Startseite Associations of strength indices and cycling economy in young adults
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Associations of strength indices and cycling economy in young adults

  • Joshua F. Feuerbacher ORCID logo und Moritz Schumann ORCID logo EMAIL logo
Veröffentlicht/Copyright: 22. Juli 2024
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Translational Exercise Biomedicine
Aus der Zeitschrift Translational Exercise Biomedicine Band 1 Heft 2

Abstract

Objectives

Cycling economy is associated with muscle strength in athletes. However, the relationship between strength capacity (i.e. maximal and explosive strength) and cycling economy in previously untrained but healthy individuals remains unclear. Therefore, this study aimed to assess the associations between cycling economy and strength performance in a population of recreationally active but untrained healthy individuals.

Methods

A total of 155 recreationally active individuals (95 males and 60 females) were included. Strength capacity was assessed through an incremental one-repetition maximum test, from which the one-repetition maximum, mean propulsive velocity, and mean propulsive power were derived as strength indices. Cycling economy was assessed using a step protocol on a cycle ergometer and gross oxygen cost and caloric unit cost were determined at submaximal intensities.

Results

Marginal R2 ranged between 0.013 and 0.062 for the gross oxygen cost and between 0.022 and 0.103 for the gross caloric unit cost, respectively. Greater cycling economy is related to higher strength levels. However, the relationship is relatively weak, explaining only 1.3–6.2 % of the variance in gross oxygen cost and 2.2–10.3 % of the variance in gross caloric unit cost.

Conclusions

Greater cycling economy in recreationally active males and females is related to higher strength levels (i.e. one-repetition maximum, mean propulsive velocity, mean propulsive power).

Keywords: physical activity; cycling economy; oxygen consumption; maximal strength; gross caloric unit cost

Introduction

Traditionally, the maximum oxygen consumption (V̇O2max) is considered a performance determinant in recreational and elite sports [1] but has also been shown to be a risk factor of cardiovascular diseases in untrained populations [2]. Similarly, also movement economy (i.e. oxygen consumption at given submaximal loads may be important for endurance performance [3, 4], but may also be linked to energy conservation in the general population. In a previous study it was shown that individuals with higher cardiovascular demands exhibit lesser daily stepping time [5]. Thus, by promoting efficient movement, the overall energy expenditure required for daily activities is minimized, allowing individuals to undertake tasks with greater ease and diminished fatigue [5, 6].

Understanding the mechanism behind fluctuations in movement economy is eminent but conclusive evidence is lacking. In athletic populations, it was shown that a greater percentage of type I fibers is related to better movement economy, as this fiber type is known to be more efficient compared to type II fibers [7]. Furthermore, in athletic populations regular heavy strength training was shown to improve movement economy [4], primarily due to enhanced neuromuscular function [8]. In a similar manner, associations between strength capacity and movement economy were also shown in previously untrained healthy [9] as well as diseased populations [10], underlining the importance of maintaining strength to perform also habitual activities with lower energetic demands [9].

While the associations between muscle strength and walking or running have been demonstrated in non-athletic populations [9, 10], evidence is lacking for cycling. Although the link between muscle strength and cycling economy is known in athletes [4, 8], its manifestation in healthy and active individuals remains unclear. Therefore, this study aimed to assess the associations between cycling economy and strength performance in a population of recreationally active but otherwise untrained healthy individuals. Expanding on previous research, we hypothesized that a higher maximal as well as explosive strength is a key indicator of enhanced cycling economy. The summary of this article is presented in Figure 1.

Figure 1: Graphical representation of this article. Figure created with BioRender.
Figure 1:

Graphical representation of this article. Figure created with BioRender.

Materials and methods

Study design

All participants completed three laboratory sessions. The first session involved familiarization with the materials and methods used in this study. The second session included an incremental ergometer test on a stationary bicycle, and the third session involved an incremental one-repetition maximum (1RM) test in the squat, with each session being separated by at least 48 h but not exceeding 5 days. However, as previous research has indicated that ovarian hormones may impact both endurance and strength performance in women, the experimental sessions (i.e. incremental ergometer test and incremental maximal strength test) were conducted at fixed points within the menstrual cycle [11]. Accordingly, the incremental ergometer test was performed within the luteal phase of the menstrual cycle, while the incremental maximal strength test was performed between days 2 and 5 of the subsequent menstrual cycle.

Participants

Participants were eligible if they met the following criteria: (1) non-smokers; (2) free from chronic or acute injuries; (3) aged between 18 and 40; and (4) physically active, but not specifically endurance and/or strength trained (more than two sessions per week). The study included both male and female participants. For female participants, only those who had a consistent menstrual cycle with regular menstrual bleedings for at least three consecutive months before participating in the study were eligible. Information regarding the menstrual cycle and monthly bleeding was obtained during a familiarization interview. Prior to inclusion, all participants received instructions regarding potential risks and provided written informed consent. This study received approval from the local institutional board and adhered to the principles of the Declaration of Helsinki. Initially, 96 males and 61 females were enrolled into the study. Two datasets (i.e. 1 male, 1 female) were excluded due to compromised V̇O2 data quality.

Incremental ergometer test

Participants underwent a graded exercise test on an electronically braked cycle ergometer (ergoline GmbH, Bitz, Germany) until volitional exhaustion. Before the test began, body mass was measured using a Sanitas system (Hans Dinslage GmbH, Uttenweiler, Germany). The test commenced with a 5-min warm-up at 80 W for males and 50 W for females. Subsequently, the resistance was incremented every 3 min by 30 W. Gas exchange was analysed using a Metalyzer 3B, which was calibrated according to manufacturer’s recommendations before each test (Cortex, Leipzig, Germany). The V̇O2peak was determined as the highest 30-s average. Subsequently, V̇O2peak was utilized to quantify the level of physical fitness and to analyse its effects on cycling economy.

The gross oxygen cost of cycling (mL·min−1·kg−1) was calculated during the final minute of each increment. Additionally, the gross caloric unit cost of cycling (kcal·min−1·kg−1) was obtained according to previous recommendations [12]. Capillary blood samples (20 μL) were collected from the earlobe, inserted into reaction capsules containing a haemolyzing agent, and then analysed using a Biosen analyser (Biosen S-Line, EKF Diagnostics, Barleben, Germany). Blood lactate concentrations were assessed at rest and in the final 20 s of each increment, in order to quantify the exercise intensity withing the incremental ergometer test.

Incremental maximal strength test

We evaluated strength capacity using load-velocity and load-power profiles during squats, from which we derived the one-repetition maximum (1RM), as well as mean propulsive velocity (MPV) and mean propulsive power (MPP) as strength indicators. The MPV as well as the eccentric displacement was recorded during each repetition using a linear velocity transducer (T-Force System, Ergotech, Murcia, Spain). The propulsive phase was defined as the part of the concentric movement where the measured acceleration exceeded gravitational acceleration. Velocity was sampled at a frequency of 1,000 Hz and processed using custom software (T-Force Dynamic Measurement System, version 2.3).

The test began with a 5-min individualized warm-up on a stationary cycle ergometer, followed by 10 squats using only the participant’s body weight and then 3–6 repetitions with the Smith machine barbell (22 kg). Depth and foot positioning were recorded to ensure consistency across all sets. Prior to each set, participants were instructed to maintain a controlled eccentric phase until the reversal point, hold the position for 1.5 s, and then perform the concentric phase with maximal velocity in a non-ballistic manner. Starting with an initial load of 22 kg, the load was progressively increased until participants could no longer lift the weight with correct technique. Load increments and the number of repetitions were adjusted based on the velocity achieved during each set. Three, two, and one repetitions were performed for high (MPV>1.0 m·s−1), moderate (0.75 m·s−1≤MPV≤1.0 m·s−1), and low velocities (≤0.75 m·s−1), respectively [13]. Rest between sets was set at 2 min.

Following the test, individual load-velocity and load-power characteristics were calculated. A linear regression was used to assess MPV at 30 %, 50 %, 70 %, and 90 % of the 1RM, while a third-degree polynomial was fitted to the MPP data.

Statistical analyses

To determine the associations of load-velocity and load-power characteristics (i.e. MPV, MPP & 1RM) with the gross oxygen cost and the gross caloric unit cost of cycling across different sub-maximal workloads, we used a generalized estimating equation model (GEE), since GEE allows for the consideration of correlations between repeated measurements or observations (e.g., 6 cluster). GEE models were calculated using submaximal intensities identified by blood lactate concentrations below 4 mmol·L−1, excluding heavy and severe exercise intensities due to the impact of the V̇O2 slow component [14]. Predictors included the following strength indices, 1RM as well as MPVs and MPPs at 30 %, 50 %, 70 %, & 90 % of 1RM. We analysed potential confounding factors such as sex and V̇O2peak that could influence movement economy, and found that only V̇O2peak demonstrated a significant effect on both gross oxygen cost and gross caloric cost. Moreover, research has highlighted V̇O2peak as a factor impacting exercise economy [15], while evidence from a previous study indicates that strength levels do not affect movement economy differently between men and women [9]. Consequently, the final GEE modelling incorporated V̇O2peak as a confounding variable in the model, along with strength indices as predictors. To mitigate multicollinearity among predictors, GEEs were computed for each strength determinant individually. Main effects were examined for each predictor across all workloads. The goodness of fit for the GEE was assessed by examining quantile-quantile plots. The presence of heteroscedasticity in each GEE model was evaluated using the Breusch-Pagan test and by visually inspecting the residuals plot. Marginal R2 following the methodology of Hardin and Hilbe [16] was computed to enhance the interpretability of the findings. All analyses were performed using R and R Studio (version, 4.3.2). GEE was calculated using the “geepack” package, version 1.3.9. Heteroscedasticity was assessed using the lmtest package, version 0.9–40. Statistical significance was defined for all tests a priori at p<0.050.

Results

Demographic characteristics and ergometer test results

The anthropometric and general performance characteristics of all participants are displayed in Table 1.

Table 1:

Anthropometric and performance data of the participants clustered by sex.

Males

n=95		 Females

n=60
Age, years 27.3 ± 5.5 25.5 ± 5.1
Height, m 1.82 ± 5.7 1.68 ± 7.2
Mass, kg 79.7 ± 8.3 64.7 ± 9.2
V̇O2peak, mL·min−1·kg−1 44.8 ± 6.8 40.2 ± 4.2
Peak power, W 278.1 ± 41.1 198.7 ± 34.2
Relative peak power, W·kg−1 3.5 ± 0.6 3.0 ± 0.6
Squat 1RM, kg 94.6 ± 22.0 64.3 ± 13.2
Relative squat 1RM, kg·kg−1 1.19 ± 0.27 1.0 ± 0.21

The incremental ergometer test results of all participants are presented in Table 2.

Table 2:

Participants incremental ergometer test data clustered by sex and workload.

Intensity 50 W 80 W 110 W 140 W
Females

n=47		 Males Females

n=60		 Males

n=93		 Females

n=59		 Males

n=93		 Females Males

n=95
Exercise intensity, %V̇O2peak 36.5 ± 5.4 – 47.5 ± 2.8 36.3 ± 5.9 60.6 ± 8.7 45.7 ± 7.0 – 55.1 ± 8.5
Blood lactate concentrations, mmol·L−1 1.1 ± 0.5 – 1.5 ± 0.7 1.1 ± 0.5 2.5 ± 1.1 1.4 ± 0.6 – 2.1 ± 1.0
Gross oxygen cost of cycling, mL·min−1·kg−1 14.3 ± 2.1 – 19.0 ± 2.8 15.9 ± 1.9 24.3 ± 3.2 20.2 ± 2.4 – 24.3 ± 2.8
Gross caloric unit cost of cycling, kcal·min−1·kg−1 0.06 ± 0.008 – 0.07 ± 0.010 0.06 ± 0.010 0.10 ± 0.012 0.08 ± 0.010 – 0.10 ± 0.011

Associations between the load-velocity characteristic and the oxygen cost of cycling

The results of the GEE modelling for the gross oxygen cost and the gross caloric unit cost of cycling are presented in Tables 3 and 4, respectively. The GEE modelling revealed a statistically significant inverse association between the strength indices and the gross oxygen cost of cycling. Marginal R2 values varied between 0.013 and 0.028 for MPVs and between 0.048 and 0.062 for MPPs. Marginal R2 for 1RM was 0.059. The negative estimates suggest that higher strength capacity is associated with a lower gross oxygen cost in both males and females. Notably, the highest marginal R2 was found for MPPs at 50 and 70 % of the 1RM (0.062 and 0.061, respectively). Statistically significant effects were observed for the variable V̇O2peak. Positive estimates for the variable V̇O2peak indicate that higher V̇O2peak is associated with increased gross oxygen consumption.

Table 3:

Generalized estimating equations illustrating the effects of predictors (i.e. MPV, MPP and 1RM, as well as V̇O2peak) on the oxygen cost of cycling (mL·min−1·kg−1). Final model used: Gross oxygen cost ∼ MPV/MPP/1RM + V̇O 2peak . The left panel (strength indices) presents the estimates and marginal R2 for the strength index (i.e. MPV, MPP and 1RM) controlled for V̇O2peak, while the right panel (V̇O2peak) displays the marginal R2 value for V̇O2peak.

Gross oxygen cost
Strength indices V̇O2peak
Predictor Estimate Std.Err Wald Pr. p-Value Marginal R2 Estimate Std.Err p-Value Marginal R2
MPV 30 % −2.004 0.838 5.71 0.017 0.013 0.193 0.019 <0.001 0.211
MPV 50 % −2.500 0.929 7.2 0.007 0.016 0.192 0.019 <0.001 0.209
MPV 70 % −2.484 0.698 12.7 <0.001 0.028 0.187 0.018 <0.001 0.205
MPV 90 % −0.154 0.725 0.04 0.830 0.000 0.182 0.018 <0.001 0.207
MPP 30 % −0.005 0.001 36.3 <0.001 0.055 0.196 0.021 <0.001 0.179
MPP 50 % −0.005 0.001 28.9 <0.001 0.062 0.199 0.022 <0.001 0.172
MPP 70 % −0.006 0.001 28.3 <0.001 0.061 0.199 0.022 <0.001 0.172
MPP 90 % −0.008 0.002 21.9 <0.001 0.048 0.194 0.021 <0.001 0.179
1RM −0.033 0.006 27.3 <0.001 0.059 0.190 0.020 <0.001 0.179

  1. MPV, mean propulsive velocity; MPP, mean propulsive power; 1RM, one-repetition maximum; Std.Err, standard error; V̇O2peak, peak oxygen consumption. Bold values indicate a statistically significant association with p≤0.050.

Table 4:

Generalized estimating equations illustrating the effects of predictors (i.e. MPV, MPP and 1RM, as well as V̇O2peak) on the caloric cost of cycling (kcal·min−1·kg−1). Final model used: Gross caloric unit cost ∼ MPV/MPP/1RM + V̇O 2peak . The left panel (strength indices) presents the estimates and marginal R2 for the strength index (i.e. MPV, MPP and 1RM) controlled for V̇O2peak, while the right panel (V̇O2peak) displays the marginal R2 value for V̇O2peak.

Gross caloric unit cost
Strength indices V̇O2peak
Predictor Estimate Std.Err Wald Pr. p-Value Marginal R2 Estimate Std.Err p-Value Marginal R2
MPV 30 % −9.90e-3 3.22e-3 9.48 0.002 0.022 7.82e-4 7.17e-5 <0.001 0.244
MPV 50 % −1.22e-2 3.46e-3 12.6 <0.001 0.029 7.75e-4 7.18e-5 <0.001 0.239
MPV 70 % −1.21e-2 2.61e-3 21.3 <0.001 0.049 7.53e-4 7.14e-5 <0.001 0.230
MPV 90 % 9.27e-4 3.27e-3 0.08 0.780 0.000 7.28e-4 6.83e-5 <0.001 0.235
MPP 30 % −2.42e-5 3.56e-6 46.3 <0.001 0.103 7.98e-4 7.87e-5 <0.001 0.215
MPP 50 % −2.39e-5 3.53e-6 45.7 <0.001 0.102 8.00e-4 7.98e-5 <0.001 0.211
MPP 70 % 7.92e-4 7.90e-5 40.3 <0.001 0.091 7.92e-4 7.90e-5 <0.001 0.211
MPP 90 % −3.05e-5 5.97e-6 26.1 <0.001 0.060 7.70e-4 7.53e-5 <0.001 0.218
1RM −2.55e-5 4.02e-6 40.3 <0.001 0.091 7.92e-4 7.90e-5 <0.001 0.211

  1. MPV, mean propulsive velocity; MPP, mean propulsive power; 1RM, one-repetition maximum; Std.Err, standard error; V̇O2peak, peak oxygen consumption. Bold values indicate a statistically significant association with p≤0.050.

Similarly, a statistically significant inverse association between the strength indices and the gross caloric unit cost of cycling was found. Marginal R2 values ranged from 0.020 to 0.024 for MPVs and from 0.048 to 0.062 for MPPs. Marginal R2 for 1RM was 0.091. The negative estimates suggest that higher strength capacity is associated with a lower gross oxygen cost in both males and females. Notably, the highest marginal R2 was found for MPPs at 30 and 50 % of the 1RM (0.103 and 0.102, respectively). Additionally, V̇O2peak was identified as a confounding factor. Positive estimates for the variable V̇O2peak indicate that higher V̇O2peak is associated with increased gross oxygen consumption.

Discussion

In this study, we investigated the associations between strength indices obtained from load-velocity and load-power profiling and parameters of cycling economy, including gross oxygen cost and gross caloric unit cost, in recreationally active healthy males and females. We found a statistically significant inverse relationship of strength indices with both gross oxygen cost and gross caloric unit cost. Marginal R2 ranged between 0.013 and 0.062 for the gross oxygen cost and between 0.022 and 0.103 for the gross caloric unit cost, respectively. Additionally, we identified V̇O2peak as a confounding factor, affecting cycling economy.

Our findings indicate that in recreationally active adult males and females, greater 1RM and MPV/MPP, obtained from load-velocity and load-power relationships, affects the gross oxygen cost and gross caloric unit cost of cycling at submaximal intensities. This is in line with other studies performed in healthy males and females, showing that in walking/running greater maximal isometric leg extensor strength is associated with improved movement economy [9]. Although the marginal R2 values indicate a weak relationship between strength capacity and gross oxygen cost as well as gross caloric cost, accounting for only up to 6 and 10 % of the variance, respectively, these findings are consistent with previous reports [9]. This suggests that the extent to which strength contributes to cycling economy is comparable to its contribution in walking and running. In contrast to walking/running economy, the mechanisms behind the improved cycling economy are still unknown. Unfortunately, the majority of evidence regarding the cycling economy is derived from studies involving athletes that engaged in strength training in order to improve athletic performance [17, 18]. These longitudinal training studies have shown that improved maximal and explosive strength led to altered muscle fiber recruitment patterns toward a more oxidative fiber type [19, 20] and delayed activation of less efficient Type II muscle fibers [8]. However, this differs from the present study, where we analysed the cross-sectional associations between the strength capacity and the cycling economy in recreationally active individuals.

Nonetheless, our data indicate that in recreationally active males and females a greater 1RM strength and higher MPV/MPP are associated with better cycling economy. While the exact mechanisms for this association remain unknown, some evidence from athletic populations points toward shorter contraction times and consequently a reduced time to achieve peak power in each pedal stroke [18]. This in turn may enhance oxygen supply by minimizing periods of restricted blood flow. Indeed, this potential mechanism is similar to walking and running, where it was shown that improved economy in males may result from longer relaxation periods within each gait cycle, leading to better muscular recovery, perfusion, and oxygen transport [21, 22]. Additionally, previous studies showed that factors contributing to enhanced walking/running economy also include neuromuscular mechanisms like improved joint stiffness and intramuscular coordination [23], but also mechanical and morphological properties of the muscle-tendon units may explain variations in running economy [24]. Interestingly, previous studies indicated a stronger association between movement economy and explosive strength, such as higher rates of force development (RFD) [10, 25] and greater contraction velocities [22], compared to maximal strength. This is in contrast to our findings, where both maximal (i.e. 1RM) and explosive strength (MPV/MPP) were similarly associated with greater cycling economy across the entire load spectrum (i.e. 30–90 % 1RM). This may be attributed to the interconnected nature of maximal and explosive strength, which share physiological and neuromuscular characteristics [26]. This is particularly true when using load-velocity relationships as indicators of strength performance, where the movement velocity is directly linked to the executed load [27].

Importantly, our data revealed V̇O2peak as a confounding factor for the observed associations, as participants with a lower V̇O2peak showed a greater exercise economy. This finding is well in line with observations in elite cyclists [28]. In the specific population of elite athletes, it appears that lower V̇O2max/V̇O2peak is compensated by a greater cycling economy/efficiency [28]. However, it is somewhat surprising that in the present study with healthy but untrained individuals V̇O2peak was inversely related to cycling economy, especially since typically a higher V̇O2peak indicates better cardiovascular fitness and aerobic capacity [29]. These traits commonly reflect a more aerobic phenotype, which would generally be expected to correlate with better economy [7]. However, a higher V̇O2peak implies a greater ability to produce energy aerobically as well, which affects substrate metabolism [30]. Notably, marginal R2 presented in this study were comparable for both gross oxygen and gross caloric unit cost, suggesting that the impact of greater strength capacity on cycling economy in this specific population seems to be independent of substrate metabolism. Importantly, substrate metabolism may account for a significant portion of the observed variance as individuals with higher oxidative capacity are likely to exhibit greater fat metabolism [31], requiring a higher V̇O2. However, this may be negligible in a population not specifically endurance-trained, as fat metabolism is only moderately developed [32]. However, apart from metabolic factors that may explain why greater V̇O2peak is associated with lower cycling economy, higher submaximal oxygen consumption can also arise from higher basal metabolic rates [33, 34] and mechanical and morphological properties of the muscle-tendon unit [35], which may aid in explaining the observed inverse relationship.

Despite the differences between highly adapted athletes and untrained but healthy individuals, the associations between strength capacity and cycling economy appear to be comparable. Even though we assessed cross-sectional associations between strength capacity and cycling economy our data may suggest that prolonged, systematic strength training could potentially enhance cycling economy in untrained individuals, as demonstrated in numerous training studies with athletes [7, 8, 36], [37], [38].

Although we expand the evidence on the relationship of cycling economy and strength capacity, this study has limitations. We evaluated the cycling economy using a step protocol, which allows to analyse various submaximal workloads. Consequently, we had to use a fixed workload (50 W, 80 W, and 110 W in females & 80 W, 110 W, and 140 W in males) leading to heterogenous exercise intensities ranging from 36.5 ± 5.4 % to 60.6 ± 8.7 % of V̇O2peak in females and 45.7 ± 7.0 % to 55.1 ± 8.5 % of V̇O2peak in males, respectively. However, we did not analyse each workload separately but much rather used GEE models for our analysis.

Conclusions

Our findings indicate that load-velocity and load-power characteristics are associated with reduced gross oxygen cost as well as gross caloric unit cost during submaximal cycling in healthy but recreationally active males and females. While these findings are in line with previous studies, this relationship explains only a small proportion of the observed variation, accounting for 1.3–6.2 % of the variance in gross oxygen cost and 2.2–10.3 % of the variance in gross caloric unit cost. This emphasizes the significance of both maximal and explosive strength capacity in facilitating cycling at lower oxygen and caloric demands. Future studies should assess whether this promotes a more efficient and sustainable physical activity level as represented by a better adherence to physical activity guidelines.

Corresponding author: Prof. Dr. Moritz Schumann, Department of Sports Medicine and Exercise Therapy, Institute of Human Movement Science and Health, University of Technology, Thüringer Weg 11, 09126 Chemnitz, Germany, E-mail:

  1. Research ethics: The study was conducted in accordance with the Declaration of Helsinki.

  2. Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.

  3. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Competing interests: The authors declare that they have no competing interests.

  5. Research funding: No funding was received for performing this study.

  6. Data availability: Data is available upon reasonable request.

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Received: 2024-05-06
Accepted: 2024-07-04
Published Online: 2024-07-22

© 2024 the author(s), published by De Gruyter on behalf of Shangai Jiao Tong University and Guangzhou Sport University

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artikel in diesem Heft

  1. Frontmatter
  2. Beyond the Olympic Games
  3. Beyond the Olympic and Paralympic Games
  4. Practical steps to develop a transcriptomic test for blood doping
  5. A unique pseudo-eligibility analysis of longitudinal laboratory performance data from a transgender female competitive cyclist
  6. Why the dominance of East Africans in distance running? A narrative review
  7. Gender equality policy of the Olympic Movement in Chinese sport governing bodies: the case of elite volleyball
  8. Movement Science
  9. Associations of strength indices and cycling economy in young adults
  10. Are calves trainable? Low-intensity calf muscle training with or without blood flow restriction: a randomized controlled trial
  11. Exercise Biology
  12. Caveolin-3 regulates slow oxidative myofiber formation in female mice
  13. Effect of aerobic intermittent exercise on the decreased cognitive ability induced by PM2.5 exposure in rats
https://doi.org/10.1515/teb-2024-0008
Schlagwörter für diesen Artikel
physical activity; cycling economy; oxygen consumption; maximal strength; gross caloric unit cost
Creative Commons
BY 4.0

Artikel in diesem Heft

  1. Frontmatter
  2. Beyond the Olympic Games
  3. Beyond the Olympic and Paralympic Games
  4. Practical steps to develop a transcriptomic test for blood doping
  5. A unique pseudo-eligibility analysis of longitudinal laboratory performance data from a transgender female competitive cyclist
  6. Why the dominance of East Africans in distance running? A narrative review
  7. Gender equality policy of the Olympic Movement in Chinese sport governing bodies: the case of elite volleyball
  8. Movement Science
  9. Associations of strength indices and cycling economy in young adults
  10. Are calves trainable? Low-intensity calf muscle training with or without blood flow restriction: a randomized controlled trial
  11. Exercise Biology
  12. Caveolin-3 regulates slow oxidative myofiber formation in female mice
  13. Effect of aerobic intermittent exercise on the decreased cognitive ability induced by PM2.5 exposure in rats
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