Mild hyperbaric oxygen therapy on subjective sleep quality, aerobic, anaerobic exercise test, brain and muscle tissue oxygenation response in Chinese University male athletes after muscle fatigue

Abstract

Background

This study aimed to investigate the recovery effects and timing sequence of Mild Hyperbaric Oxygen Therapy (MHOT) on physiological responses and exercise capacity following muscle fatigue induced by simulated cycling exercise.

Methods

This study employed a controlled crossover design. Twelve Chinese secondary national-level male athletes participated in two identical trials (CON and MHOT). Each trial consisted of one daily session for six consecutive days. In each session, participants first completed 90 min of cycling exercise to induce fatigue. Afterward, six participants underwent the CON intervention, while the other six received MHOT; the interventions were switched in the alternate trial. Physiological outcomes included the Pittsburgh Sleep Quality Index (PSQI), aerobic exercise exhaustion test (maximal oxygen uptake (V̇O₂max), maximal cycling power, maximal cycling time), Wingate anaerobic test, and brain and muscle tissue oxygenation indices. Measurements were taken at six time points across the study.

Results

After six interventions, the MHOT group showed significantly better subjective sleep quality compared with CON (p < 0.05). MHOT also increased muscle and brain oxygenated hemoglobin as well as muscle total hemoglobin (p < 0.05). At 24 h after the sixth intervention, the MHOT group demonstrated higher V̇O₂max, maximal cycling power, and cycling time compared with CON (p < 0.05). However, no significant differences were observed between MHOT and CON in Wingate test indices (p > 0.05).

Conclusions

Repeated MHOT sessions promoted recovery by improving subjective sleep quality, enhancing brain and muscle oxygenation, and augmenting aerobic exercise performance compared with CON. However, MHOT did not significantly influence anaerobic capacity as assessed by the Wingate test.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13102-026-01587-y.

Introduction

Muscle fatigue, marked by a decrease in force generation capacity post-exercise, is a common occurrence that impairs performance in sports and other strenuous activities [1]. Training and competition induced fatigue in athletes may be one frequent cause of stagnant or reduced performance in their special disciplines [2]. Due to the large amount of energy expenditure associated with excessive training, soccer players and middle-long distance athletes is highly prone to muscle fatigue, that will lead to many adverse consequences, such as burnout, injuries, sleep impairments, and reduced sports performance [2, 3]. Specific recovery strategies may assist in decrease the match-induced physiological and/or psychological alterations [4], and in order to accelerate the time to achieve full recovery and potentially reduce the risk of injury [5]. Oxygen level is closely related to exercise training and competition, at present, administration of supplemental oxygen is not prohibited by the World Anti-Doping Agency [6]. Therefore, many oxygen-related recovery methods are applied in post-training and competition recovery, such as hyperoxia [6, 7], hyperbaric oxygen therapy (HBOT) [8, 9]. Some studies suggest that hyperoxia and HBOT have positive effects on the body’s fatigue recovery and athletic performance [6–9]. However, some studies suggest their effects are insignificant [6, 9], and improper use may lead to various adverse reactions such as barotrauma, increased oxidative stress, oxygen toxicity [10, 11]. Therefore, both hyperoxia and HBOT can cause beneficial or detrimental outcomes, depending on the intensity and duration of treatment, as well as on the resilience of the subject [11].Thus, subsequently more studies are needed to clarify the link between oxygen therapy and exercise fatigue recovery.

Mild Hyperbaric Oxygen Therapy (MHOT) is characterized by exposure to slightly elevated atmospheric pressures (1.24–1.29 ATA) and enriched oxygen levels (25%–40%), and it has emerged as a promising alternative for mitigating exercise-induced fatigue [12, 13]. MHOT intervention has includes its relative accessibility, non-invasive nature, and reported safety profile in clinical contexts, which make it a practical candidate for investigation in sports recovery. Some studies believe that, MHOT can improvement of functional imbalances of autonomic (sympathetic and parasympathetic) nerves, maintenance and improvement of health, and physical fitness, and early recovery from an injury [12]. We consider that MHOT facilitate post-exercise recovery through several interconnected mechanisms: by improving mitochondrial oxidative capacity and ATP resynthesis for performance metrics; by attenuating exercise-induced oxidative stress and modulating inflammatory pathways, which may influence perceived recovery and sleep quality; and by optimizing tissue oxygen delivery and microcirculatory function [12, 13]. While existing studies have explored MHOT in combination with exercise, variability in key parameters limits result comparability, leaving a research gap in MHOT’s impact on exercise recovery specifically aim at athletic sports performance, sleep quality, and oxygen-related adaptations indicator. In addition, the lack of consensus on the efficacy of MHOT for post-exercise recovery in athletes, and the need to investigate MHOT impact on integrated physiological and performance markers.

Therefore, to address the urgent need for understanding oxygen therapy in exercise recovery and fill the critical research gap on MHOT’s role in this field, we conducted this study aims to examines the effects of single and repeated MHOT on aerobic/anaerobic exercise performance and subjective sleep quality, systemic oxygen levels, aiming to explore MHOT role in post-exercise fatigue recovery, refine MHOT in exercise recovery theoretical frameworks, and enhance MHOT practical applications. The selection of subjective sleep quality, aerobic/anaerobic performance, and muscle/brain oxygenation—is therefore grounded in their direct theoretical connection to these underlying physiological pathways, which have been suggested not conclusively demonstrated in prior MHOT research on athletic populations. We hypothesize that, a single and repeated session of MHOT can improve subjective perceived sleep quality and enhance tissue oxygen kinetics and aerobic/anaerobic capacity, thereby facilitating enhanced recovery from exercise-induced fatigue, compared to normoxia control.

Methods

Experimental design

This study employed a controlled crossover design (Fig. 1) involving 12 participants [13, 14]. Over two non-consecutive weeks, subjects completed a six-day exercise protocol designed to induce muscle fatigue. Across the study, each participant performed 12 exercise sessions. After each session in the first week, participants were randomly assigned to one of two intervention sequences using a randomization schedule: one received the control condition (CON), while the other received mild hyperbaric oxygen therapy (MHOT). In the second week, the interventions were switched so that all participants experienced both treatments. A one-week washout period separated the two cycles [13, 15]. Measurements were taken at six time points: baseline, post-1 exercise, post-1 intervention, post-6 exercises, post-6 interventions, and post 6 intervention 24 h (measurements taken 24 h after the final (sixth) intervention session). The outcomes assessed included:

Fig. 1.

Study design. CON: Control group; MHOT: Mild Hyperbaric Oxygen Therapy group

• Subjective sleep quality: Pittsburgh Sleep Quality Index (PSQI).

• Aerobic exercise capacity: maximal oxygen uptake (V̇O₂max), maximal cycling power, and maximal cycling time.

• Anaerobic exercise capacity (Wingate test): peak power, mean power, lowest power, and fatigue index.

• Tissue oxygenation: oxygenated hemoglobin (O₂Hb), deoxygenated hemoglobin (HHb), and total hemoglobin (tHb).

Participants

The sample size of our study was based on prior sports and oxygen therapy (Hyperoxia [16], HBOT [17], MHOT [13–15, 18]) similar research design. And determined using the power analysis program G*Power, the a priori power analysis was calculated using the F-test family (i.e., ANOVA repeated measures within-between interaction; power = 0.8, alpha = 0.05, effect size f = 0.5). Furthermore, this sample size was determined based on considerations of ensuring experimental feasibility and minimizing participant attrition. The cohort consisted of male collegiate athletes from Capital University of Physical Education and Sports, all classified as Chinese secondary national-level players (Sub-elite competitive tier athletes had qualified for the Chinese National University Sports Championships). They trained five times per week, averaging 8.33 ± 1.87 h per week, with a training history of 6.66 ± 2.10 years (range: 10–18 years). The sample included six middle- to long-distance runners and six soccer players. Participant characteristics were: age 20.41 ± 1.31 years, height 176.75 ± 3.49 cm, body mass 70.58 ± 6.27 kg, and V̇O₂max 47.30 ± 3.72 ml·kg⁻¹·min⁻¹. All participants underwent medical screening, which confirmed excellent health, no history of oxygen therapy, and no relevant disorders. They were instructed to refrain from exercise for one week before and throughout the study period, and additional exercise outside the study protocol was prohibited. The study was approved by the Ethical Committee of the China Institute of Sport Science (approval project number CISSLA-2020062801) and performed in accordance with the ethical standards laid down in the Declaration of Helsinki. All participating subjects were informed of the risks and benefits of the study before any data collection and then signed the approved informed consent document.

Exercise and experimental control procedures

The subjects proceeded to the laboratory before the experiment for a preliminary testing procedure. All participants completed a graded exercise test on cycle ergometer to assess their V̇O₂max. After the preliminary exercise, all subjects were familiarized with the exercise protocol.

The exercise protocol 90-minute workload was individualized, prescribed at a specific percentage of each participant’s own measured V̇O₂max to standardize relative intensity. The exercise protocol consisted of 90 min of cycling [19], segmented into three consecutive 30-minute intervals at intensities corresponding to 65%, 70%, and 75% of V̇O₂max, respectively. Beginning with a 5-minute warm-up at 100 W, the session progressed with the ergometer’s power adjusted to match the predetermined percentages of each participant’s V̇O₂max. This cycling exercise consisted of continuous pedaling without any rest intervals, maintaining a pedaling speed range of 60 to 70 rpm in exercise [20]. If a participant could not sustain ≥ 60 rpm for > 60 s despite encouragement, the test would be terminated. This cadence range was chosen to balance muscular endurance and central cardiovascular demand while cause muscular fatigue. In addition, the fatigue model is defined based on the evaluation of multiple objective fatigue verification measures used such as heart rate, rating of perceived exertion (RPE), and creatine kinase (CK) [13]. The protocol was rigorously followed once daily for six days, culminating in 12 cycles throughout the study, with the regimen standardized across both participant groups. All subjects completed the exercise program. This entire protocol was very time-consuming and exhausting for subjects, to control baseline fatigue, participants were instructed to refrain from all strenuous exercise and sport-specific training outside the experimental sessions throughout the study. Additionally, immediately before commencing the six-day fatigue protocol, their baseline state was confirmed via a short wellness questionnaire assessing muscle soreness, fatigue, sleep quality, and motivation. Only participants reporting a recovered state (scores within a normal range) proceeded with the protocol.

The study was conducted in a Beijing laboratory during autumn. Participants provided a 3-day dietary record prior to the study and maintained daily logs throughout. They were required to avoid smoking, alcohol, caffeine, and spicy foods, and others recovery modalities, or intake of supplements/ergogenic aids and to arrive fully hydrated throughout the entire experimental period. Strenuous exercise, caffeine, and alcohol were prohibited 24 h before testing. Laboratory conditions were maintained at 22–28 °C and 45–55% relative humidity. In this study, participants’ well-being and readiness were monitored daily using morning resting heart rate and a short subjective wellness questionnaire. Any significant adverse trends would have prompted a review, but none were observed. All measurements were performed by the same experimenter on a fixed schedule to minimize human-related bias and diurnal variation. Trials were conducted at the same time each day to control for circadian effects.

Treatment protocol

All participants underwent both recovery modalities. Based on previous studies [12–14], the CON group participants rested on a sofa bed in a separate, quiet room adjacent for 60 min (1 ATA, 20.9% O₂, 22–28 °C) to but isolated from the chamber laboratory, that followed an identical time-matched schedule as the MHOT. The MHOT group received intervention in a mild hyperbaric chamber (Beijing Chuangxin Kaida Technology Co., Ltd). The oxygen concentration was continuously monitored and maintained within a narrow range of 26–28% throughout all MHOT sessions, and the pressure was stabilized at 1.25 ATA, that as preset and verified by the device’s calibrated sensors. MHOT intervention chamber with temperature maintained at 24–26 °C, that participants remained Lie down in the oxygen chamber, breathed normally, and that chamber pressure was monitored continuously, added confirming participant compliance and reported comfort. CON and MHOT session lasted 60 min immediately post-exercise, once daily for six days, totaling six sessions per intervention. No another information on the research topic and the recovery method was provided to the participant to limit a potential placebo effect.

Measurement data collection

Participants completed the Chinese Pittsburgh Sleep Quality Index (CPSQI), a validated self-report tool assessing sleep quality over seven components. Each component (e.g., sleep latency, efficiency, disturbances) is scored 0–3, with total scores ranging from 0 to 21; higher scores indicate poorer sleep [21]. The standard instruction for the PSQI version specifies a measurement period of one month (i.e., four weeks). However, we drawing on the application of the PSQI in sports science research [22–24] and in consideration of the practical needs [25]– [26] of our study, we adjusted the measurement period to seven days. The CPSQI was administered before and after each one trial to evaluate subjective sleep quality and disturbances at baseline and post 6 intervention 24 h in each groups.

The aerobic V̇O₂max test was conducted on a motorized cycle ergometer (VIA sprint 150 P, Germany), with cardiopulmonary data recorded breath-by-breath (Cortex Metalyzer 3B, Germany). And use of a Monark 894E cycle ergometer, calibrated before and after the study according to the manufacturer’s instructions. The test started at 10 W and increased by 20 W every 2 min until exhaustion (pedaling speed: 60–70 rpm) [27]. Both the preliminary screening test and the main V̇O₂max test used the identical incremental protocol on the same calibrated cycle ergometer. Exhaustion criteria included: a plateau in oxygen uptake despite an increase in workload; respiratory exchange ratio (RER) > 1.1; heart rate (HR) > 90% predicted maximum; and subjective signs of exhaustion preventing further cycling. V̇O₂max (ml/min/kg), maximal power (w), and maximal cycling time (min) were measured at baseline and post 6 intervention 24 h in each groups.

The Wingate test involved a 30-second maximal sprint on a Monark 894E ergometer against a resistance of 8.3% body mass. After warm-up and a 5-second countdown, participants pedaled maximally with verbal encouragement. Flywheel revolutions were recorded electronically [28]. That Wingate power output was sampled at 10 Hz and calculated using standard formulas by the Monark Anaerobic Test software. peak power (w), mean power (w), lowest power (w), and fatigue index (w/s/kg) were measured at baseline, post-1 exercise, post-1 intervention, post-6 exercises, post-6 interventions, and 24 h after the 6 intervention.

Near-infrared spectroscopy (NIRS) was performed using PortaMon (Artinis Medical Systems) for muscle and OctaMon (Artinis) for brain oxygenation. Both devices operate at 760 and 850 nm wavelengths to measure O₂Hb (uM/L), HHb (uM/L), and tHb (uM/L) concentrations via the Beer-Lambert law [29, 30]. The PortaMon was fixed on the vastus lateralis with waterproof tape and strapping to minimize motion and ambient light. The OctaMon was attached to the forehead with a headband, and the prefrontal cortex optodes were placed over the left dorsolateral prefrontal cortex, in accordance with the international 10–20 EEG system (approximating F3 position). Data from selected channels (TX1, TX2 and TX3 in RX1 for PortaMon vastus lateralis muscle); (TX1 in RX1, TX3 in RX1, TX5 in RX2 and TX7 in RX2 for OctaMon prefrontal cortex) were recorded at 10 Hz via Bluetooth and analyzed at six time points, each test lasting 4 min. We were ensuring secure sensor fixation with adhesive and opaque coverings, conducting measurements in a controlled environment, and performing visual inspection of signal stability during data collection. During all testing, the system was connected to a personal computer via Bluetooth™ technology for data acquisition, analog-to-digital conversion and subsequent analysis. In this study, that no missing data were present and that all 12 participants completed every measurement at all time points.

Statistical analyses

All data were expressed as means ± standard deviations (X ± SD). Statistical analyses were performed using SPSS (IBM Corp, Armonk, NY). p < 0.05 was considered statistically significant. A two-way repeated measures ANOVA was applied to examine the effects of time and recovery method (CON vs. MHOT). PSQI scores and aerobic parameters (V̇O₂max, maximal power and time) were analyzed across two time points (baseline and 24 h post-6 interventions). Wingate performance indices (peak, mean, lowest power; fatigue index) and tissue oxygenation (O₂Hb, HHb, tHb) were compared across six time points. The assumptions of normality (Shapiro–Wilk test) and homogeneity of variance were tested. Mauchly’s sphericity test was used to validate the two-way repeated measures ANOVA. In the case of violation of the assumption of sphericity, significance was established by using the Greenhouse-Geisser correction. A test of simple effects was performed when a significant interaction was detected at the 0.05 level. Within-condition comparisons were performed to detect differences in the same condition at specific time points, and between-condition comparisons were performed to detect differences in condition at the same time point. Effect sizes (ES) were interpreted according to Cohen’s guidelines (small: 0.2–0.5; medium: 0.5–0.8; large: >0.8) [31].

Results

Pittsburgh sleep quality index score

The results shown in Table 1 indicated a significant time × group interaction (F(1,22) = 18, p < 0.001, η² = 0.450) and a main effect of time (F(1,22) = 12.5, p < 0.002, η² = 0.362) for PSQI total score. Post-hoc analysis showed that the MHOT group had significantly lower PSQI total score compared to both the CON group (p < 0.001, ES = 0.80) and baseline (p < 0.001, ES = 0.79) at 24 h after the sixth intervention.

Table 1.

Baseline to Pos- 6 intervention 24 h of the subjective PSQI score changes (n = 12) across two conditions

PSQI scores	CON		MHOT
	Baseline	Post 6 intervention 24 h	Baseline	Post 6 intervention 24 h
Total score	4.83 ± 1.19	5.00 ± 1.20	4.25 ± 0.75	2.41 ± 0.66* a
Sleep quality	0.75 ± 0.45	1.33 ± 0.65*	0.58 ± 0.51	0.41 ± 0.51a
Sleep latency	0.75 ± 0.45	1.16 ± 0.83	0.66 ± 0.49	0.75 ± 0.73
Sleep duration	0.66 ± 0.49	0.91 ± 0.51	0.58 ± 0.51	0.33 ± 0.49 a
Habitual sleep efficiency	0.66 ± 0.49	0.75 ± 0.45	0.58 ± 0.51	0.66 ± 0.49
Sleep disturbance	0.58 ± 0.51	0.50 ± 0.52	0.58 ± 0.51	0.41 ± 0.51
Use of sleeping medications	0 ± 0	0 ± 0	0 ± 0	0 ± 0
Daytime dysfunction	0.91 ± 0.66	1.08 ± 0.79	0.91 ± 0.66	0.83 ± 0.57

CON: Control group; MHOT: Mild Hyperbaric Oxygen Therapy group; PSQI: Pittsburgh Sleep Quality Index

* Significantly different from Baseline (p < 0.05)

a Significantly different from the CON group (p < 0.05)

A significant time × group interaction was found in PSQI sleep quality score (F(1,22) = 5.091, p = 0.034, η² = 0.188), though no main effect of time was observed (F(1,22) = 1.571, p = 0.223, η² = 0.067). Post-hoc analysis revealed that the MHOT group showed a significant decrease compared to the CON group at 24 h post-sixth intervention (p < 0.001, ES = 0.61).

No significant main effect of time (F(1,22) = 0.001, p > 0.99, η² < 0.001) or time × group interaction (F(1,22) = 3.882, p = 0.062, η² = 0.150) was observed for PSQI sleep duration score. However, simple effects analysis indicated that the MHOT group scored significantly lower than the CON group at 24 h post-intervention (p = 0.010, ES = 0.50). There were no statistically significant differences between the two groups regarding the other PSQI subscales (all p > 0.05).

Muscle and brain oxygen saturation

As shown in Fig. 2, no significant time × group interaction was found for muscle O₂Hb at TX1 (F(3.930,86.471) = 1.713, p = 0.155, η² = 0.072) or TX3 (F(4.112,90.465) = 1.047, p = 0.389, η² = 0.045), but a significant interaction was observed at TX2 (F(3.219,70.809) = 2.990, p = 0.033, η² = 0.120). A main effect of time was significant across all channels (TX1 F(3.930,86.471) = 8.547,p < 0.001, η² = 0.280; TX2 F(3.219,70.809) = 11.718,p < 0.001, η² = 0.348; TX3 F(4.112,90.465) = 7.854,p < 0.001, η² = 0.263). Post-hoc tests indicated that the MHOT group showed significantly higher O₂Hb than the CON group at TX2 and TX3 after one intervention (TX2: p = 0.003, ES = 0.92; TX3: p = 0.028, ES = 0.85), and at TX1 and TX2 after six interventions (TX1: p = 0.006, ES = 0.90; TX2: p = 0.034, ES = 0.84). Muscle tHb at TX3 showed a significant time × group interaction (F(4.359,95.909) = 2.509, p = 0.042, η² = 0.102), but not at TX1 (F(3.156,69.427) = 1.926, p = 0.131, η² = 0.080). A main effect of time was significant for both TX1 (F(3.156, 69.427) = 27.576, p < 0.001, η² = 0.556) and TX3 (F(4.359, 95.909) = 26.114, p < 0.001, η² = 0.543). Post-hoc tests revealed that the MHOT group had significantly higher tHb than the CON group at both TX1 (p = 0.003, ES = 0.92) and TX3 (p = 0.005, ES = 0.91) after six interventions. However, no significant differences were observed in muscle HHb at any channel across time points (all p > 0.05).

Fig. 2.

Effects of different interventions on muscle tissue oxygenation indicator at six-time points (TX1;TX2;TX3: O₂Hb; HHb; THb). ^a Significantly different from the CON group (p < 0.05)

Figure 3 reveals no significant time × group interaction for brain O₂Hb at TX1, TX3, TX5, TX7 (F(4.344,95.561) = 1.749,p = 0.140, η² = 0.074; F(4.120,90.630) = 1.397,p = 0.240, η² = 0.060; F(3.799,83.574) = 0.209,p = 0.926, η² = 0.009; F(3.811,83.841) = 0.677,p = 0.603, η² = 0.030, respectively). However, a significant main effect of time was observed at all channels (F(4.344,95.561) = 5.199,p < 0.001, η² = 0.191; F(4.120,90.630) = 12.917,p < 0.001, η² = 0.370; F(3.799,83.574) = 8.114,p < 0.001, η² = 0.269; F(3.811,83.841) = 10.944,p < 0.001, η² = 0.332 respectively). Post-hoc analysis indicated that the MHOT group exhibited significantly higher O₂Hb than the CON group after one intervention at TX1 (p = 0.046, ES = 0.83), TX3 (p = 0.006, ES = 0.90), and TX7 (p = 0.028, ES = 0.85), and at TX1 after six interventions (p < 0.001, ES = 0.93). No significant differences were found in brain HHb (TX1, TX3, TX5,TX7) and brain tHb (TX1, TX3, TX5,TX7) at any channel across time points (all p > 0.05).

Fig. 3.

Effects of different interventions on brain tissue oxygenation indicator at six-time points (TX1;TX3;TX5;TX7: O2Hb;HHb;THb). ^a Significantly different from the CON group (p < 0.05)

Aerobic exercise exhaustion test

Results from Fig. 4 showed no significant main effect of time on V̇O₂max (F(1, 22) = 0.634,p = 0.434, η² = 0.028). In contrast, significant main effects of time were observed for both maximal cycling power (F(1, 22) = 6.494,p = 0.018, η² = 0.228) and maximal cycling time (F(1, 22) = 9.330,p = 0.006, η² = 0.298). Significant time × group interactions were found for V̇O₂max (F(1, 22) = 29.271,p < 0.001, η² = 0.571), maximal cycling power (F(1, 22) = 6.494,p = 0.018, η² = 0.228), and maximal cycling time (F(1, 22) = 11.518,p = 0.003, η² = 0.344). Simple main effects analysis indicated that, compared with the CON group, the MHOT group exhibited significantly higher V̇O₂max (p = 0.023,ES = 0.49), maximal cycling power (p = 0.040, ES = 0.40), and maximal cycling time (p = 0.019, ES = 0.46) at 24 h after the sixth intervention. Additionally, within the MHOT group, all three measures were significantly increased at Post 6 relative to Baseline (V̇O₂max: p < 0.001, ES = 0.28; power: p = 0.002, ES = 0.31; time: p < 0.001, ES = 0.47).

Fig. 4.

Effects of different interventions on aerobic exercise test indicator at baseline and pos- 6 intervention 24 h (V̇O₂max; maximal cycling power; maximal cycling time).^* Significantly different from Baseline (p < 0.05). ^a Significantly different from the CON group (p < 0.05)

The wingate test

The results from Fig. 5 indicated that peak power, mean power, lowest power, fatigue index did not exhibit a significant time group interaction effect (F_{(2.816, 61.955)} = 1.577,p = 0.206, η² = 0.067 ; F_{(3.500,77.006)} = 1.129,p = 0.347, η² = 0.049 ; F_{(4.012,88.256)} = 0.417,p = 0.796, η² = 0.019 ; F_{(3.928,86.425)} = 0.838,p = 0.503, η² = 0.037, respectively). However, A significant main effect of time was observed for peak power (F _{(2.816, 61.955)} = 33.400, p<0.001, η² = 0.603) ; mean power (F_{(3.500, 77.006)} = 32.662,p<0.001, η² = 0.598) ; lowest power (F_{(4.012, 88.256)} = 14.979,p<0.001, η² = 0.405) ; fatigue index (F_{(3.928, 86.425)} = 3.212, p = 0.017, η² = 0.127). But, in the simple main effects analysis revealed that no significant difference in peak, mean, lowest power and fatigue index between the two groups at the six-time points(all p>0.05).

Fig. 5.

Effects of different interventions on Wingate test indicator at six-time points (peak power, mean power, lowest power, fatigue index)

Discussion

This study examined the effects of single and repeated MHOT on subjective sleep quality, aerobic/anaerobic performance, and post‑exercise cerebral/muscular oxygenation recovery. Repeated MHOT improved subjective sleep quality. Additionally, both single and repeated MHOT enhanced post‑fatigue recovery of muscle oxygen content (notably O₂Hb and tHb) in specific channels and elevated cerebral O₂Hb in specific channels. Repeated MHOT also effectively maintained aerobic capacity. However, neither single nor repeated MHOT interventions significantly altered anaerobic capacity as assessed by the Wingate test.

Muscle fatigue model

This study employed a multidimensional approach to assess exercise-induced muscle fatigue, combining subjective ratings (RPE > 19), objective fatigue verification measures (heart rate > 180 bpm, irregular breathing), and biochemical markers (elevated creatine kinase) [32, 33]. Participants demonstrated significant fatigue through impaired movement stability, inability to maintain 60 rpm cycling beyond 60 s, and pronounced muscle soreness [13, 32, 33]. The protocol successfully met fatigue induction criteria. These multi-faceted verification protocol strengthens the validity of the fatigue model despite the athletes’ diverse training backgrounds.

Impact of MHOT on subjective sleep quality after muscle fatigue

While the exercise protocol reduced subjective sleep quality (PSQI) due to fatigue, repeated MHOT interventions significantly improved self-reported sleep duration and quality. Similar PSQI improvements have been observed with HBOT in conditions including fibromyalgia [34, 35], mild traumatic brain injury [36, 37], and cerebral palsy [38]. Although not directly measured here, but we considered exploratory potential which may also underlie MHOT’s effects potential mechanisms involve enhanced tissue oxygenation, mitochondrial recovery, reduced neuronal apoptosis, and pain relief [38]. However, the PSQI is a subjective measure intended for longer-term assessment; therefore, the observed changes reflect perceived rather than objectively confirmed improvements and may be influenced by placebo effects or individual variability. Future studies should incorporate objective measures such as heart rate variability, actigraphy, polysomnography, neuroimaging, and serum biomarkers (e.g., growth hormone and melatonin) to more precisely evaluate MHOT’s impact on sleep restoration. Furthermore, in this study, we made corresponding adjustments to the measurement period of the PSQI, which differs to some extent from the standard PSQI assessment timeframe. Further research is warranted using the standard PSQI measurement duration in subsequent studies.

Impact of MHOT on tissue oxygenation content after muscle fatigue

This study demonstrated that both single and repeated MHOT interventions significantly accelerated post‑fatigue recovery of regional muscle oxygen content (notably O₂Hb and tHb) in specific channels and selectively enhanced cerebral O₂Hb in specific channels, indicating its potential to modulate localized tissue oxygen dynamics. These findings align with HBOT research reporting increased tissue oxygen saturation during therapy [39], improved tendon perfusion even under modest hyperoxia [40], and enhanced cerebral oxygen utilization in vascular dementia [41]. We hypothesize based on other work that MHOT improves tissue oxygenation through hemodynamic and microcirculatory adjustments under high oxygen tension [42], elevated parasympathetic activity with reduced heart rate [41], and increased plasma‑dissolved oxygen that meets metabolic demands [42] while attenuating peripheral fatigue and maintaining cerebral oxygenation [43]. Notably, MHOT‑induced oxygenation responses show marked interindividual variability, influenced by exercise parameters, demographic traits, metabolic profiles, baseline hemodynamics, and mitochondrial capacity. Experimental limitations such as motion artifacts, ambient light, repeated measurements, and site‑placement differences—also warrant cautious interpretation alongside complementary indicators. Future research should adopt longitudinal designs to assess intra‑individual variability, utilize high‑density NIRS for broader prefrontal monitoring, examine long‑term neuromuscular effects, and implement tightly controlled protocols to minimize event‑coding errors, thereby advancing mechanistic beyond observational constraints.

Impact of MHOT on aerobic exercise capacity after muscle fatigue

In this study, repeated MHOT interventions maintained aerobic capacity, with significant improvements in V̇O₂max, maximal power, and exercise time versus controls, consistent with prior reports of HBOT and hyperoxia enhancing aerobic performance. Research indicates that combined HBOT and exercise training over three weeks improves endurance capacity [44], while HBOT alone elevates VO₂max, power output, and anaerobic threshold, likely via increased mitochondrial respiration and biogenesis [45]. Similarly, elevated oxygen supply raises maximal aerobic power and extends endurance [46]. Rats exposed to MHOT also showed higher voluntary running activity, aligning with our findings [47]. Collectively, hyperoxia/HBOT may improve aerobic endurance through mechanisms such as enhanced oxygen delivery/utilization, faster VO₂ kinetics, accelerated gas exchange [44], elevated oxidative/glycolytic capacity and mitochondrial proteins [45], and direct mitochondrial augmentation [46]. Based on existing literature, we hypothesize that MHOT improves aerobic capacity mainly by increasing oxygen content, boosting endurance, optimizing metabolism and mitochondrial function, accelerating metabolite clearance, and maintaining homeostasis (these mechanisms were not directly tested here). However, some studies report no endurance benefit from hyperoxia in skiers [48] or acute HBOT on running performance [49], and combined HBOT-HIIT showed no added aerobic or mitochondrial improvement versus normoxia [50]. Such discrepancies may arise from variations in study design, exercise protocols, equipment, participant fitness, metrics, and individual differences. Therefore, results should be interpreted cautiously within each study’s specific context, and further sustained research is required for validation.

Impact of MHOT on anaerobic exercise capacity after muscle fatigue

This study found that both group no significant time and group interaction effect for any Wingate parameter, MHOT did not demonstrate a significant effect on anaerobic capacity as measured by the Wingate test. The evidence on hyperoxia and HBOT aligns with our results: neither oxygen-enriched air before exercise [51] nor hyperoxia during activity [52] significantly improves short-term anaerobic power. HBOT prior to competition also shows no benefit in strength-dependent sports [53]. However, conflicting findings exist. Some studies report enhanced mean power [37], improved muscle activation [16], and better recovery of maximal strength after hyperoxia exposure [6, 54]. Potential mechanisms (not directly measured in our study) may include increased muscle activation and power output [16], maintained cerebral oxygenation and phosphocreatine recovery [43], regulated lactate/pH balance, and centrally-mediated neural facilitation [55], delayed fatigue by reducing physiological disturbances [16]. Similar to hyperoxia, HBOT improves anaerobic threshold after 60 sessions over three months [56] and reduces agonist muscle fatigue during repetitive jumping [57]. The inconsistency across studies likely stems from differences in exercise modalities, oxygen administration protocols, and inter-individual variability. Moreover, force production depends not only on oxygen availability but also on factors such as pH balance and neural drive [52]. However, in this study, the lack of a statistically significant effect of MHOT on these metrics. Observed trends are presented only as descriptive, hypothesis-generating patterns, not as evidence of true intervention effects. The absence of a significant effect of MHOT on anaerobic capacity may be due to potential interference between the exercise protocol (90 min cycling) and the Wingate test. Repeated testing across six time points could also have induced participant learning/adaptation. Individual variability may have further contributed to these nonsignificant results, warranting caution in interpretation. Future studies should employ combined medium- to long-term training protocols, select representative measurement time points, and integrate sport-specific tests with strength indicators to better evaluate MHOT’s effects on anaerobic performance.

Limitations of the study

We acknowledge that this study has several limitations that should be considered when interpreting the results. First, that complete blinding of participants was not feasible in this study due to the conspicuous nature of the MHOT and rest procedure, which inherently increases the risk of placebo or expectancy effects, the lack of a sham control and the consequent potential for expectancy/placebo effects, particularly for subjective outcomes like PSQI. Second, that the inclusion of athletes from different sports (middle-distance runners and football players) introduces physiological heterogeneity that may influence responses to the intervention. And only male participants were recruited. Extending these research questions to female populations would enhance both the significance and generalizability of the findings. In Data collection and analysis, the potential for interference and cumulative fatigue between the exhaustive aerobic and anaerobic tests, which may have confounded the performance outcomes, and the possibility of individual learning/adaptation to different recovery methods may have contributed to the variability observed in certain parameters. Third, the relatively small sample, coupled with multiple comparisons, increases the risks of Type II errors, future studies with larger and more diverse samples are needed to confirm and extend these results. Finally, the study was conducted in a controlled laboratory environment using a fixed-intensity exercise protocol. While this design ensured data consistency, it may not fully capture the variability and intensity of real-world exercise conditions. And In this study that while every effort was made to conduct sessions at a consistent time of day, we acknowledge that this environmental variability (self-reported adherence, hydration, temperature and humidity)may have introduced into the physiological responses. We acknowledge that daily recovery monitoring would have provided a more comprehensive safety and data validation framework, such assessments (e.g., daily wellness questionnaires, heart rate variability) will be a key consideration for improving the methodological rigor of future related studies.

Conclusions

The findings of this study demonstrate that repeated MHOT interventions the potential multifaceted physiological impact of on post-exercise recovery. MHOT significantly ameliorate subjective sleep quality (PSQI) and accelerate the recovery of brain and muscle tissue oxygenation following muscle fatigue, while concurrently improving V̇O2max aerobic exercise test performance. In contrast, MHOT did not demonstrate a significant effect on anaerobic capacity as measured by the Wingate test. These results suggest that MHOT may offer a potential benefit for reducing muscle fatigue after endurance exercise in athletes, particularly by optimizing oxygen utilization and sustaining aerobic performance. Although the intervention did not influence anaerobic metrics, its potential in post-exercise recovery warrants further investigation. In addition, the therapeutic benefits of MHOT appear dose-dependent, with cumulative advantages observed following repeated applications rather than a single session. Therefore, Future research should explore optimal protocols and doses to maximize MHOT efficacy in sports recovery field. Based on these results, we propose MHOT can offer a potential benefit for alleviating muscle fatigue in athletes. MHOT intervention may be applied following strenuous exercise to help with faster recovery. A single session MHOT intervention limited efficacy, whereas repeated sessions or therapeutic protocol implementation yield cumulative benefits. However, We now explicitly reiterate that MHOT is not currently classified as a banned method by WADA, making it a permissible recovery methods consideration. But, strengthened the accompanying cautionary guidance by more clearly stating that the MHOT long-term health effects and optimal MHOT application protocols remain largely unknown. We now recommend that practitioners approach its use conservatively, prioritizing athlete safety and viewing it as a recommended experimental recovery conditions rather than an established recovery strategy until further longitudinal research is available.

In addition, it should be noted that this study employed a fixed single-session preset configuration of the device MHOT protocol. As noted in the efficacy of oxygen therapies can be highly sensitive to variations in parameters such as pressure, duration, frequency, and timing relative to exercise. Our findings are therefore specific only to the protocol used and may not generalize to other dosing regimens. Furthermore, while we observed group-level effects, the potential influence of individual differences (e.g., training status, physiological responsiveness) on the outcomes was not investigated. Future studies should systematically explore these protocol variables and individual factors to optimize application.

Authors’ contributions

Conceptualization: CY Qu, JX Zhao. Data curation: MX Xu, ZJ Rao. Formal analysis: CY Qu, P Huang. Funding acquisition: JX Zhao. Investigation and Methodology: CY Qu, X Geng. Writing original draft and review & editing: CY Qu, Santiago Lorenzo, JX Zhao. All authors read and approved the submitted version.

Funding

This work was supported by the General Research of Natural Science Foundation of Beijing (5212020) and the Doctoral Research Initiation Fund Project of Hebei Normal University (L2024B35) and Hebei Province Sports Science and Technology Research Project Funding (2025JT05) and Funded by Science Research Project of Hebei Education Department (BJ2026361).

Data availability

The datasets used and/or analyzed during the current study available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

This study was approved by the Ethical Committee of the China Institute of Sport Science (Approval Project Number CISSLA-2020062801), and adhered to the ethical principles of the Declaration of Helsinki. In this study, written informed consent, outlining the study’s benefits and potential risks, was obtained from all participants.

Consent for publication

Written informed consent for publication of personal and clinical information and any potentially identifying images was obtained from all participants included in this study. We declare that this manuscript is our original work and has not been published in any other journal or publication, nor is it under consideration for publication elsewhere. All listed authors have made contributions to this manuscript and have jointly approved the final version of the manuscript.

Competing interests

The authors declare no competing interests.