profiles across different exercise modalities. Compared to single time-point measurements, AUC provides a more comprehensive characterization of the metabolic response to exercise. For instance, Valborg Land et al demonstrated that lactate AUC is twice as sensitive as peak VO₂(VO2peak) in detecting the effects of exercise interventions and effectively differentiates the efficacy between distinct exercise types\u003csup\u003e[21]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1.6 RPE Measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Rating of Perceived Exertion (RPE) was assessed using the Borg RPE scale\u003csup\u003e[22]\u003c/sup\u003e (see Table 4). Participants were asked to report their RPE value immediately following the completion of each exercise session (see Table 5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Statistica Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBar graphs, box plots, and raincloud plots presented in the figures were generated using GraphPad Prism software (Version 10.4.1, GraphPad Software, USA). Other experimental data were analyzed using Python software (Version 3.12.1, Python Software Foundation, Netherlands). Continuous data were assessed for normality using the Shapiro-Wilk test. Data conforming to a normal distribution are presented as mean \u0026plusmn; standard deviation (SD). Non-normally distributed data are presented as median (interquartile range, IQR). In accordance with our hypotheses and study design:\u003c/p\u003e\n\u003cp\u003e1. For comparisons of data obtained within participants across different exercise conditions (e.g., running vs. cycling, MIIT vs. MICT vs. HIIT), paired statistical tests were used. If data met the assumption of normality (confirmed by Shapiro-Wilk test), paired *t*-tests were employed. For non-normally distributed data, Wilcoxon signed-rank tests were used.\u003c/p\u003e\n\u003cp\u003e2. For comparisons of data between genders (male vs. female) within the same exercise condition, independent samples *t*-tests were used (assuming normality).\u003c/p\u003e\n\u003cp\u003e3. All calculated AUC values were confirmed to be normally distributed; therefore, paired *t*-tests were used for their analysis across exercise conditions.Statistical significance was set at P \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Significantly Higher Lactate Accumulation in Localized Large-Muscle Group Exercise (Cycling) Compared to Whole-Body Exercise (Running)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of the Area Under the Blood Lactate-Time Curve (AUC) revealed that lactate accumulation was significantly higher during cycling ergometry compared to treadmill running, regardless of intensity (moderate: MIIT/MICT or high: HIIT) (Figure 2): During Moderate-Intensity Interval Training (MIIT), the lactate AUC for cycling (183.19\u0026nbsp;±\u0026nbsp;47.05 mmol·min/L) was 59% higher than for running (115.19\u0026nbsp;±\u0026nbsp;26.50 mmol·min/L) (P \u0026lt; 0.01). During High-Intensity Interval Training (HIIT), the lactate AUC for cycling (329.33\u0026nbsp;±\u0026nbsp;60.38 mmol·min/L) was 67% higher than for running (197.29\u0026nbsp;±\u0026nbsp;57.85 mmol·min/L) (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eThis indicates that cycling, predominantly engaging localized lower-limb muscle groups, induces greater lactate accumulation at equivalent intensities compared to running, which utilizes a whole-body, multi-muscle group synergistic metabolic pattern.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 No Significant Difference in Lactate Accumulation or Subjective Fatigue Between Intermittent and Continuous Exercise at Moderate Intensity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparison between Moderate-Intensity Interval Training (MIIT) and Moderate-Intensity Continuous Training (MICT) showed:\u003c/p\u003e\n\u003cp\u003e•Lactate Accumulation: No significant difference was observed between cycling MIIT (183.19\u0026nbsp;±\u0026nbsp;47.05 mmol·min/L) and cycling MICT (175.90\u0026nbsp;±\u0026nbsp;48.84 mmol·min/L) (P \u0026gt; 0.05). Similarly, no significant difference existed between running MIIT (115.19\u0026nbsp;±\u0026nbsp;26.50 mmol·min/L) and running MICT (136.58\u0026nbsp;±\u0026nbsp;46.25 mmol·min/L) (P \u0026gt; 0.05; Figure 3A-B).\u003c/p\u003e\n\u003cp\u003e•Subjective Fatigue (RPE): No significant differences in RPE values were found between the intermittent and continuous protocols for either exercise modality (P \u0026gt; 0.05; Figure 3C).\u003c/p\u003e\n\u003cp\u003eThese results suggest that the specific intermittent protocol employed in this study (4 minutes of exercise + 1 minute of rest) did not significantly alter lactate accumulation load or subjective fatigue perception at moderate intensity compared to continuous exercise. This may be related to the achievement of an energy metabolic steady state during the intermittent cycles that approximated that of continuous exercise. The 4-min exercise / 1-min rest pattern may have allowed a balance between lactate production during exercise bouts and mitochondrial oxidation during recovery periods, resulting in no significant difference in overall AUC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Highest Lactate Production Efficiency in Intermittent Cycling, Lowest in Intermittent Running\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis using the Lactate Production Efficiency (LPE, calculated as AUC/RPE) metric revealed significant modality-specific differences:\u003c/p\u003e\n\u003cp\u003e•High-Intensity Interval Training (HIIT): Cycling efficiency (20.48\u0026nbsp;±\u0026nbsp;4.00 mmol·min/L·scale) was 52% higher than running efficiency (13.47\u0026nbsp;±\u0026nbsp;3.51 mmol·min/L·scale) (P \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003e•Moderate-Intensity Interval Training (MIIT): Cycling efficiency (15.03\u0026nbsp;±\u0026nbsp;4.76 mmol·min/L·scale) was 62% higher than running efficiency (9.25\u0026nbsp;±\u0026nbsp;2.06 mmol·min/L·scale) (P \u0026lt; 0.01).\u003c/p\u003e\n\u003cp\u003e•Moderate-Intensity Continuous Training (MICT): No significant difference in LPE was observed between cycling and running (P \u0026gt; 0.05; Figure 4).\u003c/p\u003e\n\u003cp\u003eThese findings demonstrate that for an equivalent level of subjective fatigue perception, intermittent cycling induces a substantially stronger lactate stress response. Conversely, intermittent running exhibits the lowest lactate production efficiency among the protocols tested. This quantification provides a critical basis for the precise control of lactate exposure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Significantly Higher Lactate Accumulation in Males During Running, with Gender Differences Amplifying at Higher Intensities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGender-stratified analysis revealed significant differences in lactate AUC specifically during treadmill running (Figure 5):\u003c/p\u003e\n\u003cp\u003e•During Moderate-Intensity Interval Training (MIIT), Moderate-Intensity Continuous Training (MICT), and High-Intensity Interval Training (HIIT) running protocols, male participants exhibited significantly higher lactate AUC compared to females: 36% higher in MIIT, 57% higher in MICT, and 43% higher in HIIT (All P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e•For cycling ergometry, a significant gender difference favoring higher lactate AUC in males was observed only during Moderate-Intensity Interval Training (MIIT) (Male: 217.33\u0026nbsp;±\u0026nbsp;21.36 mmol·min/L vs. Female: 149.04\u0026nbsp;±\u0026nbsp;40.48 mmol·min/L; P \u0026lt; 0.05). The gender difference during High-Intensity Interval Training (HIIT) cycling did not reach statistical significance (P \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eThese results suggest that males exhibit greater sensitivity to the metabolic stress induced by whole-body running exercise involving multi-muscle group recruitment. This heightened sensitivity in males may be associated with gender-related differences in glycolytic enzyme activity or muscle mass.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003e4.1 Mechanistic Differences in Lactate Accumulation by Exercise Type: The Metabolic Advantage of Localized Muscle Groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study found significantly higher lactate accumulation (AUC) during cycling ergometry compared to treadmill running. This finding is closely linked to the distinct muscle recruitment patterns inherent to these exercise modalities. Cycling primarily engages large lower-limb muscle groups, such as the quadriceps femoris and hamstrings, whereas running involves synergistic activation of multiple muscle groups across the whole body. At equivalent heart rates, cycling imposes a higher relative load per contracting muscle unit within these large lower-limb groups. This concentrated effort may create an amplification effect of local metabolic stress. The high-load contractions in large lower-limb muscles rapidly deplete muscle glycogen and activate the glycolytic pathway, leading to a rate of pyruvate production that exceeds mitochondrial oxidative capacity, thereby promoting lactate accumulation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Decoupling of Subjective Fatigue and Lactate Production: Lactate Production Efficiency per Unit RPE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile lactate AUC was significantly higher during cycling than running (Figure 2), no significant difference in RPE was observed between these modalities (Figure 3C). This suggests that equivalent levels of subjective fatigue can correspond to markedly different physiological lactate loads, indicating a decoupling phenomenon between RPE and lactate production.\u003c/p\u003e\n\u003cp\u003eThe Rating of Perceived Exertion (RPE) represents the central nervous system's (CNS) integrated subjective assessment of exercise stress. Its genesis involves complex interactions between afferent physiological signals and central neural processing, rather than being solely or directly stimulated by rising lactate levels. Elevations in RPE are strongly associated with cardiovascular load afferent feedback. Increases in heart rate and stroke volume, mediated via vagal and sympathetic nerve afferents to the CNS, show a significant positive correlation with RPE\u003csup\u003e[18]\u003c/sup\u003e. For instance, the significantly higher RPE observed during HIIT compared to MIIT in this study directly corresponds to the difference in heart rate zones (77%-95% vs. 64%-76% HRmax, Table 5). RPE arises from the CNS's integration of multiple inputs: peripheral metabolic stress signals (including:lactate,pH,HR), muscle mechanoreceptor feedback, and cognitive/emotional factors. Lactate, as a peripheral metabolic marker, indirectly modulates RPE by activating muscle metaboreceptors and influencing central neurotransmitter balance, but it does not directly enter the brain to elicit the sensation of fatigue.\u003c/p\u003e\n\u003cp\u003eThis study introduces, for the first time, the novel metric \"Lactate Production Efficiency per Unit RPE\" (LPE, AUC/RPE), revealing the superior efficiency of cycling in generating lactate for a given perception of effort. Exercise prescription design must therefore consider the dynamic relationship between RPE and lactate response. For example, cycling can elicit higher lactate exposure at equivalent RPE levels, making it suitable for populations requiring highly efficient lactate elevation (e.g., for metabolic or cognitive benefits). Conversely, the \"low RPE - low lactate\" characteristic of running may be more appropriate for scenarios where fatigue perception needs to be carefully managed, such as in elderly individuals or rehabilitation patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Exercise-Specific Nature of Gender Differences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe significantly higher lactate accumulation observed in males compared to females during treadmill running aligns with previous studies reporting greater lactate production in males during high-intensity exercise when relative intensity is controlled\u003csup\u003e[23]\u003c/sup\u003e. However, a significant gender difference in cycling was only observed during moderate-intensity interval training (MIIT). This modality-specific pattern may be related to inherent gender differences in muscle fiber type composition: males typically possess a higher proportion of type II (fast-twitch, glycolytic) muscle fibers and exhibit greater glycolytic enzyme activity, while females often demonstrate a higher proportion of type I (slow-twitch, oxidative) fibers and superior oxidative metabolic capacity. For instance, citrate synthase activity, a key mitochondrial enzyme, is approximately 15% higher in females, promoting enhanced mitochondrial oxidation of pyruvate. Notably, during cycling HIIT, although lactate levels tended to be numerically higher in males, the gender difference did not reach statistical significance. The localized muscle group metabolic pattern of cycling may mask underlying gender-related metabolic differences, whereas the whole-body metabolic stress imposed by running amplifies gender-specific metabolic sensitivity. As this study did not directly measure muscle fiber types or hormone levels, future research employing molecular biological techniques is warranted to validate this hypothesis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study, by comparing lactate accumulation (AUC) and Rating of Perceived Exertion (RPE) across running vs. cycling and intermittent vs. continuous exercise modalities, has elucidated the differential impact of exercise mode on the coupling between lactate production and subjective perception. This provides novel evidence for the design of precise exercise prescriptions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.1 Key Findings: Exercise Mode Determines Lactate Production Efficiency and Fatigue Perception\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e•Localized vs. Whole-Body Exercise: Cycling, predominantly engaging the lower limbs, generated significantly higher lactate accumulation (AUC) at equivalent intensities. Crucially, its Lactate Production Efficiency per Unit RPE (LPE, AUC/RPE) was significantly superior to running during intermittent protocols (52% higher in HIIT, 62% higher in MIIT). This indicates that cycling induces lactate stress more efficiently for the same level of subjective fatigue.\u003c/p\u003e\n\u003cp\u003e•Intermittent vs. Continuous Exercise: While no significant difference in lactate accumulation was found between Moderate-Intensity Interval Training (MIIT) and Moderate-Intensity Continuous Training (MICT), High-Intensity Interval Training (HIIT) elicited substantially higher lactate AUC compared to MICT (+87% for cycling, +44% for running). This confirms high-intensity intervals as an effective strategy for augmenting lactate exposure.\u003c/p\u003e\n\u003cp\u003e•Gender Differences: Males exhibited significantly higher lactate AUC during running compared to females (+36% in MIIT, +43% in HIIT). In contrast, a gender difference in cycling was only evident during moderate-intensity intervals. This demonstrates heightened male sensitivity to the whole-body metabolic stress of running.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 Exercise Prescription Optimization Strategies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e•Populations Requiring High Lactate Exposure (e.g., metabolic regulation, muscle maintenance, cardiac rehabilitation, appetite suppression): Based on acute exercise data from healthy individuals, cycling MIIT or HIIT may be effective protocols for efficiently elevating lactate\u003csup\u003e[24]\u003c/sup\u003e. These modalities achieve potent lactate stimulation within a manageable perception of fatigue. Safety and efficacy must be validated within specific target populations.\u003c/p\u003e\n\u003cp\u003e•Populations Requiring Controlled Lactate Exposure (e.g., cancer patients, immunocompromised individuals): Running MIIT, characterized by the lowest LPE (lactate per unit RPE), should be prioritized to minimize the risk of excessive lactate accumulation.\u003c/p\u003e\n\u003cp\u003e•Gender-Specific Considerations: For males, careful control of lactate peaks during running HIIT is advised (e.g., employing phased intensity progression)\u003csup\u003e[25,26]\u003c/sup\u003e. Females can leverage cycling exercise to achieve higher lactate stimulation benefits at equivalent RPE levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3 Scientific Value and Practical Significance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is the first to propose the quantitative metric \"Lactate Production Efficiency per Unit RPE\" (LPE), revealing the critical role of exercise modality in decoupling lactate production from subjective perception. For instance, the \"high lactate - moderate RPE\" characteristic of cycling offers a novel pathway for enhancing metabolic activity in elderly populations through low-perceived-exertion exercise. Conversely, the \"low lactate - low RPE\" profile of running is better suited for special populations needing to avoid excessive stress\u003csup\u003e[27]\u003c/sup\u003e. The discovery of gender differences further underscores the necessity of prescribing exercise based on relative intensity (e.g., %HRmax) rather than absolute intensity, ensuring both safety and effectiveness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.4 Study Limitations and Future Directions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e•Sample Size and Protocol Limitations: The study involved a relatively small sample size (n=12), and the exercise protocols were laboratory-standardized, potentially limiting generalizability to real-world exercise settings. Future research should expand the sample size and include participants with varying training statuses. While preliminary conclusions are drawn from healthy individuals, factors like age-related muscle decline (sarcopenia) in the elderly or metabolic abnormalities in cancer patients may alter exercise responses, warranting further investigation.\u003c/p\u003e\n\u003cp\u003e•Depth of Mechanistic Insight: Direct measurement of metabolic enzyme activity or mitochondrial function in muscle biopsy samples was not performed. Future studies should integrate omics technologies (e.g., proteomics, metabolomics) to further elucidate the molecular mechanisms underlying exercise modality and gender differences.\u003c/p\u003e\n\u003cp\u003e•Clinical Application Extension: Lactate Production Efficiency per Unit RPE (LPE) holds promise as a potential biomarker for personalized exercise prescription. Its clinical utility, particularly for populations like individuals with diabetes or obesity, requires validation in larger cohorts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e6 AUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCZ and YZ conceptualized the study. Z-HD and L-XW contributed to methodology. YZ provided resources. CZ and RF handled visualization. YZ supervised the study. YZ funded the acquisition. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e7 Data Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this paper, which includes the privacy of the individuals involved, cannot be made public for the following reasons. These data will be shared with the respective authors upon reasonable request. If you need the data, you can contact me via my e-mail: [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSun,Shiren,et al.Lactic acid: no longer an inert and end-product of glycolysis.Physiology 32.6 (2017): 453-463.\u003c/li\u003e\n\u003cli\u003eBrooks,George A.The science and translation of lactate shuttle theory.Cell metabolism 27.4 (2018): 757-785.\u003c/li\u003e\n\u003cli\u003eZhang, Di, et al.Metabolic regulation of gene expression by histone lactylation.Nature 574.7779 (2019): 575-580.\u003c/li\u003e\n\u003cli\u003eLi, Veronica L., et al. \u0026quot;An exercise-inducible metabolite that suppresses feeding and obesity.\u0026quot; Nature 606.7915 (2022): 785-790.\u003c/li\u003e\n\u003cli\u003eHoshino, Daisuke, et al.Effects of decreased lactate accumulation after dichloroacetate administration on exercise training\u0026ndash;induced mitochondrial adaptations in mouse skeletal muscle.Physiological reports 3.9 (2015): e12555.\u003c/li\u003e\n\u003cli\u003eOhno, Y., et al.Lactate increases myotube diameter via activation of MEK/ERK pathway in C2C12 cells.Acta physiologica 223.2 (2018): e13042.\u003c/li\u003e\n\u003cli\u003eDe Silva, T. Michael, and Frank M. Faraci.Microvascular dysfunction and cognitive impairment.Cellular and molecular neurobiology 36 (2016): 241-258.\u003c/li\u003e\n\u003cli\u003eJim\u0026eacute;nez-Maldonado, Alberto, et al. The impact of high-intensity interval training on brain derived neurotrophic factor in brain: a mini-review. Frontiers in neuroscience 12 (2018): 839.\u003c/li\u003e\n\u003cli\u003eMao, Y., Zhang, J., Zhou, Q. et al. Hypoxia induces mitochondrial protein lactylation to limit oxidative phosphorylation. Cell Res 34, 13\u0026ndash;30 (2024).\u003c/li\u003e\n\u003cli\u003eLi, Veronica L., et al.An exercise-inducible metabolite that suppresses feeding and obesity.Nature 606.7915 (2022): 785-790.\u003c/li\u003e\n\u003cli\u003eMarchiq, Ibtissam, and Jacques Pouyss\u0026eacute;gur.Hypoxia, cancer metabolism and the therapeutic benefit of targeting lactate/H+ symporters.Journal of molecular medicine 94 (2016): 155-171.\u003c/li\u003e\n\u003cli\u003eMu, Xianmin, et al.Tumor-derived lactate induces M2 macrophage polarization via the activation of the ERK/STAT3 signaling pathway in breast cancer.Cell cycle 17.4 (2018): 428-438.\u003c/li\u003e\n\u003cli\u003eZhang L, Li S. Lactic acid promotes macrophage polarization through MCT-HIF1\u0026alpha; signaling in gastric cancer. Experimental Cell Research, 2020, 388(2): 111846\u003c/li\u003e\n\u003cli\u003eChen, H., Li, Y., Li, H. et al. NBS1 lactylation is required for efficient DNA repair and chemotherapy resistance. Nature 631, 663\u0026ndash;669 (2024). https://doi.org/10.1038/s41586-024-07620-9\u003c/li\u003e\n\u003cli\u003eSalido, Sof\u0026iacute;a, Alfonso Alejo-Armijo, and Joaqu\u0026iacute;n Altarejos. Synthesis and h LDH inhibitory activity of analogues to natural products with 2, 8-dioxabicyclo [3.3. 1] nonane scaffold. International Journal of Molecular Sciences 24.12 (2023): 9925.\u003c/li\u003e\n\u003cli\u003eHalford, Sarah, et al. A phase I dose-escalation study of AZD3965, an oral monocarboxylate transporter 1 inhibitor, in patients with advanced cancer. Clinical Cancer Research 29.8 (2023): 1429-1439.\u003c/li\u003e\n\u003cli\u003eGrasa, Laura, et al. Antitumor effects of lactate transport inhibition on esophageal adenocarcinoma cells. Journal of physiology and biochemistry 79.1 (2023): 147-161.\u003c/li\u003e\n\u003cli\u003eMalik, Adam A., et al.Perceptual responses to high-and moderate-intensity interval exercise in adolescents.Medicine and Science in Sports and Exercise 50.5 (2017): 1021-1030\u003c/li\u003e\n\u003cli\u003eTsukamoto H, Suga T, Takenaka S, et al. Greater impact of acute high-intensity interval exercise on post-exercise executive function compared to moderate-intensity continuous exercise. Physiol Behav, 2016, 155: 224-30\u003c/li\u003e\n\u003cli\u003eMorales, Julio B., et al. Relationship of Blood Lactate and Sweat Lactate on Exercise Intensity. International Journal of Exercise Science: Conference Proceedings. Vol. 2. No. 12. 2020.\u003c/li\u003e\n\u003cli\u003eValborgland, Torstein, et al.Blood lactate AUC is a sensitive test for evaluating the effect of exercise training on functional work capacity in patients with chronic heart failure.Rehabilitation Research and Practice (2021): 6619747.\u003c/li\u003e\n\u003cli\u003eLiguori, Gary, and American College of Sports Medicine. ACSM\u0026apos;s guidelines for exercise testing and prescription. Lippincott williams \u0026amp; wilkins, 2020\u003c/li\u003e\n\u003cli\u003eMochizuki, Yukina, et al.Sex Differences In Blood Lactate Concentration And Changes In Lifting Velocity During And After Resistance Exercise For Strength Gain And Muscle Hypertrophy.Medicine \u0026amp; Science in Sports \u0026amp; Exercise 52.7S (2020): 215.\u003c/li\u003e\n\u003cli\u003eBrennan, Louise, et al. Personalised exercise rehabilitation in cancer survivorship: the percs triage and referral system study protocol. BMC cancer 24.1 (2024): 517.\u003c/li\u003e\n\u003cli\u003eJacob, Nithin, et al. Effects of high-intensity interval training protocols on blood lactate levels and cognition in healthy adults: systematic review and meta-regression. Sports Medicine 53.5 (2023): 977-991.\u003c/li\u003e\n\u003cli\u003eCoco M, Buscemi A, Cavallari P, Massimino S, Rinella S, Tortorici MM, Maci T, Perciavalle V, Tusak M, Di Corrado D, Perciavalle V and Zappal\u0026agrave; A (2020) Executive Functions During Submaximal Exercises in Male Athletes: Role of Blood Lactate. Front. Psychol. 11:537922. \u003c/li\u003e\n\u003cli\u003eBergerot, Cristiane Decat, et al. Enhancing cancer supportive care: integrating psychosocial support, nutrition, and physical activity using telehealth solutions. JCO Global Oncology 10 (2024): e2400333.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Basic Physical Data of Participants in the Experiment\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eMale（n=6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eFemale(n=6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026eta;\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eAge(yr)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e27.2\u0026plusmn;5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e23.3\u0026plusmn;2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eHeight(cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e179.8\u0026plusmn;7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e163.5\u0026plusmn;5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.002\u003csup\u003e﹡\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eWeight(kg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e80.3\u0026plusmn;9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e53.3\u0026plusmn;5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e﹡\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eBMI(kg∙m-2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e24.4\u0026plusmn;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e19.7\u0026plusmn;1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003csup\u003e﹡\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003eVO2max(ml∙kg-1∙min-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e48.1\u0026plusmn;5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e43.2\u0026plusmn;4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20%;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 1. Basic physical data of participants.\u003csup\u003e﹡\u003c/sup\u003eSignifcantly diferent between the groups.Abbreviations:BMI,body mass index;VO\u003csub\u003e2\u003c/sub\u003emax,maximal oxygen uptake.\u003c/p\u003e\n\u003cp\u003eTable 2: Experimental Condition Settings\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1693%;\"\u003e\n \u003cp\u003eForm of\u003c/p\u003e\n \u003cp\u003eTest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2822%;\"\u003e\n \u003cp\u003eIntensity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1693%;\"\u003e\n \u003cp\u003eDuration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11287%;\"\u003e\n \u003cp\u003eRest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.87831%;\"\u003e\n \u003cp\u003eRep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.99647%;\"\u003e\n \u003cp\u003eSet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003eRest type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1693%;\"\u003e\n \u003cp\u003eMIIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2822%;\"\u003e\n \u003cp\u003e64-76%HRmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e70% HRmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1693%;\"\u003e\n \u003cp\u003e4min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11287%;\"\u003e\n \u003cp\u003e1min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.87831%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.99647%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1693%;\"\u003e\n \u003cp\u003eMICT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2822%;\"\u003e\n \u003cp\u003e64-76%HRmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e70% HRmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1693%;\"\u003e\n \u003cp\u003e30min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11287%;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.87831%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.99647%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003eNO\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1693%;\"\u003e\n \u003cp\u003eHIIT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.2822%;\"\u003e\n \u003cp\u003e77-95%HRmax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.0476%;\"\u003e\n \u003cp\u003e85%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.1693%;\"\u003e\n \u003cp\u003e4min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.11287%;\"\u003e\n \u003cp\u003e1min\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 6.87831%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.99647%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15.3439%;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3: Cardiopulmonary Exercise Test Conditions\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eGrade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eSpeed（km/h）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eIncline%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eTime（min）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003eMETs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e1/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20%;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp; Note: Incline 1 = 1.75%\u003c/p\u003e\n\u003cp\u003eTable 4: Perceived Exertion（Rating on 6-20 RPE Scale）\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.839%;\"\u003e\n \u003cp\u003eIntensity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2322%;\"\u003e\n \u003cp\u003eVery light\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2322%;\"\u003e\n \u003cp\u003eLight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2322%;\"\u003e\n \u003cp\u003e\u0026lt;\u0026lt;Moderate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2322%;\"\u003e\n \u003cp\u003eVigorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2322%;\"\u003e\n \u003cp\u003eNear-maximal to maximal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.839%;\"\u003e\n \u003cp\u003ePerceived Exertion(Rating on 6-20 RPE Scale)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2322%;\"\u003e\n \u003cp\u003e\u0026lt;9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2322%;\"\u003e\n \u003cp\u003e9-11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2322%;\"\u003e\n \u003cp\u003e12-13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2322%;\"\u003e\n \u003cp\u003e14-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.2322%;\"\u003e\n \u003cp\u003e\u0026ge;18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 5: Heart Rate (HR) and Rate of Perceived Exertion (RPE) During Moderate and High-Intensity Interval Training on Cycling and Treadmills\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5294%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1176%;\"\u003e\n \u003cp\u003eCycling（MIIT）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1176%;\"\u003e\n \u003cp\u003eTreadmills（MIIT）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1176%;\"\u003e\n \u003cp\u003eCycling（HIIT）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1176%;\"\u003e\n \u003cp\u003eTreadmills（HIIT）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5294%;\"\u003e\n \u003cp\u003eHR，bpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1176%;\"\u003e\n \u003cp\u003e132.5\u0026plusmn;4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1176%;\"\u003e\n \u003cp\u003e135.2\u0026plusmn;6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1176%;\"\u003e\n \u003cp\u003e155.6\u0026plusmn;9.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1176%;\"\u003e\n \u003cp\u003e158.8\u0026plusmn;6.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 23.5294%;\"\u003e\n \u003cp\u003eRPE,6-20scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1176%;\"\u003e\n \u003cp\u003e12.50\u0026plusmn;1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1176%;\"\u003e\n \u003cp\u003e12.58\u0026plusmn;1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1176%;\"\u003e\n \u003cp\u003e16.17\u0026plusmn;1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.1176%;\"\u003e\n \u003cp\u003e14.58\u0026plusmn;1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lactate Accumulation, Exercise Modality, Rating of Perceived Exertion (RPE), Gender Differences, Exercise Prescription","lastPublishedDoi":"10.21203/rs.3.rs-6901017/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6901017/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePURPOSE: \u0026nbsp;\u003c/strong\u003eLactate has been redefined from a metabolic waste product to a key signaling molecule regulating energy metabolism, gene expression, and disease progression. While it confers benefits like mitochondrial biogenesis and cognitive enhancement, it poses risks such as tumor microenvironment exacerbation. Precise regulation of exercise-induced lactate exposure is thus critical for population-specific prescriptions (e.g., elderly, cancer patients). This study investigated the coupling between lactate accumulation and subjective fatigue by comparing whole-body (running) vs. localized (cycling) and intermittent vs. continuous modalities, using blood lactate area-under-curve (AUC) and Rating of Perceived Exertion (RPE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eTwelve healthy adults (6M/6F) completed 3 intensities×2 modalities (MIIT/MICT/HIIT×running/cycling ergometry). Lactate AUC was calculated via trapezoidal rule. We innovatively proposed Lactate Production Efficiency (LPE = AUC/RPE) to quantify lactate exposure per unit RPE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003e1.Cycling induced 59% (MIIT, P\u0026lt;0.01) and 67% (HIIT, P\u0026lt;0.05) higher lactate AUC than running, irrespective of intensity/intermittency. HIIT cycling yielded 52% higher LPE than running (20.48 vs. 13.47 mmol·min/L·scale, P\u0026lt;0.01), indicating superior lactate stress per fatigue unit.\u003c/p\u003e\n\u003cp\u003e2.Males showed 36-43% higher running AUC than females (P\u0026lt;0.05), suggesting heightened metabolic sensitivity. HIIT increased lactate AUC by 44-87% versus MICT (P\u0026lt;0.05), confirming interval efficacy.\u003c/p\u003e\n\u003cp\u003e3.Cycling HIIT/MIIT optimized lactate elevation; running MIIT minimized lactate exposure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eWe introduce LPE as the first metric quantifying exercise-modality effects on fatigue-lactate decoupling. Key findings include: (1) metabolic stress concentration in localized exercise (cycling), (2) male-specific lactate sensitivity during whole-body running, and (3) RPE-based strategies for precision exercise prescription. This advances personalized interventions in sports medicine.\u003c/p\u003e","manuscriptTitle":"Exercise Modality Influences Lactate Production and RPE: Running vs. Cycling, Intervals vs. Continuous","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 01:00:49","doi":"10.21203/rs.3.rs-6901017/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ea219f1-3f5c-45a6-bc46-cd9cc02e7817","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55541320,"name":"Biological sciences/Physiology"},{"id":55541321,"name":"Health sciences/Health care/Health services"}],"tags":[],"updatedAt":"2026-02-12T21:23:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 01:00:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6901017","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6901017","identity":"rs-6901017","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}