Effects of 8-week ischemic preconditioning on swimming performance and power output in male 100-m freestyle swimmers: a randomized controlled trial

Abstract

Purpose

This study aimed to investigate the long-term effects of an 8-week ischemic preconditioning (IPC) intervention on the athletic performance of male freestyle swimmers.

Methods

Eighteen male 100-m freestyle swimmers were randomly assigned to either the IPC group (n = 9) or the sham operation group (n = 9), with interventions administered three times per week over the course of 8 weeks. Athletic performance was evaluated through 100-m freestyle race tests and Wingate 30-s tests at baseline, at the 4th week, and at the 9th week. The data were analyzed via two-way repeated-measures ANOVA.

Results

After 8 weeks of intervention, the IPC group demonstrated significantly improved performance in the 100-m race compared with the placebo group (57.57 ± 2.30 vs. 59.90 ± 2.30, p = 0.048, d = 1.01). The enhancement in the second half of the 100-m performance was more pronounced in the IPC group than in the placebo group (30.15 ± 1.00 vs. 32.32 ± 1.97, p = 0.009, d = 1.39). Significant increases in peak power (817.27 ± 144.77 vs. 674.93 ± 54.75, p = 0.014, d = 1.30), mean power (679.60 ± 85.12 vs. 541.64 ± 78.33, p = 0.003, d = 1.69), and blood lactate levels (16.07 ± 1.22 vs. 14.7 ± 0.97, p = 0.018, d = 1.24) were detected. Conversely, a significant decrease was noted in the fatigue index (51.28 ± 6.20 vs. 60.34 ± 10.60, p = 0.042, d = 1.04) and time to peak (2942.67 ± 1782.08 vs. 4758.00 ± 1830.71, p = 0.049, d = 1.00).

Conclusion

An 8-week IPC intervention can effectively enhance the athletic performance of freestyle swimmers, potentially by improving anaerobic power output and delaying fatigue, as measured by the Wingate 30 s test. This finding suggests a novel intervention strategy for swim training.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00421-025-05982-0.

Introduction

In competitive swimming, records in the Olympics and World Championships are continually being broken, such as the men’s 100-m freestyle record, which improved from 46.86 s at the 2022 Doha World Championships to 46.40 s at the 2024 Paris Olympics. However, swimming training methods are encountering a bottleneck. It is crucial to explore innovative scientific intervention methods to assist swimming training, enhancing physiological functions to improve athletic performance. Short-distance swimming (100 m) primarily relies on the anaerobic metabolism system for energy supply (Gastin 2001; Pyne and Sharp 2014). The start, turns, push-offs after turns, and the final sprint phase during competitions have a decisive impact on the outcomes (Morais et al. 2019; Peterson Silveira et al. 2018; Tønnessen et al. 2013). These phases require athletes to display high-intensity explosive power in an extremely short time, maximizing the output of strength and speed, which depends on the anaerobic metabolism system’s ability to supply energy rapidly (Hargreaves and Spriet 2020). The performance of this system at critical moments directly determines competitive advantage. Therefore, as short-distance swimming records continue to be surpassed, exploring innovative intervention methods to assist swimming training in enhancing the anaerobic metabolic capacity of swimmers, thereby significantly improving performance, has become a key research topic.

In recent years, ischemic preconditioning (IPC) has garnered increasing attention in the field of sports science as an emerging exercise training intervention (de Groot et al. 2010; Khoramipour et al. 2021; Ou et al. 2024; Ribeiro et al. 2019). Research has shown that IPC can significantly reduce tissue damage, particularly myocardial ischemic injury (Murry et al. 1986). IPC activates endogenous protective mechanisms by applying multiple brief ischemic (5 min) and reperfusion (5 min) stimuli to the athlete’s limbs prior to exercise (Caru et al. 2019), enhancing anaerobic metabolic performance in sports such as cycling (Lindsay et al. 2017; Patterson et al. 2015), basketball (Cheng et al. 2021), taekwondo (Ou et al. 2024), and track and field (Chen et al. 2023; Paull and Van Guilder 2019). The mechanism includes reducing proton generation or enhancing proton buffering to maintain pH levels (Carvalho and Barroso 2019; Murry et al. 1990), alleviating muscle acidosis and preserving glycolytic enzyme activity, thereby enhancing glycolytic capacity and fatigue resistance (Carvalho and Barroso 2019; Hargreaves and Spriet 2020). Similarly, experimental evidence shows that IPC can enhance the function of the glycolytic system (Paull and Van Guilder 2019) and increase the number of repetitions in resistance training, while an increase in lactate anion accumulation has also been observed (Carvalho and Barroso 2019). Additionally, by activating mitochondrial ATP-sensitive potassium channel (mK_ATP), it inhibits the opening of the mitochondrial permeability transition pore, maintaining mitochondrial function and reducing ATP consumption (Nakano et al. 2000; Riksen et al. 2004). Experimental evidence also indicates that IPC exerts a protective effect on myocardium by decreasing ATP hydrolysis (Kida et al. 1991). Therefore, it is speculated that IPC may improve the function of the phosphagen system by preserving more ATP before exercise and enhancing ATP resynthesis capacity, ultimately having a positive impact on anaerobic exercise. However, this remains a hypothesis that requires further validation. Furthermore, IPC stimulates the production of substances such as adenosine, bradykinin, and nitric oxide (NO) (Downey et al. 2007; Nakano et al. 2000), which regulate vascular tone and muscle contraction (BAILEY et al. 2012; de Groot et al. 2010; Homer-Vanniasinkam 2005; Lawson and Downey 1993).

IPC has a positive impact on short-term high-intensity exercise performance; however, existing studies primarily focus on its acute effects, lacking research on the long-term (≥ 8 weeks) adaptive effects of IPC on swimmers, particularly its influence on 100-m freestyle performance and anaerobic power accumulation benefits. Research indicates that acute IPC can enhance elite swimmers’ 100-m freestyle times (Jean-St-Michel et al. 2011), with variations in effectiveness observed at different time points (Lisbôa et al. 2016), and it also positively affects intermittent swimming tests (Ferreira et al. 2016). These findings confirm its role in short-distance freestyle; however, these conclusions are limited to acute effects and do not address the sustained impact of long-term IPC on swimming training. Although current studies have demonstrated that a 2-week IPC intervention can significantly enhance anaerobic power performance in the running anaerobic sprint test (RAST) for soccer players and that a 4-week IPC regimen combined with sprint interval training can improve performance on the Wingate 30-s test (Paradis-Deschênes et al. 2020a, b; Shannon and Carter 2024), there is a paucity of research regarding the effects of long-term IPC intervention on short-distance freestyle swimming. Moreover, owing to differences in exercise modalities, the outcomes may not be directly comparable. Therefore, investigating the impact of long-term IPC on short-distance freestyle swimming performance is crucial. This study hypothesizes that an 8-week IPC intervention will positively influence the performance and power output of male swimmers in the 100-m freestyle. The objectives are: (1) to explore the enhancement effects of long-term IPC on the 100-m freestyle; (2) to provide new auxiliary intervention strategies for long-term swimming training and to offer theoretical and practical evidence for the application of IPC in sports science.

Methods

Subjects

Eighteen university swimmers participated in this randomized controlled trial. with all subjects drawn from the same team and possessing comparable skill levels. The participants were randomly assigned into two groups via block randomization (block size = 4) (Bösel et al. 2022): one group of 9 individuals formed the IPC group, which underwent long-term IPC intervention at an intervention pressure of 220 mmHg; the other group, comprising 9 individuals, acted as the placebo group (SHAM) with an intervention pressure of 20 mmHg. The inclusion criteria were as follows: (1) adult males aged 18 years and above; (2) had a national level 2 or above swimming athlete certificate; (3) were in good health, had no injuries or illnesses in the past six months, and were able to participate in regular professional swimming training; (4) did not engage in any swimming or strength training outside the experiment during the study period; (5) had no adverse habits, strictly adhering to experimental requirements; (6) maintained regular professional swimming training over the past year; (7) all subjects are from the same swimming team; (8) dining in the same cafeteria; and (9) under the supervision of assistant coaches for daily life and training. The exclusion criteria were as follows: (1) hypertension, heart disease, or other circulatory system diseases; (2) suffering from metabolic diseases; (3) training absenteeism, failure to meet training standards, or poor training compliance; and (4) sustaining injuries during the experiment or voluntary withdrawal.

All experimental subjects were informed of the procedures prior to the experiment and provided written informed consent. The study protocol received approval from the Ethics Review Committee of Guangzhou Sport University (ID: 20242c22-95) and complied with the guidelines of the Declaration of Helsinki.

IPC Protocol

Prior to the regular swimming training, the IPC intervention was administered to the subjects in the IPC group via a specialized compression device (Theratools, lower limb set, China). The subjects were positioned supine on yoga mats, with the compression device accurately placed at the upper one-third of both thighs. The blood pressure cuff on the right leg was inflated while the left leg remained relaxed for a duration of 5 min. The right leg subsequently relaxed, and the blood pressure cuff on the left leg was inflated for another 5 min. This constituted a 10-min intervention cycle, which was repeated for a total of 4 cycles, culminating in 40 min of intervention. The IPC group was subjected to an inflation pressure of 220 mmHg, whereas the SHAM group was subjected to an inflation pressure of 20 mmHg (Cheng et al. 2021; Da Mota et al. 2019; Paradis-Deschênes et al. 2020a, b). A pressure of 20 mmHg provided the subjects with a corresponding compression sensation without obstructing blood flow, thereby minimizing the placebo effect (Paradis-Deschênes et al. 2020a, b). To further mitigate the placebo effect, the subjects were informed that the study aimed to compare the potential impact of two different cuff pressures on exercise performance. Following the intervention, the subjects remained seated for 5 min before their regular swimming training commenced (Fig. 1). In this study, in addition to the difference in intervention pressure, all other procedures were consistent between the IPC and SHAM groups.

Fig. 1.

Intervention program flowchart

Eight-week intervention, training, and testing protocol

In this experiment, the training programs for the IPC group and the SHAM group were designed and implemented by a professional coaching team, both following the same training regimen. Each training session included a warm-up phase, a main phase, and a cool-down phase. The warm-up consisted of land-based and water-based activities. The main phase was divided into three stages: the first stage (2 weeks) focused on familiarization with the procedures and adaptation to ischemic preconditioning and training intensity; the second stage (3 weeks) aimed to break through comfort zones to enhance performance; and the third stage (3 weeks) involved maintaining training volume while increasing intensity to adapt to high-intensity rhythms. Each stage included anaerobic lactate tolerance training (maximum heart rate), lactate peak training (maximum heart rate), anaerobic non-lactate training (speed/explosiveness), and low-intensity aerobic training (120–150 bpm). Low-intensity aerobic sessions were scheduled on Wednesdays to help athletes recover from physical and mental fatigue, preparing them for the next high-intensity anaerobic capacity training. The cool-down phase for the 8 weeks consisted of 200 m of relaxed swimming. Athletes trained three times a week (Monday, Wednesday, Friday), totaling 24 sessions over the 8 weeks.

IPC interventions were conducted three times a week, specifically on Mondays, Wednesdays, and Fridays, from 14:30–15:10. During each intervention session, both the IPC and SHAM groups underwent the procedures simultaneously, followed by a 5-min rest before commencing swimming training. Weekly assessments of resting heart rates upon waking on Monday mornings and the Rating of Perceived Exertion (RPE) (Ritchie 2012) were performed to monitor the subjects’ adaptation to the training load throughout the experimental period. Coaches monitored the training compliance of the experimental subjects through each training log and regulated training intensity by tracking heart rates (Polar H10) to ensure consistency across groups. The intervention, training, and testing flow chart is depicted in Fig. 2.

Fig. 2.

Flowchart of intervention, training, and testing

Basic indicator measurement methods

In this study, fundamental body indicators, including body weight, body fat, and body mass index (BMI), were assessed via a body composition analyzer (Inbody 370, Korea). The lung capacity was evaluated with a spirometer (HHTC200-FH, China), with each test being repeated twice to ensure accuracy, and the maximum value was recorded. Heart rate was monitored using a heart rate detection device (Polar H10, Finland), and real-time heart rate data were recorded via the Polar Flow app. To prevent accidental detachment or signal loss of the heart rate monitor during the swimming test, a remedial method involving carotid artery palpation was also employed. Specifically, the carotid artery was palpated with the fingertips for 10 s, and the number of heartbeats counted was multiplied by 6 to calculate the heart rate per minute (Jin et al. 2017). Following the heart rate assessment, the RPE of each subject was reported. Blood lactate levels were analyzed via a portable blood lactate analyzer (Lactate Scout 4) along with specific blood lactate test strips (Code 92). Fingertip capillary blood lactate tests were conducted at 1 min, 3 min, and 5 min postexercise, with the maximum value recorded for each subject.

100-m freestyle performance test

Maximum effort 100-m freestyle tests were conducted prior to the experiment, after the fourth week of intervention, and in the ninth week following the intervention. The preexperiment test was scheduled for the Saturday and Sunday preceding the intervention, the midterm test was arranged for the Saturday and Sunday after the completion of the fourth week of intervention, and the postexperiment test was conducted on the Friday of the ninth week, 1 week after the final intervention, to eliminate any acute effects induced by IPC (Chen et al. 2023). The test was conducted in a 50 m swimming pool, with all athletes completing standardized land stretching and water warm-up exercises beforehand to ensure they were in optimal condition. During the testing process, a starter and two timekeepers were appointed. The timekeepers have officiated at numerous domestic and international swimming competitions over the past five years. The two referees employed a double-blind method and used stopwatches (Casio HS70W, Japan) to record the times, with results accurate to two decimal places. The average of the two timekeepers’ results was taken as the final score. Before the test commenced, all the athletes adopted a crouching start position on the starting platform. Upon hearing the start command, the athletes departed from the platform. Throughout the test, they maintained a full sprint and utilized the standard front crawl stroke to complete the entire course (denoted as T100). The recorded times included the interval from the start to 15 m, the first 50-m segment (from the start signal to the first wall touch, denoted as T50), the turn (captured at the 5-m line for both entry and exit, represented as T_5in and T_5out) (Cossor et al. 1999; Nicol et al. 2019), the second 50-m segment (calculated as T100 − T50), and the full 100-m course (denoted as T100). Timing was considered when any part of the athlete’s body touched the wall.

Anaerobic power test

Anaerobic power tests (Wingate 30 s) (Le Panse et al. 2007; Michalczyk et al. 2019) were conducted prior to the experiment, after 4 weeks, and at the ninth week. The lower limb anaerobic power of the subjects was assessed via an anaerobic power bicycle (MONARK 894E, Sweden). This equipment calculates the power output per cycle via a physical formula that incorporates the set load (resistance) and pedaling frequency (revolutions per minute) of the subjects, along with the wheel circumference and resistance coefficient. The testing procedure was as follows: Before the test, each subject adjusted the bicycle seat and handlebar position to a comfortable setting, ensuring consistency in position for both the pre- and postexperiment tests. The subjects subsequently performed a 3- to 5-min warm-up by pedaling lightly at a load of 20% of the measured load. During the warm-up, the participants executed a 5-s all-out sprint at the end of each minute. After completing the specific warm-up, the participants engaged in dynamic stretching. At the beginning of the test, the participants first performed an all-out sprint without any load. Once maximum speed was achieved (typically within 1–3 s), participants were required to perform a 30-s all-out sprint with an additional load. The load was set at 0.075 kg per kilogram of body weight, following the Bar-Or protocol (Bar-Or 1987; Durkalec-Michalski et al. 2017), and the resistance coefficient was verified via Monark 894E software. After the additional load was applied, the 30-s test officially commenced. During the test, the participants were prohibited from lifting their hips off the bicycle seat or releasing their hands from the handlebars. Following 30 s of maximal cycling, the subjects continued with 2–5 min of relaxed cycling. In this study, peak power (PP), average power (AP), the fatigue index (FI%), and the time to reach peak power (tPP) were utilized as indicators for evaluating anaerobic power.

Data analysis

In this study, Microsoft Excel 2021 was utilized for data organization, whereas IBM SPSS Statistics 25.0 software was employed for statistical analysis. The data are expressed as the mean ± standard deviation and the corresponding 95% confidence interval. The Shapiro‒Wilk test was employed to assess the normality of all variables. Normality was confirmed for all variables (p > 0.05) except for the RPE, for which nonparametric tests were applied. Independent sample t tests were conducted for baseline indicators that conformed to a normal distribution, including age, height, weight, BMI, body fat percentage, training years, and lung capacity. Two-factor analysis of variance (ANOVA)—group (placebo group vs. ischemic preconditioning group) × time (baseline vs. mid-training vs. posttraining)—was performed on the variables requiring repeated measures, including the first 15 m performance, the first 50 m split performance, the second 50 m split performance, overall 100 m performance, turn performance, blood lactate levels, heart rate, and Wingate 30 s test indicators. The effect size (η²) was calculated, with values interpreted as trivial when 0 ≤ η² ≤ 0.01, small when 0.01 < η² ≤ 0.06, medium when 0.06 < η² ≤ 0.14, and large when η² > 0.14. For pairwise comparisons, Cohen’s d value was calculated, with d = 0.2 indicating a small effect, d = 0.5 indicating a medium effect, and d = 0.8 indicating a large effect, on the basis of Cohen’s criteria (Field 2024). All repeated measures ANOVAs were subjected to Bonferroni multiple comparison correction. R 4.4.3 software was used to perform Z score standardization and density distribution calculations and analyses on the variables measured after 8 weeks of intervention, along with grouped correlation visualization plotting and analysis. Statistical significance was defined as p < 0.05.

Results

Subjects

Eighteen male swimmers participated in this experiment. No significant differences were detected (p > 0.05) in the basic characteristics of the subjects between the IPC group and the SHAM group (Table 1).

Table 1.

Inter-group statistical analysis table of basic characteristics of subjects (N = 18)

	$\overline{X}\pm S$		p	95% CI	d
	SHAM (n = 9)	IPC (n = 9)
Age (Y)	19.11 ± 0.78	18.89 ± 0.60	0.509	− 0.47 to 0.92	0.32
Height (cm)	180.86 ± 2.69	178.29 ± 1.99	0.455	− 4.53 to 9.67	1.09
Weight (kg)	72.7 ± 11.59	70.9 ± 7.63	0.643	− 7.62 to 12.00	0.18
Training duration (Y)	11.22 ± 5.07	10.22 ± 1.79	0.807	− 4.24 to 3.35	0.26
BMI (kg/m2)	22.12 ± 2.11	22.31 ± 2.38	0.861	− 2.44 to 2.06	0.08
Body fat percentage (%)	12.4 ± 5.51	14.3 ± 2.84	0.371	− 6.28 to 2.48	0.43
Vital capacity (ml)	5440 ± 520.56	5040.44 ± 728.18	0.199	− 238.39 to 1032.07	0.63

d denotes the effect size

100-m freestyle performance test results

In this study, participants underwent three 100-m freestyle tests: one before the intervention, one after 4 weeks of intervention, and one after 8 weeks of intervention. Repeated-measures ANOVA was utilized, with pairwise comparisons performed following Bonferroni multiple comparison correction. The data are presented as the means ± standard deviations, accompanied by the corresponding 95% confidence intervals (Table 2). The results of the main effect test for overall performance across the three 100-m freestyle tests indicated F = 3.49, p = 0.08, η² = 0.179, with a 95% CI of -0.29–4.64. This suggests marginal significance between subjects and a large effect size, indicating potential real differences among the overall groups. Pairwise comparisons between groups, adjusted for Bonferroni correction, revealed that in the test conducted after 8 weeks of intervention, the total time for the 100-m freestyle in the IPC group (57.57 ± 2.30) was significantly lower than that in the SHAM group (59.90 ± 2.30), F = 4.598, p = 0.048, d = 1.01, 95% CI = 0.03 to 4.63. The performance in the IPC group was significantly better than that in the SHAM group, indicating a large effect size. However, the results from the test conducted after 4 weeks of intervention revealed no significant difference between the IPC and SHAM groups, F = 2.478, p = 0.135, d = 0.74, 95% CI = -0.75–5.08. The results of the within-subject effect test for time demonstrated that F = 18.673, p < 0.001, η² = 0.539, with the third test significantly differing from the first two tests (p < 0.001), and no difference was observed between the first two tests, indicating a significant improvement in performance during the later stage (Fig. 3a).

Table 2.

Statistical table of repeated measures ANOVA results for subjects’ 100 m freestyle performance (N = 18)

	$\overline{X}\pm S$		F	p	95% CI	η2	d
	SHAM (n = 9)	IPC (n = 9)
100-m freestyle result
Between-subjects effects			3.49	0.08†	− 0.29 to 4.64	0.179
Pre (s)	61.15 ± 2.57	59.11 ± 2.26	3.169	0.094†	− 0.39 to 4.45		0.84
Mid (s)	61.34 ± 3.09	59.18 ± 2.73	2.478	0.135	− 0.75 to 5.08		0.74
Post (s)	59.90 ± 2.30	57.57 ± 2.30	4.598	0.048*	0.03 to 4.63		1.01
Performance in the first 15 m
Between-subjects effects			0.002	0.967	− 0.415 to 0.40	0
Pre (s)	6.62 ± 0.46	6.61 ± 0.41	0.003	0.958	− 0.42 to 0.45		0.02
Mid (s)	6.49 ± 0.59	6.57 ± 0.32	0.123	0.73	− 0.55 to 0.40		0.17
Post (s)	6.17 ± 0.42	6.13 ± 0.42	0.048	0.829	− 0.38 to 0.46		0.10
First 50 m split time
Between-subjects effects			1.972	0.179	− 0.37 to 1.82	0.11
Pre (s)	29.07 ± 0.97	28.39 ± 1.24	1.649	0.217	− 0.44 to 1.78		0.61
Mid (s)	29.06 ± 1.51	27.72 ± 1.06	4.789	0.044*	0.04 to 2.65		1.03
Post (s)	27.58 ± 1.02	27.43 ± 1.42	0.071	0.793	− 1.08 to 1.39		0.12
The split time for the last 50 m
Between-subjects effects			3.865	0.067†	− 0.114 to 3.014	0.195
Pre (s)	32.08 ± 1.99	30.72 ± 1.16	3.142	0.095†	− 0.266 to 2.99		0.83
Mid (s)	32.28 ± 1.91	31.46 ± 1.80	0.88	0.362	− 1.033 to 2.67		0.44
Post (s)	32.32 ± 1.97	30.15 ± 1.00	8.678	0.009*	0.61 to 3.73		1.39
Freestyle turn performance
Between-subjects effects			4.295	0.055†	− 0.006 to 0.542	0.212
Pre (s)	5.76 ± 0.30	5.55 ± 0.28	2.193	0.158	− 0.088 to 0.497		0.72
Mid (s)	5.79 ± 0.35	5.4 ± 0.29	6.402	0.022*	0.063 to 0.712		1.21
Post (s)	5.58 ± 0.22	5.37 ± 0.32	2.7	0.12	− 0.062 to 0.486		0.76

^†p value indicates marginal significance (0.05 < p < 0.10), with a statistical power (1 − β) of 0.78, suggesting a potential risk of Type II error. *p < 0.05; after Bonferroni correction for multiple comparisons, significant differences were observed between the IPC group and the SHAM group. η² represents the effect size for between-subjects comparisons, and d denotes the effect size d for between-subjects pairwise comparisons. Pre denotes the preintervention test, Mid denotes the test conducted after 4 weeks of intervention, and Post refers to the test administered 1 week after the completion of the 8-week intervention

Fig. 3.

Scatter box plot of 100-m freestyle performance and anaerobic power test results. Pre denotes the preintervention test, Mid denotes the test conducted after 4 weeks of intervention, and Post refers to the test administered 1 week after the completion of the 8-week intervention. The box plot illustrates the interquartile range (IQR) from the 25th to the 75th percentile, with the horizontal line within the box indicating the median. The small square within the box represents the mean, whereas the whiskers depict the mean ± standard deviation. Individual data points are represented as scatter points, with blue indicating the SHAM group and red indicating the IPC group. Note that some scatter points may overlap because of identical data values. A significance level of *p < 0.05 indicates significant differences between the IPC and SHAM groups in pairwise comparisons, following Bonferroni correction for multiple comparisons

A comparison of performance over the first 15 m across the three tests revealed a significant within-subject effect, with F = 23.562, p < 0.001, η² = 0.596. Pairwise comparisons revealed significant differences between the third test and the first two tests (p < 0.001), whereas no significant difference was detected between the first two tests. The interaction effect between group and time was F = 0.387, p = 0.682, η² = 0, indicating that there was no significant interaction effect. The between-subject effect test for groups revealed F = 0.002, p = 0.967, η² = 0, with no significant differences found in the intergroup comparisons for each test, suggesting no significant intergroup differences in 15-m performance across the three tests (Fig. 3b).

A comparison of the first 50-m segment times across the three tests revealed a significant main effect of time, with F = 16.728, p < 0.001, η² = 0.511, indicating that the performance of all the subjects changed significantly over time (Fig. 3c). The analysis of the interaction effect between group and time yielded F = 3.371, p = 0.036, η² = 0.188, suggesting a significant interaction effect; this finding indicates that the trends in the changes in the first 50-m segment times over time differed between the IPC group and the SHAM group. The overall between-group effect was not significant, with F = 1.972, p = 0.179, η² = 0.11, and the 95% CI ranged from − 0.415 to 0.40. After 4 weeks of intervention, the intergroup comparison revealed that the IPC group (27.72 ± 1.06) performed significantly better than the SHAM group did (29.06 ± 1.51), with F = 4.789, p = 0.044, d = 1.03, and a 95% CI of 0.04–2.65. The effect size was large, indicating substantial practical differences. However, after 8 weeks of intervention, the performance gap between the IPC group (27.43 ± 1.42) and the SHAM group (27.58 ± 1.02) narrowed, resulting in no significant difference (p = 0.793).

A comparison of performance in the last 50-m segment across the three tests revealed that the within-subject effect test indicated a marginally significant main effect of time, F = 3.296, p = 0.079, η² = 0.171. The interaction effect was also marginally significant, F = 3.518, p = 0.070, η² = 0.180, suggesting a trend toward differences among the groups. However, owing to the borderline significance, caution is warranted when interpreting these results. The between-subject effect test yielded a marginally significant result, F = 3.865, p = 0.067, η² = 0.195, and a 95% CI of − 0.114 to 3.014, with a large effect size, indicating that the differences between groups may hold practical significance. Furthermore, the postintervention results at 8 weeks demonstrated that the IPC group (30.15 ± 1.00) significantly outperformed the SHAM group (32.32 ± 1.97), F = 8.678, p = 0.009, d = 1.39, and 95% CI of 0.61–3.73, reflecting a large effect size and practical significance (Fig. 3d).

The within-subjects test results for turning performance across the three tests indicated F = 7.811, p = 0.002, η² = 0.328, demonstrating a significant main effect of time, with subjects’ turning performance changing notably over time (Fig. 3e). The between-subjects effect test results showed F = 4.295, p = 0.055, η² = 0.212, with a 95% CI of − 0.006 to 0.542, suggesting a marginally significant overall between-group effect with a large effect size, implying that the differences between groups may hold practical significance. The mean turning performance within the SHAM group exhibited no significant change during the first 4 weeks but showed a noticeable trend change in the final 4 weeks. In contrast, the IPC group demonstrated a significant trend change during the first 4 weeks, followed by a smaller change in the last 4 weeks. Furthermore, a significant difference in turning performance was observed between the IPC group and the SHAM group after 4 weeks of intervention (5.4 ± 0.29 vs. 5.79 ± 0.35), with F = 6.402, p = 0.022, d = 1.21, and a 95% CI ranging from 0.063–0.712, indicating a large effect size and a substantial actual difference. The turning performance of the IPC group was significantly better than that of the SHAM group following 4 weeks of intervention.

Anaerobic power test results

The results of the anaerobic power test, which included the PP, AP, FI%, and tPP data from the Wingate 30 s test, are presented as the means ± standard deviations, along with the corresponding 95% confidence intervals (Table 3). The main effect of time on PP was significant, F = 8.387, p = 0.007, η² = 0.344. Additionally, the interaction effect between time and group was significant, F = 9.463, p = 0.004, η² = 0.372, indicating a large effect size. The pattern of change in PP over time differed between the IPC and SHAM groups. The between-subject effect was not significant, F = 0.498, p = 0.491, η² = 0.03, and the 95% CI ranged from -150.49–75.32, indicating that there was no significant overall difference between the IPC and SHAM groups. The results from pairwise comparisons revealed that after 8 weeks of intervention, the PP of the IPC group (817.27 ± 144.77) was significantly greater than that of the SHAM group (674.93 ± 54.75), F = 7.611, p = 0.014, d = 1.30, and 95% CI of − 251.71 to 32.96, demonstrating a large effect size. No significant differences were observed between the groups at baseline and at the 4-week follow-up (p = 0.946, p = 0.719) (Fig. 3f).

Table 3.

Statistical table of repeated measures ANOVA results for subjects’ Wingate 30 s test (N = 18)

	$\overline{X}\pm S$		F	p	95% CI	η2	d
	SHAM (n = 9)	IPC (n = 9)
Wingate 30 s PP
Between-subjects effects			0.498	0.491	− 150.49 to 75.32	0.03
Pre (W)	661.81 ± 54.58	658.37 ± 140.40	0.005	0.946	− 103.00 to 109.89		0.03
Mid (W)	719.22 ± 141.44	693.04 ± 160.66	0.135	0.719	− 125.08 to 177.43		0.17
Post (W)	674.93 ± 54.75	817.27 ± 144.77	7.611	0.014*	− 251.71 to − 32.96		1.30
Wingate 30 s AP
Between-subjects effects			0.568	0.462	− 120.10 to 57.11	0.034
Pre (W)	533.22 ± 97.82	510.88 ± 99.97	0.23	0.638	− 76.49 to 121.18		0.23
Mid (W)	542.77 ± 88.18	521.64 ± 102.43	0.22	0.645	− 74.38 to 116.63		0.22
Post (W)	541.64 ± 78.33	679.60 ± 85.12	12.799	0.003*	− 219.70 to − 56.21		1.69
Wingate 30 s FI%
Between-subjects effects			1.135	0.302	− 5.00 to 15.104	0.066
Pre (%)	64.43 ± 7.39	58.36 ± 5.58	3.873	0.067†	− 0.47 to 12.63		0.93
Mid (%)	64.60 ± 22.04	64.59 ± 28.92	0	0.999	− 25.68 to 25.71		0.00
Post (%)	60.34 ± 10.60	51.28 ± 6.20	4.911	0.042*	0.393 to 17.74		1.04
Wingate 30 s tPP
Between-subjects effects			0.019	0.893	− 2104.69 to 2394.55	0.001
Pre (ms)	3810.11 ± 2565.16	5778.44 ± 3956.71	1.568	0.228	− 5300.45 to 1363.78		0.59
Mid (ms)	5165.78 ± 6604.82	4578.00 ± 5066.50	0.045	0.835	− 5294.43 to 6469.98		0.10
Post (ms)	4758.00 ± 1830.71	2942.67 ± 1782.08	4.544	0.049*	9.98v3620.69		1.00

^†p value indicates marginal significance (0.05 < p < 0.10), with a statistical power (1 − β) of 0.78, suggesting a potential risk of Type II error. *p < 0.05; after Bonferroni correction for multiple comparisons, significant differences were observed between the IPC group and the SHAM group. η² represents the effect size for between-subjects comparisons, and d denotes the effect size d for between-subjects pairwise comparisons. Pre denotes the preintervention test, Mid denotes the test conducted after 4 weeks of intervention, and Post refers to the test administered 1 week after the completion of the 8-week intervention

The time main effect of the AP index was significant, F = 41.794, p < 0.001, η² = 0.723, indicating a substantial change in the AP index over time. Additionally, the interaction effect between time and group for the AP index was significant, F = 37.802, p < 0.001, η² = 0.703, suggesting distinct patterns of change in the AP index over time between the IPC group and the SHAM group. The between-subject effect of AP was not significant, F = 0.568, p = 0.462, η² = 0.034, with a 95% CI of − 120.10 to 57.11, indicating that there was no significant overall difference between the IPC and SHAM groups. The results from pairwise comparisons revealed that after 8 weeks of intervention, the AP of the IPC group (679.60 ± 85.12) was significantly greater than that of the SHAM group (541.64 ± 78.33), with F = 12.799, p = 0.003, d = 1.69, and a 95% CI of − 219.70 to 56.21, indicating a large effect size. No significant differences were observed between the groups at baseline and at 4 weeks (p = 0.638, p = 0.645) (Fig. 3g).

The main effect of time on the FI% index was not significant (F = 1.495, p = 0.241, η² = 0.085). Similarly, the interaction between time and group did not reach significance (F = 0.403, p = 0.552, η² = 0.025), nor did the between-subjects effect (F = 1.135, p = 0.302, η² = 0.066). However, pairwise comparisons between the IPC group and the SHAM group revealed that, following 8 weeks of intervention, the FI% in the IPC group (51.28 ± 6.20) was significantly lower than that in the SHAM group (60.34 ± 10.60), with F = 4.911, p = 0.042, d = 1.04, and a 95% CI ranging from 0.393–17.74, indicating a large effect size. No significant differences were observed between the groups at baseline and at 4 weeks (p = 0.067, p = 0.999) (Fig. 3h).

The main effects of time (F = 0.344, p = 0.627, η² = 0.021), the interaction between time and group (F = 0.991, p = 0.356, η² = 0.058), and the between-subjects effect (F = 0.019, p = 0.893, η² = 0.001) for the tPP index were not significant. However, the pairwise comparison results between the IPC group and the SHAM group indicated that, following 8 weeks of intervention, the tPP index in the IPC group (2942.67 ± 1782.08) was significantly lower than that in the SHAM group (4758.00 ± 1830.71), with F = 4.544, p = 0.049, d = 1.00, 95% CI 9.98–3620.69, reflecting a large effect size. No significant differences were observed between the groups at baseline and at 4 weeks (p = 0.228, p = 0.835) (Fig. 3i).

Results of the blood lactate level, heart rate, and RPE

Following the 100-m freestyle test, blood lactate levels, heart rate, and RPE were measured. The RPE variable exhibited a nonnormal distribution and was thus analyzed via nonparametric tests. Comparisons of blood lactate levels and heart rates between groups were performed via repeated-measures ANOVA. The data are expressed as the means ± standard deviations (Table 4). The statistical analysis of blood lactate (Fig. 4a) revealed a significant within-subject time effect (F = 51.41, p < 0.001, η² = 0.763) as well as a significant time × group interaction (F = 4.879, p = 0.014, η² = 0.234), both indicating a large effect size. Notably, blood lactate levels significantly differed across various testing times. Furthermore, the patterns of change in blood lactate over time varied between the IPC and SHAM groups. The main effect between the IPC and SHAM groups was marginally significant (F = 3.71, p = 0.072, η² = 0.188, 95% CI − 1.47 to 0.07), indicating a large effect size. Pairwise comparisons revealed that during the 4-week intervention, the blood lactate level in the IPC group (12.96 ± 0.90) was significantly greater than that in the SHAM group (11.88 ± 3.09), with F = 5.51, p = 0.032, d = 0.47, and a 95% CI of − 2.05 to − 0.10, indicating a small-to-medium effect size. After 8 weeks of intervention, the blood lactate level in the IPC group (16.07 ± 1.22) was also significantly greater than that in the SHAM group (14.7 ± 0.97), with F = 6.882, p = 0.018, d = 1.24, and a 95% CI of − 2.47 to 0.26, reflecting a large effect size. No significant difference in blood lactate levels was found between the groups at baseline (p = 0.507). Additionally, there were no significant differences in the RPE (Fig. 4b) or heart rate (Fig. 4c) between the groups across the pre, mid-, and postintervention measurements. The morning heart rate (Fig. 4d) and RPE (Fig. 4e) of the subjects were measured every Monday morning, and the results revealed no significant differences.

Table 4.

Statistical analysis table of subjects’ blood lactate, heart rate, and RPE (N = 18)

	$\overline{X}\pm S$		F	p	95% CI	η2	d
	SHAM (n = 9)	IPC (n = 9)
BLA after 100-m freestyle test
Between-subjects effects			3.711	0.072†	− 1.47 to 0.07	0.188
Pre (mmol/L)	13.9 ± 1.04	13.56 ± 1.12	0.46	0.507	− 0.73 to 1.42		0.31
Mid (mmol/L)	11.88 ± 3.09	12.96 ± 0.90	5.51	0.032*	− 2.05 to − 0.10		0.47
Post (mmol/L)	14.7 ± 0.97	16.07 ± 1.22	6.882	0.018*	− 2.47 to − 0.26		1.24
RPE after 100-m freestyle test
Pre	17.11 ± 0.83	17.22 ± 0.78		0.922			0.14
Mid	17.56 ± 1.01	17.78 ± 1.09		0.739			0.21
Post	18.33 ± 0.50	18.00 ± 1.00		0.552			0.42
HR after 100-m freestyle test
Between-subjects effects			0	0.986	− 8.48 to 8.63	0
Pre (bpm)	188.00 ± 6.71	185.33 ± 8.71	0.529	0.478	− 5.11 to 10.44		0.34
Mid (bpm)	181.33 ± 17.89	183.11 ± 18.20	0.044	0.837	− 19.81 to 16.25		0.10
Post (bpm)	188.67 ± 6.78	189.33 ± 5.29	0.054	0.819	− 6.75 to 5.41		0.11

The ^†p value indicates marginal significance (0.05 < p < 0.10), with a statistical power (1 − β) of 0.78, suggesting a potential risk of Type II error. *p < 0.05, significant differences were observed between the IPC group and the SHAM group after BONFERRONI multiple comparison correction. η² represents the effect size for between-subjects comparisons, and d denotes the effect size for between-subjects pairwise comparisons

Fig. 4.

Blood lactate, RPE, and heart rate scatter box plots and 8-week heart rate and RPE change curve charts

Results of Z score standardization, density distribution, and correlation analysis of the variable indicators

The posttest results revealed significant differences in various variables between the groups, suggesting the application of Z score standardization for 100-m freestyle performance, the Wingate 30 s test, and related physiological and biochemical variables assessed following the 8-week intervention in both the IPC and SHAM groups (Fig. 5a). This figure demonstrates that the changes in each variable effectively differentiate between the IPC and SHAM groups. In the IPC group, the trends for turn performance, tPP, RPE, FI, the latter 50-m performance, and overall 100-m performance were lower than those in the SHAM group, whereas the trends for PP, blood lactate, and AP were greater. Additionally, a density plot (Fig. 5b) was generated from the Z score values of each indicator variable, clearly illustrating the differing trends between the IPC and SHAM groups. The IPC group exhibited a leftward distribution and a downward trend in the performance of the last 50-m segment, 100-m performance, and tPP data. Conversely, the PP, AP, and BLA of the IPC group displayed a rightward distribution, indicating an increasing trend. Furthermore, a group correlation analysis based on the Z score values of each indicator variable was conducted, resulting in a correlation heatmap (Fig. 5c). In the IPC group, a significant positive correlation (p < 0.05) was observed among the performance indicators of the 100-m freestyle. Conversely, the performance indicators of the 100-m freestyle were negatively correlated with anaerobic power indicators and physiological-biochemical indicators, which were more concentrated and pronounced than those of the SHAM group. Notably, in the IPC group, the blood lactate concentration was significantly negatively correlated (p < 0.05) with overall performance in the 100-m freestyle, performance in the last 50 m, and performance in the first 15 m.

Fig. 5.

Z score plot, density plot, and correlation heatmap of test variable metrics. a Z score representation. The color gradient represents the magnitude of the Z score, with smaller Z scores depicted in blue and larger Z scores depicted in red. The size of the circles corresponds to the absolute value of the Z score, where larger absolute Z scores are represented by larger circles and smaller absolute Z scores are represented by smaller circles. Panel b shows the density distribution plot of the Z scores for the test metrics, where the blue area represents the SHAM group and the red area represents the IPC group. Panel c displays a heatmap of correlations among the test metrics, where larger circles indicate greater absolute values of correlation, and smaller circles indicate smaller absolute values of correlation. Redder colors signify stronger positive correlations, whereas bluer colors indicate stronger negative correlations. Statistical significance is denoted as ***p < 0.001, **p < 0.01, and *p < 0.05

Discussion

This study demonstrated that 8 weeks of IPC significantly enhanced the 100-m freestyle performance of male short-distance swimmers. After the 8-week period, the IPC group exhibited a time improvement of 2.33 s compared with the SHAM group, with the most notable performance gains occurring in the final 50 m of the test. Additionally, the improvement in performance was significantly correlated with increased blood lactate accumulation. Furthermore, the 8-week IPC regimen led to substantial increases in PP and AP during the Wingate 30 s anaerobic power assessment but also significantly decreased FI and tPP. This study is the first to substantiate the positive effects of long-term IPC on 100-m freestyle performance, distinguishing it from prior research focused on the acute effects of IPC on swimming performance. This study addresses a critical gap in the understanding of how long-term intervention models influence freestyle training and offers new evidence for the application of IPC in the field of sports science.

Our study indicates that, compared with a placebo intervention, an 8-week IPC intervention effectively enhances performance in the 100-m freestyle, potentially related to improved energy metabolism within the body. Studies have indicated that IPC may enhance pre-exercise pH levels and alleviate the decline in pH during exercise by either reducing proton production or enhancing proton buffering capacity (Carvalho and Barroso 2019; Murry et al. 1990). Once muscle acidosis is mitigated, the activity of glycolytic enzymes is maintained, thereby exhibiting improved glycolytic capacity and fatigue resistance (Carvalho and Barroso 2019; Hargreaves and Spriet 2020), which positively impacts anaerobic performance. Additionally, studies suggest that IPC may regulate energy metabolism by opening mK_ATP channels, which conserve energy expenditure in resting tissues (Pang et al. 1997; Pell et al. 1998). IPC may also promote ATP retention effects by inducing more efficient muscle contractions, enhancing the efficiency of excitation‒contraction coupling, and reducing the activity of ineffective ion pumps (Pang et al. 1995). This optimization leads to more economical energy consumption during exercise (de Groot et al. 2010; Walker and Yellon 1992), potentially benefiting explosive power-related athletic performance. Furthermore, the results of our anaerobic power test corroborate this perspective, as the 8-week IPC intervention improved AP and PP in the Wingate 30-s test, positively influencing athletes’ explosive power and anaerobic endurance performance. However, these findings are not entirely consistent with those of previous studies. Earlier research has indicated that IPC does not enhance the performance of high-level athletes in 5 × 6-s repeated cycling sprints (Gibson et al. 2015), nor does it improve the 30-m repeated sprint running performance of team sport athletes (Gibson et al. 2013). Moreover, IPC has been shown not to enhance the maximal sprint performance of sprinters over 10 or 20 m (Thompson et al. 2018). Collectively, these studies suggest that IPC may not improve explosive athletic performance. This may be attributed to two factors. First, previous research has focused primarily on the impact of acute IPC interventions on explosive exercise performance, neglecting the effects of long-term interventions. Long-term IPC interventions may lead to significant improvements in energy metabolism and muscle contraction efficiency through cumulative effects, which can be sensitively detected via the Wingate 30-s test. Second, although IPC enhances the physiological functions of athletes, the translation of these physiological improvements into enhanced athletic performance is influenced by various factors, such as energy supply, intermuscular coordination, muscle volume, neural reflexes, and environmental conditions (Majumdar and Robergs 2011; MORIN et al. 2011; Nuell et al. 2020; Sandbakk et al. 2011). The Wingate 30-s test, characterized by fixed conditions, regular exercise patterns, and precise recording equipment, can significantly mitigate the impact of external factors, thereby more accurately capturing performance differences. This explains the lack of a significant difference in the performance of the first 15 m in the 100-m freestyle test after 8 weeks, whereas the PPs in the anaerobic power test were significantly different. This finding is consistent with those of previous studies (Gibson et al. 2013). However, this study hypothesizes the potential physiological mechanisms through which an 8-week IPC intervention may enhance 100-m freestyle performance, suggesting that future research could explore specific physiological mechanisms in terms of improvements in energy metabolism efficiency and muscle contraction efficiency.

This study revealed a significant increase in blood lactate levels in the IPC group (16.07 ± 1.22) compared with those in the SHAM group (14.7 ± 0.97, p = 0.018). Correlation analysis revealed a negative correlation between blood lactate levels and 100-m freestyle performance in the IPC group (Fig. 5c), suggesting that IPC may increase lactate production by increasing the activity of glycolytic enzymes, such as phosphofructokinase (Li et al. 2020). Swimmers were able to maintain higher power outputs despite increased lactate accumulation, a phenomenon that is consistent with previous research indicating that IPC enhances anaerobic capacity in elite athletes (Mavroudi et al. 2023). In short-distance swimming competitions, the energy supply relies primarily on anaerobic metabolism (Gastin 2001; Ruiz-Navarro et al. 2025), with the glycolytic system playing a dominant role (Mougios 2020). Additionally, studies have demonstrated that greater blood lactate accumulation is correlated with faster swimming speeds (Morais et al. 2023; Terzi et al. 2021), and both peak blood lactate levels and the rate of accumulation are positively associated with swimming speed (Mavroudi et al. 2023). Furthermore, the Wingate 30 s anaerobic power assessment conducted after 8 weeks revealed a significant decrease in tPP, indicating that athletes in the IPC group achieved peak power more rapidly. Coupled with higher PP and lower FI%, these findings suggest that athletes in the IPC group sustained the test at a higher power level, demonstrating greater average power and superior anaerobic endurance performance. Thus, the 8-week IPC intervention increased the energy supply from the glycolytic system, providing athletes with improved anaerobic endurance capabilities. However, while anaerobic metabolism plays a critical role in 100-m swimming, it is essential to acknowledge the influence of aerobic metabolism on performance in 100-m freestyle swimming. Previous studies have indicated that during full-effort 30-s exercise, 50% of the energy in the final 5 s is derived from aerobic metabolism (Hargreaves and Spriet 2020; Parolin et al. 1999). Additionally, research has shown that acute IPC can increase the maximum oxygen uptake of professional athletes by 3% (de Groot et al. 2010). IPC may enhances mitochondrial function, facilitating the entry of acetyl-CoA into the tricarboxylic acid cycle more efficiently, thereby improving aerobic metabolism, promoting oxidative phosphorylation, and generating a greater energy supply (Jean-St-Michel et al. 2011). This is particularly significant for energy provision during the latter half of the 100-m freestyle. However, this study lacks specific evidence that IPC can improve energy metabolism efficiency, and it relies on inferences drawn from previous research. Future studies can validate this through plasma metabolomics and proteomics, and explore the contribution rates of aerobic and anaerobic improvements.

We further explored the impact of an 8-week IPC intervention on segmental performance in the 100-m freestyle. The study focused on analyzing critical phases of the race, including the first 15 m, the first 50 m, the last 50 m, and the turn times, as these stages are essential components of overall performance in the 100-m freestyle (Marinho et al. 2020; Peterson Silveira et al. 2018; Veiga et al. 2016). Our findings indicate that, following the 8-week intervention, only performance in the last 50 m significantly improved. This result aligns with the notable reduction in FI% observed in the anaerobic power test, suggesting that long-term IPC intervention may positively influence athletes’ endurance and performance during the sprint phase. The observed increase in 100-m freestyle performance can be attributed to improved endurance in the last 50 m and increased fatigue resistance. Previous studies have demonstrated that IPC can stimulate the endogenous production of substances such as adenosine, bradykinin, and NO (Das et al. 2008; Koda et al. 2010; Yin et al. 2022). Elevated adenosine levels can promote vasodilation, thereby augmenting blood flow to muscles (de Groot et al. 2010) and mitigating the decline in vascular function after high-intensity exercise (Enko et al. 2011; Kraemer et al. 2011). Additionally, NO plays a crucial role in regulating microvascular dilation and possesses anti-inflammatory properties (Tapuria et al. 2008). These mechanisms collectively enhance microcirculation and oxygenation, ultimately increasing muscle endurance. Additionally, animal experiments have shown that IPC enhances antioxidant capacity and reduces oxidative damage by activating the Nrf2/HO-1 signaling pathway( He et al. 2019; Sandberg et al. 2014), which may explain the observed reduction in the fatigue index. IPC also enhances the activity of the pentose phosphate pathway, which aids in improving resistance to oxidative stress and alleviating the accumulation of muscle fat resulting from high-intensity exercise (Ou et al. 2024). Furthermore, studies indicate that after 10 consecutive days of IPC intervention, the expression of substances such as acetylsalicylic acid ester, ethionamide, and piperic acid in the body is significantly upregulated. These substances contribute positively to the body’s anti-inflammatory response (Du et al. 2023). Moreover, a metabolomics study following acute IPC intervention and subsequent high-intensity exercise revealed that metabolites associated with antioxidant stress and inflammatory responses were significantly upregulated during high-intensity exercise (Ou et al. 2024). Therefore, this study suggests that an 8-week IPC intervention may enhance the body’s ability to resist fatigue in the latter half of the 100-m freestyle by increasing endogenous protective substances and improving antioxidant stress and inflammatory responses, thereby maintaining a higher power output. However, these inferences of possibilities are based on assumptions from prior research. Future studies can further elucidate the capacity of long-term IPC to enhance the body’s resistance to fatigue by monitoring changes in substances such as adenosine, bradykinin, and nitric oxide, as well as assessing vascular function.

Our study revealed that after 4 weeks of IPC intervention, there was no significant difference in 100-m freestyle performance or first 15-m performance between the two groups of experimental subjects, which aligns with the results of the anaerobic power test. No significant differences were observed in the PP, AP, FI%, or tPP of the Wingate 30 s test between the groups. These findings suggest that a mere 4 weeks of IPC intervention is insufficient to reliably enhance the athletic performance and anaerobic power of swimmers. This finding contrasts with a study involving 4 weeks of IPC combined with sprint interval training (Bouffard et al. 2021), which may be attributed to the integration of IPC with specific training on a power bicycle in that study, whereas our research focused primarily on water training, resulting in divergent outcomes due to differing exercise modalities and training methodologies. Furthermore, another study indicated that acute IPC can improve the performance of elite 100-m freestyle swimmers by an average of 0.7 s (Jean-St-Michel et al. 2011). The results of our study contradict this, potentially because the exercise test was not conducted immediately after the fourth week of training but was scheduled for the day following the training session, at which point the acute effects of IPC on enhancing athletic performance may have already diminished (Lisbôa et al. 2016). Additionally, the long-term benefits of IPC in improving athletic performance have not yet stabilized. The significant effects observed after 8 weeks imply that long-term interventions necessitate a period of cumulative adaptation, which may be associated with IPC’s role in promoting angiogenesis (Kawata et al. 2001). Research has shown that continuous daily IPC for 4 weeks significantly increases the content of Vascular Endothelial Growth Factor (VEGF), which is a key substance that promotes angiogenesis (Kawata et al. 2001; Kimura et al. 2007). Other studies have indicated that after stimulating angiogenesis with VEGF for 32 days, a cessation of 8 days results in the presence of only a few blood vessels, which are functionally unstable (Dor et al. 2002). Therefore, a longer duration of VEGF stimulation is required for the stabilization of new blood vessel function. In our study, we intervened only three times a week, which necessitates a longer duration for the stabilization of new blood vessel function. This explains the effectiveness of 8 weeks of IPC compared to the ineffectiveness of 4 weeks. Furthermore, the effectiveness of long-term IPC may also be related to factors such as mitochondrial biogenesis, antioxidant stress, and inflammatory responses, which require further research for validation (Marocolo et al. 2025). Despite the significant differences noted in the midterm test results for the first 50 m of swimming, turning performance, and blood lactate levels between groups, these differences were insufficient to yield a significant improvement in 100-m freestyle performance. Furthermore, correlation analysis of posttest indicators revealed that improvements in 100-m freestyle performance within the IPC group were significantly positively correlated with enhancements in the first 15 m, the first 50 m, the last 50 m, and turn performance, suggesting that performance at each stage contributes to overall 100-m performance. In contrast, the 100-m performance in the SHAM group was only correlated with turn performance and the last 50 m. Consequently, this study demonstrates that a 4-week IPC intervention positively impacts the performance of 100-m freestyle swimmers; however, this effect is modest, and at least an 8-week IPC intervention is necessary to achieve significant differences.

This preliminary study investigated the effects of an 8-week IPC on 100-m freestyle performance; however, it has significant limitations. First, due to the significant pressure differences between groups, complete blinding could not be achieved, resulting in potential placebo or nocebo effects. Therefore, the results regarding exercise performance should be interpreted with caution, and future studies should implement improved blinding designs and evaluations. Second, the absence of multiomics data (e.g., metabolomics/proteomics) and muscle biopsy data restricts the ability to draw mechanistic insights, leaving them largely theoretical. Third, this study primarily involves male subjects and has a limited sample size, which constrains the generalizability of the findings. Additionally, the small sample size may increase the sensitivity of the results to extreme values or randomness, thus reducing the precision of the estimates and limiting our ability to detect smaller but potentially meaningful effects. In interpreting the main findings, we will closely relate them to the reported effect sizes and confidence intervals, avoiding overinterpretation of statistical significance, while emphasizing the direction of the effects and their possible ranges. Future research should include female athletes and larger populations to enhance its applicability. Fourth, the lack of follow-up testing impedes the evaluation of the long-term effects of IPC, which should ideally track the duration of these effects at 2-week, 3-week, and 4-week intervals. Fifth, the observed 2.33-s improvement in the IPC group’s 100-m performance compared with the SHAM group may be influenced by the athletes’ competitive level, necessitating further validation of the intervention’s effects on higher-level athletes. Fifth, This study did not systematically control and record nutritional status, sleep quality, and sporadic daily physical activities unrelated to swimming. These factors may act as confounding variables affecting the results of athletic performance and may partially explain the individual differences observed. However, measures such as controlling the homogeneity of the experimental subjects, assessing the effects of interventions, and monitoring daily routines have ensured, to some extent, the stability of the experimental results. Future research should attempt to monitor (e.g., using dietary logs, activity trackers, and sleep monitoring devices) or standardize these variables more rigorously to more accurately assess the independent effects of IPC. Lastly, this study employed Two-way repeated-measures ANOVA to examine both group and time effects. While mixed-effects models offer advantages in addressing individual random effects, and analysis of covariance (ANCOVA) provides more nuanced control over baseline differences, the selection of Two-way repeated-measures ANOVA in this study was justified by its compatibility with the research design, the lack of significant baseline differences across groups, and the integrity of the data. All analyses were subjected to Bonferroni correction to mitigate the risk of multiple comparison errors. Future research may benefit from the adoption of mixed-effects models to investigate individual differences more comprehensively.

Conclusion

The findings of this study indicate that integrating IPC into a periodized training plan during an 8-week precompetition phase can effectively enhance anaerobic endurance in swimmers, thereby optimizing competition performance. This training strategy offers significant guidance for coaches aiming to maximize athletes’ physiological adaptations during critical training phases. Specifically, implementing a bilateral thigh IPC intervention three times per week, with each session lasting 40 min over 8 weeks, can lead to substantial improvements in athletes’ 100-m freestyle performance and anaerobic power output as measured by the Wingate 30 s test.

In practical training applications, it is advisable for coaches to incorporate long-term IPC interventions into training programs to further increase athletic performance potential. Notably, this intervention protocol is also applicable to other sports that demand high anaerobic power output. Importantly, IPC intervention should be sustained for at least 8 weeks to potentially achieve stable physiological effects, with these effects lasting for more than a week. Consequently, coaches can initiate IPC intervention 8 weeks prior to major competitions to ensure that the competition period aligns with the optimal efficacy window. However, the specific temporal characteristics of the dissipation of long-term IPC effects still require further investigation for comprehensive elucidation. Additionally, while this study observed a synchronous enhancement of baseline anaerobic capacity (assessed via the Wingate 30 s anaerobic power assessment) and swimming performance, due to the ecological validity limitations of the testing methods, it is recommended that future research incorporate anaerobic power assessment methods specific to swimming (such as tethered swimming sprints) to more directly explore the impact of IPC on swimming propulsion power and efficiency.

Supplementary Information

Below is the link to the electronic supplementary material.

Author contributions

W.W.Y. and Z.Y.O. contributed equally as co-first authors. W.W.H., W.W.Y., X.Y., G.Q.X. and Z.Y.O. designed and planned the study. X.Y., W.W.H., and W.W.Y. performed the experiments. G.Q.X., Z.Y.O., W.W.Y., S.B.Z., and X.Y.B. analyzed the data. Z.Y.O. and G.Q.X. drafted the manuscript with critical revisions from all the authors. All the authors read and approved the final manuscript.

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Special Fund for Biomedical and Health Key Fields of General Universities in Guangdong Province (2023ZDZX 2033) and the Guangdong Province General University Innovation Team Project (2023WCXTD011).

Data availability

The data are available from the corresponding author upon reasonable request. Correspondence and requests for materials should be addressed to the corresponding author.

Declarations

Conflict of interest

The authors have no competing interests related to this work.

Ethical approval

The studies involving humans were approved by the Ethics Committee of Guangzhou Sport University (ID number: 20242c22-95). The studies were conducted in accordance with local legislation and institutional requirements. The participants provided written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.

Consent for publication

The work described has not been published previously. The article is not under consideration for publication elsewhere. The article’s publication is approved by all authors and tacitly or explicitly by the responsible authorities where the work was carried out. If accepted, the article will not be published elsewhere in the same form, in English or in any other language, including electronically, without the written consent of the copyright holder.