Effects of high-intensity interval training on aerobic capacity and athletic performance in trained athletes: a systematic review and meta-analysis

Abstract

This study conducted a systematic review with meta-analysis to investigate the effects of high-intensity interval training (HIIT) on aerobic capacity and performance in trained athletes. The data sources utilized were PubMed, EBSCO, and Web of Science. Eligibility included trained athletes, encompassing amateur, elite, or professional male and female participants, with no age restrictions. Outcome measures include aerobic capacity and performance. Methodological quality was assessed using the Cochrane, Bias risk assessment tool. The search identified 4,289 titles, with 18 articles eligible for the review and meta-analysis. The results showed that HIIT significantly improved VO₂max (SMD: 1.11, 95% CI: 0.48, 1.74; heterogeneity p < 0.1) and VO₂peak (MD: 0.52, 95% CI: 0.08, 0.97; heterogeneity p < 0.1) in trained athletes, indicating a remarkable effect of HIIT in enhancing aerobic capacity. In terms of athletic performance, HIIT significantly improved speed (MD: -0.72, 95% CI: -1.41, -0.03; heterogeneity p < 0.1) and agility (MD: -0.93, 95% CI: -1.79, -0.06; heterogeneity p < 0.1). However, no significant effects of HIIT were observed on HRmax, jump, and power, suggesting that its main advantages are focused on cardiopulmonary endurance and efficiency-related indicators rather than maximal strength or explosive power.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13102-025-01479-7.

Introduction

With the continuous advancement of competitive sports worldwide, enhancing athletes’ physical fitness and performance has become a central focus of sports science research. Aerobic capacity (such as maximal oxygen uptake, VO₂max) and sport performance(e.g., running, jump, agility, and power) are widely recognized as key indicators of athletic performance [1]. A review of the relevant literature reveals that current research hotspots are increasingly focused on training methods, and with the emergence of novel approaches, the adoption of scientifically grounded and efficient training strategies has become a clear trend. A review of the relevant literature reveals that current research hotspots are increasingly focused on training methods, with particular emphasis on approaches such as High-Intensity Interval Training (HIIT). Recent studies suggest that these new approaches offer more efficient training methods to enhance athletic capacity. In recent years, HIIT has gained popularity in training practices due to its notable advantages, including improving cardiorespiratory and metabolic capacity, enhancing endurance, and reducing training time compared to traditional continuous training methods [2, 3].

Numerous studies have demonstrated that HIIT is more effective than traditional moderate-intensity continuous training (MICT) in enhancing VO₂max and athletic performance [1–3]. In well-trained athletes, a key challenge in optimizing training lies in how to further improve their physical capacity, which is already close to the physiological limit, within a limited timeframe. Against this backdrop, HIIT has attracted widespread attention and has been shown by numerous studies to exert positive effects in endurance sports, team sports, and even combat sports [4–6].

Although existing studies have confirmed the potential benefits of HIIT for athletes, there remains some controversy regarding its specific effects on well-trained athletes (VO₂max > 60 mL/kg/min) [7]. On the one hand, there is significant heterogeneity in the HIIT protocols used across studies, including training modes, cycle durations, intensity settings, and recovery strategies, which makes direct comparison of results difficult [8]. On the other hand, sport-specific characteristics and training backgrounds of athletes significantly influence their physiological adaptations to HIIT [9]. For example, professional soccer players often experience smaller VO₂max improvements from HIIT (3–5%) compared to amateur athletes (8–12%), which may be due to their baseline levels being closer to physiological limits [10]. In light of these methodological differences and population heterogeneity, it is particularly necessary to quantify the overall effects of HIIT using systematic review and meta-analysis approaches. By integrating data from controlled trials, the standardized effect sizes (Cohen’s d) of HIIT on aerobic capacity (e.g., VO₂max, lactate threshold) and sport-specific performance (e.g., time-trial results, repeated sprint ability) can be identified, while subgroup analyses may reveal dose-response relationships between training variables and effect size. This will provide evidence-based support for coaches to design individualized HIIT programs, especially in terms of application strategies across different seasonal phases (preparatory vs. competition period).

Therefore, this study aims to use systematic review and meta-analysis methods to investigate the effects of HIIT on aerobic capacity and athletic performance in trained athletes, to provide scientific guidance for optimizing athletic training programs.

Materials and methods

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement have been followed in reporting the systematic review and meta-analyses [11], and will be registered in PROSPERO (CRD420251126491).

This systematic review and meta-analysis adhered to the guidelines established by the Cochrane Collaboration and the PRISMA framework [12]. The study design was based on the PICOS principle, namely population, intervention, comparator, outcomes, and study design.

In terms of population, the included participants were trained athletes of all ages, genders, and competitive levels, while individuals with acute or chronic diseases, injuries, or other medical restrictions were excluded.

The intervention was HIIT, including but not limited to running, cycling, or other common forms of intermittent aerobic training. The control group received traditional training (such as MICT) or no exercise intervention.

The primary outcomes included aerobic capacity (e.g., VO₂max, HRmax, VO₂peak) and exercise performance-related parameters (e.g., speed, power, Jump etc.). The mean and standard deviation (Mean ± SD) of each indicator were extracted as the basis for effect size calculation.

The included study types were randomized controlled trials (RCTs) and crossover design studies to ensure comparability and high quality. Literature screening, data extraction, and risk of bias assessment were independently performed by two researchers, with disagreements resolved by a third researcher.

Eligibility criteria

Detailed information regarding the inclusion and exclusion criteria for this systematic review and meta-analysis is presented in Table 1. All original studies published in peer-reviewed journals or in pre-publication status were considered eligible for inclusion.

Table 1.

Inclusion and exclusion criteria

Item	Inclusion criteria	Exclusion criteria
Population	Participants are trained athletes, including men and women at amateur, elite or professional levels, with no age limit.	Participants were non-athletes, such as non-professional athletes or people who did not train regularly.
Intervention	HIIT was used as the main intervention.	The intervention protocol is unclear or does not specify training parameters (e.g., intensity, frequency, duration).
Comparator	Including control groups such as MICT, low-intensity training or no training intervention or other interventions other than HIIT.	Lack of control group or unclear intervention.
Outcome	Studies should report at least one result related to aerobic capacity (e.g. VO₂max, maximum heart rate, etc.) or athletic performance (e.g. speed, strength, etc.), with mean and standard deviation provided.	Other indicators of aerobic exercise or exercise performance.
Study design	RCTs or other controlled trials were included with clear intervention procedures and control conditions.	A non-randomized controlled trial or an observational study without a control group.
Additional criteria	Peer reviewed, original, full-text studies written in English.	Data from the same study population were reprinted in different articles, and the one with the most complete data was retained. The full text of the article could not be obtained or the required statistics (such as mean and standard deviation) could not be obtained after contacting the author. Written in other language than those selected (English). Reviews, letters to editors, trial registrations, proposals for protocols, editorials, book chapters, conference abstracts.

Two researchers (K.Q. and L.T.) independently screened the titles and abstracts of the retrieved studies, followed by independent full-text reviews according to the inclusion criteria. In cases of disagreement during the screening process, the two researchers discussed and re-evaluated the studies; if consensus could not be reached, a third researcher (Q.X.) made the final decision. Literature management was conducted using EndNote X9.3.3 software, which was employed to remove duplicate records through both automated and manual functions.

Information sources

On July 1, 2025, we systematically searched the following three electronic databases: PubMed, EBSCO, and Web of Science, to identify literature related to the effects of HIIT on aerobic capacity and athletic performance in trained athletes. To further supplement potentially missed studies, we also manually traced the reference lists of the included articles. In addition, all included studies were rigorously checked to exclude articles with obvious errors or those that had been retracted.

Search strategy

The search strategy utilized a combination of Boolean operators “AND” and “OR” to maximize the retrieval of relevant studies. No filters or limitations were applied for publication date, or study design, ensuring a broad and comprehensive search. This approach aimed to capture as many relevant studies as possible without restricting the scope. The specific search methodology was as Table S1.

Extraction of data

Two review authors (K.Q. and L.T.) independently pilot-tested the data extraction using a Microsoft Excel template. Subsequently, one review author (K.Q.) extracted the data from all English reports, while the second review author (L.T.) independently cross-checked all extracted data. Disagreements regarding the included studies were resolved through discussion between the reviewers and a third author (Q.X.). Reasons for excluding full-text articles were documented, and all records were systematically logged using a Microsoft Excel spreadsheet (Microsoft Corporation, Redmond, WA, USA).

Data items

For aerobic capacity, we extracted the following outcomes: (i) VO₂max, (ii) HRmax, and (iii) VO₂peak. These indicators adequately reflect the aerobic capacity of athletes. To further reflect athletic performance, the extracted outcome measures included: (i) Jump, (ii) Speed, (iii) Agility, and (iv) Power.

In addition to the outcome measures, we systematically collected the following basic information from the included studies: (i) study authors and year of publication, (ii) country where the study was conducted, (iii) detailed characteristics of participants (athlete level, sample size, mean age, and sex), and (iv) specific assessment indicators and variables extracted from each outcome. Moreover, to better interpret the intervention effects, we extracted detailed information on HIIT interventions from each study, including training modality, training intensity, training frequency, duration per session, and total intervention period.

Risk of bias assessment

The quality of the included studies was assessed using the Cochrane risk of bias assessment tool, focusing on seven domains: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, completeness of outcome data, selective reporting, and other potential sources of bias. Each item was classified as low risk, high risk, or unclear risk according to the standards of the Cochrane Handbook for Systematic Reviews of Interventions [13]. All studies were independently evaluated by two researchers, and in case of disagreement, resolution was achieved through discussion or adjudication by a third researcher to ensure objectivity and consistency of the assessment results.

Statistical methods

Among the 18 studies included in this research, differences existed in measurement dimensions across studies; therefore, relevant indicators needed to be merged during the data extraction process. Since the data were presented in the form of continuous variables, conversions were performed according to established public methods. All data analyses were conducted using Review Manager version 5.3 or Stata version 17.0, and standardized mean differences (SMD) with 95% confidence intervals (CI) were calculated based on a random-effects model. When heterogeneity was present, further analyses were performed to explore its sources, and corresponding methods were applied to address heterogeneity in order to minimize its impact. In addition, forest plots were used to conduct pooled analyses of effect sizes for the included studies, and funnel plots were employed to examine publication bias.

Equity, diversity and inclusion statement

Regarding the inclusion criteria, no restrictions were imposed on participant characteristics (such as gender, cultural background), research settings (such as region, community), or publication language (English), in order to maximize the coverage of potential data. The review team consisted of researchers from diverse cultural backgrounds and career stages, with an emphasis on fairness, diversity, and inclusiveness. In addition, this study paid attention to the current research trends in meta-analyses, striving to encompass a wide range of sample characteristics to enhance the generalizability and practical value of the results.

Results

Study identification and selection

An initial search was conducted across three electronic databases, yielding a total of 4,289 records, of which, after removing 1,582 duplicates, 2,707 records were screened based on their titles and abstracts. From this screening process, 2,661 records were excluded, and the full texts of 46 manuscripts were analyzed in detail. 28 papers were excluded based on specific criteria: (i) 8 studies experimental group not only includes HIIT, but also combines HIIT with other trainings; (ii) 10 studies outcome metrics are not available; (iii) 4 studies no control group; (iv) 6 studies full text cannot be obtained. As a result, this review includes a total of 18 studies, as illustrated in Fig. 1.

Fig. 1.

Flow chart diagram identifying the screening process and the studies included in the present scoping review

Risk of bias assessment

The quality of the included studies was assessed using the Cochrane risk-of-bias tool, and the results are presented in Fig. 2. Overall, most studies were judged to be at low risk of bias in terms of random sequence generation (selection bias), blinding of outcome assessment (detection bias), completeness of outcome data (attrition bias), and selective reporting (reporting bias), indicating relatively high methodological quality in these domains. However, in allocation concealment (selection bias) and blinding of participants and personnel (performance bias), a certain proportion of studies showed unclear or high risk, suggesting deficiencies in group allocation concealment and blinding implementation that may affect internal validity of the results. In addition, some studies were rated as having unclear risk in the category of other bias. In summary, although the majority of studies demonstrated good quality in the main risk-of-bias domains, limitations remained in allocation concealment and blinding implementation, which warrants caution when interpreting the findings. Detailed risk-of-bias assessments for each study are provided in Fig. 3.

Fig. 2.

Summary of the Risk of bias assessment. Percentages calculated from the remaining 18 records

Fig. 3.

Risk of bias assessment results among the remaining 18 records

Study characteristics

Table 2 provides a summary of the essential information extracted from each study, covering the country of origin, participants’ competitive level, sample size, age, analyzed outcomes, and related variables. A total of 18 studies and 518 athletes were included in this study [14–31]. Among them, 15 studies included male athletes [15–19, 21, 22, 24–31], while 8 studies included female athletes [14, 15, 19, 20, 23–26]. In addition, 11 studies reported outcomes related to aerobic capacity [15–17, 20–22, 24–27, 29, 31], and 12 studies reported outcomes related to athletic performance [14–19, 23, 25, 27–30]. As shown in Fig. 4, the included athletes represented 10 different sports, including gymnastics [14], judo [15, 22], basketball [16, 23, 27], running [17], badminton [18], taekwondo [19, 25], field hockey [20], canoe polo [21], swimming [26], and soccer [28–31]. Additionally, one study did not report the specific type of athletes involved [24].

Table 2.

Characteristics of the studies included in the present review, outcomes examined, and variables assessed

Study	Country	Competitive level	Sample size(n)		Age (years)	Sex	Outcome	Variable extracted
			Experimental group	Control group
Ma et al (2024) [14]	Poland	Regional	24	25	16.2 ± 1.3	F	Athletic performance	Countermovement jump
Zhang et al (2024) [15]	China	Regional	16 (EG1)	16	20.5 ± 1.2	M, F	Aerobic capacity and performance	VO₂max, Modified T-test, 30-m sprint, Countermovement jump, Medicine ball put, 20-m multistage shuttle run
Zhang et al (2024) [15]	China	Regional	16 (EG2)	16	20.5 ± 1.2	M, F	Aerobic capacity and performance	VO₂max, Modified T-test, 30-m sprint, Countermovement jump, Medicine ball put, 20-m multistage shuttle run
Kumari et al (2023) [16]	India	Regional	20	20	21.5 ± 2.1	M	Aerobic capacity and performance	VO₂max, T Test, control dribble, 1 min shooting, wall passing, seated chest pass, vertical jump
Possamai et al (2024)[17]	Brazil	Regional	10	9	26.8 ± 6.5	M	Aerobic capacity and performance	VO₂max, Countermovement jump, Lower-body maximal power, Lower-body maximal strength, 1500 m and 5000 m time-trial
Ko et al (2021) [18]	Korea	Regional	16	16	15.5 ± 1.44	M	Athletic performance	Isokinetic Muscle Function
Alex et al (2021) [19]	Chile	National	8	8	19.5 ± 4.6	M, F	Athletic performance	Jump Ability, Linear Sprint in 5 m (5 M), 20-Metre Shuttle Run Test (20MSR)
Lindsey et al (2018) [20]	USA	Regional	6	8	19.29 ± 0.91	F	Aerobic capacity	VO₂peak
Mohsen et al (2017) [21]	Iran	National	14	7	24 ± 3	M	Aerobic capacity	VO₂peak
Franchini et al (2016) [22]	Brazil	Regional	9 (EG1)	8	24.4 ± 6.2	M	Aerobic capacity	HRmax, VO₂peak
Franchini et al (2016) [22]	Brazil	Regional	9 (EG2)	8	25 ± 6.9	M	Aerobic capacity	HRmax, VO₂peak
Franchini et al (2016) [22]	Brazil	Regional	9 (EG3)	8	24.9 ± 5.8	M	Aerobic capacity	HRmax, VO₂peak
Aschendorf et al (2018) [23]	Germany	Regional	11	13	15.1 ± 1.1	F	Athletic performance	Sprint test、Vertical jump test、Standing long jump
Menz et al (2015) [24]	Austria	Regional	19	16	25.5 ± 2.5	M, F	Aerobic capacity	HRmax, VO₂max
Lynne et al (2017) [25]	USA	Regional	16	17	20 ± 1	M, F	Aerobic capacity and performance	HRmax, VO₂max, Vertical jump, T-test, Sit-ups
Jin et al (2025) [26]	China	Regional	12	12	20.56 ± 1.5	M, F	Aerobic capacity	VO₂max
Arslan et al (2022) [27]	Turkey	Regional	16	16	14.5 ± 0.5	M	Aerobic capacity and performance	VO₂max, Countermovement jump, Sprinting and Repeated Sprint Tests
Filipe et al (2022) [28]	Portugal	Regional	20	20	16.4 ± 0.5	M	Athletic performance	Countermovement jump, Standing broad jump, Linear sprint test
Thomakos et al (2024) [29]	Greece	Regional	13	13	18.4 ± 0.9	M	Aerobic capacity and performance	VO₂max, Countermovement jump
Liu et al (2024) [30]	Thailand	Regional	15	15	17.6 ± 0.6	M	Athletic performance	Countermovement jump,30-meter linear sprint
Zhang et al (2025) [31]	China	Regional	10	10	25 ± 3.1	M	Aerobic capacity	VO₂max

EG1 Experimental group 1, EG2 Experimental group 2, EG3 Experimental group 3

Fig. 4.

Main outcomes analyzed and main formats of athlete category analyzed

Table 3 presents the descriptive statistics of the HIIT groups and control groups included in the review. Specifically, it summarizes the athlete category, the specific mode of HIIT, intervention intensity, session duration, total intervention duration, training frequency, work-to-rest ratio, as well as the intervention approach of the control group. Participants in the HIIT groups replaced their usual training with HIIT training during the intervention period. It should be noted that participants in the HIIT groups did not engage in any additional training during the study period, ensuring the independence and comparability of the intervention effects.

Table 3.

Group intervention characteristics

Meta-analysis

Aerobic capacity

Figure 5 analyzed the effects of HIIT on athletes’ aerobic capacity. Regarding VO₂max, ten studies provided relevant data comparing HIIT with regular training or other non-HIIT interventions, with a pooled sample size of 293 participants. The meta-analysis revealed that the HIIT group showed significantly greater improvements in VO₂max compared with the control group (SMD = 1.11, 95% CI: 0.48 to 1.74, p < 0.001). However, this result exhibited large heterogeneity (I² = 83.1%, p < 0.001). The random effects model yielded a τ² of −0.119, indicating that despite the significant overall effect, there was minimal variation in actual effects across studies, likely reflecting the consistency of the research, funnel plots appeared symmetrical and that sensitivity analyses confirmed the robustness of the results.

Fig. 5.

Forest plot of post-intervention aerobic capacity value comparison between experimental and control groups. The blue diamond reflects the overall comparisons. The funnel chart is on the right

For HRmax, five studies reported comparative results, with a pooled sample size of 119 participants. The analysis indicated no significant difference between the HIIT group and the control group in HRmax improvement (SMD = 0.81, 95% CI: −0.08 to 1.70, p = 0.075), with moderate heterogeneity (I² = 52.8%, p = 0.075). The random effect model showed a τ² of −0.123, indicating high consistency in the HRmax between studies with minimal variability, funnel plots appeared symmetrical and that sensitivity analyses confirmed the robustness of the results.

Regarding VO₂peak, five studies provided relevant data with a pooled sample size of 86 participants. The analysis showed that the HIIT group had significantly greater improvements in VO₂peak compared with the control group (SMD = 0.78, 95% CI: 0.58 to 0.97, p < 0.001), with extremely low heterogeneity among studies (I² = 0%, p = 0.523), suggesting a high degree of consistency. Publication bias analysis revealed no obvious concerns. The random effect model showed a τ² of −0.165, which further supported the consistency of the results and there was no significant difference between the studies.

Athletic performance

Figure 6 analyzed the effects of HIIT on athletes’ athletic performance. For Jump, twelve studies with a total sample size of 373 participants were included. The results showed no statistically significant difference between the HIIT group and the control group in improving Jump performance (SMD = −0.23, 95% CI: −0.55 to 0.09, p = 0.16), with significant heterogeneity among studies (I² = 83.1%, p < 0.001). The random effect model showed a τ² of 0.011, indicating a relatively low level of heterogeneity despite the significant I²value. This suggests that although there is variability among studies, the effects are largely consistent. No obvious issues of publication bias were detected, and the funnel plot showed symmetry.

Fig. 6.

Forest plot of post-intervention aerobic performance value comparison between experimental and control groups. The blue diamond reflects the overall comparisons. The funnel chart is on the right

For Speed, eight studies provided relevant data, with a combined sample size of 225 participants. The results demonstrated that the HIIT group was significantly superior to the control group in improving Speed (SMD = −1.22, 95% CI: −2.00 to −0.43, p = 0.002), with low heterogeneity (I² = 0%, p = 0.523). The random effect model showed a τ² of 0.181, indicating that the studies had low heterogeneity and consistent results. This result suggests a large effect size (SMD < −1), reflecting a substantial improvement in Speed. No obvious issues of publication bias were detected.

For Agility, six studies were included, with a total sample size of 185 participants. The results indicated that the HIIT group was significantly better than the control group in enhancing Agility (SMD = −2.17, 95% CI: −3.14 to −1.20, p < 0.001), with low heterogeneity among studies (I² = 0%, p = 0.523). The random effect model showed a τ² of 0.199, again suggesting low heterogeneity. The large effect size (SMD > −2) indicates that HIIT significantly enhances Agility. No obvious issues of publication bias were detected.

For Power, six studies provided relevant data, with a combined sample size of 179 participants. The results showed no statistically significant difference between the HIIT group and the control group in improving Power performance (SMD = −0.12, 95% CI: −0.72 to 0.47, p = 0.69), with low heterogeneity among studies (I² = 0%, p = 0.523). The random effect model showed a τ² of −0.075, indicating no significant heterogeneity between studies. This result suggests that HIIT does not significantly improve Power in this group of athletes. No obvious issues of publication bias were detected.

Publication bias and sensitivity analysis

In Figs. 5 and 6, publication bias analysis revealed no significant risk, suggesting that the results are relatively robust. In Figure S1, we conducted a sensitivity analysis, and the exclusion of individual studies had little impact on the overall effect estimates, indicating a high level of consistency and reliability in this meta-analysis. Subsequently, we performed Egger’s test, and the results (Table S2) showed that HRmax (p = 0.300), VO₂peak (p = 0.168), Jump (p = 0.608), Agility (p = 0.076), and Power (p = 0.871) did not indicate significant publication bias, further supporting the robustness of the findings. Although the results for VO₂max (p = 0.007) and Speed (p = 0.017) were below 0.05, suggesting potential publication bias, the influence on the overall conclusions was limited when combined with funnel plots and sensitivity analyses. Therefore, the overall analysis results remain reliable and robust.

Discussion

This systematic review and meta-analysis aimed to investigate the effects of HIIT on aerobic capacity and performance in trained athletes. The results demonstrated that HIIT exerted positive effects on multiple key indicators, with significant improvements in VO₂max and VO₂peak enhancing athletes’ aerobic capacity, while speed and agility showed notable gains in aerobic performance. These findings suggest that HIIT not only effectively strengthens cardiopulmonary function but also facilitates improvements in exercise capacities closely related to athletic performance.

The present meta-analysis revealed that HIIT significantly improved VO₂max and VO₂peak in terms of aerobic capacity. VO₂max, as a core indicator of cardiorespiratory fitness, is an essential parameter for evaluating maximal oxygen uptake and overall cardiopulmonary function [32]. Multiple trials included in this study consistently demonstrated improvements in this measure. In addition, the significant enhancement of VO₂peak further emphasizes the advantage of HIIT in improving oxygen utilization under maximal exercise loads, indicating that HIIT not only elevates overall aerobic fitness but also optimizes functional responses during maximal exertion [33]. These findings align with both theoretical frameworks and empirical evidence from previous studies, thereby confirming the unique value of HIIT in cardiorespiratory endurance training [34]. The potential mechanisms may lie in the alternating high-intensity loads and recovery phases of HIIT, which effectively induce dual adaptations in both the cardiovascular and muscular systems [35, 36], including increased stroke volume, enhanced capillary density, and improved mitochondrial function [37]. These adaptive responses collectively contribute to more efficient oxygen delivery and utilization. Moreover, since HIIT can trigger strong metabolic and circulatory responses within a relatively short time, compared with traditional training, it may serve as a more efficient training approach for athletes with limited training periods [7]. It is noteworthy that HIIT did not show a significant effect on HRmax. As an indicator reflecting the maximal working capacity of the heart, HRmax is largely constrained by genetic factors and age [38], making it difficult to achieve substantial changes solely through training.

In terms of athletic performance, the meta-analysis results indicated that HIIT produced significant improvements in both speed and agility. The enhancement of speed may be closely related to the unique high-intensity, intermittent stimuli of HIIT. Repeated bouts of high-intensity sprints and recovery phases can induce neuromuscular adaptations, including improved motor unit recruitment efficiency [39], enhanced conduction velocity of muscle fibers, and optimization of the phosphagen system and glycolytic capacity [40]. These adaptations enable athletes to achieve faster sprinting ability and greater explosive speed, thereby demonstrating superior movement efficiency in competition and sport-specific training [41]. At the same time, frequent high-intensity stimuli may also improve lactate clearance rate and rapid recovery capacity [42], thus allowing athletes to perform better when repeatedly executing high-intensity actions in a short time. The significant improvement in agility further underscores the positive role of HIIT in sport-specific performance. High-intensity movements, abrupt stops, and directional changes frequently occurring in HIIT training contribute to enhanced neuromuscular control and multi-joint coordination [43]. This finding suggests that HIIT not only strengthens athletes’ aerobic and anaerobic metabolic capacities but also improves, to some extent, their flexibility and responsiveness closely related to sport-specific skills, which is particularly important for competitive sports requiring rapid starts, abrupt stops, and directional changes. The core of HIIT lies in repeated high-intensity aerobic/anaerobic metabolic loads; although it improves energy metabolism efficiency and neuromuscular endurance, it does not significantly enhance maximal strength or explosive power output [41]. The HIIT protocols included in the studies mostly focused on optimizing aerobic metabolism and cardiorespiratory load in their intervention design, which may explain why improvements in jump and power were not significant.

From a practical perspective, the findings of this study highlight the importance of HIIT as an effective training strategy for enhancing aerobic capacity and performance in trained athletes. Improvements in VO₂max and VO₂peak provide strong support for endurance-related adaptations, while enhancements in speed and agility suggest that HIIT can be translated into advantages in sport-specific performance. For coaches, incorporating HIIT into training programs may help simultaneously optimize athletes’ physiological fitness and sport-specific performance.

This study still has certain limitations. First, although 18 studies were included, the overall number remains limited, and some studies had relatively small sample sizes, which may weaken statistical power and restrict the generalizability of the findings. Furthermore, some of the included studies did not specify the athletes’ sport disciplines. Despite this, these studies were retained in the analysis due to their methodological rigor and statistical quality, which provided valuable data for the overall findings. Second, differences in training programs, such as intensity settings, work-to-rest ratios, and duration, as well as variations in participant profiles, limit direct comparisons among the studies. These factors contribute to the high heterogeneity observed in the meta-analysis. However, despite these challenges, the review achieved internally consistent results with low bias, which represents a significant accomplishment. Third, in some studies, the baseline performance of the control group was superior to that of the experimental group, which may have influenced the comparison of intervention effects and potentially underestimated the true effect of HIIT. Finally, shortcomings in study design regarding randomization, allocation concealment, and blinding in some trials may have increased the risk of bias. Therefore, the findings of this study should be interpreted and applied within a reasonable scope, acknowledging that some of the included trials had small sample sizes.

In future research, firstly, more systematic comparisons and optimizations should be conducted regarding the core elements of HIIT (such as intensity distribution, interval duration, and intervention period) to determine the optimal training prescription. In addition, future studies may consider integrating advanced monitoring technologies, such as wearable devices, to achieve precise tracking of athletes’ physiological and performance changes during HIIT. Finally, it is recommended that future research strictly adhere to randomized controlled trial protocols in study design to improve methodological quality, thereby further verifying the true effects of HIIT on aerobic capacity and performance in athletes, and providing coaches and practitioners with more evidence-based training recommendations.

Conclusions

This systematic review and meta-analysis demonstrated that HIIT was associated with statistically significant improvements in VO₂max, VO₂peak, speed, and agility, while no significant changes were observed in HRmax, jump, or power. On the other hand, it is important to emphasize that the positive outcomes observed are mainly due to the optimization of the cardiorespiratory component, which enhances oxygen delivery, utilization, and efficiency, rather than to gains in neuromuscular strength or explosive power. It should be noted that the effects of HIIT may be influenced by factors such as athletes’ baseline level, training program design, and the type of intervention used in the control group. For coaches and practitioners, incorporating HIIT into athletes’ conditioning programs can efficiently optimize aerobic fitness and sport-specific performance within a limited time frame. Overall, HIIT is a scientifically sound and practical training model that provides a powerful means to enhance aerobic adaptations in competitive athletes.

Authors’ contributions

Kai Qi, Liang Tan, Qi Xu, Yifan Xu conceived of the study, Kai Qi, Liang Tan, Qi Xu conducted research on materials, and Kai Qi wrote the manuscript., and they along with Aiguo Chen and Adam Kawczyński wrote, revised and approved the article.

Funding

The author(s) reported there is no funding associated with thework featured in this article.

Data availability

The anonymized dataset used for analysis will be made available from the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.