Jihai Li
Liuhong Zang- Institute of Physical Education, Xinjiang Normal University, Urumqi, Xinjiang, China
Background: To systematically assess the impact of various exercise modalities and dosages on the body composition of college students through a comprehensive review of randomized controlled trials (RCTs).
Methods: We conducted a comprehensive search of relevant randomized controlled trials (RCTs) in eight databases, covering data from the inception of each database to August 2024. Following the literature screening, two investigators independently conducted data extraction and assessed the risk of bias. Network meta-analysis (NMA) was conducted using Stata 17.0 with random-effects modeling, while dose-response analysis was performed utilizing R version 4.3.1.
Results: A total of 43 randomized controlled trials (RCTs), encompassing 3,154 participants, were included in the analysis. Aerobic exercise, combined exercise, high-intensity interval training (HIIT), mind-body exercise, and calisthenics demonstrated significant effects on reducing body mass index (BMI) compared to control groups. Surface under the cumulative ranking (SUCRA) probability rankings indicated that calisthenics had the highest likelihood of being the most effective intervention for BMI reduction, whereas resistance exercise was associated with the lowest likelihood. The dose-response analysis revealed that the threshold exercise dose for overall exercise to lower BMI was 310 METs-min/week, with the predicted maximum significant response dose being 1,300 METs-min/week, beyond which there was minimal change in the intervention effect. Additionally, distinct nonlinear dose-response relationships were observed for aerobic exercise, combined exercise, HIIT, mind-body exercise, and aerobics.
Conclusion: No significant differences in the effectiveness of exercise interventions on body composition were observed across exercise types. However, based on the SUCRA analysis, calisthenics emerged as the preferred intervention, succeeded by a combination of exercises. The optimal exercise dosage for enhancing body composition was identified as 1,300 METs-min/week, with the threshold for a significant effect being relatively low.
Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024587032
1 Introduction
With shifts in lifestyle and dietary patterns, global obesity rates have increased threefold over the past 35 years (Bray et al., 2016), and projections indicate that by 2030, over 1 billion individuals will be classified as obese (WHO, 2024; Haslam and James, 2005). Obesity has emerged as a predominant health challenge worldwide, with strong correlations to an array of diseases, including cardiovascular, digestive, respiratory, musculoskeletal, depression, and anxiety (Haidar and Cosman, 2011; Kivimäki et al., 2022), obesity may also negatively affect an individual’s emotional intelligence (Gilyana et al., 2023). The direct and indirect economic costs attributable to obesity and its comorbidities are estimated to reach $2 trillion globally (González-Muniesa et al., 2017). The rapid advancement of digital technology has contributed to a sedentary lifestyle among college students, with more than 50% of students in the United States not meeting physical activity recommendations (Haase et al., 2004). A survey encompassing 23 countries revealed that a majority of college students in these nations engage in low levels of physical activity (Ezati et al., 2020). Concurrently, changes in dietary habits within university settings have contributed to a multifaceted set of factors driving the annual increase in obesity rates among college students (Lv et al., 2019; Vankim and Nelson, 2013).
Obesity interventions primarily encompass lifestyle modifications, dietary restrictions, augmented physical activity, and medical interventions, including pharmacotherapy and surgery (Bray et al., 2016). Exercise stands out as a straightforward and potent intervention, with various exercise modalities demonstrating efficacy in reducing body mass index (BMI), for example, a study by Batrakoulis et al. states that Combined training is the most effective type of exercise for improving obesity (Batrakoulis et al., 2022). However, there is a lack of consensus on the most effective type of exercise (Morze et al., 2021; O’Donoghue et al., 2021; Andreato et al., 2019; Wang et al., 2022; Wang H. et al., 2023; Hao et al., 2023), and scant research exists that scrutinizes the relationship between exercise dosage and BMI changes. Concurrently, college students, immersed in a communal campus environment and grappling with substantial academic demands (Meehan and Howells, 2019; Bayram and Bilgel, 2008), constitute a distinctive cohort within the adult population. Hence, we included pertinent randomized controlled trials (RCTs) focusing on college students and employed a synthesis of network meta-analysis and dose-response modeling to elucidate the optimal exercise type and dosage for obesity reduction in this demographic. This investigation aims to furnish college students with actionable insights for crafting personalized exercise regimens as part of obesity interventions.
2 Methods
2.1 Registration
This systematic review and NMA were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement guidelines (Hutton et al., 2015). The study protocol was registered in the International Prospective Register of Systematic Reviews ID: CRD42024587032.
2.2 Literature search strategy
Searches were conducted across five English-language databases—PubMed, Embase, Cochrane, Web of Science, and EBSCO—as well as three Chinese-language databases: CNKI, Wanfang, and VIP. For PubMed, Cochrane, and Embase, the search employed a blend of subject headings and keywords. The search strategy adhered to the PICOS framework, encompassing randomized controlled trials (RCTs) published in English and Chinese from the inception of the databases through August 2024. For the Chinese databases, the search was limited to core journals. Additionally, the references of the included articles were scrutinized to identify any omitted studies. The detailed search strategy is presented in Supplementary Appendix 1.
2.3 Eligibility criteria
Inclusion criteria: (1) Study type: randomized controlled trials (RCTs), language limited to English and Chinese. (2) Subjects: college undergraduates, specialists and postgraduates aged 18–28 years old. (3) Interventions: The experimental group received at least 4 weeks of planned physical activity, and the types of exercise included aerobic exercise (including walking, running, cycling, etc.), resistance exercise (exercise that overcomes self-weight or external resistance), combined exercise (exercise that alternates aerobic and resistance exercises), high-intensity interval training (HIIT), mind-body exercises (exercises performed by integrating consciousness, breathing and body, such as Baduanjin, yoga, Pilates, etc.), and calisthenics (including all kinds of aerobics, dance, aerobics, etc.).
Exclusion criteria: (1) Literature with duplicated data published. (2) Literature where full text could not be found such as dissertations, review papers, research proposals and conference papers. (3) Acute exercise or animal studies. (4) Exercise mixed with other interventions.
2.4 Literature screening and data extraction
Two researchers extracted all data independently, and in case of disagreement, a third researcher intervened to resolve the disagreement by consensus, integrating the opinions of all three. The following information was extracted: lead author, year of publication, age, subject characteristics (number of participants, physical attributes), and intervention details (type of exercise, duration of individual exercise sessions, frequency, and duration of the intervention). In cases of incomplete information or missing data, requests for additional data could be made to the authors via e-mail as necessary.
2.5 Risk of bias and GRADE assessment
Two investigators independently assessed the risk of bias (ROB) of the included studies according to the Cochrane Risk of Bias Tool (Higgins et al., 2011). Because blinding participants to an exercise intervention is difficult, this component was not included in the overall ROB score, and a total of six entries were evaluated in this study: (1) allocation generation, (2) concealment of allocation, (3) blinding of outcome assessment, (4) incomplete outcome data addressed, (5) freedom from selective reporting bias, and (6) other forms of bias. The ROB of each study was comprehensively rated on the basis of the risk evaluation of each entry, and the rating methodology was based on existing studies: if there was no high ROB for each of the above entries and the number of unclear ROBs was ≤3, the study was categorized as low ROB; if there was one high ROB, or no high ROB but the number of unclear ROBs was ≥4, the study was categorized as medium ROB; and all other cases were rated as high ROB (Cipriani et al., 2018).
We assessed the certainty of evidence contributing to the network estimates of the outcomes with the Grading of Recommendations Assessment, Development and Evaluation (GRADE) framework.
2.6 Methods of counting exercise doses
In this study, dose-response analysis of overall exercise and body composition of different exercise types of interventions was further conducted on the basis of NMA by calculating weekly energy expenditure (i.e., metabolic equivalents, METs) for exercise dosimetry and expressed as METs-min/week. The calculation was performed by first rating the exercise intensity against the exercise level classification scale based on the physiological indices given in the original literature to indicate exercise intensity [maximal heart rate reserve, percentage of maximal heart rate, percentage of maximal oxygen uptake, or perceived exercise intensity (RPE)], and then estimating the METs consumed per minute of exercise in relation to the age and physical characteristics of the subjects (e.g., the presence of underlying diseases, etc.), and finally comparing the METs were multiplied with the single exercise time and the number of exercises per week to obtain METs-min/week (Garber et al., 2011; Ainsworth et al., 2000; Wasfy and Baggish, 2016; Ainsworth et al., 2011). If no exercise intensity indicator was given in the text, METs expended per minute were assessed based on the type of exercise described in the literature against the 2024 Compendium of Physical Activity for Adults (Herrmann et al., 2024), and finally METs-min/week was obtained by multiplying METs with the duration of a single exercise session and the number of exercise sessions per week.
2.7 Statistical analysis
2.7.1 Network meta-analysis
In this study, effect sizes were combined using pre- and post-intervention mean and standard deviation change values to minimize the impact of baseline differences. The change in standard deviation was calculated following the Cochrane Handbook (version 6.3) and converted using the formula provided therein. The calculation of combined effect sizes and 95% confidence intervals (CI) was performed using a random-effects model in Stata 17.0 software, adhering to the PRISMA guidelines for network Meta-analysis (Hutton et al., 2015), employing a frequency-based approach (Salanti, 2012). Weighted Mean Difference (WMD) was utilized for calculations, as the units of the primary outcome indicators were consistent. Relationships between movement types were visualized through a network evidence graph, where lines connecting nodes indicate direct comparisons, with line thickness proportional to the number of studies and node size proportional to the sample sizes. The consistency of each closed loop was assessed by calculating the inconsistency factor and its 95% CI (Chaimani et al., 2013). The inconsistency model was applied to test for inconsistency, and if P > 0.05, the consistency model would be employed to continue the analysis (Shim et al., 2017). The efficacy of each intervention type was ranked by the surface under the cumulative ranking (SUCRA), with a SUCRA value of 1 indicating the best effect and 0 indicating the worst effect (Salanti et al., 2011; Mbuagbaw et al., 2017). Funnel plots were inspected for signs of publication bias.
2.7.2 Dose-response analysis
The dose-response relationship analysis between exercise and BMI index was conducted using a random-effects Bayesian model-based Network Meta-Analysis (MBNMA) (Mawdsley et al., 2016). Initially, we validated the key assumptions of MBNMA through network transmissibility (Higgins et al., 2012), data consistency (Wheeler et al., 2010), and network connectivity (Ter et al., 2019). Subsequently, a quadratic model was selected for the dose-response analysis (Shim and Lee, 2019) by comparing the Deviance Information Criterion (DIC) and the fitted plots of each nonlinear model. The standardized mean difference (SMD) was utilized as the effect size measure in the dose analysis, with 95% CI to ascertain validity; results were considered statistically significant if the 95% CI did not encompass 0 (Borg et al., 2024). The dose-response analysis was executed using the “MBNMAdose” package in R (version 4.3.1), and visualization was accomplished using the “ggplot2” package.
3 Results
3.1 Literature selection
As shown in Figure 1, a total of 5,104 potentially eligible studies were identified, with 5,091 coming from searches of eight databases and 13 from reference lists. After removing 2,457 duplicate studies, 2,647 remained for screening. Of these, 2,571 were removed following title and abstract screening, and 33 were removed after downloading and reading the full text, resulting in 43 RCTs included in the analysis (Alexander and Machado, 2024; Chaudhary et al., 2022; Eather et al., 2019; Eimarieskandari et al., 2012; Fisher et al., 2015; Ghorbani et al., 2014; Heydari et al., 2012; Kong et al., 2016; Li et al., 2022; Liu et al., 2023; Moravveji et al., 2019; Nie et al., 2018; Pourabdi et al., 2013; Saltan and Ankaralı, 2021; Sun et al., 2020; Suwannakul et al., 2024; Wang Y. et al., 2023; Xiaolin, 2023; Ye et al., 2022; Zhang et al., 2015; Zhang and Jiang, 2023; An et al., 2022; Cai et al., 2019; Cao et al., 2020; Chen et al., 2020; Chen, 2018; Chen and Zhang, 2022; Fu et al., 2019; Gao et al., 2017; Huang, 2005; Jiao et al., 2021; Li and Chu, 2019; Li et al., 2021; Lin et al., 2016; Liu et al., 2016; Ma and Yuan, 2004; Qi et al., 2013; Xiao et al., 2022; Yan et al., 2011; Yang et al., 2019; Yang and Fu, 2010).
Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram depicting the study selection process.
3.2 Characteristics of the included studies
The characteristics of the included studies are summarized in Table 1. A total of 3,154 current university students participated in the study, with 2,018 assigned to the experimental group and 1,136 to the control group. The intervention periods across the studies varied from 4 to 40 weeks, with 38 studies (88%) having an intervention period of 8 weeks or longer. The length of a single exercise session spanned from 15 to 90 min, with 36 studies (84%) involving sessions that lasted 30 min or longer. The frequency of exercise sessions per week ranged from 2 to 5, with 38 studies (88%) conducting 3 or more sessions weekly.
Table 1. Characteristics of the included studies.
3.3 Results of risk of bias assessment
The bias risk assessment results are detailed in Supplementary Appendix 2. Among the studies, 42 detailed their allocation methods, 21 described their allocation concealment, and 10 reported blinding of outcome assessments. Dropout rates were reported by 34 studies; one of these, with a dropout rate exceeding 20%, was rated as high risk. The remaining 40 studies indicated a low risk of selective reporting. In terms of other biases, four studies were rated high risk due to small sample sizes (fewer than 10 participants in any group). Overall, the studies were rated as follows: 37 low risk, 4 medium risk, and 2 high risk.
3.4 Results of network meta-analysis
Figure 2 displays the NMA plots for eligible studies examining the effects of various exercise types on body composition. As shown in the plots, aerobic exercise was the most frequently included intervention, whereas resistance exercise was the least common.
Figure 2. Network plot presenting the effects of different exercise types on body composition in university students. (A): Control; (B): Aerobic exercise; (C): Resistance exercise; (D): Combined exercise; (E): HIIT; (F): Mind-body exercise; (G): Calisthenics.
Supplementary Appendix 3 illustrates the contributions of direct and indirect comparisons to the NMA, as well as the number of studies involved in each direct comparison. All outcome indicators were assessed using loop-specific heterogeneity estimates, an inconsistency model, and node-splitting analysis, as detailed in Supplementary Appendix 4.
The loop-specific heterogeneity estimates demonstrated good consistency within each closed loop for depression, anxiety, and stress. The inconsistency model showed p-values greater than 0.05 for all three conditions, suggesting no significant inconsistency. The node-splitting analysis also revealed no inconsistencies in either direct or indirect evidence, confirming the reliability of the results.
The results of the network meta-analysis are presented in Table 2. Compared with the control group, aerobic exercise [MD = −1.04, 95% CI (−1.48, −0.61), P < 0.0001], combined exercise [MD = −1.31, 95% CI (−1.92, −0.69), P < 0.0001], HIIT [MD = −1.03, 95% CI (−1.50, −0.55), P < 0.0001], physical and mental exercise [MD = −1.28, 95% CI (−1.81, −0.74), P < 0.0001], and calisthenics [MD = −1.50, 95% CI (−2.08, −0.91), P < 0.0001] had a significant effect on college students’ body composition. In cross-comparisons, no significant differences were found between any two exercise types. Supplementary Appendix 5 displays a forest plot of the outcome metrics, including 95% CI and 95% prediction intervals (95% PI).
Table 2. Results of network Meta-analysis.
The SUCRA probability ranking is depicted in Figure 3, with the detailed SUCRA ranking results provided in Supplementary Appendix 3. The exercise type with the highest probability of being the most effective for reducing BMI was calisthenics, with a SUCRA value of 84.7%. This was followed by combined exercise, with a SUCRA value of 72.4%. Conversely, resistance exercise had the lowest probability of being effective in influencing body composition, with a SUCRA value of 24.9%.
Figure 3. SUCRA Probability Ranking Chart, the figure shows the ranking of the effects of each exercise type on the body composition interventions. (A) Control; (B) Aerobic exercise; (C) Resistance exercise; (D) Combined exercise; (E) HIIT; (F) Mind-body exercise; (G) Calisthenics.
The analysis funnel plot (Figure 4) was tested for publication bias for the outcome indicators, and it was found that the symmetry of the funnel plot for the outcome indicators was better, with less impact of publication bias or small sample effect.
Figure 4. Funnel plot of network meta-analysis, according to this figure, the publication bias status of the included literature can be analyzed. (A) Control; (B) Aerobic exercise; (C) Resistance exercise; (D) Combined exercise; (E) HIIT; (F) Mind-body exercise; (G) Calisthenics.
3.5 Dose-response results
The key assumptions for network meta-analysis were first validated through assessments of network connectivity, data consistency, and transferability (Ter et al., 2019; Donegan et al., 2013; Watt et al., 2019), as detailed in Supplementary Appendix 7. Subsequently, by comparing the Deviance Information Criterion (DIC) and fitted plots of various models—including Emax, restricted cubic spline, and nonparametric models—we selected the quadratic model as the analytical model for this study (Supplementary Appendix 8). Furthermore, we conducted a series of tests to ensure the stability of the chosen model, as described in Supplementary Appendix 8 (Mawdsley et al., 2016; Dias et al., 2013).
Figure 5 illustrates the nonlinear dose-response relationship between overall exercise and body composition. The predictions indicate a significant response to the effect of holistic exercise on body composition starting at 310 METs-min/week (where the 95% CI upper limit is less than 0), with the maximum effect achieved when the exercise dose reached 1,300 METs-min/week (SMD = −0.91; 95% CI [-1.25, −0.54]; SD = 0.19). The effect of exercise on body composition exhibited a moderate effect at doses up to 600 METs-min/week [equivalent to the energy expenditure at the lower limit of physical activity recommended by the World Health Organization (WHO); SMD = −0.59; 95% CI (−0.87, −0.28); SD = 0.15], and a strong effect at doses up to 1,200 METs-min/week [equivalent to the upper limit of physical activity recommended by the WHO; SMD = −0.9; 95% CI (−1.22, −0.55); SD = 0.17] (Bull et al., 2020).
Figure 5. Overall exercise dose-response relationship. The green line is the minimum response measure that produces a significant effect. The red line is the dose that produces the maximum response effect. The shaded area is the WHO recommended range of physical activity.
Figure 6 illustrates the dose-response relationships for six types of exercise affecting body composition. The predicted results indicated a statistically non-significant dose-response relationship only for resistance exercise. In contrast, aerobic, combined, HIIT, mind-body, and calisthenics exercises showed non-linear dose-response relationships.
Figure 6. Dose-response relationship by exercise type. The black line is the minimum response measure that produces a significant effect. The shaded area is the WHO recommended range of physical activity. (A) Aerobic exercise; (B) Resistance exercise; (C) Combined exercise; (D) HIIT; (E) Mind-body exercise; (F) Calisthenics.
Mind-body exercise had the lowest minimum significant dose at 410 METs-min/week (SMD = −0.44), but the significant effect ceased at doses up to 1,030 METs-min/week (SMD = −0.88). Aerobic exercise required the highest minimum significant dose of 870 METs-min/week (SMD = −0.61), with its effect on body composition increasing more rapidly as the exercise dose increased. The minimum significant dose for aerobic exercise was 580 METs-min/week (SMD = −0.49), and its effect increased very gradually as the exercise dose exceeded 1,300 METs-min/week (SMD = −0.76). Combined exercise had a minimum response dose of 740 METs-min/week (SMD = −0.76), with its effect increasing more smoothly and gradually with increasing exercise dose. For HIIT, the minimum response dose was 460 METs-min/week (SMD = −0.44), and the significant effect disappeared when the dose was increased to 1,450 METs-min/week (SMD = −0.87). A plot of the dose-response relationship, including the original study dataset, is provided in Supplementary Appendix 9.
The ranked results in Supplementary Appendix 10 show that combined exercise (1,500 METs-min/week) had the highest probability of producing the greatest impact (SMD = −1.56), the second exercise was calisthenics (1,200 METs-min/week; SMD = −1.32), and the last ranked was resistance exercise.
3.6 GRADE assessment
Supplementary Appendix 11 shows the SUCRA-ranked GRADE assessments for each comparison for each outcome indicator and for the treatment measures for the outcome indicators. Overall, most of the comparative GRADE assessment ratings were rated as “Low.” SUCRA GRADE assessments were rated as “Moderate”.
4 Discussion
This systematic review and NMA encompassed 43 RCTs involving 3,154 participants, aiming to evaluate the impact of various exercise types on body composition in college students. Aerobic exercise, combined exercise, HIIT, mind-body exercise, and calisthenics all significantly reduced BMI. However, no significant differences in effectiveness were observed between the types of exercise. Additionally, dose-response analyses predicted changes in the effects of overall exercise and each specific type on body composition, revealing a nonlinear relationship between exercise dose and body composition. The analyses also indicated a low dose threshold for the impact of exercise interventions on body composition.
4.1 Results of network meta-analysis
After entering college, lifestyle changes and increased sedentary behavior have contributed to rising obesity rates among college students (Lv et al., 2019; Vankim and Nelson, 2013). Although numerous studies have shown that different types of exercise have varying effects on body composition, the results are inconsistent (Irandoust et al., 2022). It is still unclear which type of exercise is most effective for altering body composition in college students and what exercise dose is most beneficial when designing a training program. By incorporating a large number of RCTs and conducting both direct and indirect analyses, this study determined that aerobic exercise, combined exercise, HIIT, mind-body exercise, and calisthenics all significantly reduced BMI in college students. The SUCRA results indicate the following ranking: Calisthenics > Combined exercise > Mind-body exercise > Aerobic exercise > HIIT > Resistance exercise. Calisthenics had the highest likelihood of being the most effective exercise type, while resistance exercise was most likely to be the least effective for this population. These findings align with the analyses by Morze et al. and Wang et al. (Morze et al., 2021; Wang et al., 2022), although their studies included all adults. Also our study is in line with the findings of AL-Mhanna et al. (Al-Mhanna et al., 2024a; Al-Mhanna et al., 2024b) and Batrakoulis et al. (Batrakoulis et al., 2022) that Combined exercise is the type of exercise that has a significant effect in intervening in obesity.
4.2 Results of dose-response analysis
We also discovered a nonlinear dose-response relationship between overall exercise and BMI for all types of exercise. The minimum effective dose for overall exercise was estimated to be 310 METs-min/week, a threshold that is quite low and equivalent to 90 min of slow walking (3.5 METs-min) or 80 min of cycling (4 METs-min). The maximum response dose is anticipated to be 1,300 METs-min/week, which is equivalent to 170 min of jogging (7.5 METs-min) or 160 min of basketball or badminton (8 METs-min) (Herrmann et al., 2024). College students who engage in these activities for an average of 20–25 min per day outside of school hours can effectively improve their body composition and lower their BMI. Beyond 1,300 METs-min/week, the increase in the intervention effect becomes very gradual. Our findings suggest that exceeding the physical activity doses recommended by the WHO can provide additional benefits in reducing the BMI index among college students (Bull et al., 2020). Therefore, college students should aim to exercise up to 1,300 METs-min/week of energy expenditure, as time and physical condition permit.
In addition, we observed varying dose-response relationships between different exercise types and BMI. The dose-response relationships for aerobic exercise, combined exercise, HIIT, physical and mental exercise, and calisthenics were all statistically significant. The intervention effects of aerobic exercise, combined exercise, and calisthenics progressively strengthened with increasing exercise dose. However, the effects of HIIT and physical and mental exercise lost significance after reaching certain doses of 1,450 METs-min/week and 1,030 METs-min/week, respectively. Ranking analyses revealed that combined exercise (at 1,500 METs-min/week) had the highest probability of producing the greatest effect (SMD = −1.56), followed by calisthenics (at 1,200 METs-min/week; SMD = −1.32). Resistance exercise was the last in the ranking. These findings align with the SUCRA probability ranking results.
4.3 Strengths and limitations
The strengths of this study are threefold. Firstly, we synthesized both direct and indirect comparisons to rank the effectiveness of various exercise types on college students’ body composition, offering new and more reliable evidence. Secondly, our study recognized that both exercise type and dose are crucial factors in exercise program development. By integrating network meta-analysis with a novel dose-response network analysis, we examined the dose-response relationships between overall exercise, individual exercise types, and BMI. This combination of methods enabled us to more comprehensively predict the optimal exercise prescription for lowering BMI in college students. Lastly, all included studies were RCTs, enhancing the reliability of our evidence.
However, the study is not without limitations. Firstly, of the included RCTs, only 10 reported the use of blinding in outcome assessment, with 2 rated as high risk of bias and 4 as medium risk. This could have introduced some degree of error. Secondly, the limitation of this study also lies in the fact that subgroup analyses were not conducted based on factors such as gender, geographic location, and duration and frequency of exercise interventions, and this aspect was focused on in subsequent studies to enhance the applicability of the findings to different populations. Again, the population selected for this study was a group of college students, and the findings are less applicable to other age groups; in subsequent studies, attention should be paid to conducting research based on subjects of different ages. Finally, this study only examined the effect of a single intervention, exercise, on body composition, and future studies will have to draw on Al Mhanna et al.'s (Al-Mhanna et al., 2023) study to explore the optimal intervention for intervening on body composition in conjunction with diet.
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.
Author contributions
JL: Conceptualization, Data curation, Methodology, Software, Validation, Visualization, Writing–original draft, Writing–review and editing. LZ: Formal Analysis, Writing–review and editing. SH: Data curation, Writing–review and editing. HW: Project administration, Writing–review and editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This study is supported by the National Foundation of Philosophy and Social Sciences of China funded by the Chinese Government (22BTY021).
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.
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.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fphys.2025.1537937/full#supplementary-material
References
Ainsworth B. E., Haskell W. L., Herrmann S. D., Meckes N., Bassett D. R., Tudor-Locke C., et al. (2011). 2011 Compendium of Physical Activities: a second update of codes and MET values. Med. Sci. Sports Exerc 43, 1575–1581. doi:10.1249/MSS.0b013e31821ece12
Ainsworth B. E., Haskell W. L., Whitt M. C., Irwin M. L., Swartz A. M., Strath S. J., et al. (2000). Compendium of physical activities: an update of activity codes and MET intensities. Med. Sci. Sports Exerc 32, S498–S504. doi:10.1097/00005768-200009001-00009
Alexander M., Machado L. (2024). Chronic exercise and neuropsychological function in healthy young adults: a randomised controlled trial investigating a running intervention. Cogn. Process 25, 241–258. doi:10.1007/s10339-024-01177-1
Al-Mhanna S. B., Batrakoulis A., Mohamed M., Alkhamees N. H., Sheeha B. B., Ibrahim Z. M., et al. (2024b). Home-based circuit training improves blood lipid profile, liver function, musculoskeletal fitness, and health-related quality of life in overweight/obese older adult patients with knee osteoarthritis and type 2 diabetes: a randomized controlled trial during the COVID-19 pandemic. BMC Sports Sci. Med. Rehabil. 16, 125. doi:10.1186/s13102-024-00915-4
Al-Mhanna S. B., Batrakoulis A., Wan Ghazali W. S., Mohamed M., Aldayel A., Alhussain M. H., et al. (2024a). Effects of combined aerobic and resistance training on glycemic control, blood pressure, inflammation, cardiorespiratory fitness and quality of life in patients with type 2 diabetes and overweight/obesity: a systematic review and meta-analysis. PeerJ 12, e17525. doi:10.7717/peerj.17525
Al-Mhanna S. B., Rocha-Rodriguesc S., Mohamed M., Batrakoulis A., Aldhahi M. I., Afolabi H. A., et al. (2023). Effects of combined aerobic exercise and diet on cardiometabolic health in patients with obesity and type 2 diabetes: a systematic review and meta-analysis. BMC Sports Sci. Med. Rehabil. 15, 165. doi:10.1186/s13102-023-00766-5
An Y., Zhang P., Zhang X., Xi W. (2022). Effect analysis of moderate intensity exercise prescription on the physical intervention of female college students. Chin. J. Sch. Health, 1500–1504+1508. doi:10.16835/j.cnki.1000-9817.2022.10.014
Andreato L. V., Esteves J. V., Coimbra D. R., Moraes A. J. P., de Carvalho T. (2019). The influence of high-intensity interval training on anthropometric variables of adults with overweight or obesity: a systematic review and network meta-analysis. Obes. Rev. 20, 142–155. doi:10.1111/obr.12766
Batrakoulis A., Jamurtas A. Z., Metsios G. S., Perivoliotis K., Liguori G., Feito Y., et al. (2022). Comparative efficacy of 5 exercise types on cardiometabolic health in overweight and obese adults: a systematic review and network meta-analysis of 81 randomized controlled trials. Circ. Cardiovasc Qual. Outcomes 15, e008243. doi:10.1161/CIRCOUTCOMES.121.008243
Bayram N., Bilgel N. (2008). The prevalence and socio-demographic correlations of depression, anxiety and stress among a group of university students. Soc. Psychiatry Psychiatr. Epidemiol. 43, 667–672. doi:10.1007/s00127-008-0345-x
Borg D. N., Impellizzeri F. M., Borg S. J., Hutchins K. P., Stewart I. B., Jones T., et al. (2024). Meta-analysis prediction intervals are under reported in sport and exercise medicine. Scand. J. Med. Sci. Sports 34, e14603. doi:10.1111/sms.14603
Bray G. A., Frühbeck G., Ryan D. H., Wilding J. P. H. (2016). Management of obesity. Lancet. 387, 1947–1956. doi:10.1016/S0140-6736(16)00271-3
Bull F. C., Al-Ansari S. S., Biddle S., Borodulin K., Buman M. P., Cardon G., et al. (2020). World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br. J. Sports Med. 54, 1451–1462. doi:10.1136/bjsports-2020-102955
Cai Y., Ning L., Wang G., Lei Y., Long L. (2019). Effect of exercise prescription and mobile APP for health education on the physical self-esteem, and mental health among female college students. Chin. J. Sch. Health, 1503–1505. doi:10.16835/j.cnki.1000-9817.2019.10.018
Cao L., Shi W., Liu H., Zeng Y. (2020). Effect of walking on body composition and hemodynamics for obese college students. J. Hydrodyn., 608–616. doi:10.16076/j.cnki.cjhd.2020.05.008
Chaimani A., Higgins J. P. T., Mavridis D., Spyridonos P., Salanti G. (2013). Graphical tools for network meta-analysis in STATA. PLoS One 8, e76654. doi:10.1371/journal.pone.0076654
Chaudhary M., Chadha V., Mishra R., Sodhi H. S., Ahmed Q. R. (2022). To compare the effects of yoga program and walking exercise on cardiac function in young adults. Eur. J. Mol. and Clin. Med. 9.
Chen J., Li Y., Wu Y., Su X. (2020). Effects of Pilates exercise on depressive body composition and serum inflammatory factors in obese female college students. Chin. J. Sch. Health, 783–786. doi:10.16835/j.cnki.1000-9817.2020.05.040
Chen L. (2018). Effect of 16 Weeks aerobic exercise on fat oxidation kinetics in overweight female college students. J. Shenyang Sport Univ. 37, 87–91.
Chen S., Zhang P. (2022). Effects of high intensity interval training and crossover point training on blood lipid metabolism in overweight female university students. Chin. J. Sch. Health, 1495–1499. doi:10.16835/j.cnki.1000-9817.2022.10.013
Cipriani A., Furukawa T. A., Salanti G., Chaimani A., Atkinson L. Z., Ogawa Y., et al. (2018). Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis. Lancet 391, 1357–1366. doi:10.1016/S0140-6736(17)32802-7
Dias S., Sutton A. J., Ades A. E., Welton N. J. (2013). Evidence synthesis for decision making 2: a generalized linear modeling framework for pairwise and network meta-analysis of randomized controlled trials. Med. Decis. Mak. 33, 607–617. doi:10.1177/0272989X12458724
Donegan S., Williamson P., D’Alessandro U., Tudur Smith C. (2013). Assessing key assumptions of network meta-analysis: a review of methods. Res. Synth. Methods 4, 291–323. doi:10.1002/jrsm.1085
Eather N., Riley N., Miller A., Smith V., Poole A., Vincze L., et al. (2019). Efficacy and feasibility of HIIT training for university students: the Uni-HIIT RCT. J. Sci. Med. Sport 22, 596–601. doi:10.1016/j.jsams.2018.11.016
Eimarieskandari R., Zilaeibouri S., Zilaeibouri M., Ahangarpour A. (2012). Comparing two modes of exercise training with different intensity on body composition in obese young girls. Sci. Mov. Health 12, 473–478.
Ezati M., Keshavarz M., Barandouzi Z. A., Montazeri A. (2020). The effect of regular aerobic exercise on sleep quality and fatigue among female student dormitory residents. BMC Sports Sci. Med. Rehabil. 12, 44. doi:10.1186/s13102-020-00190-z
Fisher G., Brown A. W., Bohan Brown M. M., Alcorn A., Noles C., Winwood L., et al. (2015). High intensity interval-vs moderate intensity- training for improving cardiometabolic health in overweight or obese males: a randomized controlled trial. PLoS One 10, e0138853. doi:10.1371/journal.pone.0138853
Fu F., Wang G., Hu Y., Yang L. (2019). Study on the effects of dance yoga on the physical fitness and mental health of female college students. J. Guangzhou Sport Univ., 86–90. doi:10.13830/j.cnki.cn44-1129/g8.2019.04.023
Gao Y., Wang G., Yang W., Qiao X. (2017). Effects of high-intensity interval training and aerobic exercise on lipid metabolism and chronic inflammation in obese youths. Chin. J. Sports Med., 628–632+650. doi:10.13830/j.cnki.cn44-1129/g8.2019.04.023
Garber C. E., Blissmer B., Deschenes M. R., Franklin B. A., Lamonte M. J., Lee I.-M., et al. (2011). American College of Sports Medicine position stand. Quantity and quality of exercise for developing and maintaining cardiorespiratory, musculoskeletal, and neuromotor fitness in apparently healthy adults: guidance for prescribing exercise. Med. Sci. Sports Exerc 43, 1334–1359. doi:10.1249/MSS.0b013e318213fefb
Ghorbani F., Heidarimoghadam R., Karami M., Fathi K., Minasian V., Bahram M. E. (2014). The effect of six-week aerobic training program on cardiovascular fitness, body composition and mental health among female students. J. Res. Health Sci. 14, 264–267.
Gilyana M., Batrakoulis A., Zisi V. (2023). Physical activity, body image, and emotional intelligence differences in adults with overweight and obesity. Diseases 11, 71. doi:10.3390/diseases11020071
González-Muniesa P., Mártinez-González M.-A., Hu F. B., Després J.-P., Matsuzawa Y., Loos R. J. F., et al. (2017). Obesity. Nat. Rev. Dis. Prim. 3, 17034. doi:10.1038/nrdp.2017.34
Haase A., Steptoe A., Sallis J. F., Wardle J. (2004). Leisure-time physical activity in university students from 23 countries: associations with health beliefs, risk awareness, and national economic development. Prev. Med. 39, 182–190. doi:10.1016/j.ypmed.2004.01.028
Haidar Y. M., Cosman B. C. (2011). Obesity epidemiology. Clin. Colon Rectal Surg. 24, 205–210. doi:10.1055/s-0031-1295684
Hao Z., Liu K., Qi W., Zhang X., Zhou L., Chen P. (2023). Which exercise interventions are more helpful in treating primary obesity in young adults? A systematic review and Bayesian network meta-analysis. Arch. Med. Sci. 19, 865–883. doi:10.5114/aoms/153479
Haslam D. W., James W. P. T. (2005). Obesity. Lancet. 366, 1197–1209. doi:10.1016/S0140-6736(05)67483-1
Herrmann S. D., Willis E. A., Ainsworth B. E., Barreira T. V., Hastert M., Kracht C. L., et al. (2024). 2024 Adult Compendium of Physical Activities: a third update of the energy costs of human activities. J. Sport Health Sci. 13, 6–12. doi:10.1016/j.jshs.2023.10.010
Heydari M., Freund J., Boutcher S. H. (2012). The effect of high-intensity intermittent exercise on body composition of overweight young males. J. Obes. 2012, 480467. doi:10.1155/2012/480467
Higgins J. P. T., Altman D. G., Gøtzsche P. C., Jüni P., Moher D., Oxman A. D., et al. (2011). The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ 343, d5928. doi:10.1136/bmj.d5928
Higgins J. P. T., Jackson D., Barrett J. K., Lu G., Ades A. E., White I. R. (2012). Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Res. Synth. Methods 3, 98–110. doi:10.1002/jrsm.1044
Huang X. (2005). Research on effects of aerobic calisthencis on the lipid metabolism and relative hormone among obese female college students. J. Beijing Sport Univ., 1214–1216.
Hutton B., Salanti G., Caldwell D. M., Chaimani A., Schmid C. H., Cameron C., et al. (2015). The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann. Intern Med. 162, 777–784. doi:10.7326/M14-2385
Irandoust K., Hamzehloo A., Youzbashi L., Taheri M., Ben Saad H. (2022). High intensity interval training and L-Arginine supplementation decrease interleukin-6 levels in adult trained males. Tunis. Med. 100, 696–705.
Jiao X., Ji H., Chen J. (2021). Effects of traditional Wuqinxi on physical fitness and mental health of female college students. Chin. J. Sch. Health, 1323–1327. doi:10.16835/j.cnki.1000-9817.2021.09.011
Kivimäki M., Strandberg T., Pentti J., Nyberg S. T., Frank P., Jokela M., et al. (2022). Body-mass index and risk of obesity-related complex multimorbidity: an observational multicohort study. Lancet Diabetes Endocrinol. 10, 253–263. doi:10.1016/S2213-8587(22)00033-X
Kong Z., Sun S., Liu M., Shi Q. (2016). Short-term high-intensity interval training on body composition and blood glucose in overweight and obese young women. J. Diabetes Res. 2016, 4073618. doi:10.1155/2016/4073618
Li D., Chu J. (2019). Effects of comprehensive yoga intervention program on cardiopulmonary function and physical fitness among female college students. Chin. J. Sch. Health, 89–91+95. doi:10.16835/j.cnki.1000-9817.2019.01.024
Li H., Zhang Y., Zhao L., Song Y., Yu J., Mao S. (2021). Study on the energy expenditure and fat-reducing effect of overfat female undergraduates practicing flow yoga. Chin. J. Sports Med., 614–619. doi:10.16038/j.1000-6710.2021.08.004
Li R., Yan R., Cheng W., Ren H. (2022). Effect of resistance training on heart rate variability of anxious female college students. Front. Public Health 10, 1050469. doi:10.3389/fpubh.2022.1050469
Lin J., Zhao H., Huang X., liu X., Zhang R. (2016). Effect of high intensity interval training on the body Composition,Blood lipid and fasting insulin level of obese female college students. Chin. Gen. Pract. 19, 2139–2144.
Liu H., Chen S., Ji H., Dai Z. (2023). Effects of time-restricted feeding and walking exercise on the physical health of female college students with hidden obesity: a randomized trial. Front. Public Health 11, 1020887. doi:10.3389/fpubh.2023.1020887
Liu H., Liu Z., Wang C. (2016). Effect of high intensity interval training on lose weight in obese young women. J. Shandong Sport Univ., 95–98. doi:10.14104/j.cnki.1006-2076.2016.06.017
Lv Y., Qin X., Jia H., Chen S., Sun W., Wang X. (2019). The association between gut microbiota composition and BMI in Chinese male college students, as analysed by next-generation sequencing. Br. J. Nutr. 122, 986–995. doi:10.1017/S0007114519001909
Ma G., Yuan L. (2004). The influence of middle compulsories in national aerobie gymnastics exercise standard on the body shape of female university students. J. Tianjin Sport Univ., 74–77.
Mawdsley D., Bennetts M., Dias S., Boucher M., Welton N. J. (2016). Model-based network meta-analysis: a framework for evidence synthesis of clinical trial data. CPT Pharmacometrics Syst. Pharmacol. 5, 393–401. doi:10.1002/psp4.12091
Mbuagbaw L., Rochwerg B., Jaeschke R., Heels-Andsell D., Alhazzani W., Thabane L., et al. (2017). Approaches to interpreting and choosing the best treatments in network meta-analyses. Syst. Rev. 6, 79. doi:10.1186/s13643-017-0473-z
Meehan C., Howells K. (2019). In search of the feeling of “belonging” in higher education: undergraduate students transition into higher education. J. Furth. High. Educ. 43, 1376–1390. doi:10.1080/0309877x.2018.1490702
Moravveji A., Sayyah M., Shamsnia E., Vakili Z. (2019). Comparing the prolonged effect of interval versus continuous aerobic exercise on blood inflammatory marker of Visfatin level and body mass index of sedentary overweigh/fat female college students. AIMS Public Health 6, 568–576. doi:10.3934/publichealth.2019.4.568
Morze J., Rücker G., Danielewicz A., Przybyłowicz K., Neuenschwander M., Schlesinger S., et al. (2021). Impact of different training modalities on anthropometric outcomes in patients with obesity: a systematic review and network meta-analysis. Obes. Rev. 22, e13218. doi:10.1111/obr.13218
Nie J., Zhang H., Kong Z., George K., Little J. P., Tong T. K., et al. (2018). Impact of high-intensity interval training and moderate-intensity continuous training on resting and postexercise cardiac troponin T concentration. Exp. Physiol. 103, 370–380. doi:10.1113/EP086767
O’Donoghue G., Blake C., Cunningham C., Lennon O., Perrotta C. (2021). What exercise prescription is optimal to improve body composition and cardiorespiratory fitness in adults living with obesity? A network meta-analysis. Obes. Rev. 22, e13137. doi:10.1111/obr.13137
Pourabdi K., Shakeriyan S., Pourabdi Z., Janbozorgi M. (2013). Effects of short-term interval training courses on fitness and weight loss of untrained girls. Ann. Appl. Sport Sci. 1, 1–9.
Qi Y., Huang J., Tan S. (2013). Comparison of weight loss effects carried out by HIlT andContinuous aerobic exercise of female obese college students. China Sports Sci. Tech., 30–33. doi:10.16470/j.csst.2013.01.005
Salanti G. (2012). Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Res. Synth. Methods 3, 80–97. doi:10.1002/jrsm.1037
Salanti G., Ades A. E., Ioannidis J. P. A. (2011). Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. J. Clin. Epidemiol. 64, 163–171. doi:10.1016/j.jclinepi.2010.03.016
Saltan A., Ankaralı H. (2021). Does Pilates effect on depression status, pain, functionality, and quality of life in university students? A randomized controlled study. Perspect. Psychiatr. Care 57, 198–205. doi:10.1111/ppc.12547
Shim S., Yoon B.-H., Shin I.-S., Bae J.-M. (2017). Network meta-analysis: application and practice using Stata. Epidemiol. Health 39, e2017047. doi:10.4178/epih.e2017047
Shim S. R., Lee J. (2019). Dose-response meta-analysis: application and practice using the R software. Epidemiol. Health 41, e2019006. doi:10.4178/epih.e2019006
Sun J., Cheng W., Fan Z., Zhang X. (2020). Influence of high-intensity intermittent training on glycolipid metabolism in obese male college students. Ann. Palliat. Med. 9, 2013–2019. doi:10.21037/apm-20-1105
Suwannakul B., Sangkarit N., Thammachai A., Tapanya W. (2024). Effects of Surya Namaskar yoga on perceived stress, anthropometric parameters, and physical fitness in overweight and obese female university students: a randomized controlled trial. Hong Kong Physiother. J., 1–11. doi:10.1142/s1013702525500027
Ter V. E., van Oijen M. G. H., van Laarhoven H. W. M. (2019). The use of (network) meta-analysis in clinical oncology. Front. Oncol. 9, 822. doi:10.3389/fonc.2019.00822
Vankim N. A., Nelson T. F. (2013). Vigorous physical activity, mental health, perceived stress, and socializing among college students. Am. J. Health Promot 28, 7–15. doi:10.4278/ajhp.111101-QUAN-395
Wang H., Cheng R., Xie L., Hu F. (2023a). Comparative efficacy of exercise training modes on systemic metabolic health in adults with overweight and obesity: a network meta-analysis of randomized controlled trials. Front. Endocrinol. (Lausanne) 14, 1294362. doi:10.3389/fendo.2023.1294362
Wang S., Zhou H., Zhao C., He H. (2022). Effect of exercise training on body composition and inflammatory cytokine levels in overweight and obese individuals: a systematic review and network meta-analysis. Front. Immunol. 13, 921085. doi:10.3389/fimmu.2022.921085
Wang Y., Jia N., Zhou Y., Fu L., Fan L., Li B. (2023b). A comparison of the effects of remote coaching HIIT training and combined exercise training on the physical and mental health of university students. Front. Psychol. 14, 1182332. doi:10.3389/fpsyg.2023.1182332
Wasfy M. M., Baggish A. L. (2016). Exercise dose in clinical practice. Circulation 133, 2297–2313. doi:10.1161/CIRCULATIONAHA.116.018093
Watt J., Tricco A. C., Straus S., Veroniki A. A., Naglie G., Drucker A. M. (2019). Research techniques made simple: network meta-analysis. J. Invest. Dermatol. 139, 4–12. doi:10.1016/j.jid.2018.10.028
Wheeler D. C., Hickson D. A., Waller L. A. (2010). Assessing local model adequacy in bayesian hierarchical models using the partitioned deviance information criterion. Comput. Stat. Data Anal. 54, 1657–1671. doi:10.1016/j.csda.2010.01.025
WHO (2024). Obesity and overweight. Available at: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (Accessed September 8, 2024).
Xiao Z., Hu H., Hu J., Yang M., Peng Y., Zhou H. (2022). Mechanism of action of 10-week aerobic exercise versus combined aerobic and resistance exercise in improving the microcirculation in obese college students. Chin. Gen. Pract. 25, 2349–2355+2362.
Xiaolin C. (2023). Impacto del entrenamiento por intervalos de alta intensidad en la composición corporal de estudiantes universitarias. Rev. Bras. Med. do Esporte 29, e2023. doi:10.1590/1517-8692202329012023_0053
Yan X., Liu M., Li N., Guo M., Ma L. (2011). Effects of different types of yoga exercise on body morphology and cardiovascular function of female college students. Chin. J. Sports Med., 748–751. doi:10.16038/j.1000-6710.2011.08.015
Yang B., Li W., Chen D., Li Y. (2019). Effect evaluation of different weight loss methods for overweight/obese youth in a university Hubei Province. Chin. Health Educ., 557–560. doi:10.16168/j.cnki.issn.1002-9982.2019.06.018
Yang X., Fu L. (2010). Evaluation of the effectiveness of weight loss interventions for obese female college students. Chin. J. Puble Healh 26, 151.
Ye Y., Zhao F., Sun S., Xiong J., Zheng G. (2022). The effect of Baduanjin exercise on health-related physical fitness of college students: a randomized controlled trial. Front. Public Health 10, 965544. doi:10.3389/fpubh.2022.965544
Zhang B., Guo Y., Liu X. (2009). An experimental research on the influence of aerbic exercise on female university students ’physical condition. J. Beijing Sport Univ., 72–74. doi:10.19582/j.cnki.11-3785/g8.2009.04.021
Zhang H., K Tong T., Qiu W., Wang J., Nie J., He Y. (2015). Effect of high-intensity interval training protocol on abdominal fat reduction in overweight Chinese women: a randomized controlled trial. Kinesiology 47, 57–66.
Zhang Y., Jiang X. (2023). The effect of Baduanjin exercise on the physical and mental health of college students: a randomized controlled trial. Med. Baltim. 102, e34897. doi:10.1097/MD.0000000000034897
Keywords: obesity, body composition, BMI, exercise, dose, systematic evaluation, network meta-analysis
Citation: Li J, Zang L, Hao S and Wang H (2025) Comparative efficacy of different exercise types on body composition in university students: a systematic review and meta-analysis of randomized controlled trials. Front. Physiol. 16:1537937. doi: 10.3389/fphys.2025.1537937
Received: 02 December 2024; Accepted: 13 February 2025;
Published: 22 April 2025.
Edited by:
Tarak Driss, Université Paris Nanterre, FranceReviewed by:
Alexios Batrakoulis, University of Thessaly, GreeceFernanda Thomazini, Federal University of São Paulo, Brazil
Copyright © 2025 Li, Zang, Hao and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Hui Wang, MTAwNjM2MzAxNkBxcS5jb20=