Comparative of Different Interval Training Methods on Athletes' Oxygen Uptake: A Systematic Review and Meta-analysis

doi:10.21203/rs.3.rs-4954697/v1

Download PDF

Research Article

Comparative of Different Interval Training Methods on Athletes' Oxygen Uptake: A Systematic Review and Meta-analysis

https://doi.org/10.21203/rs.3.rs-4954697/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background

This systematic review aimed to 1) investigate the comparative efficacy of high-intensity interval training (HIIT), sprint interval training (SIT), and repetitive sprint training (RST) on athletes' oxygen uptake, 2) examine the effects of program protocols for each training method on enhancing oxygen uptake, 3) provide evidence-based insights to inform future research.

Methods

Data sources, Web of Science, PubMed, Scopus, PsycINFO, SPORTDiscus, CINAHL, Medline, and Embase. Eligibility criteria, between-groups controlled experimental studies (2000–present) that investigated the effect of improving athletes' oxygen uptake levels by RST, HIIT, and SIT. Study selection and analysis, sensitivity, and indirectness in the network were assessed by two independent investigators. A frequentist network meta-analysis was performed to examine pre-post intervention differences in oxygen uptake between groups.

Results

32 studies (n=768) with 42 comparisons were included in the network. Compared with MICT, RST significantly increased the oxygen uptake (SMD: 0.87, 95% CI 0.44 to 1.33), whereas HIIT (SMD: 0.81, 95% CI 0.50 to 1.11), and SIT (SMD: 0.43, 95% CI 0.16 to 0.70) significantly elevated the oxygen uptake in athletes. Compared with CON, HIIT (SMD: 0.87, 95% CI 0.39 to 1.90), and RST (SMD: 0.71, 95% CI 0.40 to 1.01). Ranking on the basis of the P-score was consistent with that derived from the SUCRA: RST > HIIT > SIT > CON > MICT. In SIT, the total number of repetitions per session (β: -0.01, 95% CI -0.03 to -0.01), and interval duration (β: -0.01, 95% CI -0.0016 to -0.0002) showed a significant dose‒response relationship.

Conclusions

A 6-week running-based HIIT program with work‒recovery intervals of 2‒3 days per week improved athletes' oxygen uptake levels. With work‒recovery intervals of 30 seconds and 60‒90 seconds, respectively, 3 days/week, SIT with less volume may prove more efficacious.

Systematic review registration

PROSPERO CRD42023435021.

High-intensity interval training

Sprint interval training

Repeat sprint training

Athlete

Aerobic capacity

Oxygen uptake

Meta-analysis

Aerobic capacity is critical to the competitive performance of athletes, and common training methods used to improve aerobic capacity include interval training (IT) and continuous training (CT). In recent years, there has been a gradual shift in the field of aerobic training, practice, and research from CT to high-intensity interval training (HIIT) [1]. Moderate-intensity continuous training (MICT) has long been considered an effective strategy for improving aerobic endurance. Extensive research has demonstrated that this approach can substantially increase maximal oxygen uptake, accelerate fat metabolism, and enhance cardiovascular capacity when practiced over an extended period [2–4]. In contrast, HIIT is distinguished by its high efficiency and time efficiency. This type of training is postulated to increase anaerobic metabolism and aerobic capacity in athletes more expeditiously, particularly in certain circumstances. However, the enhancement effect may vary significantly depending on individual differences and the specific design of the training regimen [5, 6].

The use of IT methods in competitive training dates back to 1912, when the10,000 meter champion Hannes Kolehmainen used intervals to improve his long-distance running performance [7]. In 1952, Czech middle-distance runner Ernil Zatopek first used HIIT (≥ 85% VO_2max) to train and win three gold medals at the Helsinki Olympics. Moreover, Woldemargerschler and Hans Reinddel, coaches and physiologists at the University of Freiburg in Germany, first described HIIT in a scientific journal [8]. Since then, scientists have studied HIIT and its physiological mechanisms at different intensities, interval durations, and exercise modalities [9]. In the 1960s, experimental studies based on power monitoring revealed that HIIT enabled athletes to maintain higher physiological responses, such as increased VO_2max, HR, and blood lactate concentrations, for longer periods, and that increasing the duration of the intermittent work period led to an increase in physiological responses [10, 11].

The primary interval-type training methods can be categorized into three key criteria: loading intensity, single interval work duration time, and recovery duration time. High-intensity interval training (HIIT), sprint interval training (SIT), and repeat sprint training (RST) are all forms of interval training characterized by brief periods of high-intensity exercise interspersed with rest [12–14]. HIIT is a type of training performed at intensities below or equal to the 100% VO_2max intensity, aiming to complete as many intervals as possible at higher intensities rather than sprinting continuously at the same intensity throughout a single session [15]. In contrast, SIT involves exercising at intensities often exceeding the athlete's 100% VO_2max intensity, necessitating maximum effort and workload rate during each interval. This approach can elicit a more significant stimulus response in the athlete's organism, as they are required to push themselves to their limits during each training cycle. Typical SIT patterns involve 30 seconds of exercise on a Wingate power bike, followed by 4–4.5 minutes of passive recovery or low-intensity active recovery. RST is also a supramaximal oxygen uptake intensity training modality characterized by short sprints (≤ 10 seconds) and brief recovery times (≤ 60 seconds) per interval. Primarily a short sprint training modality, RST typically involves short sprints of 20–60 meters, which may lead to a more pronounced decrease in sprinting capacity during repeated sprints [16, 17].

In recent years, a substantial number of review studies on HIIT, SIT, and RST have been conducted [14, 18, 19]. Systematic reviews have primarily examined the impact of diverse training regimens on VO_2max [20]. These investigations have not identified notable discrepancies between HIIT and SIT in enhancing oxygen uptake in the general population [21]. Moreover, meta-analytic studies on groups of athletes are relatively lacking [22–24], and few meta-analytic studies have compared multiple training methods simultaneously. This study primarily uses methods of systematic review and meta-analysis to investigate the degree of benefit of different interval training methods for improving oxygen uptake in athletes. Aiming: 1) To explore the ranking of the training effects of the three training methods via reticulated meta-analysis. 2) To study the effects of the program protocols of the three training methods on the improvement of the level of oxygen uptake. And provide scientific insight and precise guidance for future research.

This study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines [25], and it has been registered in the PROSPERO database (CRD42023435021).

Search strategy

A literature search screening was conducted following the PRISMA guidelines and standards, utilizing the Web of Science, PubMed, Scopus, Psyinfo, SportDiscus, CINAHL, Medline, and EMBASE databases (Figure S1). The search spanned from database establishment to May 19, 2024. The English-language literature was searched for all fields containing HIIT, SIT, and RST in the subject, title, and abstract. Duplicates were eliminated via Endnote20 software.

Study selection

The eligibility of the studies for inclusion was independently assessed by two authors, and the final decision regarding inclusion was made after a summary of the comments was prepared and discussed with another author. The reference lists of all included studies were examined during the secondary search.

The literature screening was based on the PICOS criteria for evidence-based medicine, and the specific literature inclusion criteria were as follows: (1) all participants were athletes; (2) the type of literature was experimental articles using a between-groups design, with the control group consisting of either MICT or conventional training (CON). (3) The experimental outcome metrics included those reflecting the level of oxygen uptake, such as VO_2max or VO_2peak. (4) The training intervention lasted for a minimum of two weeks. (5) The intervention was conducted in one of three modes: HIIT, SIT, or RST. (Table S1)

Data extraction

The initial extraction of relevant data was conducted by two authors, with a third party serving as an additional check. The primary data extracted included the authors' names, year of publication, study design, participant characteristics, training protocols for the experimental groups, measurements of oxygen uptake levels, and means and standard deviations (SD) of the baseline and endpoint oxygen uptake levels.

Methodological quality of the included studies

Two authors used the 11-item Physiotherapy Evidence Database (PEDro scale) for independent assessment. The quality of the literature was categorized as excellent (9–10 points), good (6–8 points), fair (4–5 points), or poor (≤ 3 points) on the basis of the composite score of the scale [26].

Statistical analysis

For descriptive statistics, we used the standardized mean (with the standard deviation, SD) as a measure of central tendency. A random-effects model was employed in frequentist network meta-analysis to obtain direct and indirect treatment effect estimates. This approach was preferred over the fixed-effects model because of its ability to account for the variability of true effect sizes and its application beyond included studies [27].

Frequentist network meta-analysis (NMA) was performed using the netmeta package in R [28]. We used Cochran's Q statistic (Q-test) to determine pairwise between-study heterogeneity. Additionally, τ² was calculated to quantify the variance between studies. I² was employed to evaluate the percentage of variance attributed to between-study heterogeneity. It was assumed that heterogeneity was common across the entire network. The statistical details of the frequentist NMA are described in detail on the MetaInsight online platform [27], which is the software used for this study's analysis. Sensitivity analyses were conducted to examine whether the efficacy results differed by article quality and subject characteristics.

Transitivity within the network was assessed through a visual inspection of study characteristics, including mean age, intervention duration, athlete level, and outcome measures. Primary meta-regression analyses were performed within comparisons to evaluate the potential influence of these characteristics on effect sizes. To rank relative training effects, we employed the P-score proposed by Rücker and Schwarzer [29].

Forest plots were used to visualize the NMA results, with relative training effects ranked in descending order. An assessment of inconsistency for all studies was made to compare observed standardized mean differences (direct evidence from pairwise comparisons) with NMA estimations (indirect evidence). A p value < 0.05 was selected as the level of significance for inconsistency. All the mean differences were accompanied by a 95% confidence interval (CI). NMA statistical analysis was performed via the MetaInsight online platform and the netmeta package in R [27, 28].

Pairwise meta-analyses were conducted using the metafor package in R [30]. Subgroup analyses were performed for exercise mode, intervention period, age, and the ratio of single training work‒recovery time. In subgroup analyses, categorical moderators and predictors in meta-regression analyses were employed as predictors because of their potential influence on effect sizes.

To explore the dose‒response relationship of different training methods on oxygen uptake levels, a series of random effects meta-regression analyses were conducted, incorporating predictors related to training programs. Constrained maximum likelihood was used in modeling these analyses. To evaluate the models for the dose‒response relationship, linear, quadratic, and cubic polynomial models were fitted. The model with the smallest value of bias was selected by correcting for the results of the Akaike information criterion (AICc) [31]. Potential for publication bias was evaluated through the inspection of funnel plots and the application of Egger's bias test [32].

Upon searching, 16,591 potentially relevant studies were found. After excluding studies based on titles and abstracts, a total of 329 full-text records were selected. Following review, a total of 32 studies were included in the main analysis (Figure S1).

Study characteristics

In 42 pairwise comparisons, 768 participants were assigned to one of three training groups or two control groups (Figure 1). Pairwise comparisons included HIIT versus CON (n = 8) [18, 33–39], SIT versus CON (n = 7) [39–44], RST versus CON (n = 7) [18, 45–50], HIIT versus MICT (n = 5) [51–55], SIT versus MICT (n = 7) [43, 52, 56–59], HIIT versus SIT (n = 5) [39, 52, 60–62], RST versus HIIT (n = 2) [18, 63], and CON versus MICT (n = 1) [43].

The methodological quality of the included studies was generally fair-to-good, as indicated by PEDro scale scores ranging from 4 to 7. A summary of the methodological quality assessment for each included study and individual rating scales is provided in Table S4.

***Figure 1 near here***

Network meta-analysis

The network meta-analysis included 8 pairwise comparisons and 26 indirect comparisons. Compared with MICT, RST significantly increased athletes' oxygen uptake [SMD = 0.87, 95% CI (0.44, 1.33), p < 0.05]. Similarly, HIIT [SMD = 0.81, 95% CI (0.50, 1.11), p < 0.05] and SIT [SMD = 0.43, 95% CI (0.16, 0.70), p < 0.05] significantly elevated oxygen uptake in athletes. Compared with CON, both HIIT and RST significantly increase oxygen uptake in athletes, with HIIT demonstrating a greater effect size than RST [SMD = 0.87, 95% CI (0.39, 1.90), p < 0.05] and RST [SMD = 0.71, 95% CI (0.40, 1.01), p < 0.05]. Ranking on the basis of the P-score was consistent with that derived from the SUCRA analysis: RST > HIIT > SIT > CON > MICT (Table S5).

The network meta-analysis revealed low heterogeneity among studies (τ² = 0; I² = 0%). No evidence of heterogeneity was observed in the remaining comparisons. Furthermore, no inconsistency was detected among the tables of interest (Table S6), and the Q-test did not reveal any significant inconsistency between comparisons (Q = 4.17; df = 11; p = 0.96). Sensitivity analysis confirmed the robustness of the findings (Table S7).

Pairwise meta-analysis

Pairwise meta-analysis corroborated findings from the network meta-analysis (Table 1). Compared with CON, the RST had a statistically significant combined effect on oxygen uptake [SMD = 0.76, 95% CI: 0.43, 1.09), p < 0.01]. Similarly, HIIT had a statistically significant combined effect [SMD = 0.52, 95% CI: 0.19, 0.84), p < 0.01]. In contrast, SIT was not significantly different from CON [SMD = 0.33, 95% CI: -0.01, 0.68), p > 0.05]. Compared with MICT, HIIT demonstrated a statistically significant combined effect on oxygen uptake [SMD = 0.87, 95% CI [0.45, 1.29], p < 0.01], indicating moderate heterogeneity (I² = 45%). The combined effect of SIT was notable [SMD = 0.39, 95% CI: 0.08, 0.71), p < 0.05].

A meta-analysis of 5 studies revealed no statistically significant difference between SIT and HIIT [SMD = -0.34, 95% CI: -0.09, 0.76], p > 0.05]. However, a notable degree of heterogeneity among studies was observed (I² = 68.50%, p < 0.01), which was attributed to Pierros’s study [61] (Table S14).

Table 1. Results on the comparative effectiveness of the interventions from the network and pairwise meta-analyses

RST

-0.09 [-0.65; 0.47]

(N=2; I²=0%)

NA^†

0.76 [0.43; 1.09]

(N=7; I²=0%)

NA^†

0.06 [-0.30; 0.41]

(N=34; I²=0%)

HIIT

0.34 [-0.09; 0.76]

(N=5; I²=60.4%)

0.54 [0.21; 0.86]

(N=8; I²=0%)

0.87 [0.45; 1.29]

(N=5; I²=45%)

0.44 [0.05; 0.83]

(N=34; I²=0%)

0.38 [0.10; 0.65]

(N=34; I²=0%)

SIT

0.34 [0.00; 0.68]

(N=7; I²=0%)

0.39 [0.08; 0.71]

(N=;7 I²=0%)

0.71 [0.40; 1.01]

(N=34; I²=0%)

0.65 [0.39; 0.90]

(N=34; I²=0%)

0.27 [-0.01; 0.55]

(N=34; I²=0%)

CON

0.04 [-0.94; 1.02]

(N=1; I²=NA*)

0.87 [0.44; 1.30]

(N=34; I²=0%)

0.81 [0.50; 1.11]

(N=34; I²=0%)

0.43 [0.16; 0.70]

(N=34; I²=0%)

0.16 [-0.17; 0.50]

(N=34; I²=0%)

MICT

Note: Results are presented in Lower triangle for network meta-analysis and Upper triangle for pairwise meta-analyses. Estimates are displayed as columns vs rows for network meta-analyses and rows vs columns for pairwise meta-analyses, both of which are expressed as standardized mean differences (SMD). A positive SMD indicates the superiority of the first treatment over the comparison treatment. *, No evidence on I² is available, as there was only one study for that comparison. †, No studies compared combination treatment versus no treatment. N, number of studies in the comparison. NA, not available.

Subgroup analysis

Age

A subgroup analysis of HIIT versus CON revealed that 4 studies [18, 34, 36, 39] demonstrated a significant advantage in improving oxygen uptake in athletes aged 19-23 years [SMD = 0.73, 95% CI (0.20, 1.27), p < 0.01, I² = 0%]. For HIIT versus MICT, 2 studies [53, 54] reported that athletes over the age of 23 years presented superior outcomes [SMD=1.1, 95% CI (0.21, 1.98), p < 0.05, I²=55%] (Tables S10, S11).

Training mode

In an analysis of HIIT versus CON, 4 studies [18, 34, 35, 38] demonstrated that running form-based HIIT yielded a notable advantage [SMD = 0.72, 95% CI (0.20, 1.25), p < 0.01, I² = 34.5%]. For HIIT versus MICT, 2 studies [53, 54] reported that running form-based HIIT yielded a notable advantage [SMD = 1.1, 95% CI (0.21, 1.98), p < 0.05, I² = 55%] (Tables S10, S11).

Training regimen

A meta-analysis comparing HIIT to CON revealed that 5 studies [18, 35, 36, 38, 39] reported a significant increase in oxygen uptake in athletes following 3‒6 weeks of the HIIT training regimen [SMD = 0.54, 95% CI (0.11, 0.98), p < 0.05, I² = 10.4%]. For HIIT versus MICT, 2 studies [51, 53] reported that HIIT exceeding 6 weeks resulted in superior outcomes [SMD = 1.36, 95% CI (0.77, 1.94), p < 0.01, I² = 0%], the regimen of the intervention was identified as a significant moderator (Tables S10, S11).

Frequency

The analysis of HIIT versus CON revealed that 3 studies [18, 35, 38] demonstrated a significant increase in the level of oxygen uptake in athletes following 3 days/week of HIIT training [SMD = 0.62, 95% CI (0.02, 1.23), p < 0.05, I² = 39.9%]. For HIIT versus MICT, a single study [53] indicated that HIIT training 2 days/week was more efficacious [SMD = 1.49, 95% CI [0.79, 2.19], p < 0.01, I² = 0%). Four studies [43, 52, 57, 58] reported a significant advantage of SIT training 3 days/week over MICT [SMD = 0.40, 95% CI (0.02, 0.78), p < 0.05, I² = 0%] (Tables S10, S11, S13).

Work‒recovery ratio

In the comparative analysis of HIIT and CON, 4 studies [18, 34, 35, 37] demonstrated that HIIT training with a work‒recovery ratio of < 1 resulted in a significant advantage [SMD = 0.82, 95% CI (0.36, 1.24), p < 0.01, I² = 0%]. Three studies [51, 53, 54] reported that compared with MICT, HIIT with a work‒recovery ratio of 1 had a superior effect [SMD = 1.12, 95% CI (0.56, 1.69), p < 0.01, I² = 19.1%]. In SIT versus MICT, 6 studies [52, 56–59] reported that a SIT work‒recovery ratio of 1:2 exhibited a more pronounced advantage [SMD = 0.37, 95% CI (0.04, 0.70), p < 0.05, I² = 0%] (Tables S10, S11, S13).

Team sport

A comparative study of HIIT and CON suggested that 3 studies [33–35] showed that, compared with nonteam sports programs, HIIT training yielded superior outcomes in team programs [SMD = 0.79, 95% CI (0.13, 1.46), p < 0.05, I² = 37.5%]. For HIIT versus MICT, 4 studies [51–53, 55] reported that nonteam programs yielded superior outcomes [SMD = 0.78, 95% CI (0.07, 1.49), p < 0.05, I² = 55.4%] (Tables S10, S11).

Effect of dose‒response

In the meta-regression analysis, the AICc test demonstrated that the linear model achieved an optimal balance between goodness of fit and model simplicity except for the interval work duration in SIT (Table S15). The total number of repetitions trained per day on the SIT was identified as the sole variable significantly associated with changes in athletes' oxygen uptake [β = -0.01, 95% CI (-0.03, -0.01), p < 0.05]. Considering the work duration in SIT, the AICc model indicated that the quadratic model exhibited the optimal fit, with work duration² evincing a negative correlation with athlete oxygen uptake levels [β = -0.01, 95% CI (-0.0016, -0.0002), p < 0.05]. The findings indicate that an interval duration of 30 seconds exhibited a notable advantage (Figure S6).

The primary finding of this systematic review and meta-analysis was that RST, HIIT, and SIT were efficacious training techniques for enhancing the oxygen uptake capacity of athletes. However, no single training modality was identified as being inherently superior to the others. Instead, a ranking of RST > HIIT > SIT was observed based on the P-score.

Despite a decline in the maximum heart rate (HR_max) and oxygen uptake kinetics with age [64, 65], older athletes may still benefit from high-intensity training. Research has shown that older athletes can achieve greater aerobic gains when engaging in larger training intensities [66]. However, our study did not find a significant interaction effect between athlete age and the three training methods. Given that training experience and age are positively correlated, long-term training adaptations in the central cardiovascular system may limit further improvements in central capacity. For elite athletes of advanced age, consideration should be given to peripheral-stimulating training methods, such as the SIT and RST, to reasonably improve aerobic capacity. Among the three training methods evaluated, HIIT based on running demonstrated the greatest improvement effect, whereas SIT based on Wingate cycling showed a significant improvement effect. Notably, using running as an intervention mode offers a cost-effective and easily implementable approach for team-based projects. Our analysis did not reveal that team sport project was a significant regulator of aerobic capacity improvement.

Subgroup analysis revealed that extending the RST regimen beyond 6 weeks did not yield further improvements compared with implementing RST over a period of 3‒6 weeks. This finding is consistent with the hypothesis proposed by Thurlow et al. [67], which suggests that the majority of adaptation to the RST occurs within the initial 6-week phase and then plateaus [68]. Similarly, we did not identify a significant interaction effect between the SIT training regimen and improvements in athletes' oxygen uptake levels. Previous studies have indicated that the optimal improvement effect of SIT on maximal oxygen uptake in athletes is achieved at 3 weeks [69]. These findings align with our findings that 3‒6 weeks of SIT produced a better effect, although this result was not significant. When combined with the dose‒response results, it was concluded that no more than 6 weeks of SIT may be necessary for optimal improvement in oxygen uptake. Notably, our findings suggest that performing HIIT for a longer regimen is associated with greater improvements in oxygen uptake levels in athletes (Tables S10, S11, and Figure S5). Subgroup analysis indicated that 3‒6 weeks of HIIT produced the most significant improvements, although more than 6 weeks of HIIT yielded superior outcomes compared with MICT. However, only 2 studies provided support for this result; thus, these findings should be interpreted with caution. This finding is consistent with those of Wang et al., who reported that 6‒9 weeks of HIIT was the most effective regimen for improving VO_2max in athletes [70]. Overall, the improvement effect of HIIT appears to increase with increasing number of intervention regimens. Given that a considerable amount of time is dedicated to training by athletes, considering the advantages of diverse training methods is essential. The RST and SIT are superior choices for enhancing oxygen uptake in the short term, whereas HIIT is more suitable for incorporation into long-term athletic training regimens.

An optimized training frequency will yield superior training results for coaches and athletes alike. The results demonstrated that scheduling the RST for both 2 and 3 days/week yielded significant outcomes. However, this result is contrary to that of Thurlow et al. [67]. Given that Thurlow et al.'s systematic review evaluated a combination of sprint times, intermittent running performance, CMJ height, and COD ability, it was concluded that an RST of 2 days per week had the most beneficial effect. Our study focused exclusively on improvements in oxygen uptake levels, which may explain the discrepancy. The application of HIIT 2 to 3 days per week yielded superior outcomes. When these findings were integrated with the outcomes of the dose‒response analysis, it became evident that the impact of HIIT tended to decrease with the number of days dedicated to training per week (Tables S10, S11, and Figure S5). However, this trend was not statistically significant. In the case of SIT training, the optimal frequency was determined to be 3 days per week.

The training protocols of RST, HIIT, and SIT involve repeated bouts of intense training interspersed with periods of recovery. The total workload of interval training is determined by the number of intervals, work intensity, duration of interval work, and recovery time [71]. The work‒recovery ratio (WRR) refers to the ratio of work time to recovery time for a single training. The WRR has a significant effect on the development of aerobic and anaerobic capacity as well as the overall effectiveness of the training program. The biological adaptation mechanism indicates that the body's recovery process following strenuous exercise involves the replenishment of phosphocreatine (PCr) (half-life of 30 seconds, complete recovery in approximately 3 minutes), the removal of H⁺, and the restoration of the acid‒base balance of the primary motor muscle groups (6‒10 minutes) [72, 73]. From a training perspective, it is essential to allow sufficient recovery intervals to facilitate metabolic homeostasis and maintain appropriate training intensity. An imbalance between the demand placed on the body and its recovery capacity during these intervals could result in premature fatigue, potentially impacting the ability to complete the training program.

When WRR > 1 is employed in SIT, only 64% of the total workload is accomplished due to inadequate recovery, despite athletes sprinting to exhaustion on each occasion [74]. We found that a significant binomial dose‒response was observed for the work interval duration of SIT, indicating an optimal work interval of 30 seconds (Table S15, Figure S6). Furthermore, on the basis of subgroup analysis, a WRR of 1:2 or less (i.e., more recovery time) clearly results in superior improvement (Tables S12, S13). Trials of SIT versus RST have demonstrated that a recovery time of less than 30 seconds may result in the inability to achieve the desired intensity in subsequent training [75–77]. Beneficial changes have been shown in a multitude of exercise modalities with prolonged recovery time within a single session [76–79], but recovery periods exceeding 120 seconds result in low aerobic demand and hinder the capacity to induce endurance adaptations [77, 80].

Unfortunately, the training protocol factor was not identified as a significant moderator of RST training effects in the subgroup versus dose‒response effect analysis. SIT and RST with shorter sprint durations are fueled primarily by ATP and PCr. However, the relatively brief recovery duration impedes sufficient replenishment of ATP and PCr, potentially leading to an increase in aerobic contribution during training [81]. In SIT, a recovery time of less than 20‒30 seconds restricts the resynthesis of PCr, thereby increasing the organism's reliance on aerobic energy sources during subsequent sprints. However, this may result in athletes being unable to complete subsequent sprint training at an optimal level of quality and quantity.

To achieve the optimal training intensity for HIIT, maintaining the intensity at approximately 100% of VO_2max is essential [82]. This approach facilitates the recruitment of type II muscle fibers and ensures the achievement of maximal cardiac output, which is necessary for effective stimulation of the central system. A previous study indicated that to attain and sustain VO_2max, the duration of a single training session should be 2‒3 minutes, with a WRR of 1, with the objective of more effectively oxidizing lactate [83]. This finding is corroborated by the results of our meta-analysis, which indicated that a HIIT’s WRR of 1 or less (i.e., longer rest duration) was associated with superior improvement. There is currently no evidence to suggest that extending the interval recovery duration of HIIT results in enhanced benefits by maintaining oxygen uptake levels during recovery, thereby enabling VO_2max to be reached with greater expediency during the subsequent interval training session. In maximal sprinting, the relative proportions of different energy supply systems depend on the combination of sprint duration and recovery time. Therefore, WRR should be targeted with different training methods to focus on the development of the required performance qualities.

Owing to the limitations of the literature, we were unable to conduct a subgroup analysis of recovery modes. It is evident that the approach taken during recovery, whether active or passive, is as important as the overall effect of interval training. Previous studies have indicated that active recovery may lead to reduced muscle oxygenation levels, impede PCr resynthesis, and potentially negatively affect subsequent sprint performance [84, 85]. The sprint times (4‒15 s) versus recovery times (21‒30 s) were shorter in these studies, with the negative effects focused on decreased sprinting ability and peak power. These outcomes were related to the competition between PCr resynthesis and lactate oxidation during active recovery. This finding is corroborated by a recent study showing that localized hypoxia affects muscle function and accelerates central and peripheral fatigue development [86]. The evidence suggests that active recovery (40% VO_2max) during longer interval recovery periods (30 seconds of sprinting and 4 minutes of active recovery) does not negatively impact aerobic capacity development. Instead, it appears to induce adaptations that enhance endurance by increasing overall oxygen consumption and cardiorespiratory demands [87, 88].

Recent studies have indicated that SIT with a reduced total training volume yields comparable outcomes to conventional 30 s Wingate SIT in enhancing both aerobic and anaerobic performance. Furthermore, recent research has revealed that reducing the interval work duration within a single interval training session can elicit a similar physiological response, when maintaining the same WRR [20, 89]. It was demonstrated that peak power production is sufficient to elicit an adaptive response to SIT, with nearly half of the total work output from a single SIT session occurring within the initial 10 seconds of sprinting [79]. A review of the number of SIT repetitions revealed a gradual decline in improvement magnitude with each additional training session, particularly after the fourth repetition [90]. One potential explanation for this phenomenon is that the majority of glycogen and PCr are depleted within the initial 15 seconds of the sprint, with the efficacy of this process diminishing with each subsequent repetition. Furthermore, an increase in SIT sprints or an extension of sprint duration may also have a detrimental effect on neuromuscular activity and average power output [91]. Our findings indicate a negative correlation between the total number of sprints performed in a single SIT session and improvements in oxygen uptake (Figure S6). This suggests that reducing the overall number of SIT sprints may lead to increased gains.

Limitations

Several factors must be considered when interpreting the results of this study. First, the incorporation of nonrandomized trials in the analysis may introduce a risk of bias and imprecision in the findings. Nevertheless, our methodology enabled a more comprehensive synthesis of available evidence from RST, HIIT, and SIT, and we evaluated a lower overall risk of bias. Second, all interventions were conducted in conjunction with regular training, thereby precluding the isolated effect of each training method from being ascertained. Furthermore, the reliance on maximal or peak oxygen uptake as metrics has inherent limitations when programs with diverse training regimens are evaluated. Additionally, maximal oxygen uptake is primarily determined by innate factors, and there are no universally applicable thresholds to elucidate the specific effects of changes in oxygen uptake levels in a practical sense.

In light of these limitations, future practical explorations are needed to address several knowledge gaps. Comparative studies of interval training based on power measurements and load monitoring are necessary, as are studies that strengthen interval training monitoring in underage athletes. Investigating whether there are discrepancies in the efficacy of the three training modalities across diverse programs would be beneficial. Additionally, future research should consider the optimal recovery period for active versus passive recovery. Furthermore, additional scientific experimental studies are needed to provide conclusive evidence regarding the dose‒response relationship and the underlying mechanisms of oxygen uptake level enhancement, demonstrating that interval training improves oxygen uptake levels.

The results of the reticulated meta-analysis and systematic review indicated that there was no statistically significant difference in the effectiveness of different interval training methods in enhancing maximal oxygen uptake capacity. Consequently, all three training methods were found to be effective in improving the level of oxygen uptake in athletes. A 6-week running-based HIIT program with work‒recovery intervals of 2‒3 days per week was found to be effective in improving athletes' oxygen uptake levels. With work‒recovery intervals of 30 seconds and 60 to 90 seconds, respectively, 3 days per week, SIT with less volume may prove more efficacious. Athletes and coaches may wish to consider targeted training methods to improve oxygen uptake in future training programs, which can be based on athlete age, program characteristics, and training regimens.

Acknowledgements

Thanks to H. Wang for encouragement, and thanks to J. Zhang for valuable discussion.

Author contributions

Qiushi Yang: Writing–original draft, Data curation, Conceptualization, Methodology. Junli Wang: Writing–review & editing, Supervision, Conceptualization.

Funding

This work was supported by the “Late-stage funded project of the National Social Science Fund of China in 2021” (Grant No. 21FTYB013, Investigator: Junli Wang), and the “Social Science Project of Jiangsu Provincial Education Department” (Grant No. 2021SJZDA173, Investigator: Junli Wang), and the “Innovation Plan of China University of Mining and Technology” (Grant No. 2023WLJCRCZL181, Investigator: Qiushi Yang).

Data availability

Data is provided within the manuscript or supplementary information files.

Ethics approval and consent to participate

Not applicable, this systematic review and meta-analysis was registered in PROSPERO.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

LI Y. Effect of High-Intensity Interval Training on Different Training Populations. China Sport Science. 2015;35:59–75.
Wewege M, van den Berg R, Ward RE, Keech A. The effects of high-intensity interval training vs. moderate-intensity continuous training on body composition in overweight and obese adults: a systematic review and meta-analysis. Obesity Reviews. 2017;18:635–46.
Ramos JS, Dalleck LC, Tjonna AE, Beetham KS, Coombes JS. The Impact of High-Intensity Interval Training Versus Moderate-Intensity Continuous Training on Vascular Function: a Systematic Review and Meta-Analysis. Sports Medicine. 2015;45:679–92.
Milanović Z, Sporiš G, Weston M. Effectiveness of High-Intensity Interval Training (HIT) and Continuous Endurance Training for VO2max Improvements: A Systematic Review and Meta-Analysis of Controlled Trials. Sports Medicine. 2015;45:1469–81.
Psilander N, Wang L, Westergren J, Tonkonogi M, Sahlin K. Mitochondrial gene expression in elite cyclists: Effects of high-intensity interval exercise. Eur J Appl Physiol. 2010;110:597–606.
Burgomaster KA, Howarth KR, Phillips SM, Rakobowchuk M, Macdonald MJ, McGee SL, et al. Similar metabolic adaptations during exercise after low volume sprint interval and traditional endurance training in humans. J Physiol. 2008;586:151–60.
Billat L. Interval training for performance: A scientific and empirical practice. special recommendations for middle- and long-distance running. part I: aerobic interval training. Sports Med. 2001;31:13–31.
Tschakert G, Hofmann P. High-intensity intermittent exercise: Methodological and physiological aspects. Int J Sports Physiol Perform. 2013;8:600–10.
CHEN X ping, CHU Y fang. Evolution and Development of Classical Theory and Methods of Athletic Training. China Sport Science. 2013;33:91–7.
Astrand I, Astrand PO, Christensen EH, Hedman R. Intermittent muscular work. Acta Physiol Scand. 1960;48:448–53.
Astrand I, Astrand PO, Christensen EH, Hedman R. Myohemoglobin as an oxygen-store in man. Acta Physiol Scand. 1960;48:454–60.
Girard O, Mendez-Villanueva A, Bishop D. Repeated-sprint ability part I: Factors contributing to fatigue. Sports Medicine. 2011;41:673–94.
MacInnis MJ, Gibala MJ. Physiological adaptations to interval training and the role of exercise intensity. J Physiol. 2017;595:2915–30.
Gibala MJ, Little JP. Physiological basis of brief vigorous exercise to improve health. J Physiol. 2020;598:61–9.
Tschakert G, Hofmann P. High-intensity intermittent exercise: Methodological and physiological aspects. Int J Sports Physiol Perform. 2013;8:600–10.
Bishop D, Claudius B. Effects of induced metabolic alkalosis on prolonged intermittent-sprint performance. Med Sci Sports Exerc. 2005;37:759.
Bishop D, Edge J, Goodman C. Muscle buffer capacity and aerobic fitness are associated with repeated-sprint ability in women. Eur J Appl Physiol. 2004;92.
Fernandez-Fernandez J, Zimek R, Wiewelhove T, Ferrauti A. High-intensity interval training vs. Repeated-sprint training in tennis. The Journal of Strength & Conditioning Research. 2012;26:53.
Gillen JB, Gibala MJ. Is high-intensity interval training a time-efficient exercise strategy to improve health and fitness? Applied Physiology, Nutrition, and Metabolism. 2014;39:409–12.
García-De Frutos JM, Orquín-Castrillón FJ, Marcos-Pardo PJ, Rubio-Arias JÁ, Martínez-Rodríguez A. Acute effects of work rest interval duration of 3 HIIT protocols on cycling power in trained young adults. Int J Environ Res Public Health. 2021;18:4225.
de Oliveira-Nunes SG, Castro A, Sardeli AV, Cavaglieri CR, Chacon-Mikahil MPT. HIIT vs. SIT: What is the better to improve V˙o2max? A systematic review and meta-analysis. Int J Environ Res Public Health. 2021;18:13120.
Zhang ZY, Ji HS, He JX, Huang LJ, Ding SC, Sun J, et al. A meta-analysis of the effects of high-intensity interval training and small-sided games on sprint performance in adolescents. Strength Cond J. 2023;45:587.
Engel FA, Ackermann A, Chtourou H, Sperlich B. High-intensity interval training performed by young athletes: A systematic review and meta-analysis. Frontiers in Physiology. 2018;9 JUL.
Ma X, Cao Z, Zhu Z, Chen X, Wen D, Cao Z. VO2max (VO2peak) in elite athletes under high-intensity interval training: A meta-analysis. Heliyon. 2023;9:e16663.
Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. Syst Rev. 2021;10:89.
Maher CG, Sherrington C, Herbert RD, Moseley AM, Elkins M. Reliability of the PEDro scale for rating quality of randomized controlled trials. Phys Ther. 2003;83:713–21.
Owen RK, Bradbury N, Xin Y, Cooper N, Sutton A. MetaInsight: An interactive web‐based tool for analyzing, interrogating, and visualizing network meta‐analyses using R‐shiny and netmeta. Res Synth Methods. 2019;10:569–81.
Balduzzi S, Rücker G, Nikolakopoulou A, Papakonstantinou T, Salanti G, Efthimiou O, et al. netmeta : An R Package for Network Meta-Analysis Using Frequentist Methods. J Stat Softw. 2023;106.
Rücker G, Schwarzer G. Ranking treatments in frequentist network meta-analysis works without resampling methods. BMC Med Res Methodol. 2015;15:58.
Viechtbauer W. Conducting Meta-Analyses in R with the metafor Package. J Stat Softw. 2010;36.
Kletting P, Glatting G. Model selection for time-activity curves: The corrected akaike information criterion and the F-test. Z Med Phys. 2009;19:200–6.
Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315:629–34.
Quezada-Muñoz Y, Rodríguez-Artigas P, Aravena-Sagardia P, Barramuño M, Herrera-Valenzuela T, Guzmán-Muñoz E, et al. Effects of a High-Intensity Interval Training Program on Body Composition and Physical Fitness in Female Field Hockey Players. International Journal of Morphology. 2021;39:1323–30.
Helgerud J, Engen LC, Wisløff U, Hoff J. Aerobic endurance training improves soccer performance. Med Sci Sports Exerc. 2001;33:1925.
Impellizzeri F, Rampinini E, Maffiuletti N, Castagna C, Bizzini M, Wisloff U. Effects of aerobic training on the exercise-induced decline in short-passing ability in junior soccer players. Appl Physiol Nutr Metab. 2008;33:1192–8.
Driller M, Fell J, Gregory J, Kitic C, Williams A. The effects of high-intensity interval training in well-trained rowers. Int J Sports Physiol Perform. 2009;4:110–21.
Breil FA, Weber SN, Koller S, Hoppeler H, Vogt M. Block training periodization in alpine skiing: Effects of 11-day HIT on VO2max and performance. Eur J Appl Physiol. 2010;109:1077–86.
Menz V, Strobl J, Faulhaber M, Gatterer H, Burtscher M. Effect of 3-week high-intensity interval training on VO2max, total haemoglobin mass, plasma and blood volume in well-trained athletes. Eur J Appl Physiol. 2015;115:2349–56.
Qu C, Tang Y. Effects of Different Intensive Intermittent Training on Maximum Oxygen Uptake and Output Power of Elite Road Cyclist. Journal of Capital University of Physical Education and Sports. 2019;31:448–53.
Kilen A, Larsson TH, Jørgensen M, Johansen L, Jørgensen S, Nordsborg NB. Effects of 12 weeks high-intensity & reduced-volume training in elite athletes. PLoS One. 2014;9:e95025.
Clark B, Costa VP, O’Brien BJ, Guglielmo LG, Paton CD. Effects of a seven day overload-period of high-intensity training on performance and physiology of competitive cyclists. PLoS One. 2014;9:e115308.
Sheykhlouvand M, Khalili E, Gharaat M, Arazi H, Khalafi M, Tarverdizadeh B. Practical model of low-volume paddling-based sprint interval training improves aerobic and anaerobic performances in professional female canoe polo athletes. J Strength Cond Res. 2018;32:2375–82.
Papandreou A, Philippou A, Zacharogiannis E, Maridaki M. Physiological adaptations to high-intensity interval and continuous training in kayak athletes. J Strength Cond Res. 2020;34:2258–66.
Mitranun W. Supramaximal vs functional high-intensity interval training effects on macrovascular reactivity in young male athletes. Songklanakarin Journal of Science and Technology. 2018;40:710–7.
Boer PH, Van Aswegen M. Effect of combined versus repeated sprint training on physical parameters in sub-elite football players in south africa. Journal of Physical Education and Sport. 2016;16:964–71.
Gantois P, Batista G, Dantas M, Fortes L, Machado D, Mortatti A, et al. Short-term effects of repeated-sprint training on vertical jump ability and aerobic fitness in collegiate volleyball players during pre-season. 2022;15:1040–51.
Gantois P, Batista GR, Aidar FJ, Nakamura FY, de Lima-Júnior D, Cirilo-Sousa MS, et al. Repeated sprint training improves both anaerobic and aerobic fitness in basketball players. Isokinet Exerc Sci. 2019;27:97–105.
Maggioni M, Bonato M, Stahn A, La Torre A, Agnello L, Vernillo G, et al. Effects of ball-drills and repeated sprint ability training in basketball players. Int J Sports Physiol Perform. 2018;14:1–24.
Ouergui I, Messaoudi H, Chtourou H, Wagner MO, Bouassida A, Bouhlel E, et al. Repeated sprint training vs. repeated high-intensity technique training in adolescent taekwondo athletes—a randomized controlled trial. Int J Environ Res Public Health. 2020;17:1–14.
Erylmaz S, Kaynak K, Polat M, Aydoğan S. Effects of repeated sprint training on isocapnic buffering phase in volleyball players. Revista Brasileira de Medicina do Esporte. 2018;24:286–90.
Sheykhlouvand M, Arazi H, Astorino TA, Suzuki K. Effects of a new form of resistance-type high-intensity interval training on cardiac structure, hemodynamics, and physiological and performance adaptations in well-trained kayak sprint athletes. Front Physiol. 2022;13.
Fereshtian S, Sheykhlouvand M, Forbes S, Agha-Alinejad H, Gharaat M. Physiological and performance responses to high-intensity interval training in female inline speed skaters. Apunts Medicina de l’Esport. 2017;52:131–8.
Sarkar S, Debnath M, Das M, Bandyopadhyay A, Dey SK, Datta G. Effect of high intensity interval training on antioxidant status, inflammatory response and muscle damage indices in endurance team male players. Apunts Sports Medicine. 2021;56:100352.
Sperlich B, De Marées M, Koehler K, Linville J, Holmberg H-C, Mester J. Effects of 5 weeks of high-intensity interval training vs. Volume training in 14-year-old soccer players. The Journal of Strength & Conditioning Research. 2011;25:1271.
Yang M, Lee M, Hsu S, Chan K. Effects of high‐intensity interval training on canoeing performance. Eur J Sport Sci. 2017;17:814–20.
Diker G, Darendeli A, Chamari K, Dellal A, Müniroğlu S, Ön S, et al. Recovery time variation during sprint interval training impacts amateur soccer players adaptations - a pilot study. Biol Sport. 2023;40:417–24.
Macpherson TW, Weston M. The effect of low-volume sprint interval training on the development and subsequent maintenance of aerobic fitness in soccer players. Int J Sports Physiol Perform. 2015;10:332–8.
Fang B, Kim Y, Choi M. Effect of cycle-based high-intensity interval training and moderate to moderate-intensity continuous training in adolescent soccer players. Healthcare (Switzerland). 2021;9.
Rago V, Krustrup P, Mohr M. Performance and submaximal adaptations to additional speed-endurance training vs. continuous moderate-intensity aerobic training in male endurance athletes. J Hum Kinet. 2022;83:277–85.
Meckel Y, Gefen Y, Nemet D, Eliakim A. Influence of short vs. long repetition sprint training on selected fitness components in young soccer players. J Strength Cond Res. 2012;26:1845–51.
Pierros T, Spyrou K. Effects of high-intensity interval training versus sprint interval training during the second wave of COVID-19 lockdown on soccer players. Apunts Sports Medicine. 2023;58.
Rønnestad BR, Haugen OC, Dæhlin TE. Superior on-ice performance after short-interval vs. long-interval training in well-trained adolescent ice hockey players. J Strength Cond Res. 2021;35:S76–80.
Bravo DF, Impellizzeri FM, Rampinini E, Castagna C, Bishop D, Wisloff U. Sprint vs. Interval training in football. Int J Sports Med. 2008;29:668–74.
Tanaka H, Monahan KD, Seals DR. Age-predicted maximal heart rate revisited. J Am Coll Cardiol. 2001;37:153–6.
Norris SR, Petersen SR. Effects of endurance training on transient oxygen uptake responses in cyclists. J Sports Sci. 1998;16:733–8.
Hill DW, Halcomb JN, Stevens EC. Oxygen uptake kinetics during severe intensity running and cycling. Eur J Appl Physiol. 2003;89:612–8.
Thurlow F, Huynh M, Townshend A, McLaren SJ, James LP, Taylor JM, et al. The Effects of Repeated-Sprint Training on Physical Fitness and Physiological Adaptation in Athletes: A Systematic Review and Meta-Analysis. Sports Medicine. 2024;54:953–74.
Sloth M, Sloth D, Overgaard K, Dalgas U. Effects of sprint interval training on <scp>VO</scp> _2max and aerobic exercise performance: A systematic review and meta‐analysis. Scand J Med Sci Sports. 2013;23.
YANG Q, LI D, XIE H, JI H, LU J, HE J, et al. Effects of sprint interval training on maximal oxygen uptake in athletes: a meta-analysis. J Sports Med Phys Fitness. 2021. https://doi.org/10.23736/S0022-4707.21.12815-4.
Wang Z, Wang J. The effects of high-intensity interval training versus moderate-intensity continuous training on athletes’ aerobic endurance performance parameters. Eur J Appl Physiol. 2024. https://doi.org/10.1007/s00421-024-05532-0.
Tschakert G, Hofmann P. High-intensity intermittent exercise: Methodological and physiological aspects. Int J Sports Physiol Perform. 2013;8:600–10.
Menzies P, Menzies C, McIntyre L, Paterson P, Wilson J, Kemi OJ. Blood lactate clearance during active recovery after an intense running bout depends on the intensity of the active recovery. J Sports Sci. 2010;28:975–82.
McMahon S, Jenkins D. Factors Affecting the Rate of Phosphocreatine Resynthesis Following Intense Exercise. Sports Medicine. 2002;32:761–84.
Laursen PB, Jenkins DG. The scientific basis for high-intensity interval training: Optimising training programmes and maximising performance in highly trained endurance athletes. Sports Medicine. 2002;32:53–73.
Gosselin LE, Kozlowski KF, DeVinney-Boymel L, Hambridge C. Metabolic response of different high-intensity aerobic interval exercise protocols. J Strength Cond Res. 2012;26:2866–71.
Toubekis AG, Douda HT, Tokmakidis SP. Influence of different rest intervals during active or passive recovery on repeated sprint swimming performance. Eur J Appl Physiol. 2005;93:694–700.
Kavaliauskas M, Aspe RR, Babraj J. High-intensity cycling training: The effect of work-to-rest intervals on running performance measures. J Strength Cond Res. 2015;29:2229–36.
McEwan G, Arthur R, Phillips SM, Gibson N, Easton C. Interval running with self-selected recovery: Physiology, performance, and perception. Eur J Sport Sci. 2018;18:1058–67.
Hazell TJ, MacPherson REK, Gravelle BMR, Lemon PWR. 10 or 30-s sprint interval training bouts enhance both aerobic and anaerobic performance. Eur J Appl Physiol. 2010;110:153–60.
Shi Q, Tong TK, Sun S, Kong Z, Chan CK, Liu W, et al. Influence of recovery duration during 6-s sprint interval exercise on time spent at high rates of oxygen uptake. J Exerc Sci Fit. 2018;16:16–20.
Lloyd Jones MC, Morris MG, Jakeman JR. Impact of time and work:Rest ratio matched sprint interval training programmes on performance: a randomised controlled trial. J Sci Med Sport. 2017;20:1034–8.
Altenburg TM, Degens H, van Mechelen W, Sargeant AJ, de Haan A. Recruitment of single muscle fibers during submaximal cycling exercise. J Appl Physiol (1985). 2007;103:1752–6.
Buchheit M, Laursen PB. High-intensity interval training, solutions to the programming puzzle: Part I: Cardiopulmonary emphasis. Sports Medicine. 2013;43:313–38.
Dupont G, Moalla W, Matran R, Berthoin S. Effect of short recovery intensities on the performance during two wingate tests. Med Sci Sports Exerc. 2007;39:1170–6.
Buchheit M, Cormie P, Abbiss CR, Ahmaidi S, Nosaka KK, Laursen PB. Muscle deoxygenation during repeated sprint running: Effect of active vs. Passive recovery. Int J Sports Med. 2009;30:418–25.
Fennell CRJ, Hopker JG. The acute physiological and perceptual effects of recovery interval intensity during cycling-based high-intensity interval training. Eur J Appl Physiol. 2021;121:425–34.
Nalbandian HM, Radak Z, Takeda M. Active recovery between interval bouts reduces blood lactate while improving subsequent exercise performance in trained men. Sports. 2017;5:40.
Yamagishi T, Babraj J. Active recovery induces greater endurance adaptations when performing sprint interval training. The Journal of Strength & Conditioning Research. 2019;33:922.
McKie GL, Islam H, Townsend LK, Robertson-Wilson J, Eys M, Hazell TJ. Modified sprint interval training protocols: Physiological and psychological responses to 4 weeks of training. Applied Physiology, Nutrition and Metabolism. 2018;43:595–601.
Vollaard NBJ, Metcalfe RS, Williams S. Effect of number of sprints in an SIT session on change in v O2max: A meta-analysis. Medicine and Science in Sports and Exercise. 2017;49:1147–56.
St Clair Gibson A, Schabort EJ, Noakes TD. Reduced neuromuscular activity and force generation during prolonged cycling. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology. 2001;281:R187–96.

No competing interests reported.

Download PDF

Reviewers invited by journal
01 Sep, 2024
Editor assigned by journal
01 Sep, 2024
Editor invited by journal
29 Aug, 2024
Submission checks completed at journal
27 Aug, 2024
First submitted to journal
27 Aug, 2024

You are reading this latest preprint version

Comparative of Different Interval Training Methods on Athletes' Oxygen Uptake: A Systematic Review and Meta-analysis

Status:

Version 1

Abstract

Figures

Background

Methods

Search strategy

Study selection

Data extraction

Methodological quality of the included studies

Statistical analysis

Results

Study characteristics

Network meta-analysis

Pairwise meta-analysis

Subgroup analysis

Age

Training mode

Training regimen

Frequency

Work‒recovery ratio

Team sport

Effect of dose‒response

Discussion

Limitations

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1