Body composition and anaerobic power: correlational analysis in male athletes

Background: Body composition monitoring is vital to improve functional performance outcomes such as power output and fatigue resistance in athletes.

Objective: To assess the correlation between body composition parameters (body fat percentage, percentage of muscle mass, and visceral fat VF) and anaerobic performance measures, specifically relative peak power (RPP) and fatigue index (FIWAnT), in male athletes. Along with exploring the potential influence of sport type, training frequency and smoking status.

Methods: A cross-sectional study of 31 healthy male athletes aged 18–35 years was conducted. Participants were categorized by weekly training frequency and sport type. Bioelectrical Impedance Analysis was used to assess Body composition, and the 30 s Wingate test for Anaerobic performance.

Results: A significant positive correlation was found between muscle percentage and both RPP (r = 0.51, p < 0.01) and average RPP (r = 0.47, p < 0.01). A significant negative correlation was found between average RPP and both Fat percentage (r = −0.45, p < 0.05) and VF (r = −0.50, p < 0.05). No significant correlation was found between FIWAnT and any body composition measures.

Conclusion: Body composition has a critical role in the integrity of anaerobic performance among athletes.

1 Introduction

The performance of athletes is multifactorial, including strength, endurance, and recovery capacity; modulated by body composition and fitness level (1). Beyond training, current research increasingly explores how various lifestyle habits modulate body composition and anaerobic outcomes. Factors like chronic stress, poor sleep quality, nutritional deficiencies, and tobacco use are known to alter hormonal balance, increase inflammation, and negatively affect muscle repair and body composition (2), further impacting anaerobic capacity.

Figure 1

Figure 1. Presents WAnT outcomes [(a) RPP, (b) average RPP, (c) fatigue index] broken down by type of sport, frequency of training, and smoking status. As previously reported, no statistically significant differences were observed across any comparison.

Body composition in athletes, like muscle mass (MM) and fat mass (FM), needs to be monitored to improve functional outcomes such as power output and fatigue resistance (3, 4). While the general influence of FM is known, recent international studies highlight the specific role of VF accumulation, even in athletes. VF is highly metabolically active and is linked to chronic low-grade inflammation, which can potentially impair muscle function, metabolic efficiency, and recovery, thereby negatively impacting power-to-mass ratio (5).

Bioelectrical Impedance Analysis (BIA) is a practical, validated tool to assess the parameters of body composition in athletes (6), measuring estimates for muscle and fat percentage, and Visceral Fat (VF) with high convenience and reliability (7), BIA has been used effectively to track seasonal changes and training adaptations in athletes, presenting strong associations with the outcomes of performance (8).

The impact of consistent structured training on anaerobic performance is well-documented. Regular training enhances both the capacity to generate high power and the ability to tolerate fatigue, primarily by increasing glycolytic enzyme activity, buffering capacity, and the size of fast-twitch muscle fibers (9). The Wingate Anaerobic Test (WAnT) is commonly recognized to evaluate high-intensity, short-duration performance; It measures peak anaerobic power and fatigue index (FIWAnT) (10). It is used across diverse athletic populations to assess explosive strength and fatigue resistance (11). Anaerobic power reflects the capacity to perform high-intensity, short-duration work relying primarily on the phosphocreatine (PCr) and glycolytic systems (12). It appears to be sensitive to body composition, with prior studies suggesting that lean MM is positively associated with power output, while high FM often has a negative association (13).

Studies have examined the role of strength training programs on anaerobic power in specific team sports, such as volleyball (9), demonstrating training-specific adaptation. Concurrently, other research has highlighted the relationship between specific body composition profile and high-level performance in highly specialized sports, such as elite motorcycle speedway riders (14).

In addition to those physiological components, the performance of athletes is also modulated by several aspects of their current lifestyle and habits, including diet, physical activity, sleep, and factors such as tobacco use (2, 15). Smoking is often overlooked in athletic research, although it has been associated with impaired cardiovascular and pulmonary function, which may affect anaerobic capacity and fatigue levels (16). Recent systematic reviews confirm that nicotine dependence and withdrawal can negatively influence exercise-related physical ability and sport performance, underscoring the importance of considering smoking status as a potential confounding factor in athletic studies (17). Also, anaerobic output was improved after regular training by increasing endurance and muscular strength (18). The social relevance of this investigation lies in informing targeted training and nutritional strategies to enhance power and sports health monitoring in athletes (19).

As sports science evolves, understanding how detailed body composition and daily habits affect the performance metrics is important. While the correlation between lean mass and power is established, the integrated influence of specific adverse factors, visceral fat, and chronic lifestyle factors like smoking, on anaerobic performance in a homogenous group of trained male athletes remains poorly defined in the current literature. Furthermore, more determinants of FIWAnT distinct from peak power require further investigation using morphological metrics. Therefore, the aim of this study was to evaluate the relationship between body composition parameters (fat percentage, muscle percentage, and VF) and anaerobic performance; relative peak power (RPP) and FIWAnT, in male athletes, while also exploring the potential influence of smoking status, sport type and training frequency. Based on current literature, we hypothesized that RPP would be positively correlated with body fat percentage and VF levels. Also, the lack of significance in anaerobic performance would be observed across categories due to a homogenous profile of the trained sample.

2 Methodology

2.1 Study design and participants

This observational cross-sectional study was conducted of 31 male athletes aged between 18 and 35 years to investigate how anaerobic performance is influenced by body composition. Participants were recruited from training centers and various sports clubs in Amman, Jordan, through social media outreach and direct contact. To minimize bias, consistent recruitment messaging was used, emphasizing the study's focus on trained athletes. Participants were included according to the criteria: apparently healthy and had a minimum of two years of consistent training experience. The health status was determined based on self-reported information, specifically the absence of any chronic diseases or medication use.

Exclusion criteria included: the presence of any chronic disease, current use of medication, and female sex. Interested individuals were briefed on the study's objectives and procedures and were asked to sign informed consent forms before data collection. A formal sample size calculation was not performed to determine the minimum required number of participants. However, a post-hoc power analysis was conducted using G*power software (version 3.1.9.4) based on the strongest findings: the correlation between RPP and percentage of muscle mass (r = 0.51). Assuming an alpha level of 0.05 (two-tailed) and a sample size of n = 31, the achieved statistical power for this effect was calculated to be power = 0.89. Conversely, for smaller effects (e.g., r = 0.30), the achieved power drops below the conventional 0.80 threshold. This calculation confirms that while the study was adequately powered to detect large effects, it was underpowered to detect small or moderate correlations, increasing the likelihood of type II errors in non-significant findings.

Consequently, the study relies on a convenient sample of 31 athletes. We acknowledge that this limited sample size may have resulted in low statistical power, particularly for detecting moderate effects or differences in group comparison. The absence of this power analysis is a recognized methodological limitation of this study.

2.2 Sport classification

Athletes were categorized to better understand the differences in sport-specific demands and training load; Training Frequency (the number of training days per week: ≤4 days/week and >4 days/week). And sport Type in which participants were classified into two broad categories: team and individual sports. This approach was used to compare general differences in chronic demand. However, the lack of a more specific classification limits the analysis by potentially masking obvious differences between distinct physiological phenotypes. The intensity of training commitment and the nature of the sport were factored into the study the possibility of potential differences in body composition and anaerobic performance related to them.

2.3 Body composition assessment

Body composition was assessed using the InBody 770 (Inbody Co., LTD, Seoul, Korea; Frequencies: 1, 5, 250, 500, and 1,000 kHz) multi-frequency segmental BIA device (20). Participants were requested to fast for at least 4 h prior to the assessment. Weight, FM, and percentage, MM, and percentage, and VF levels were provided by the BIA assessment.

2.4 WAnT

Following the BIA assessment, participants consumed a light snack (plain biscuit and water) and were asked if they were well rested before performing the WAnT. They were asked to wear comfortable clothing. The test was conducted on a cycle ergometer (Monark 894E, Sweden) connected to a computer system for accurate monitoring, in which the test was started with a simple warm-up of light pedaling for 10 min (21). Then, each athlete was instructed to pedal for 30 s at maximal effort against a 7.5% kg of body weight resistance. The task was to perform work to achieve maximal power with a quick rotation frequency and try to maintain it as long as possible. Encouragement was provided by verbal motivation to reach maximal effort. After the test, the subject remained on the cycle ergometer for 5 min for safety reasons (22). The test yielded values for maximum peak power, RPP, average peak power, average RPP, and FIWAnT.

2.5 Smoking status

Participants were asked if they were smoking any type, including cigarettes, shisha, or electronic devices like vapes, and recorded it using a self-reported questionnaire.

2.6 Statistical analysis

All statistical analyses were performed using Jamovi 2.6.26 (Computer Software, Retrieved from https://www.jamovi.org) (23). The Shapiro–Wilk test was used to check for normality of each variable. Based on that, Spearman's correlation was used for the Variables that were not normally distributed (e.g., FM, height, VF) to find the associations between body composition parameters and anaerobic performance measures. And for group comparisons, the Kruskal–Wallis test was used for the non-normally distributed to analyze WAnT outcomes. While One-way ANOVA was used to analyze FIWAnT, which was normally distributed. Three-way ANOVA was used to investigate the interaction effects between training frequency, sport type, and smoking status on FIWAnT. A significance level of p < 0.05 was set for all analyses.

Potential confounding factors, such as age and duration of training, were considered for statistical adjustment. However, due to the small sample size, sophisticated multivariate modeling procedures were deemed statistically inappropriate. Using complex adjustments could compromise statistical power. Instead, we focus on interpreting the primary correlational data while recognizing that these unadjusted factors constitute a study limitation.

2.7 Ethical approval

The Institutional Review Board at The University of Jordan (IRB at UJ) approved the research proposal submitted by Dr. Hadeel Ali Ghazzawi from the School of Agriculture, Decision No. (346/2025) The IRB at the Deanship of Scientific Research, The University of Jordan.

As participation was entirely voluntary, participants were free to exit the study at any time. Written invitations detailing the goal of the study were sent out, and each participant was requested to provide written informed consent digitally. All parts of the investigation involved the protection and anonymization of personal data. Written informed consent has been obtained from the sample participants to publish this paper.

3 Results

3.1 Descriptive statistics and normality

Data from 31 male athletes were analyzed (Table 1). Normality testing using the Shapiro–Wilk test revealed that FM, height, and VF did not follow a normal distribution (p < 0.05).

Table 1

Table 1. Descriptive characteristics for the participants (n = 31).

3.2 Correlation analysis

As detailed in Table 2, the percentage of muscle mass demonstrated a strong, significant positive correlation with both RPP and average RPP. Conversely, body fat percentage and VF were significantly and negatively associated with power output.

Table 2

Table 2. Spearman's correlation between body composition measurements and wingate test output.

Table 3

Table 3. Group comparison between smoking, type of sport, and frequency of training.

Table 4

Table 4. Interactions between groups with fatigue index FIWAnT.

3.3 Group comparisons

In Table 3, smoking showed no significant differences in fat percentage, muscle percentage, VF, RPP, average RPP, or FIWAnT (p > 0.05 for all). Sport type and training frequency also showed no statistically significant effect on body composition or Wingate outcomes (all p > 0.05), as shown in Figure 1.

3.4 FIWAnT

A three-way ANOVA was used for FIWAnT in Table 4. A statistically significant interaction effect was observed between training frequency, sport type, and smoking [F (1,23) = 5.04, p = 0.035], indicating that the combination of these factors influenced FIWAnT. No main effects or two-way interactions were significant on their own (p > 0.05).

4 Discussion

This study was conducted to evaluate the relationship between fat percentage, muscle percentage, and VF as the body composition variables and the WAnT indices measuring the anaerobic performance in a sample of male athletes. In addition, the influence of training frequency, sport type, and smoking status on body composition and performance was assessed. The primary findings of this investigation are summarized as follows: i) a statistically significant positive correlation exists between the percentage of muscle mass and the RPP. ii) body fat percentage and VF exhibit a significant negative correlation with average RPP. iii) No significant correlation was found between any body composition variables and the FIWAnT, nor were there significant differences across training or lifestyle groups.

One of the significant findings was the strong negative correlation between fat percentage and both RPP and average RPP, suggesting that reduced anaerobic capacity is influenced by increased body fat. Fat percentage has different effects regarding athletes, as supported by previous literature, performance and metabolic flexibility. The excess FM can diminish performance and act as a non-functional weight in high-intensity, short-duration effort like the WAnT (24–26). Metabolic flexibility, where the ability of the body to switch between carbohydrate and fat as a fuel is impaired, which leads to a compromised exercise capacity (27).

To the contrary, muscle percentage correlated positively with both power measures, emphasizing the importance of MM in supporting anaerobic yield. This is consistent with the well-established understanding of the physiology of MM being responsible directly for providing the force required during anaerobic strength (26, 28–30). Particularly fast-twitch fibers, which can supply more power and energy for short, intense activity (31). Moreover, MM contributes to better performance, not only by the composition of muscle (32), which means increased MM will facilitate energy without oxygen more rapidly through the breakdown of glucose and phosphocreatine (30).

Different correlations between body composition variables were found, and they may collectively influence anaerobic performance. For instance, the strong inverse correlation between fat percentage and muscle percentage is expected, and the strong positive correlation between fat percentage and VF suggests that an increase in overall body fat is linked to higher accumulation of fat around the internal organs, which has been shown to be detrimental to performance (33).

A negative correlation was found between FIWAnT and RPP and average RPP, which indicates that the accumulation of fat around internal organs may compromise performance. The exact mechanisms require further investigation, but VF is often associated with metabolic disorders, which lead to a reduced availability of energy during intense performance (34). Also, the heart rate recovery after exercise is affected by the degree of VF, which delays the ability to reach the resting state efficiently (5).

Correlations between FIWAnT and any body composition indices were found to be not significant, this suggests that fatigue is rarely caused by one issue, but the result of multiple factors; such as dehydration, which impairs anaerobic performance by reducing blood volume and raising core temperature, which negatively affects muscle function and leads to a decline in power output, directly influencing FIWAnT (35). While studies have shown lean MM to be crucial for power (36), our results indicate that the subsequent decline in power FIWAnT during the 30 s WAnT is driven by processes that override the volume of muscle or FM. Also, the primary drivers of this fatigue are metabolic, specifically the rapid depletion of phosphocreatine PCr and the accumulation of lactate and hydrogen ions from glycolysis (12). Furthermore, fatigue is not just a muscle-level event; it also involves neuromuscular factors. The decline in power during a WAnT results from both peripheral fatigue at the muscle and central fatigue in the nervous system, with an individual's ability to voluntarily activate motor units being crucial for fatigue resistance (37). Therefore, the FIWAnT may be more sensitive to acute variables like hydration status (35) and training quality (38) than stable morphological measures like MM or fat percentage. This result strongly suggests the use of multivariable models in future research to assess metabolic, neuromuscular, and morphological contributions to fatigue resistance simultaneously.

No statistically significant differences in RPP, average RPP, or FIWAnT across sport type, training frequency, or smoking status in terms of group comparisons. This indicates that such factors may not substantially alter the anaerobic output within this sample of trained athletes due to the homogeneity of the sample (39). A high level of conditioning likely minimizes the physiological impact of these factors on anaerobic output. Also, the overall health of the athlete may mitigate the effects of smoking on short-term performance (40). Although a small sample size could have affected the statistical power.

However, a significant interaction was observed between smoking status, sport type, and training days on FIWAnT, suggesting that combined training behaviors and lifestyle may affect fatigue and recovery or the depletion of energy during anaerobic performance (41, 42). The type of sports, specifically team sports that usually require more anaerobic capacity, enhance the anaerobic power and the physical fitness, resulting in reduced fatigue over time (19, 43). Also, the frequency of resistance training has been shown to increase the anaerobic performance of athletes, leading to better recovery and less fatigue, by distributing the same volume of training over more sessions (18, 44). Further investigation is needed with a larger sample size to reveal more about these associations.

The absence of statistically significant differences across intergroup comparisons warrants a deeper interpretation. These null results may reflect one of two possibilities. First, it is likely a factor of the homogeneous profile of the sample; all participants were consistently trained male athletes, suggesting a ceiling effect of consistent physiological conditioning that minimized measurable differences in anaerobic capacity between groups. Second, the findings must be interpreted in light low statistical power inherent to the small sample size.

This cross-sectional study is fundamentally limited by its design, which prohibits causal inference and is susceptible to selection bias due to the homogeneous sample (young, male athletes). Methodological constraints, including reliance on self-reported smoking status and lack of strict fasting monitoring before BIA, compromise data accuracy. While our results support optimizing body composition for power, the low statistical power and limited generalizability necessitate future longitudinal studies. These studies must employ larger, diverse samples and multivariable analysis to comprehensively model the complex interplay of chronic lifestyle factors and body composition on FIWAnT.

5 Conclusion

Body composition has a critical role in the integrity of anaerobic performance among athletes. Specifically concerning power output. The results provide clear practical implications for training prescription and nutritional guidance in high-intensity sport. Strategies prioritize reducing FM, especially VF, while enhancing and maintaining MM to maximize RPP outputs. Furthermore, this study significantly contributes by highlighting the complexity of fatigue; the absence of correlation between body composition variables and the FIWAnT requires moving beyond univariate body metrics to fully understand fatigue resistance. While these findings are valuable, their generalizability is limited by the small sample size and the cross-sectional design. Future research should employ larger, more diverse cohorts in multivariable and longitudinal models to fully capture the combined physiological and lifestyle influence on anaerobic performance and fatigue in athletes.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The studies involving humans were approved by Decision No. (346/2025): The IRB at UJ approves the conduct of the research referred to above provided that: It complies with the guidelines stated in the Declaration of Helsinki and the International Council for Harmonization (ICH). The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

HA: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. RO: Data curation, Methodology, Writing – review & editing. AA: Data curation, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing. HG: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This research was funded by the Deanship of Scientific Research at the University of Jordan (2025-148/2024).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1. Furrer R, Hawley JA, Handschin C. The molecular athlete: exercise physiology from mechanisms to medals. Physiol Rev. (2023) 103(3):1693–787. doi: 10.1152/physrev.00017.2022

PubMed Abstract | Crossref Full Text | Google Scholar

2. Sawada Y, Ichikawa H, Ebine N, Minamiyama Y, Alharbi AAD, Iwamoto N, et al. Effects of high-intensity anaerobic exercise on the scavenging activity of various reactive oxygen species and free radicals in athletes. Nutrients. (2023) 15(1):222. doi: 10.3390/nu15010222

PubMed Abstract | Crossref Full Text | Google Scholar

3. Campa F, Toselli S, Mazzilli M, Gobbo LA, Coratella G. Assessment of body composition in athletes a narrative review of available methods with special reference to quantitative and qualitative bioimpedance analysis. Nutrients. (2021) 13(5):1620. doi: 10.3390/nu13051620

PubMed Abstract | Crossref Full Text | Google Scholar

4. Lopez P, Taaffe DR, Galvão DA, Newton RU, Nonemacher ER, Wendt VM, et al. Resistance training effectiveness on body composition and body weight outcomes in individuals with overweight and obesity across the lifespan: a systematic review and meta-analysis. Obes Rev. (2022) 23(5):e13428. doi: 10.1111/obr.13428

PubMed Abstract | Crossref Full Text | Google Scholar

5. Amato A, Petrigna L, Sortino M, Musumeci G. Visceral fat affects heart rate recovery but not the heart rate response post-single bout of vigorous exercise: a cross-sectional study in non-obese and healthy participants. Sports. (2024) 12(12):323. doi: 10.3390/sports12120323

PubMed Abstract | Crossref Full Text | Google Scholar

6. Cataldi D, Bennett JP, Wong MC, Quon BK, Liu YE, Kelly NN, et al. Accuracy and precision of multiple body composition methods and associations with muscle strength in athletes of varying hydration: the Da Kine study. Clin Nutr. (2024) 43(1):284–94. doi: 10.1016/j.clnu.2023.11.040

PubMed Abstract | Crossref Full Text | Google Scholar

7. Lee LC, Hsu PS, Hsieh KC, Chen YY, Chu LP, Lu HK, et al. Standing 8-electrode bioelectrical impedance analysis as an alternative method to estimate visceral fat area and body fat mass in athletes. Int J Gen Med. (2021) 14:539–48. doi: 10.2147/IJGM.S281418

PubMed Abstract | Crossref Full Text | Google Scholar

8. Martins PC, Gobbo LA, Silva DAS. Bioelectrical impedance vector analysis (BIVA) in university athletes. J Int Soc Sports Nutr. (2021) 18(1):7. doi: 10.1186/s12970-020-00403-3

PubMed Abstract | Crossref Full Text | Google Scholar

9. Biçer M. The effect of an eight-week strength training program supported with functional sports equipment on male volleyball players’ anaerobic and aerobic power. Sci Sports. (2021) 36(2):137.e1–e9. doi: 10.1016/j.scispo.2020.02.006

Crossref Full Text | Google Scholar

10. Liu Y, Yan J, Gong Z, Liu Q. The impact of the wingate test on anaerobic power in the lower limbs of athletes with varied duration and load. Front Physiol. (2025) 16:1582875. doi: 10.3389/fphys.2025.1582875

PubMed Abstract | Crossref Full Text | Google Scholar

11. Harvey L, Bousson M, McLellan C, Lovell D. The effect of previous wingate performance using one body region on subsequent wingate performance using a different body region. J Hum Kinet. (2017) 56(1):119–26. doi: 10.1515/hukin-2017-0029

PubMed Abstract | Crossref Full Text | Google Scholar

12. Cheng AJ, Place N, Westerblad H. Molecular basis for exercise-induced fatigue: the importance of strictly controlled cellular Ca2+ handling. Cold Spring Harb Perspect Med. (2018) 8(2):a029710. doi: 10.1101/cshperspect.a029710

PubMed Abstract | Crossref Full Text | Google Scholar

13. Chalhoub D, Boudreau R, Greenspan S, Newman AB, Zmuda J, Frank-Wilson AW, et al. Associations between lean mass, muscle strength and power, and skeletal size, density and strength in older men. J Bone Miner Res. (2018) 33(9):1612–21. doi: 10.1002/jbmr.3458

PubMed Abstract | Crossref Full Text | Google Scholar

14. Michalik K, Szczepan S, Markowski M, Zatoń M. The relationship among body composition and anaerobic capacity and the sport level of elite male motorcycle speedway riders. Front Physiol. (2022) 13:812958. doi: 10.3389/fphys.2022.812958

PubMed Abstract | Crossref Full Text | Google Scholar

15. Tereso D, Paulo R, Petrica J, Duarte-Mendes P, Gamonales JM, Ibáñez SJ. Assessment of body composition, lower limbs power, and anaerobic power of senior soccer players in Portugal: differences according to the competitive level. Int J Environ Res Public Health. (2021) 18(15):8069. doi: 10.3390/ijerph18158069

PubMed Abstract | Crossref Full Text | Google Scholar

16. Šaranović SĐ, Vićić J, Pešić I, Tomovic M, Batinic Ð, Antic M, et al. The influence of tobacco use on pulmonary function in elite athletes. Int J Environ Res Public Health. (2019) 16(19):3515. doi: 10.3390/ijerph16193515

Crossref Full Text | Google Scholar

17. Bao K, Zheng K, Zhou X, Chen B, He Z, Zhu D. The effects of nicotine withdrawal on exercise-related physical ability and sports performance in nicotine addicts: a systematic review and meta-analysis. J Int Soc Sports Nutr. (2024) 21(1):2302383. Taylor and Francis Ltd. doi: 10.1080/15502783.2024.2302383

PubMed Abstract | Crossref Full Text | Google Scholar

18. Shiau K, Tsao TH, Bin YC. Effects of single versus multiple bouts of resistance training on maximal strength and anaerobic performance. J Hum Kinet. (2018) 62(1):231–40. doi: 10.1515/hukin-2017-0122

PubMed Abstract | Crossref Full Text | Google Scholar

19. Gottlieb R, Shalom A, Calleja-Gonzalez J. Physiology of basketball-field tests. Review article. J Hum Kinet. (2021) 77:159–67. Sciendo. doi: 10.2478/hukin-2021-0018

PubMed Abstract | Crossref Full Text | Google Scholar

20. Brewer GJ, Blue MNM, Hirsch KR, Saylor HE, Gould LM, Nelson AG, et al. Validation of InBody 770 bioelectrical impedance analysis compared to a four-compartment model criterion in young adults. Clin Physiol Funct Imaging. (2021) 41(4):317–25. doi: 10.1111/cpf.12700

PubMed Abstract | Crossref Full Text | Google Scholar

21. Yanaoka T, Hamada Y, Kashiwabara K, Kurata K, Yamamoto R, Miyashita M, et al. Very-short-duration, low-intensity half-time re-warm up increases subsequent intermittent sprint performance. J Strength Cond Res. (2018) 32(11):3258–66. doi: 10.1519/JSC.0000000000002781

PubMed Abstract | Crossref Full Text | Google Scholar

22. Michalik K, Smolarek M, Ochmann B, Zatoń M. Determination of optimal load in the wingate anaerobic test is not depend on number of sprints included in mathematical models. Front Physiol. (2023) 14:1146076. doi: 10.3389/fphys.2023.1146076

PubMed Abstract | Crossref Full Text | Google Scholar

23. Al Hassani W, El Achhab Y, Taiebine M, Nejjari C. Development and validation of a scale to measure job satisfaction among human resources for health in Morocco. Cureus. (2024) 16(4):e57438. doi: 10.7759/cureus.57438

PubMed Abstract | Crossref Full Text | Google Scholar

24. Pletcher ER, Lovalekar M, Coleman LC, Beals K, Nindl BC, Allison KF. Decreased percent body fat but not body mass is associated with better performance on combat fitness test in male and female marines. J Strength Cond Res. (2023) 37(4):887–93. doi: 10.1519/JSC.0000000000004335

PubMed Abstract | Crossref Full Text | Google Scholar

25. Brechney GC, Cannon J, Goodman SP. Effects of weight cutting on exercise performance in combat athletes: a meta-analysis. Int J Sports Physiol Perform. (2022) 17(7):995–1010. doi: 10.1123/ijspp.2021-0104

PubMed Abstract | Crossref Full Text | Google Scholar

26. Galán-Rioja MÁ, González-Mohíno F, Sanders D, Mellado J, González-Ravé JM. Effects of body weight vsLean body mass on wingate anaerobic test performance in endurance athletes. Int J Sports Med. (2020) 41(8):545–51. doi: 10.1055/a-1114-6206

Crossref Full Text | Google Scholar

27. Melanson EL, MacLean PS, Hill JO. Exercise improves fat metabolism in muscle but does not increase 24-h fat oxidation. Exerc Sport Sci Rev. (2009) 37:93–101. Lippincott Williams and Wilkins. doi: 10.1097/JES.0b013e31819c2f0b

PubMed Abstract | Crossref Full Text | Google Scholar

28. Viecelli C, Aguayo D. May the force and mass be with you—evidence-based contribution of mechano-biological descriptors of resistance exercise. Front Physiol. (2022) 12:686119. doi: 10.3389/fphys.2021.686119

PubMed Abstract | Crossref Full Text | Google Scholar

29. Kristian Berg O, Sung Kwon O, Hureau TJ, Clifton HL, Le Fur Y, Jeong EK, et al. Maximal strength training increases muscle force generating capacity and the anaerobic ATP synthesis flux without altering the cost of contraction in elderly. Exp Gerontol. (2018) 111:154–61. doi: 10.1016/j.exger.2018.07.013

PubMed Abstract | Crossref Full Text | Google Scholar

30. Sahlin K. Muscle energetics during explosive activities and potential effects of nutrition and training. Sports Med. (2014) 44:167–73. Springer International Publishing. doi: 10.1007/s40279-014-0256-9

Crossref Full Text | Google Scholar

31. Martín-Rodríguez A, Belinchón-deMiguel P, Rubio-Zarapuz A, Tornero-Aguilera JF, Martínez-Guardado I, Villanueva-Tobaldo CV, et al. Advances in understanding the interplay between dietary practices, body composition, and sports performance in athletes. Nutrients. (2024) 16(4):571. doi: 10.3390/nu16040571

Crossref Full Text | Google Scholar

32. Zinner C, Morales-Alamo D, Ørtenblad N, Larsen FJ, Schiffer TA, Willis SJ, et al. The physiological mechanisms of performance enhancement with sprint interval training differ between the upper and lower extremities in humans. Front Physiol. (2016) 7:426. doi: 10.3389/fphys.2016.00426

PubMed Abstract | Crossref Full Text | Google Scholar

33. Vissers D, Hens W, Taeymans J, Baeyens JP, Poortmans J, Van Gaal L. The effect of exercise on visceral adipose tissue in overweight adults: a systematic review and meta-analysis. PLoS One. (2013) 8(2):e56415. doi: 10.1371/journal.pone.0056415

PubMed Abstract | Crossref Full Text | Google Scholar

34. Kutac P, Bunc V, Buzga M, Krajcigr M, Sigmund M. The effect of regular running on body weight and fat tissue of individuals aged 18 to 65. J Physiol Anthropol. (2023) 42(1):28. doi: 10.1186/s40101-023-00348-x

PubMed Abstract | Crossref Full Text | Google Scholar

35. Maughan RJ, Shirreffs SM. Dehydration and rehydration in competative sport. Scand J Med Sci Sports. (2010) 20:40–7. Blackwell Munksgaard. doi: 10.1111/j.1600-0838.2010.01207.x

PubMed Abstract | Crossref Full Text | Google Scholar

36. Byrd M, Switalla JR, Eastman JE, Wallace BJ, Clasey JL, Bergstrom HC. Contributions of body-composition characteristics to critical power and anaerobic work capacity. Int J Sports Physiol Perform. (2018) 13(2):189–93. doi: 10.1123/ijspp.2016-0810

PubMed Abstract | Crossref Full Text | Google Scholar

37. Mileva KN, Sumners DP, Bowtell JL. Decline in voluntary activation contributes to reduced maximal performance of fatigued human lower limb muscles. Eur J Appl Physiol. (2012) 112(12):3959–70. doi: 10.1007/s00421-012-2381-1

PubMed Abstract | Crossref Full Text | Google Scholar

38. Wang Q, Qian J, Pan H, Ju Q. Relationship between body composition and upper limb physical fitness among Chinese students: 4-year longitudinal follow-up and experimental study. Front Physiol. (2023) 14:1104018. doi: 10.3389/fphys.2023.1104018

PubMed Abstract | Crossref Full Text | Google Scholar

39. Bergkamp TLG, Niessen ASM, den Hartigh RJR, Frencken WGP, Meijer RR. Methodological issues in soccer talent identification research. Sports Med. (2019) 49(9):1317–35. doi: 10.1007/s40279-019-01113-w

PubMed Abstract | Crossref Full Text | Google Scholar

40. Wu AD, Lindson N, Hartmann-Boyce J, Wahedi A, Hajizadeh A, Theodoulou A, et al. Smoking cessation for secondary prevention of cardiovascular disease. Cochrane Database Syst Rev. (2022) 8(8):CD014936. doi: 10.1002/14651858.CD014936.pub2

PubMed Abstract | Crossref Full Text | Google Scholar

41. Loy BD, O’Connor PJ, Dishman RK. The effect of a single bout of exercise on energy and fatigue states: a systematic review and meta-analysis. Fatigue. (2013) 1(4):223–42. doi: 10.1080/21641846.2013.843266

Crossref Full Text | Google Scholar

42. Bogdanis GC. Effects of physical activity and inactivity on muscle fatigue. Front Physiol. (2012) 3:142. doi: 10.3389/fphys.2012.00142

PubMed Abstract | Crossref Full Text | Google Scholar

43. Johnston RD, Gabbett TJ, Jenkins DG. Influence of playing standard and physical fitness on activity profiles and post-match fatigue during intensified junior rugby league competition. Sports Med Open. (2015) 1(1):18. doi: 10.1186/s40798-015-0015-y

PubMed Abstract | Crossref Full Text | Google Scholar

44. Johnsen E, van den Tillaar R. Effects of training frequency on muscular strength for trained men under volume matched conditions. PeerJ. (2021):9:e10781. doi: 10.7717/peerj.10781

Crossref Full Text | Google Scholar