Impaired oxygen consumption kinetics and autonomic dysfunction contribute to increased fatigue in obese young males during sustained exercise
Article information
Abstract
Obesity is associated with reduced exercise tolerance, yet the physiological mechanisms underlying this impairment remain unclear. This study examined whether oxygen uptake (V̇O2) kinetics reflect autonomic regulation during prolonged moderate-intensity exercise in normal-weight and obese males. This cross-sectional study included nine normal-weight and nine obese males (aged 20–22) who performed 30 min of constant-load cycling at 70% of ventilatory threshold to assess V̇O2 kinetics and heart rate variability (HRV) responses. V̇O2 kinetics parameters—including the time constant (τ), which reflects the rate of cardiopulmonary and muscular adjustment to exercise, and the V̇O2 drift slope—were analyzed. HRV was assessed at rest and during exercise. Changes in HRV and their correlations with V̇O2 kinetics and perceived fatigue were evaluated. The obese group had a significantly longer τ than the normal-weight group, despite similar relative intensities. During exercise, HRV indices—including overall HRV, low-frequency, very low-frequency, and the standard deviation along the line of identity (SD2) —were significantly lower in the obese group, indicating attenuated autonomic responsiveness. In the normal-weight group, the slope was strongly correlated with the changes in the root mean square of successive differences, the high-frequency power, the standard deviation perpendicular to the line of identity (SD1), and SD1/SD2 (r=−0.769 to −0.857; P<0.05), whereas these associations were absent in the obese group. These findings suggest that in normal-weight individuals, V̇O2 kinetics are closely linked to autonomic modulation during prolonged exercise, whereas this link is disrupted in obesity, potentially leading to reduced aerobic efficiency and greater exercise intolerance.
INTRODUCTION
Obesity is a global public health concern linked to a variety of physiological impairments that impair the body’s ability to perform and recover from physical activity (Franssen et al., 2023). Among these impairments, exercise intolerance—defined as a reduced ability to initiate or sustain physical activity—represents a significant barrier to the success of exercise-based interventions in individuals with obesity (Franssen et al., 2023; Hansen et al., 2014). This intolerance arises from a complex interaction of peripheral metabolic limitations, altered autonomic regulation, and impaired oxygen delivery and utilization (Hansen et al., 2014).
The kinetic response to oxygen uptake (V̇O2) is a critical factor in determining exercise capacity. It is indicative of the dynamic adaptation of the cardiopulmonary and muscular systems to a shift in metabolic demand at the onset of exercise (Poole and Jones, 2012). The rate at which V̇O2 approaches steady-state levels is determined by the time constant (τ), which is a critical parameter in V̇O2 kinetics (Poole and Jones, 2012). Obese individuals frequently exhibit a longer τ of V̇O2 kinetic, which suggests that the body requires more time to utilize oxygen and deliver it to the muscles (Green et al., 2018; Loftin et al., 2005; Salvadego et al., 2010). This is likely the result of inefficient mitochondria, fewer capillaries, and issues with blood vessel function. This slow kinetic response results in accelerated lactate accumulation and fatigue by increasing the reliance on anaerobic metabolism during the early phase of exercise (Green et al., 2018; Salvadego et al., 2010). In addition, V̇O2 drift—a gradual rise in V̇O2 during sustained prolonged submaximal exercise—tends to be exaggerated in obese individuals, further indicating a reduced capacity to maintain metabolic stability (Loftin et al., 2005).
Fatigue during prolonged moderate-intensity exercise is modulated not only by peripheral factors but also by central and autonomic nervous system regulation (Tornero-Aguilera et al., 2022). Heart rate variability (HRV), a non-invasive index of autonomic nervous system activity, has been widely used to monitor autonomic function during exercise (Gan et al., 2024). In healthy individuals, parasympathetic withdrawal and sympathetic activation are normal adaptive responses to exercise. However, obesity is associated with a persistent sympathovagal imbalance, characterized by diminished vagal tone and reduced parasympathetic reactivation (Indumathy et al., 2015; Kaufman et al., 2007; Phoemsapthawee et al., 2017; Rodríguez-Colón et al., 2011). HRV indices sensitive to parasympathetic modulation—such as the root mean square of successive differences (RMSSD), the standard deviation perpendicular to the line of identity (SD1), and high-frequency (HF) power (Shaffer and Ginsberg, 2017)—are often blunted in this population, reflecting impaired autonomic flexibility (Rossi et al., 2015; Yadav et al., 2017). This autonomic dysfunction contributes to reduced cardiovascular adaptability, increased perceived exertion, and early fatigue during prolonged effort (Chen et al., 2023; Gan et al., 2024; Ni et al., 2022; Vasquez-Bonilla et al., 2024).
Emerging evidence indicates that the interplay between autonomic regulation and V̇O2 kinetics may be essential for comprehending exercise intolerance in obesity. However, the extent to which this relationship is maintained or disrupted during prolonged moderate-intensity exercise is still uncertain. Mechanistic insights into the unique physiological profiles of obese versus normal-weight individuals may be obtained by investigating the relationships between τ of V̇O2 kinetic, V̇O2 drift slope, HRV responses, and fatigues.
Therefore, the present study aimed to examine the kinetic response of V̇O2 and its relationship to autonomic regulation in normal-weight and obese young males during prolonged moderate-intensity exercise. We hypothesized that the relationship between V̇O2 kinetics and dynamic changes in HRV would differ between normal-weight and obese individuals, suggesting potential group-specific patterns in autonomic and metabolic regulation during prolonged moderate-intensity exercise. This research aims to better understand the mechanisms underlying exercise intolerance in obesity and to contribute to the development of more targeted strategies to improve physical function and health outcomes in this population.
MATERIALS AND METHODS
Participants
This comparative cross-sectional study enrolled 18 sedentary young male participants (9 obese and 9 normal-weight) based on predefined inclusion criteria. Initially, 32 individuals were screened following recruitment through university-wide advertisements and word-of-mouth communication. Eligibility assessment included sedentary status, health screening, and anthropometric classification. Participants were then categorized into groups according to the World Health Organization’s regional body mass index (BMI) criteria for Asian populations. (World Health Organization Western Pacific Region, 2000). The obese group (n=9) included individuals with BMI ≥25.0 kg/m2 (age, 20.9±1.7 years; body mass, 92.0±16.9 kg; BMI, 32.5±5.9 kg/m2; body fat percentage (%BF), 36.5%±6.9%), while the normal-weight group (n=9) had a BMI of 18.5–22.9 kg/m2 (age, 20.7±1.3 years; body mass, 68.1±7.7 kg; BMI, 22.2±1.4 kg/m2; %BF, 25.6%±6.4%).
All participants were sedentary, described as “any waking behavior characterized by an energy expenditure ≤1.5 METs while in a sitting, reclining, or lying posture” (Viir and Veraksitš, 2012). Sedentary status was verified through a self-reported physical activity questionnaire adapted from the International Physical Activity Questionnaire short form (Craig et al., 2017). All participants were nonsmokers and free from cardiovascular, metabolic, neurological, respiratory, or orthopedic conditions, as confirmed by a self-reported health status questionnaire and a physician-conducted screening interview. This sample represents the same cohort described in our previous publication (Phoemsapthawee et al., 2024). Baseline characteristics are summarized in Table 1. All participants provided written informed consent after receiving detailed information about the study procedures, risks, and benefits. The study protocol adhered to the principles of the Declaration of Helsinki and was approved by the Kasetsart University Research Ethics Committee (COA no. COA61/076).
Baseline characteristics of the participants
Study design
This study employed a cross-sectional design with a random order of participant groups (obese or normal-weight). Each participant attended the laboratory on three separate occasions: one session for familiarization and two sessions for physiological testing. All trials were scheduled at the same time of day for each participant to minimize circadian effects, with at least 72 hr between visits to avoid residual physiological influences. Participants were instructed to abstain from alcohol and caffeine for at least 12 hr prior to each trial, to maintain adequate hydration, and to limit physical activity to routine daily activities during the 24 hr preceding each visit.
During the first visit, participants were familiarized with the laboratory setting, cycle ergometer, and gas exchange system. They were instructed to maintain a constant pedaling cadence of 60 revolutions per minute (rpm) without using handrails for support. The second visit involved a maximal graded exercise test on a cycle ergometer to determine peak oxygen uptake (V̇O2peak) and ventilatory threshold (VT). During the third visit, participants completed two constant-load cycling trials at an intensity corresponding to 70% of VT to evaluate V̇O2 kinetics, autonomic responses, and fatigue under prolonged steady-state conditions (Fig. 1).
Study design. V̇O2peak, peak oxygen consumption; HRV, heart rate variability; VT, ventilatory threshold.
To standardize nutritional status prior to exercise, participants were instructed to fast for a minimum of 2 hr and to avoid caffeine and alcohol for at least 12 hr before each visit. Upon arrival at the laboratory, a standardized light meal (~70 kcal: 75% carbohydrate, 12% protein, 13% fat) and 50 mL of water were provided. All testing began one hour after meal consumption to ensure consistent pre-exercise energy availability.
Exercise testing protocol
Maximal graded exercise test
All exercise testing sessions were conducted under controlled laboratory conditions (ambient temperature: 22°C–24°C; relative humidity: 40%–60%) and scheduled in the morning to minimize circadian influences on physiological responses. To determine maximal aerobic capacity and to establish individualized workloads for subsequent submaximal protocols, participants first completed an incremental cycling test using an electromagnetically braked cycle ergometer (VIAsprint 150P, Ergoline GmbH).
The incremental exercise protocol began with a 5-min seated rest, followed by 3 minutes of unloaded cycling as a warm-up. The initial workload was set at 50 W and increased by 25 W every 2 min until volitional exhaustion. Participants were instructed to maintain a cadence of 60–70 revolutions per min throughout the test, with verbal encouragement provided to promote maximal effort (Guazzi et al., 2012). Upon termination, a 3-min active recovery phase was performed using free-load cycling. Heart rate (HR) and blood pressure (BP) were monitored at each stage to ensure participant safety. Expired gases were continuously collected and analyzed on a breath-by-breath basis using a calibrated portable metabolic cart (JAEGER Oxycon Mobile, CareFusion). The rating of perceived exertion was recorded at the end of each stage using the Borg 6–20 scale. V̇O2peak was defined as the highest 30-sec average V̇O2 value obtained during the final stage of the test (Green and Askew, 2018). VT was determined using the V-slope method and confirmed by visual inspection of ventilatory equivalents (V̇E/V̇O2 and V̇E/V̇CO2) and end-tidal gas tensions. The workload corresponding to 70% of VT was then used for the constant-load cycling protocol in the subsequent experimental session, based on Salvadego et al. (2010) who examined gas exchange kinetics in obese adolescents.
Cardiac function was simultaneously assessed using thoracic bioimpedance (PhysioFlow, Manatec Biomédical), which provided continuous measurements of cardiac output, cardiac index, stroke volume, and stroke volume index. These data were recorded in real time and averaged over 1-min intervals during rest and throughout the exercise test. BP was measured with an automated brachial sphygmomanometer (Tango M2, SunTech Medical Inc.), with three resting measurements taken at 2-min intervals following a 10-min seated rest. During exercise, BP was assessed at the end of each stage.
Prolonged moderate-intensity exercise
Participants completed two constant-load cycling tests on separate days to assess the dynamic V̇O2 response during moderate-intensity exercise. Each session began with 3 min of unloaded cycling to establish baseline values, followed by 30 min of continuous cycling at 70% of VT. This workload was selected based on Salvadego et al. (2010) who demonstrated that similar submaximal intensities provide stable physiological responses suitable for evaluating gas exchange kinetics in obese individuals. A 30-min duration was chosen to represent prolonged moderate-intensity exercise, allowing sufficient time to assess V̇O2 kinetics and autonomic modulation beyond the early transient phase (McNulty and Robergs, 2017). Throughout the tests, breath-by-breath V̇O2 was measured continuously alongside HR and BP to evaluate cardiopulmonary responses under steady-state conditions. This design enabled detailed characterization of V̇O2 kinetics and autonomic responses during prolonged moderate-intensity cycling.
Measurement and analysis
V̇O2 kinetics modeling
Average V̇O2 values were computed every 10 sec for kinetics analysis during the transition from rest to constant-load exercise. To eliminate the cardiodynamic phase, the initial 20 sec of data were excluded. The remaining response was modeled using a hybrid two-phase approach, consisting of an exponential function for the fundamental component and a linear function representing the continued rise in V̇O2 over time (Linnarsson, 1974). The equation that best fitted the experimental data is presented in equation 1.
Where ΔV̇O2 represents the change in V̇O2 from baseline; A is the amplitude of the exponential response; TD is the time delay; τ is the time constant, indicating the rate of adjustment toward steady state; and k is the slope of the linear drift component that reflects V̇O2 drift during prolonged exercise.
First, the linear slope k was determined from the segment spanning the 540th sec (Jacobsen et al., 1998) to the end of the 30-min bout using a first-order polynomial regression (polyfit function in MATLAB, MathWorks Inc.). This linear component, k(t–TD), was subtracted from the measured V̇O2 signal to isolate the exponential component.
Then, the isolated exponential signal during the linear segment (i.e., post-540 sec) was averaged and used as the asymptotic value of the exponential function (A). The τ and time delay (TD) of V̇O2 kinetics were iteratively adjusted to optimize the fit of the exponential model to the V̇O2 data recorded from exercise onset to the 540th sec. The model fit quality was evaluated using the coefficient of determination (R2), with higher values indicating a better match between modeled and observed data.
This approach allowed precise quantification of the dynamic components of the V̇O2 response, providing insight into both early metabolic adjustment and longer-term stability during moderate-intensity exercise.
Heart rate variability
All tests were conducted under standardized laboratory conditions. Participants arrived at 8:00 a.m. and remained seated for a 15-min rest period, followed by an additional 5-min seated phase to ensure physiological stability prior to testing. HRV was continuously recorded at rest (5 min) and throughout the exercise session using a single-lead ECG device (eMotion Faros, Mega Electronics). For fatigue-related analysis, HRV parameters were derived from R-R intervals recorded during the final 5 min of the 30-min constant-load exercise (i.e., 25–30 min). This time window was selected to capture autonomic responses during the later phase of sustained moderate-intensity exercise, when fatigue-related shifts in autonomic modulation are more pronounced. Data were processed using Kubios HRV software (Kubios HRV, Kubios Oy), which applied validated artifact correction algorithms to remove ectopic beats and noise.
Time-domain HRV indices were calculated from normal-to-normal (NN) intervals and included the standard deviation of NN intervals (SDNN), representing overall HRV, RMSSD, an index of short-term parasympathetic activity, and the percentage of successive NN intervals that differ by more than 50 milliseconds (pNN50), which also reflects parasympathetic modulation and is often used to assess vagal tone. Frequency-domain parameters included HF (0.15–0.40 Hz) power, reflecting parasympathetic modulation; low-frequency (LF: 0.04–0.15 Hz) power, reflecting both sympathetic and parasympathetic influences; and very low-frequency (VLF: 0.001–0.04 Hz) power. The LF/HF ratio was also calculated as an indicator of sympathovagal balance (Shaffer and Ginsberg, 2017).
Nonlinear HRV analysis was also performed using Poincaré plot geometry to provide complementary insight into autonomic control. Specifically, SD1, the standard deviation perpendicular to the line of identity, was calculated to reflect short-term beat-to-beat variability mediated primarily by parasympathetic activity. SD1 is mathematically related to RMSSD but represents variability in geometric rather than temporal form (Brennan et al., 2001). HRV parameters were extracted from two stable segments: the baseline period, representing seated rest prior to exercise, and the exercise session. These time windows were selected to assess autonomic function during the resting and exercising phases. This analytical approach is consistent with previous studies demonstrating that HRV indices—particularly RMSSD, SDNN, HF power, the LF/HF ratio and SD1—are sensitive to autonomic fatigue responses during prolonged exercise (Kaikkonen et al., 2007; Schmitt et al., 2013; Vasquez-Bonilla et al., 2024).
Subjective fatigue responses
Perceived fatigue during the 30-minute cycling session was assessed using the rating of fatigue (ROF) scale (Micklewright et al., 2017). Participants reported their ROF scores at 5-min intervals throughout the session. The rate of change in perceived fatigue (ΔROF) was calculated and used as an indicator of fatigue accumulation.
Statistical analysis
All data were tested for normality using the Shapiro–Wilk test. Between-group differences (obese vs. normal-weight) were assessed using independent samples t-tests for normally distributed variables and Mann–Whitney U-tests for no-normally distributed data. HRV variables were logarithmically transformed using the natural logarithm (Ln) to meet the normality assumption required for parametric analysis. Correlation analyses were conducted to examine associations between τ of V̇O2 kinetic, V̇O2 drift slope, ΔROF and ΔHRV, using Pearson or Spearman correlation coefficients based on data distribution. All statistical tests were two-tailed, with significance set at P<0.05. Analyses were performed using IBM SPSS Statistics ver. 22.0 (IBM Co., USA).
The size of this effect shows a 35% difference in the time constant between the obesity and normal-weight groups (Green et al., 2018), using a significance level of 0.05, a desired statistical power of 0.80, and a large effect size (Cohen d=1.22). A power analysis was performed prior to the study utilizing an independent samples t-test, revealing that at least 18 participants (9 from each group) were necessary to detect significant differences between the groups. The G*Power software (ver. 3.1.9.7, Heinrich-Heine-Universität Düsseldorf) was utilized to determine the sample size.
RESULTS
Participant characteristics
Baseline characteristics of the participants are shown in Table 1. There were no significant differences between the normal-weight and obese groups in age, resting HR, BP, or physical activity levels. However, the obese group had significantly higher body mass (P<0.01), BMI (P<0.01), %BF (P<0.01), and fat mass (P<0.01) compared to the normal-weight group. During resting conditions, cardiac index (P<0.05) and stroke volume index (P<0.05) were significantly lower in the obese group, despite similar cardiac output and stroke volume index. Maximal graded exercise testing revealed significantly lower V̇O2peak in the obese group (P<0.01), along with reduced cardiac index at peak (P<0.05) and lower VT in absolute terms (P<0.05). However, no significant differences were observed in peak HR, rating of perceived exertion, or external load at VT. Physical activity profiles were comparable between groups, with no differences in daily durations of inactivity, light, moderate, or vigorous physical activity (Table 1).
V̇O2 kinetics and fatigue responses
Table 2 summarizes the estimated V̇O2 kinetics and perceived fatigue responses during prolonged moderate-intensity cycling at 70% of VT in the normal-weight and obese groups. Owing to procedural limitations, four participants (two normal-weight, two obese) did not complete the protocol and were excluded, resulting in a final analytic sample of 14 participants. There were no significant differences between the normal-weight and obese groups in relative exercise intensity, expressed as %V̇O2peak, or in oxygen pulse (V̇O2/HR). Similarly, no significant group differences were observed in the amplitude of resting V̇O2 or exercise amplitude (A). A significant difference was observed in the τ of V̇O2 kinetic, with the obese group exhibiting a longer τ of V̇O2 kinetic than the normal-weight group (P<0.05), indicating a slower V̇O2 kinetic response in obese individuals. Fig. 2 illustrates the temporal V̇O2 responses of representative individuals from the normal-weight and obese groups. The ΔROF was also significantly higher in the obese group compared to the normal-weight group (P< 0.05). However, no significant group differences were found in the slope of V̇O2 drift (Table 2).
Estimated parameters of V̇O2 kinetics and rate of fatigue during cycling at an intensity corresponding to 70% ventilatory threshold
Representative responses of oxygen uptake kinetics during prolonged moderate-intensity exercise at 70% of the ventilatory threshold in a normal-weight (A) and an obese (B) participant. Plain line of best fit is derived from the empirical model (equation 1). The asymptote of the fundamental component is depicted with a dashed line.
HRV responses
HRV indices during rest and moderate-intensity cycling at 70% VT are presented in Table 3. At rest, there were no significant differences in time-domain, frequency-domain, or nonlinear HRV indices between the normal-weight and obese groups. However, during exercise, several indices revealed a significant group difference, indicating altered autonomic regulation in obese participants. The obese group exhibited a significantly lower LnSDNN compared to the normal-weight group (P<0.05), reflecting reduced overall HRV under physical stress. Frequency-domain analyses revealed significantly lower LnLF (P<0.05), LnVLF (P<0.05), and marginally lower LnHF power in the obese group (P=0.050), suggesting a dampened parasympathetic and slower regulatory capacity during exercise. Additionally, the nonlinear index LnSD2, which reflects long-term variability and baroreflex sensitivity, was significantly reduced in the obese group (P<0.05). Other indices such as LnRMSSD, LnSD1, and the LnSD1/SD2 ratio showed a trend toward decreased vagal modulation in obese individuals during exercise but did not reach statistical significance (Table 3).
Heart rate variability indices at rest and during cycling at an intensity corresponding to 70% ventilatory threshold
Associations between V̇O2 kinetics, fatigue, and autonomic regulation
Correlation analyses between V̇O2 kinetic parameters (τ and V̇O2 drift slope), subjective fatigue (ΔROF), and changes in HRV indices (ΔHRV) are summarized in Table 4. In the normal-weight group, a significant inverse correlation was found between the slope of V̇O2 drift and changes in ΔRMSSD (r=−0.769, P<0.05), ΔHF (r=−0.857, P<0.05), ΔSD1 (r=−0.769, P<0.05), and ΔSD1/SD2 (r=−0.798, P<0.05). These findings indicate that greater reductions in parasympathetic activity and short-term variability during exercise are associated with a steeper V̇O2 drift slope, suggesting that autonomic withdrawal may contribute to less stable V̇O2 kinetics in normal-weight individuals (Table 4).
Correlation coefficients between the V̇O2 kinetics, rate of fatigue, and changes in heart rate variability parameters among participants
By contrast, the obese group showed no significant correlations between V̇O2 drift slope and changes in HRV. However, a strong inverse correlation was observed between τ of V̇O2 kinetic and ΔLF/HF (r=−0.845, P<0.05), implying that delayed V̇O2 kinetics may reflect impaired autonomic balance, specifically reduced sympathetic-parasympathetic modulation (Table 4).
DISCUSSION
This study is the first to demonstrate that V̇O2 kinetics during prolonged moderate-intensity exercise are differentially associated with autonomic regulation in normal-weight and obese young males. Despite performing exercise at comparable relative intensities and displaying similar V̇O2 amplitudes, the obese group exhibited significantly slower V̇O2 kinetics, as indicated by a prolonged τ of V̇O2 kinetic, greater perceived fatigue, and attenuated autonomic responsiveness. Baseline assessments further revealed that the obese group had lower cardiorespiratory fitness and cardiac functional capacity, as reflected by reduced V̇O2peak and cardiac index.
The present study revealed a substantial impact of obesity on V̇O2 kinetics, with young obese participants (BMI, ~33 kg/m2) exhibiting a 32% longer τ of V̇O2 kinetic compared to normal-weight controls. This level of impairment is comparable to that observed in middle-aged individuals with obesity (BMI, ~32 kg/m2), who exhibit approximately a 35% increase in τ of V̇O2 kinetic (Green et al., 2018). Although smaller in magnitude, consistent elevations in τ of V̇O2 kinetic—ranging from 20% to 25%—have also been reported in obese children and adolescents (Loftin et al., 2005; Salvadego et al., 2010). Collectively, these findings highlight the heightened sensitivity of V̇O2 kinetics to excess adiposity across different age groups and clinical populations. The prolonged τ of V̇O2 kinetic observed in the obese group reflects a delayed adjustment in oxidative metabolism following the onset of exercise, suggesting impaired oxygen delivery and/or utilization (Murias et al., 2014; Poole and Jones, 2012; Wagner, 2012).
This slower V̇O2 kinetic response is consistent with previous findings (Green et al., 2018; Lambrick et al., 2013; Salvadego et al., 2010) and may be attributed to structural and functional impairments commonly associated with obesity, including reduced mitochondrial density (Shimizu et al., 2014), capillary rarefaction (Paavonsalo et al., 2020; Shimizu et al., 2014), and endothelial dysfunction (Virdis et al., 2019). These limitations hinder the efficient matching of oxygen supply to muscular demand, particularly during the initial stages of exercise when rapid metabolic adaptation is essential (Grassi, 2005). Although V̇O2 drift slope did not differ significantly between groups, the obese group reported higher ratings of perceived fatigue. This suggests that fatigue in these individuals may be more closely linked to early-phase metabolic inefficiency rather than late-phase V̇O2 changes. The prolonged τ of V̇O2 kinetic may contribute to greater anaerobic reliance during exercise initiation, accelerating the onset of fatigue (Cannon et al., 2011). Nevertheless, this interpretation is warrants further investigation through studies specifically aimed at elucidating the mechanistic relationship between early-phase oxygen kinetics and fatigue development.
Autonomic modulation during exercise was significantly impaired in the obese group, as evidenced by marked reductions in SDNN, LF power, VLF power, and SD2 compared to the normal-weight group. These alterations reflect attenuated overall HRV and compromised long-term autonomic regulation (Facioli et al., 2021; Goldstein et al., 2011; Shaffer and Ginsberg, 2017; Vasquez-Bonilla et al., 2024). In particular, lower SD2 and VLF values may indicate a reduced capacity of the cardiovascular system to regulate peripheral circulation and BP under sustained physiological stress (Stauss et al., 2009; Volianitis and Secher, 2016). Importantly, these impairments were not apparent at rest, suggesting that autonomic dysfunction in individuals with obesity may remain latent until unmasked by physical stress. Previous studies have reported that obesity is significantly associated with reduced HRV, indicative of heightened sympathetic activity insufficiently counterbalanced by parasympathetic modulation—a pattern that has been observed even in children (Kaufman et al., 2007; Rodríguez-Colón et al., 2011). In contrast, the normal-weight group maintained higher HRV indices during exercise, consistent with greater autonomic adaptability, a shorter τ of V̇O2 kinetic, and lower perceived fatigue. These findings suggest more effective autonomic-metabolic integration in the normal-weight group (Messina et al., 2017), contributing to superior exercise tolerance and physiological resilience.
Correlational analyses further support these observations. In the normal-weight group, the slope of V̇O2 drift was significantly and negatively correlated with indices of parasympathetic activity—ΔRMSSD, ΔHF, and ΔSD1 (Goldberger et al., 2006; Shaffer and Ginsberg, 2017)—as well as with the ratio ΔSD1/SD2, an established marker of sympathovagal balance (Hsu et al., 2012). These associations suggest that enhanced parasympathetic modulation and preserved autonomic balance contribute to more stable V̇O2 kinetics during prolonged exercise (Chen et al., 2023; Gan et al., 2024). Collectively, these findings indicate that in normal-weight individuals, the V̇O2 drift slope is closely linked to dynamic shifts in autonomic regulation, reflecting a tighter integration between neural control and metabolic demand (Messina et al., 2017).
In contrast, such relationships were not observed in the obese group, indicating a disrupted integration between autonomic and metabolic control systems (Calcaterra et al., 2021; Garg et al., 2013; Guarino et al., 2017; Rossi et al., 2015; Yadav et al., 2017). The only notable association in the obese group was a significant inverse correlation between τ of V̇O2 kinetic and ΔLF/HF, potentially reflecting sympathovagal imbalance. Previous studies have similarly reported a maladaptive shift toward sympathetic dominance in individuals with obesity (Rossi et al., 2015; Yadav et al., 2017). Collectively, these findings suggest that in obesity, exercise-induced fatigue may result from a combination of delayed metabolic activation and diminished autonomic adaptability. This underscores the importance of addressing both metabolic and autonomic factors when evaluating exercise intolerance and developing personalized interventions for obese individuals.
The clinical implications of these findings are particularly relevant for developing personalized exercise prescriptions in individuals with obesity. The observed dissociation between autonomic regulation and V̇O2 kinetics suggests that conventional approaches—based solely on external workload or V̇O2 targets—may underestimate internal physiological strain. Blunted vagal modulation and reduced sympathovagal balance likely contribute to diminished exercise tolerance and impaired cardiovascular adaptability during prolonged activity. Incorporating non-invasive assessments of HRV and V̇O2 kinetics may help identify individuals with impaired autonomic-metabolic integration, allowing for more precise and individualized exercise interventions aimed at improving tolerance and physiological efficiency.
This study has several limitations. First, the small sample size may have reduced statistical power and limited the generalizability of findings. Second, although pre-test instructions were standardized, variations in dietary intake and physical activity adherence may have influenced the results. Third, although HRV was employed as a non-invasive proxy for autonomic function, it does not directly reflect sympathetic nerve activity or central regulation and should be interpreted with caution. HRV may also be influenced by factors such as respiration rate, emotional state, physical fitness, and circadian variation, which were not directly controlled. However, efforts were made to minimize these confounders by conducting all tests at the same time of day under standardized laboratory conditions. Finally, the cross-sectional design precludes any causal inferences. Future research should utilize longitudinal or interventional designs and include larger, more diverse populations to determine whether improving autonomic function can enhance V̇O2 kinetics and exercise capacity in individuals with obesity.
In conclusion, this study demonstrates that young males with obesity exhibit significantly prolonged τ of V̇O2 kinetic, higher perceived fatigue, and blunted autonomic responsiveness during prolonged moderate-intensity exercise. Notably, while V̇O2 drift slope was strongly associated with changes in autonomic regulation in normal-weight individuals, such associations were absent in their obese counterparts. In the obese group, only τ of V̇O2 kinetic showed a significant relationship with sympathovagal balance, suggesting a disruption in the normal integration between metabolic and autonomic systems. These findings highlight the importance of considering both metabolic and autonomic mechanisms when assessing exercise intolerance and developing personalized exercise interventions for individuals with obesity.
Notes
CONFLICT OF INTEREST
No potential conflict of interest relevant to this article was reported.
ACKNOWLEDGMENTS
We would like to thank the participants for their enthusiastic participation throughout the study. The authors received no financial support for this article.