Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Open Access

Peer-reviewed

Research Article

Effects of various living-low and training-high modes with distinct training prescriptions on sea-level performance: A network meta-analysis

  • Xinmiao Feng,

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    Affiliation Sports Coaching College, Beijing Sport University, Haidian, Beijing, China

    ⨯
  • Yonghui Chen,

    Roles Data curation

    Affiliation Sports Coaching College, Beijing Sport University, Haidian, Beijing, China

    ⨯
  • Teishuai Yan,

    Roles Software

    Affiliation Sports Coaching College, Beijing Sport University, Haidian, Beijing, China

    ⨯
  • Hongyuan Lu,

    Roles Data curation

    Affiliation Sports Coaching College, Beijing Sport University, Haidian, Beijing, China

    ⨯
  • Chuangang Wang,

    Roles Data curation

    Affiliation Sports Coaching College, Beijing Sport University, Haidian, Beijing, China

    ⨯
  • Linin Zhao

    Roles Conceptualization

    fengxinmiao666@163.com

    Affiliation Sports Coaching College, Beijing Sport University, Haidian, Beijing, China

    ⨯

Abstract

This study aimed to separately compare and rank the effect of various living-low and training-high (LLTH) modes on aerobic and anaerobic performances in athletes, focusing on training intensity, modality, and volume, through network meta-analysis. We systematically searched PubMed, Web of Science, Embase, EBSCO, and Cochrane from their inception date to June 30, 2023. Based on the hypoxic training modality and the intensity and duration of work intervals, LLTH was divided into intermittent hypoxic exposure, continuous hypoxic training, repeated sprint training in hypoxia (RSH; work interval: 5–10 s and rest interval: approximately 30 s), interval sprint training in hypoxia (ISH; work interval: 15–30 s), short-duration high-intensity interval training (s-IHT; short work interval: 1–2 min), long-duration high-intensity interval training (l-IHT; long work interval: > 5 min), and continuous and interval training under hypoxia. A meta-analysis was conducted to determine the standardized mean differences (SMDs) among the effects of various hypoxic interventions on aerobic and anaerobic performances. From 2,072 originally identified titles, 56 studies were included in the analysis. The pooled data from 53 studies showed that only l-IHT (SMDs: 0.78 [95% credible interval; CrI, 0.52–1.05]) and RSH (SMDs: 0.30 [95% CrI, 0.10–0.50]) compared with normoxic training effectively improved athletes’ aerobic performance. Furthermore, the pooled data from 29 studies revealed that active intermittent hypoxic training compared with normoxic training can effectively improve anaerobic performance, with SMDs ranging from 0.97 (95% CrI, 0.12–1.81) for l-IHT to 0.32 (95% CrI, 0.05–0.59) for RSH. When adopting a program for LLTH, sufficient duration and work intensity intervals are key to achieving optimal improvements in athletes’ overall performance, regardless of the potential improvement in aerobic or anaerobic performance. Nevertheless, it is essential to acknowledge that this study incorporated merely one study on the improvement of anaerobic performance by l-IHT, undermining the credibility of the results. Accordingly, more related studies are needed in the future to provide evidence-based support. It seems difficult to achieve beneficial adaptive changes in performance with intermittent passive hypoxic exposure and continuous low-intensity hypoxic training.

Citation: Feng X, Chen Y, Yan T, Lu H, Wang C, Zhao L (2024) Effects of various living-low and training-high modes with distinct training prescriptions on sea-level performance: A network meta-analysis. PLoS ONE 19(4): e0297007. https://doi.org/10.1371/journal.pone.0297007

Editor: Raphael Faiss, University of Lausanne, SWITZERLAND

Received: September 26, 2023; Accepted: December 22, 2023; Published: April 18, 2024

Copyright: © 2024 Feng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The hypoxic training techniques of “Live High-Train High” (LHTH) and “Live High-Train Low” (LHTL), which necessitate athletes to spend daily prolonged durations in high altitudes, have been used for over half of a century [1, 2]. This camp-style hypoxic technique is often employed by individual and team-sport athletes during the pre-season training phase to gain a competitive edge. However, research has suggested that such long-term hypoxic exposure training can potentially cause a range of detrimental effects in athletes (such as muscle cell deterioration and immunological disruptions etc.) [3–8]. In recent years, with the evolution of hypoxia equipment and to mitigate the drawbacks associated with chronic hypoxia [9], most studies have focused on LLTH modes that expose athletes to only discrete hypoxia during specific training or rest periods [10–12]. Recent reviews have introduced updated panorama for hypoxia training [13, 14] and divided LLTH into two major categories (active and passive) based on the parameters of the training regimen. The passive hypoxic paradigm is intermittent hypoxic exposure (IHE), where athletes are exposed to short (<3 hours) yet intense passive hypoxia during rest periods [15]. The active hypoxic paradigm encompasses continuous hypoxia training (CHT), which is a moderate-intensity training that lasts for approximately 30 minutes [16], and intermittent (moderate-high intensity) hypoxia training. According to the training duration and ratio of work to rest, intermittent training can be divided into repeated sprint training in hypoxia (RSH; work interval: 5–10 s and rest interval: approximately 30 s) [17], interval sprint training in hypoxia (ISH; work interval: 15–30 s) [18], high-intensity interval training (IHT; short duration: 1–2 min [19] and long duration: > 5 min [20]). In addition, some studies have combined continuous and intermittent hypoxia training aimed at optimizing aerobic and anaerobic capabilities concurrently [21–23].

Current literature has suggested that LLTH variants cannot trigger adequate hypoxic stimuli to induce related hematological alterations [24–26]. The associated primary mechanism is to induce muscular and peripheral adaptations [15, 27] by adding extra hypoxic load during training. However, the ongoing discourse about the effectiveness of various LLTH variants remains unresolved [28–31]. Critics argue that restriction of oxygen availability could substantially decrease absolute training intensity [31, 32]. This could potentially induce degenerative changes, subsequently limiting the enhancement of sea-level performance. The first meta-analysis conducted by Bonetti and Hopkins (2009) on hypoxic training suggested that compared with normoxic training, LLTH does not significantly enhance athletes’ performance. However, some perspectives have highlighted that previous studies did not consider the potential impact of variable training prescriptions [10, 14]. Recent meta-analyses have also suggested that high-intensity interval training [33] or repeated sprint training [34] in a hypoxic condition can effectively improve performance or aerobic capacity in athletes. Training at different duration and intensities has been shown to place diverse physiological demands on the body, leading to potential differences in subsequent adaptive changes, irrespective of whether the environment is normoxic or hypoxic [28, 35]. Hence, discerning the disparities in performance outcomes across diverse training modalities of LLTH is crucial for its practical application.

Compared with the usual meta-analysis used in previous studies, network meta-analysis (NMA) can generate a clear hierarchy between various interventions by synthesizing the results of direct and indirect comparisons to derive more comprehensive and definitive comparison results [36, 37]. Therefore, LLTH variants were meticulously divided based on their various training parameters. Subsequently, we conducted an NMA to comprehensively compare and rank the performance (i.e., aerobic and anaerobic) enhancement effects of various hypoxic training modes in athletes.

Methods

This meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses NMA (S1 Checklist and S1 File) [38]. Additionally, the study protocol has been registered with PROSPERO under registration number CRD42023421683.

Data source and search

The following English electronic databases were searched systematically: PubMed, Web of Science, Embase, EBSCO, and Cochrane from their inception date to June 30, 2023. The following search syntax was utilized: (“altitude” OR “hypoxia”) AND (“high-intensity interval training” OR “repeated sprint training” OR “interval sprint training” OR “continuous training” OR “intermittent exposure” OR “performance”) (S1 Table). we manually searched all review articles related to altitude or hypoxic training and traced additional possible studies by reviewing their reference lists.

Study selection

The standards were based on the PICOS approach (participants, interventions, comparators, outcomes, and study design). The participants were athletes who had received training but had not been exposed to hypoxia in the last 6 months. There were no criteria regarding the sport type and training level of the participants (youth teams, university teams, national teams, professional club teams, etc.). We categorized LLTH into seven modes based on the training type, intensity, and volume (duration of work interval) (for detailed classification and definition, see Fig 1 and Table 1 in S2 File) (Table 1). All the training plans for the control group are implemented in a normoxic environment. In the face-to-face studies included in the selected systematic reviews, the comparator should be any of the seven hypoxic modes. At least one test result of interest should have been obtained in the study. The results were mainly divided into two categories according to the metabolic characteristics in the test: aerobic and anaerobic performances. The reference indicators for aerobic and anaerobic performances include some test results that have been proven to be highly correlated. We have chosen the Incremental Treadmill Test (Time to exhaustion (min) etc.), 3 min All-Out Test (average power output (W) etc.), YYIR test level I and II (Ddistance Covered (m)), Run test (distance ≥ 1000 m; duration (seconds or minutes)), Cycle test (durations ≥ 10 min; duration (seconds or minutes)), Swiming test (distance ≥ 400 m; duration (seconds or minutes)) and constant-load test (Time to exhaustion (seconds or minutes) etc.) as the reference indicatorfor aerobic performance, and the Wingate Test (average power output (W) etc.), RAS test (peak power output (W)), Maximal anaerobic test (duration (seconds or minutes)), Supramaximal time to fatigue test (duration (seconds or minutes)), Run test (distance ≤ 800 m, or durations ≤ 2 min; duration (seconds or minutes)) and Swiming test (≤ 200 m; duration (seconds or minutes)) as the standard for anaerobic performance, for details on the selection of reference indicators, S4 File. In designing this study, we mainly considered randomized controlled studies (including the front and back parts of crossover randomized controlled studies) and conducted a meta-network analysis of studies on various types of hypoxic and normoxic training. We excluded studies on a single exercise in an acute hypoxia condition. In addition, studies that included special training interventions (i.e., cold, hot, or humid environments, blood flow restriction intervention, etc.) or special nutritional supplements (i.e., nitrate, caffeine, etc.) were excluded.

thumbnail
Download:
Fig 1. Search terms and outcomes.

https://doi.org/10.1371/journal.pone.0297007.g001

thumbnail
Download:
Table 1. Definition of LLTH modes.

https://doi.org/10.1371/journal.pone.0297007.t001

Data extraction and quality evaluation

All relevant articles retrieved from the aforementioned electronic databases were stored in EndNote X9 reference manager, and three investigators reviewed and selected the retrieved articles based on the aforementioned reference criteria. Subsequently, relevant data was extracted from the qualified articles. Information extracted included publication information (author and year), research participants (sample size, gender ratio, participants’ sports, and training level), experimental design (type of experiment), intervention measures (hypoxia mode and hypoxia dosage [km/h]; details of the hypoxic dose model are provided in S3 File), duration and frequency of training (training plan and supplementary training), and test results (the outcome measures selected per study are shown in the Table 2). The hypoxic dosage between different hypoxic types was coordinated using the “kilometer hours” model [39]. The dosage model was defined as km·h = (m/1000)× h (“m” represents the altitude of the exposure environment; “h” represents the total exposure duration).

thumbnail
Download:
Table 2. Characteristics of included studies.

https://doi.org/10.1371/journal.pone.0297007.t002

The methodological quality of the Included articles was evaluated by two independent reviewers using the Physiotherapy Evidence Database (PEDro) scale [40]. The PEDro scale includes 11 items made up of three items from the Jadad scale and nine items from the Delphi list. The PEDro scale score for randomized controlled trials ranges from 0 (low quality) to 10 (high quality), and a score of ≥6 represents high-quality research. The first item on the PEDro scale (eligibility criteria specified) is used to establish external validity; thus, the score is not included in the total score. Any disagreement during the above process was resolved by a review group within the team (HY, YH, TS and XM) through consensus and arbitration.

Statistical analysis

We used the R software (version 3.6.3) package netmeta (www.rproject.org) to perform the NMA, combining direct and indirect comparisons in a frequency model [41, 42]. The standardized mean difference (SMD) and 95% credible interval (CI) was used as effect size indicators. We used the random effects NMA model to collate the size of the study effect. The circle size in the network evidence graph represents the sample size, and the lines between the circles represent direct comparisons between two types of physical activities or interventions. The width of the connecting line reflects the number of studies that directly compared the two interventions. If no connecting line is established between the two types of physical activities, we performed indirect comparisons using NMA. In addition, the SMD and 95% CI of all paired comparisons are reported, and the effect of each physical activity intervention on maximal oxygen uptake compared with that observed in the control group is reported in a league table using a forest plot. We ranked the types of physical activities using P scores based on the degree of improvement in maximal oxygen uptake among athletes. The P score ranges from 0 to 1, with a higher score indicating a greater improvement in aerobic capacity [43]. We used the tau squared (τ2) test and p-value to qualitatively analyze heterogeneity between the studies [44, 45]. The larger the τ2 and the smaller the p-value, the bigger the heterogeneity. Conversely, the smaller the τ2 and the bigger the p-value, the smaller the heterogeneity. In addition, we used I2, which is distributed between 0% and 100%, to quantitatively analyze the heterogeneity between the study results. An I2 less than 25% indicated low heterogeneity, ranging from 25% to 50% indicated medium heterogeneity; and >75% indicated high heterogeneity. Therefore, when I2 was >50%, the heterogeneity was significant. We used global and local methods to test for inconsistency in the study results. For global inconsistency, we used the design-by-treatment test to evaluate inconsistency [46]. In addition, we used the node-splitting method in the R netmeta package for the local inconsistency test [47]. We conducted network meta-regression analysis using the R gemtc package to analyze potential sources of heterogeneity (publication year, sample size, mean age, percent male, exercise duration, exercise frequency, and total time per session). We compared adjusted funnel plots to evaluate the risk of publication bias under specific circumstances. Additionally, we analyzed the funnel plots using the Egger method. A p<0.05 indicated publication bias. We evaluated the sensitivity of our study by repeating each NMA after excluding studies with high risk of bias.

Results

Study characteristics and quality assessment

The search process of the systematic reviews is shown in Fig 1. After excluding 2,292 reports based on the title and abstract, 444 full-text articles were retrieved. During the examination of the full texts, we selected and included 56 studies with interesting results (the citations included studies are provided in S3 Table). A total of 1,040 participants, most of whom were male (n = 964/92.69%), were included in the 56 studies. Additionally, most participants were endurance athletes (n = 770/74.04%). All the participants in the included studies, except two who were boxing and tennis players, were team-sports athletes (n = 270/25.96%). The sample size for the studies ranged from 4 to 25. The training period ranged from 5 to 56 days (average, 28.9 days, standard deviation [SD] = 13.214), and the weekly exercise training frequency ranged from 2 to 7 (average frequency, 3.44, SD = 1.67). The specific parameters of the training programs in each study are shown in Table 2 (the scoring details per study are provided in S2 Table). The PEDro scale was used to determine the quality of the included study, with results showing an average score of 6.327±1.203 and indicating a generally high methodological quality. Only three studies had scores below the predetermined threshold of 5 points.

Network meta‑analysis

Aerobic performance.

Fig 2 displays a network diagram of the qualified aerobic performance comparisons; all hypoxic training methods were compared with normoxic training at least once. A total of 53 studies reported changes in their primary outcome, aerobic performance among 1,021 participants (98.17%). The forest plot (Fig 3) shows that only l-IHT and RSH were significantly more effective than normoxic training in improving athletes’ aerobic performance, with SMDs of 0.78 (95% credible interval [CrI], 0.52–1.05) for l-IHT and 0.30 (95% CrI, 0.10–0.50) for RSH. Based on the P scores, l-IHT had the best effect (P score: 1.00). The league plot (Table 3) results showed that l-IHT can improve an athlete’s aerobic performance better than all the other hypoxic modes, with an SMD ranging from 0.49 and 0.78. The I2 value for aerobic performance was 8% (low heterogeneity). The global Q score for inconsistency was 0.55 with a p-value of 0.6774 (Statistical methods in details, evaluation of heterogeneity and inconsistency in S5 and S6 Files).

thumbnail
Download:
Fig 2. Network plot of aerobic performance.

The size of the nodes corresponds to the number of participants randomized to each hypoxic training. Exercise type with direct comparisons are linked with a line; its thickness corresponds to the number of trials evaluating the comparison. IHE “intermittent hypoxic exposure”; CHT “continuous hypoxic training”; RSH “Repeated sprint training in hypoxia”; ISH “Interval sprint training in hypoxia”; s-IHT “Short-trem high-intensity Interval training”; l-IHT “Long-term high-intensity interval training”; C+I “Continuous and Interval training under Hypoxia”; CON “control group (normoxic training)”.

https://doi.org/10.1371/journal.pone.0297007.g002

thumbnail
Download:
Fig 3. Forest plot change in effect of aerobic performance.

Various LLTH modes are ranked according to the surface under the curved cumulative ranking probabilities. Treatments crossing the y-axis are not signifcantly diferent from CON. The n value represents the number of studies that were directly compared to the control group. SMD “standardized Mean Diference”; CrI “Credible Interval”; IHE “intermittent hypoxic exposure”; CHT “continuous hypoxic training”; RSH “Repeated sprint training in hypoxia”; ISH “Interval sprint training in hypoxia”; s-IHT “Short-trem high-intensity Interval training”; l-IHT “Long-term high-intensity interval training”; C+I “Continuous and Interval training under Hypoxia”; CON “control group”.

https://doi.org/10.1371/journal.pone.0297007.g003

thumbnail
Download:
Table 3. League table for changes in aerobic performance associated with various LLTH modes.

https://doi.org/10.1371/journal.pone.0297007.t003

Anaerobic performance

Fig 4 shows the network graph of anaerobic performance comparisons, where only RSH and ISH were directly compared. 29 studies reported changes in anaerobic performance in the main results of 568 participants (54.62%). Compared with conventional oxygen training, all intermittent hypoxia training methods (l-IHT, ISH, s-IHT, and RSH) showed significant improvements in anaerobic performance, with SMDs (95% CrI) ranging from 0.97 (0.12–1.81) for l-IHT to 0.32 (0.05–0.59) for RSH, and l-IHT ranks first with a P-score of 0.95 (Fig 5). In addition, the NMA results (Table 4) showed that all intermittent hypoxic training could improve anaerobic performance better than continuous and interval training under hypoxia and normoxic training. However, it should be noted that this study only included one study on the improvement of anaerobic performance by l-IHT. The I2 value for anaerobic performance was 20.5% (low heterogeneity). The global Q score for inconsistency was 0.14 with a p-value of 0.7037 (Statistical methods in details, evaluation of heterogeneity and inconsistency in S5 and S6 Files).

thumbnail
Download:
Fig 4. Network plot of anaerobic performance.

The size of the nodes corresponds to the number of participants randomized to each hypoxic training. Exercise type with direct comparisons are linked with a line; its thickness corresponds to the number of trials evaluating the comparison. IHE “intermittent hypoxic exposure”; CHT “continuous hypoxic training”; RSH “Repeated sprint training in hypoxia”; ISH “Interval sprint training in hypoxia”; s-IHT “Short-trem high-intensity Interval training”; l-IHT “Long-term high-intensity interval training”; C+I “Continuous and Interval training under Hypoxia”; CON “control group (normoxic training)”.

https://doi.org/10.1371/journal.pone.0297007.g004

thumbnail
Download:
Fig 5. Forest plot change in effect of anaerobic performance.

Various LLTH modes are ranked according to the surface under the curved cumulative ranking probabilities. Treatments crossing the y-axis are not signifcantly diferent from CON. The n value represents the number of studies that were directly compared to the control group. SMD “standardized Mean Diference”; CrI “Credible Interval”; IHE “intermittent hypoxic exposure”; CHT “continuous hypoxic training”; RSH “Repeated sprint training in hypoxia”; ISH “Interval sprint training in hypoxia”; s-IHT “Short-trem high-intensity Interval training”; l-IHT “Long-term high-intensity interval training”; C+I “Continuous and Interval training under Hypoxia”; CON “control group (normoxic training)”.

https://doi.org/10.1371/journal.pone.0297007.g005

thumbnail
Download:
Table 4. League table for changes in anaerobic performance associated with various LLTH modes.

https://doi.org/10.1371/journal.pone.0297007.t004

Discussion

This study classified LLTH into several specialized hypoxic modes according to the type, intensity, and volume of training prescription and subsequently used NMA to comprehensively compare and rank the effects of the various hypoxic modes on athletes’ aerobic and anaerobic performances. The results showed that only active intermittent hypoxic modes (l-IHT, s-IHT, RSH, and ISH) compared with normoxic training were effective in improving athletes’ performance. Of these, for both aerobic and anaerobic performances, l-IHT with a high volume (longer duration of training interval) and intensity showed the best results. It seems difficult to achieve beneficial adaptive changes in performance with intermittent passive hypoxic exposure and continuous low-intensity hypoxic training.

The pooled data indicates that among various LLTH modes, only intermittent modes with high-intensity and large-volume training had significant large effects on enhancing aerobic performance. The findings substantiate the perspective that high intensity is integral to enhancing an athlete’s endurance during hypoxic training [10, 15, 35]. In a hypoxic environment, the diminished oxygen content inevitably affects arterial oxygen pressure (SaO2), reducing oxygen availability to muscle and brain tissues and constraining aerobic metabolic function [48]. Such physiological responses could notably impair performances during training with high aerobic components [49–51]. Additionally, it has been confirmed that high-level athletes are likely to encounter greater impairments [52, 53]. Absolute exercise intensity in the hypoxic group that is not only markedly lower than the normoxic group but also falls short of the threshold that could invoke beneficial adaptations is observed when CHT, requiring a relatively lesser intensity, is implemented [10, 54]. Conversely, matching a higher training intensity (VT2/anaerobic threshold/80% VO2max) during intermittent hypoxia training can mobilize a larger proportion of anaerobic metabolism to participate in energy supply [55], limiting the decrease in absolute exercise intensity [56]. Presently, it is understood that CHT exhibits a strong correlation with the amplification of aerobic performance and metabolic adaptations in active muscles. Hypoxic training with adequate intensity may enhance muscle oxidative capacity by optimizing substrate selection and augmenting mitochondrial function [57–59]. As high-intensity training continues, a large amount of lactic acid gradually accumulates in the tissues, which strongly stimulates the buffering capacity of the muscles [27]. Previous reports have indicated that after high-intensity hypoxic training, compared with normoxic training, the monocarboxylate transporter (MCT-1/4) related to H+ management shows significant adaptive changes [23, 60, 61], and the density of the capillaries in the working muscles (the ratio of muscle fibers to capillaries) also increases significantly [54, 62].

In addition to high intensity, longer work-interval duration (higher volume of training and hypoxic dose) is closely related to the improvement of endurance and aerobic capability [63–66]. Our results support this hypothesis, as both ISH and s-IHT did not show superiority over normoxic training. Adequate training volume cannot only pose greater challenge to aerobic metabolism and pH regulation but can also trigger special adaptations with more hypoxic dose application. A decrease in tissue Fraction of Inspired Oxygen (FiO2) catalyzes the accumulation of Hypoxia-Inducible Factor 1 (HIF-1) [67–69], a transcription factor that decays rapidly in normoxic environments [70]. This transcription factor has been validated to effectively activate regulatory factors related to the aforementioned metabolic adaptations [71, 72]. Furthermore, after high-intensity hypoxic training, various adaptation mechanisms closely related to aerobic performance (such as myoglobin concentration, citrate synthase, exercise economy, etc.) have been widely reported [9, 59, 73, 74]. Moreover, several studies have enhanced the comprehension of these physiological adaptations following l-IHT by evaluating related mRNA [9, 72, 75]. However, no studies have directly explored the impact of specific training variables (intensity and volume) on the amplification of aerobic performance in athletes during hypoxic training.

Beyond training intensity and volume, the degree of hypoxia is also a significantly vital element of training prescription. Studies on acute hypoxia have evidenced that exposure to moderate altitudes significantly impairs SaO2 and disturb dynamic balance [76]. The degree of stimulation increases with increasing altitude [77]. However, Karayigit et al. (2022) conducted separate investigations into the acute impacts of moderate and high hypoxia levels on the high-intensity intermittent performance of athletes. The studies revealed no substantial differences between both conditions, and no significant detriments were observed compared with that observed in the normoxic group. Additionally, Warnier et al.’s research found no notable variation in performance enhancement (measured according to incremental test results) across the 2000 m, 3000 m, and 4000 m hypoxic groups following a six-week course of hypoxia training [78]. However, given the scarcity of studies and small sample size, these findings are insufficient to conclusively assert any uniform impact of varied hypoxia levels in high-intensity training on athletes’ aerobic capacity. Finally, the physiological responses and adaptations post-hypoxia demonstrate discernible individual variations, which cannot be ignored [53]. Several studies have indicated that elite athletes, compared with athletes at the lower training levels, endure more significant damage in hypoxic conditions, primarily due to the restrictions in pulmonary gas exchange [52]. This finding is corroborated by the strong correlation between sea-level and hypoxic VO2max [79]. While the oxidative capacity of elite athletes may have reached its limit after extensive years of training, Ponsot et al. reported that post-IHT, the skeletal muscles of high-level athletes exhibit qualitative adaptation (increased Km for ADP) rather than a quantitative one [58]. These adaptations can contribute to improved endurance performance through the better integration of energy demand and utilization. This re-emphasizes that, especially for elite athletes with extended years of training experience, sufficient duration of work intervals is the key to enhancing aerobic performance.

This is the first study attempting to explore the impact of various LLTH variants on athletes’ anaerobic performance using meta-analysis. The results show that all active intermittent hypoxic modes compared with normoxic training can effectively improve athletes’ anaerobic performance. Further, Although the results demonstrated that hypoxic training with high-intensity and long-duration working interval (l-IHT) has the best improvement effect on anaerobic performance [19], it is regrettable that this study only included one related research, which greatly reduced the statistical power and result credibility. As highlighted, athletes striving to match the same external work rate (absolute exercise intensity) under hypoxic conditions, equivalent to normoxic conditions, require the considerate use of anaerobic metabolism for energy. The training intensity executed directly correlates with the stimulation of anaerobic metabolism—the higher the intensity, the greater the stimulation, and the longer the duration, the more profound the buffering capacity stimulation. Studies on IHT indicate significant correlation between enhancement of anaerobic performance and adaptation of glycolytic enzyme capacity (phosphofructokinase) [63, 65, 80], MCT-1/4 [23], and capillary density. Moreover, the current literature substantiates that moderate acute hypoxia does not curtail athletes’ anaerobic function [81]. A noticeable but substantial reduction in resting SaO2, from 95% to 92%, occurs when athletes train above sea level (ambient PIO2 = 159 mmHg) to a moderate altitude (3000 m, ambient PIO2 = 110 mmHg). The situation intensifies at higher altitudes (5000 m, ambient PIO2 = 85 mmHg), where SaO2 plummets to 80% or lower [32]. The significant decrease in SaO2 implies greater demand for and stimulation of anaerobic metabolism. Nevertheless, caution is needed as the more severe the hypoxia, the greater the interference with autonomic nervous system activity [82, 83], leading to the accumulation of fatigue and stress. Therefore, the real-time monitoring of physiological characteristics and training intensity is vital in ensuring the successful execution of hypoxia training.

Czuba et al. (2017) highlighted the essential role of supplementary strength training in enhancing anaerobic performance. Some studies have suggested that hypoxic exposure can have harmful effects on muscle tissue, reducing protein synthesis [54, 84, 85] and leading to muscle fiber atrophy. However, short-term hypoxic exposure, particularly when combined with resistance training, could have a reverse effect through the stimulation of muscle protein synthesis [86, 87], which is beneficial for improving anaerobic performance.

Interestingly, the combination of both continuous and intermittent training in a hypoxic condition has not shown significant enhancement in either aerobic or anaerobic performances. This type of combined hypoxia regimen can be divided into two as follows: 1) continuous and intermittent training sessions that are performed separately each week [22, 88, 89] and 2) continuous and intermittent training conducted during a session [21, 23, 90, 91]. The result of comprehensive data indicates that engaging in additional continuous low-intensity training under hypoxia will not produce additional effects and may even deepen fatigue. The significant effects shown by some combined hypoxia programs may also be due to the contribution of high-intensity intermittent training sessions. Pure passive hypoxic exposure compared with IHE has not shown any enhanced effect on athletic performance, which is consistent with most reviews. Our recent study also proves that IHE cannot improve the maximum oxygen uptake of athletes [15, 30, 92, 93]. Finally, it is worth mentioning that, apart from l-IHT, RSH is the only hypoxic technique that can simultaneously improve aerobic and anaerobic performances (though marginally). Compared with intermittent hypoxic training, RSH has shorter but more intense work intervals with insufficient recovery in between workouts [28]. This insufficiency stimulates special physiological reactions among athletes after an RSH intervention, with type II muscle fibers displaying greater degree of recruitment and oxygenation capabilities [28, 34]. The correlation between oxidative tendencies (a non-hematological central and peripheral mechanisms) in fast muscle fibers and the enhancement in aerobic and anaerobic performance presents a noticeable trend [17, 94, 95]. Nonetheless, several studies emphatically assert that athletes’ VO2max and endurance test results did not improve subsequent to RSH [34, 96–98], but it is noteworthy that Galvin et al. proposed that the amplifying impact of RSH on anaerobic or aerobic performance is closely related to the work-rest ratio [17]. Nevertheless, the marginal enhancement of both aerobic and anaerobic performance may merely constitute supplementary advantages of RSH. In practical application, the primary aim of RSH is to bolster the repeated sprint ability (mixed-oxide metabolism) of team or racquet-sports athletes [28, 34, 92].

Limitations

While this study confirms an impact of LLTH on aerobic and anaerobic performance based on the intensity, volume, and type of hypoxic training, several confounding factors—unquantifiable in this study—might affect the study outcomes. These include the degree of hypoxia, the integration of additional normoxic training, training frequency, and overall session volume. The effect of these particular arrangements on real-world applications is significant and necessitates further investigation. Furthermore, the characteristics of the research population are specific to males, as they were the majority (92.69%). Some studies have postulated that females exhibit lower SaO2 sensitivity to hypoxic stimuli than that observed in males [99, 100], insinuating minimal impact of hypoxic training on female athletes’ performance. Consequently, we cannot definitively determine whether our conclusions are applicable to female athletes. Although some reports have suggested that female athletes can benefit from hypoxic training, the overall sample size is substantially small, and the statistical power of the results is relatively weak. Besides gender, other characteristics such as the sports athletes participate in and their competitive levels are also worthy of further investigation. Unfortunately, the current number of studies included does not support conducting methods like subgroup analysis (after grouping by characteristics, the limited number of studies would severely compromise the credibility of the outcomes and the network connectivity of meta-analysis).

Finally, it is important to note that while the results indicated that l-IHT had the most significant impact on enhancing anaerobic performance, these findings should not be regarded as conclusive due to the small number of pertinent studies analyzed. Further research is necessary to investigate the effects of interval hypoxic training with longer work durations on anaerobic performance, which is vital for the effective practical implementation of hypoxic technique.

Conclusion

Among the various LLTH strategies, only active intermittent hypoxic modes compared with normoxic training have shown significant enhancements of athletic performance. Intermittent hypoxic training with adequate work-interval durations demonstrated the most advantageous effects on aerobic performances. Neither IHE nor CHT was proven effective.

Supporting information

S1 Checklist. PRISMA checklist.

https://doi.org/10.1371/journal.pone.0297007.s001

(DOCX)

S1 Table. Search strategy.

https://doi.org/10.1371/journal.pone.0297007.s002

(DOCX)

S2 Table. Physiotherapy evidence database (PEDro) scores of the included studies.

https://doi.org/10.1371/journal.pone.0297007.s003

(DOCX)

S3 Table. The included studies.

https://doi.org/10.1371/journal.pone.0297007.s004

(DOCX)

S1 File. Protocol.

https://doi.org/10.1371/journal.pone.0297007.s005

(DOCX)

S2 File. Classification (figure) and definition (table) of various LLTH modes.

https://doi.org/10.1371/journal.pone.0297007.s006

(DOCX)

S3 File. Hypoxic dose model.

https://doi.org/10.1371/journal.pone.0297007.s007

(DOCX)

S4 File. Selection of reference indicators.

https://doi.org/10.1371/journal.pone.0297007.s008

(DOCX)

S5 File. Statistical methods in details.

https://doi.org/10.1371/journal.pone.0297007.s009

(DOCX)

S6 File. Evaluation of heterogeneity and inconsistency.

https://doi.org/10.1371/journal.pone.0297007.s010

(DOCX)

Acknowledgments

We are very grateful to Ms. Fengyu Shi, Mr. Bowen Li and Mr. Yuze Li for their support in data verifcation.

References

  1. 1. Buskirk ER, Kollias J, Akers RF, Prokop EK, Reategui EP. Maximal performance at altitude and on return from altitude in conditioned runners. J Appl Physiol. 1967;23(2):259–66. Epub 1967/08/01. pmid:6033527.
  2. 2. Levine BD, Stray-Gundersen J. "Living high-training low": effect of moderate-altitude acclimatization with low-altitude training on performance. J Appl Physiol (1985). 1997;83(1):102–12. Epub 1997/07/01. pmid:9216951.
  3. 3. Schommer K, Menold E, Subudhi AW, Bärtsch P. Health risk for athletes at moderate altitude and normobaric hypoxia. Br J Sports Med. 2012;46(11):828–32. Epub 2012/07/31. pmid:22842235.
  4. 4. Roach GD, Schmidt WF, Aughey RJ, Bourdon PC, Soria R, Claros JC, et al. The sleep of elite athletes at sea level and high altitude: a comparison of sea-level natives and high-altitude natives (ISA3600). Br J Sports Med. 2013;47 (Suppl 1):i114–20. Epub 2013/12/07. pmid:24282197; PubMed Central PMCID: PMC3903309.
  5. 5. Green HJ, Sutton JR, Cymerman A, Young PM, Houston CS. Operation Everest II: adaptations in human skeletal muscle. J Appl Physiol (1985). 1989;66(5):2454–61. Epub 1989/05/01. pmid:2745306.
  6. 6. Mazzeo RS. Altitude, exercise and immune function. Exerc Immunol Rev. 2005;11:6–16. Epub 2006/01/03. pmid:16385840.
  7. 7. Beidleman BA, Fulco CS, Cymerman A, Staab JE, Buller MJ, Muza SR. New metric of hypoxic dose predicts altitude acclimatization status following various ascent profiles. Physiol Rep. 2019;7(20):e14263. Epub 2019/10/30. pmid:31660703; PubMed Central PMCID: PMC6817994.
  8. 8. Milledge JS. Acute mountain sickness. Lancet. 1998;100(6):494–8.
  9. 9. Hoppeler H, Vogt M. Muscle tissue adaptations to hypoxia. J Exp Biol. 2001;204(Pt 18):3133–9. Epub 2001/10/03. pmid:11581327.
  10. 10. McLean BD, Gore CJ, Kemp J. Application of ’live low-train high’ for enhancing normoxic exercise performance in team sport athletes. Sports Med. 2014;44(9):1275–87. Epub 2014/05/23. pmid:24849544.
  11. 11. Hamlin MJ, Hellemans J. Effect of intermittent normobaric hypoxic exposure at rest on haematological, physiological, and performance parameters in multi-sport athletes. J Sports Sci. 2007;25(4):431–41. Epub 2007/03/17. pmid:17365530.
  12. 12. Goods PS, Dawson B, Landers GJ, Gore CJ, Peeling P. No Additional Benefit of Repeat-Sprint Training in Hypoxia than in Normoxia on Sea-Level Repeat-Sprint Ability. J Sports Sci Med. 2015;14(3):681–8. Epub 2015/09/04. pmid:26336357; PubMed Central PMCID: PMC4541135.
  13. 13. Millet GP, Faiss R, Brocherie F, Girard O. Hypoxic training and team sports: a challenge to traditional methods? Br J Sports Med. 2013;47 (Suppl 1):i6–7. Epub 2013/12/07. pmid:24282210; PubMed Central PMCID: PMC3903151.
  14. 14. Girard O, Brocherie F, Goods PSR, Millet GP. An Updated Panorama of "Living Low-Training High" Altitude/Hypoxic Methods. Front Sports Act Living. 2020;2:26. Epub 2020/12/22. pmid:33345020; PubMed Central PMCID: PMC7739748.
  15. 15. Millet GP, Roels B, Schmitt L, Woorons X, Richalet JP. Combining hypoxic methods for peak performance. Sports Med. 2010;40(1):1–25. Epub 2009/12/22. pmid:20020784.
  16. 16. Hendriksen IJ, Meeuwsen T. The effect of intermittent training in hypobaric hypoxia on sea-level exercise: a cross-over study in humans. Eur J Appl Physiol. 2003;88(4–5):396–403. Epub 2003/01/16. pmid:12527969.
  17. 17. Galvin HM, Cooke K, Sumners DP, Mileva KN, Bowtell JL. Repeated sprint training in normobaric hypoxia. Br J Sports Med. 2013;47 (Suppl 1):i74–9. Epub 2013/12/07. pmid:24282212; PubMed Central PMCID: PMC3903144.
  18. 18. Richardson AJ, Gibson OR. Simulated hypoxia does not further improve aerobic capacity during sprint interval training. J Sports Med Phys Fitness. 2015;55(10):1099–106. Epub 2014/07/17. pmid:25028984.
  19. 19. Arezzolo D, Coffey VG, Byrne NM, Doering TM. Effects of Eight Interval Training Sessions in Hypoxia on Anaerobic, Aerobic, and High Intensity Work Capacity in Endurance Cyclists. High Alt Med Biol. 2020;21(4):370–7. Epub 2020/08/25. pmid:32830992.
  20. 20. Dufour SP, Ponsot E, Zoll J, Doutreleau S, Lonsdorfer-Wolf E, Geny B, et al. Exercise training in normobaric hypoxia in endurance runners. I. Improvement in aerobic performance capacity. J Appl Physiol (1985). 2006;100(4):1238–48. Epub 2006/03/17. pmid:16540709.
  21. 21. Kim SW, Jung WS, Kim JW, Nam SS, Park HY. Aerobic Continuous and Interval Training under Hypoxia Enhances Endurance Exercise Performance with Hemodynamic and Autonomic Nervous System Function in Amateur Male Swimmers. Int J Environ Res Public Health. 2021;18(8). Epub 2021/05/01. pmid:33918616; PubMed Central PMCID: PMC8068973.
  22. 22. Roels B, Bentley DJ, Coste O, Mercier J, Millet GP. Effects of intermittent hypoxic training on cycling performance in well-trained athletes. Eur J Appl Physiol. 2007;101(3):359–68. Epub 2007/07/20. pmid:17636319.
  23. 23. Millet G, Bentley DJ, Roels B, Mc Naughton LR, Mercier J, Cameron-Smith D. Effects of intermittent training on anaerobic performance and MCT transporters in athletes. PLoS One. 2014;9(5):e95092. Epub 2014/05/07. pmid:24797797; PubMed Central PMCID: PMC4010422.
  24. 24. Truijens MJ, Toussaint HM, Dow J, Levine BD. Effect of high-intensity hypoxic training on sea-level swimming performances. J Appl Physiol (1985). 2003;94(2):733–43. Epub 2002/10/23. pmid:12391107.
  25. 25. Vallier JM, Chateau P, Guezennec CY. Effects of physical training in a hypobaric chamber on the physical performance of competitive triathletes. Eur J Appl Physiol Occup Physiol. 1996;73(5):471–8. Epub 1996/01/01. pmid:8803509.
  26. 26. Julian CG, Gore CJ, Wilber RL, Daniels JT, Fredericson M, Stray-Gundersen J, et al. Intermittent normobaric hypoxia does not alter performance or erythropoietic markers in highly trained distance runners. J Appl Physiol (1985). 2004;96(5):1800–7. Epub 2003/12/16. pmid:14672967.
  27. 27. Gore CJ, Clark SA, Saunders PU. Nonhematological mechanisms of improved sea-level performance after hypoxic exposure. Med Sci Sports Exerc. 2007;39(9):1600–9. Epub 2007/09/07. pmid:17805094.
  28. 28. Faiss R, Girard O, Millet GP. Advancing hypoxic training in team sports: from intermittent hypoxic training to repeated sprint training in hypoxia. Br J Sports Med. 2013;47 (Suppl 1):i45–50. Epub 2013/12/07. pmid:24282207; PubMed Central PMCID: PMC3903143.
  29. 29. Siebenmann C, Dempsey JA. Hypoxic Training Is Not Beneficial in Elite Athletes. Med Sci Sports Exerc. 2020;52(2):519–22. Epub 2020/01/16. pmid:31939915.
  30. 30. Lundby C, Millet GP, Calbet JA, Bärtsch P, Subudhi AW. Does ’altitude training’ increase exercise performance in elite athletes? Br J Sports Med. 2012;46(11):792–5. Epub 2012/07/17. pmid:22797528.
  31. 31. Lundby C, Robach P. Does ’altitude training’ increase exercise performance in elite athletes? Exp Physiol. 2016;101(7):783–8. Epub 2016/05/14. pmid:27173805.
  32. 32. Mazzeo RS. Physiological responses to exercise at altitude: an update. Sports Med. 2008;38(1):1–8. Epub 2007/12/18. pmid:18081363.
  33. 33. Westmacott A, Sanal-Hayes NEM, McLaughlin M, Mair JL, Hayes LD. High-Intensity Interval Training (HIIT) in Hypoxia Improves Maximal Aerobic Capacity More Than HIIT in Normoxia: A Systematic Review, Meta-Analysis, and Meta-Regression. Int J Environ Res Public Health. 2022;19(21). Epub 2022/11/12. pmid:36361141; PubMed Central PMCID: PMC9658399.
  34. 34. Brocherie F, Girard O, Faiss R, Millet GP. Effects of Repeated-Sprint Training in Hypoxia on Sea-Level Performance: A Meta-Analysis. Sports Med. 2017;47(8):1651–60. Epub 2017/02/15. pmid:28194720.
  35. 35. Millet GP, Faiss R. Hypoxic conditions and exercise-to-rest ratio are likely paramount. Sports Med. 2012;42(12):1081–3; author reply 3–5. Epub 2012/10/31. pmid:23106429.
  36. 36. Leucht S, Chaimani A, Cipriani AS, Davis JM, Furukawa TA, Salanti G. Network meta-analyses should be the highest level of evidence in treatment guidelines. Eur Arch Psychiatry Clin Neurosci. 2016;266(6):477–80. Epub 2016/07/21. pmid:27435721.
  37. 37. Salanti G. Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis: many names, many benefits, many concerns for the next generation evidence synthesis tool. Res Synth Methods. 2012;3(2):80–97. Epub 2012/06/01. pmid:26062083.
  38. 38. Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Journal of clinical epidemiology. 2009;62(10):1006–12. MEDLINE:19631508.
  39. 39. Garvican-Lewis LA, Sharpe K, Gore CJ. Time for a new metric for hypoxic dose? J Appl Physiol (1985). 2016;121(1):352–5. Epub 2016/02/27. pmid:26917695.
  40. 40. Smart NA, Waldron M, Ismail H, Giallauria F, Vigorito C, Cornelissen V, et al. Validation of a new tool for the assessment of study quality and reporting in exercise training studies: TESTEX. Int J Evid Based Healthc. 2015;13(1):9–18. Epub 2015/03/04. pmid:25734864.
  41. 41. Rücker G. Network meta-analysis, electrical networks and graph theory. Res Synth Methods. 2012;3(4):312–24. Epub 2012/12/01. pmid:26053424.
  42. 42. Shim SR, Kim SJ, Lee J, Rücker G. Network meta-analysis: application and practice using R software. Epidemiol Health. 2019;41:e2019013. Epub 2019/04/20. pmid:30999733; PubMed Central PMCID: PMC6635665.
  43. 43. Rücker G, Schwarzer G. Ranking treatments in frequentist network meta-analysis works without resampling methods. BMC Med Res Methodol. 2015;15:58. Epub 2015/08/01. pmid:26227148; PubMed Central PMCID: PMC4521472.
  44. 44. Huedo-Medina TB, Sánchez-Meca J, Marín-Martínez F, Botella J. Assessing heterogeneity in meta-analysis: Q statistic or I2 index? Psychol Methods. 2006;11(2):193–206. Epub 2006/06/21. pmid:16784338.
  45. 45. Sidik K, Jonkman JN. A comparison of heterogeneity variance estimators in combining results of studies. Stat Med. 2007;26(9):1964–81. Epub 2006/09/07. pmid:16955539.
  46. 46. Higgins JP, Jackson D, Barrett JK, Lu G, Ades AE, White IR. Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Res Synth Methods. 2012;3(2):98–110. Epub 2012/06/01. pmid:26062084; PubMed Central PMCID: PMC4433772.
  47. 47. Dias S, Welton NJ, Caldwell DM, Ades AE. Checking consistency in mixed treatment comparison meta-analysis. Stat Med. 2010;29(7–8):932–44. Epub 2010/03/10. pmid:20213715.
  48. 48. West JB, Lahiri S, Gill MB, Milledge JS, Pugh LG, Ward MP. Arterial oxygen saturation during exercise at high altitude. J Appl Physiol. 1962;17:617–21. Epub 1962/07/01. pmid:14006303.
  49. 49. Faulkner JA, Kollias J, Favour CB, Buskirk ER, Balke B. Maximum aerobic capacity and running performance at altitude. J Appl Physiol. 1968;24(5):685–91. Epub 1968/05/01. pmid:5647647.
  50. 50. Péronnet F, Thibault G, Cousineau DL. A theoretical analysis of the effect of altitude on running performance. J Appl Physiol (1985). 1991;70(1):399–404. Epub 1991/01/01. pmid:2010398.
  51. 51. Hess H, Hostler D. Respiratory Muscle Training Effects on Performance in Hypo- and Hyperbaria. Aerosp Med Hum Perform. 2018;89(11):996–1001. Epub 2018/10/26. pmid:30352652.
  52. 52. Mollard P, Woorons X, Letournel M, Cornolo J, Lamberto C, Beaudry M, et al. Role of maximal heart rate and arterial O2 saturation on the decrement of VO2max in moderate acute hypoxia in trained and untrained men. Int J Sports Med. 2007;28(3):186–92. Epub 2006/10/07. pmid:17024632.
  53. 53. Chapman RF. The individual response to training and competition at altitude. Br J Sports Med. 2013;47 (Suppl 1):i40–4. Epub 2013/12/07. pmid:24282206; PubMed Central PMCID: PMC3903142.
  54. 54. Desplanches D, Hoppeler H, Linossier MT, Denis C, Claassen H, Dormois D, et al. Effects of training in normoxia and normobaric hypoxia on human muscle ultrastructure. Pflugers Arch. 1993;425(3–4):263–7. Epub 1993/11/01. pmid:8309787.
  55. 55. Roberts AC, Reeves JT, Butterfield GE, Mazzeo RS, Sutton JR, Wolfel EE, et al. Altitude and beta-blockade augment glucose utilization during submaximal exercise. J Appl Physiol (1985). 1996;80(2):605–15. Epub 1996/02/01. pmid:8929605.
  56. 56. Deb SK, Brown DR, Gough LA, McLellan CP, Swinton PA, Andy Sparks S, et al. Quantifying the effects of acute hypoxic exposure on exercise performance and capacity: A systematic review and meta-regression. Eur J Sport Sci. 2018;18(2):243–56. Epub 2017/12/09. pmid:29220311.
  57. 57. Roels B, Thomas C, Bentley DJ, Mercier J, Hayot M, Millet G. Effects of intermittent hypoxic training on amino and fatty acid oxidative combustion in human permeabilized muscle fibers. J Appl Physiol (1985). 2007;102(1):79–86. Epub 2006/09/23. pmid:16990498.
  58. 58. Ponsot E, Dufour SP, Zoll J, Doutrelau S, N’Guessan B, Geny B, et al. Exercise training in normobaric hypoxia in endurance runners. II. Improvement of mitochondrial properties in skeletal muscle. J Appl Physiol (1985). 2006;100(4):1249–57. Epub 2005/12/13. pmid:16339351.
  59. 59. Terrados N, Jansson E, Sylvén C, Kaijser L. Is hypoxia a stimulus for synthesis of oxidative enzymes and myoglobin? J Appl Physiol (1985). 1990;68(6):2369–72. Epub 1990/06/01. pmid:2384418.
  60. 60. Pilegaard H, Domino K, Noland T, Juel C, Hellsten Y, Halestrap AP, et al. Effect of high-intensity exercise training on lactate/H+ transport capacity in human skeletal muscle. Am J Physiol. 1999;276(2):E255–61. Epub 1999/02/10. pmid:9950784.
  61. 61. Bell GJ, Wenger HA. The effect of one-legged sprint training on intramuscular pH and nonbicarbonate buffering capacity. Eur J Appl Physiol Occup Physiol. 1988;58(1–2):158–64. Epub 1988/01/01. pmid:3203662.
  62. 62. Terrados N, Melichna J, Sylvén C, Jansson E, Kaijser L. Effects of training at simulated altitude on performance and muscle metabolic capacity in competitive road cyclists. Eur J Appl Physiol Occup Physiol. 1988;57(2):203–9. Epub 1988/01/01. pmid:3349988.
  63. 63. Czuba M, Wilk R, Karpiński J, Chalimoniuk M, Zajac A, Langfort J. Intermittent hypoxic training improves anaerobic performance in competitive swimmers when implemented into a direct competition mesocycle. PLoS One. 2017;12(8):e0180380. Epub 2017/08/02. pmid:28763443; PubMed Central PMCID: PMC5538675.
  64. 64. Czuba M, Fidos-Czuba O, Płoszczyca K, Zając A, Langfort J. Comparison of the effect of intermittent hypoxic training vs. the live high, train low strategy on aerobic capacity and sports performance in cyclists in normoxia. Biol Sport. 2018;35(1):39–48. Epub 2018/09/22. pmid:30237660; PubMed Central PMCID: PMC6135973.
  65. 65. Morton JP, Cable NT. Effects of intermittent hypoxic training on aerobic and anaerobic performance. Ergonomics. 2005;48(11–14):1535–46. Epub 2005/12/13. pmid:16338719.
  66. 66. Czuba M, Bril G, Płoszczyca K, Piotrowicz Z, Chalimoniuk M, Roczniok R, et al. Intermittent Hypoxic Training at Lactate Threshold Intensity Improves Aiming Performance in Well-Trained Biathletes with Little Change of Cardiovascular Variables. Biomed Res Int. 2019;2019:1287506. Epub 2019/10/31. pmid:31662969; PubMed Central PMCID: PMC6778904.
  67. 67. Kallio PJ, Wilson WJ, O’Brien S, Makino Y, Poellinger L. Regulation of the hypoxia-inducible transcription factor 1alpha by the ubiquitin-proteasome pathway. J Biol Chem. 1999;274(10):6519–25. Epub 1999/02/26. pmid:10037745.
  68. 68. Jewell UR, Kvietikova I, Scheid A, Bauer C, Wenger RH, Gassmann M. Induction of HIF-1alpha in response to hypoxia is instantaneous. Faseb j. 2001;15(7):1312–4. Epub 2001/05/10. pmid:11344124.
  69. 69. Däpp C, Gassmann M, Hoppeler H, Flück M. Hypoxia-induced gene activity in disused oxidative muscle. Adv Exp Med Biol. 2006;588:171–88. Epub 2006/11/09. pmid:17089889.
  70. 70. Salceda S, Caro J. Hypoxia-inducible factor 1alpha (HIF-1alpha) protein is rapidly degraded by the ubiquitin-proteasome system under normoxic conditions. Its stabilization by hypoxia depends on redox-induced changes. J Biol Chem. 1997;272(36):22642–7. Epub 1997/09/05. pmid:9278421.
  71. 71. Semenza GL. Regulation of mammalian O2 homeostasis by hypoxia-inducible factor 1. Annu Rev Cell Dev Biol. 1999;15:551–78. Epub 1999/12/28. pmid:10611972.
  72. 72. Zoll J, Ponsot E, Dufour S, Doutreleau S, Ventura-Clapier R, Vogt M, et al. Exercise training in normobaric hypoxia in endurance runners. III. Muscular adjustments of selected gene transcripts. J Appl Physiol (1985). 2006;100(4):1258–66. Epub 2006/03/17. pmid:16540710.
  73. 73. Melissa L, MacDougall JD, Tarnopolsky MA, Cipriano N, Green HJ. Skeletal muscle adaptations to training under normobaric hypoxic versus normoxic conditions. Med Sci Sports Exerc. 1997;29(2):238–43. Epub 1997/02/01. pmid:9044229.
  74. 74. Barnes KR, Kilding AE. Strategies to improve running economy. Sports Med. 2015;45(1):37–56. Epub 2014/08/29. pmid:25164465.
  75. 75. Vogt M, Puntschart A, Geiser J, Zuleger C, Billeter R, Hoppeler H. Molecular adaptations in human skeletal muscle to endurance training under simulated hypoxic conditions. J Appl Physiol (1985). 2001;91(1):173–82. Epub 2001/06/16. pmid:11408428.
  76. 76. Xu J, Zeng J, Yan Y, Xu F. Hypoxic Exercise Exacerbates Hypoxemia and Acute Mountain Sickness in Obesity: A Case Analysis. Int J Environ Res Public Health. 2021;18(17). Epub 2021/09/11. pmid:34501667; PubMed Central PMCID: PMC8430682.
  77. 77. Bärtsch P, Saltin B. General introduction to altitude adaptation and mountain sickness. Scand J Med Sci Sports. 2008;18 Suppl 1:1–10. Epub 2008/09/09. pmid:18665947.
  78. 78. Warnier G, Benoit N, Naslain D, Lambrecht S, Francaux M, Deldicque L. Effects of Sprint Interval Training at Different Altitudes on Cycling Performance at Sea-Level. Sports (Basel). 2020;8(11). Epub 2020/11/22. pmid:33217937; PubMed Central PMCID: PMC7698804.
  79. 79. Lawler J, Powers SK, Thompson D. Linear relationship between VO2max and VO2max decrement during exposure to acute hypoxia. J Appl Physiol (1985). 1988;64(4):1486–92. Epub 1988/04/01. pmid:3378983.
  80. 80. Puype J, Van Proeyen K, Raymackers JM, Deldicque L, Hespel P. Sprint interval training in hypoxia stimulates glycolytic enzyme activity. Med Sci Sports Exerc. 2013;45(11):2166–74. Epub 2013/04/23. pmid:23604068.
  81. 81. Friedmann B, Frese F, Menold E, Bärtsch P. Effects of acute moderate hypoxia on anaerobic capacity in endurance-trained runners. Eur J Appl Physiol. 2007;101(1):67–73. Epub 2007/05/09. pmid:17486360.
  82. 82. Fukuda-Matsuda E, Yamada M, Tanobe K, Saito S. Peripheral circulation monitored by surface temperature and autonomic nervous function in hypobaric hypoxic environment: effects of submaximal exercise. Int J Environ Health Res. 2007;17(1):53–60. Epub 2007/03/17. pmid:17365080.
  83. 83. Gutknecht AP, Gonzalez-Figueres M, Brioche T, Maurelli O, Perrey S, Favier FB. Maximizing anaerobic performance with repeated-sprint training in hypoxia: In search of an optimal altitude based on pulse oxygen saturation monitoring. Front Physiol. 2022;13:1010086. Epub 2022/11/01. pmid:36311239; PubMed Central PMCID: PMC9597871.
  84. 84. Imoberdorf R, Garlick PJ, McNurlan MA, Casella GA, Marini JC, Turgay M, et al. Skeletal muscle protein synthesis after active or passive ascent to high altitude. Med Sci Sports Exerc. 2006;38(6):1082–7. Epub 2006/06/16. pmid:16775549.
  85. 85. Vogt M, Hoppeler H. Is hypoxia training good for muscles and exercise performance? Prog Cardiovasc Dis. 2010;52(6):525–33. Epub 2010/04/27. pmid:20417346.
  86. 86. Chycki J, Czuba M, Gołaś A, Zając A, Fidos-Czuba O, Młynarz A, et al. Neuroendocrine Responses and Body Composition Changes Following Resistance Training Under Normobaric Hypoxia. J Hum Kinet. 2016;53:91–8. Epub 2017/02/06. pmid:28149414; PubMed Central PMCID: PMC5260579.
  87. 87. Gamonales JM, Rojas-Valverde D, Vasquez J, Martinez-Guardado I, Azofeifa-Mora C, Sanchez-Urena B, et al. An Update to a Comprehensive Assessment of the Methods and Effectiveness of Resistance Training in Normobaric Hypoxia for the Development of Strength and Muscular Hypertrophy. Applied Sciences-Basel. 2023;13(2). WOS:000916711700001.
  88. 88. Ramos-Campo DJ, Martínez-Sánchez F, Esteban-García P, Rubio-Arias JA, Clemente-Suarez VJ, Jiménez-Díaz JF. The effects of intermittent hypoxia training on hematological and aerobic performance in triathletes. Acta Physiol Hung. 2015;102(4):409–18. Epub 2015/12/23. pmid:26690033.
  89. 89. Robach P, Bonne T, Flück D, Bürgi S, Toigo M, Jacobs RA, et al. Hypoxic training: effect on mitochondrial function and aerobic performance in hypoxia. Med Sci Sports Exerc. 2014;46(10):1936–45. Epub 2014/03/29. pmid:24674976.
  90. 90. Hamlin MJ, Marshall HC, Hellemans J, Ainslie PN, Anglem N. Effect of intermittent hypoxic training on 20 km time trial and 30 s anaerobic performance. Scand J Med Sci Sports. 2010;20(4):651–61. Epub 2009/10/02. pmid:19793215.
  91. 91. Park HY, Jung WS, Kim J, Hwang H, Lim K. Efficacy of intermittent hypoxic training on hemodynamic function and exercise performance in competitive swimmers. J Exerc Nutrition Biochem. 2018;22(4):32–8. Epub 2019/01/22. pmid:30661329; PubMed Central PMCID: PMC6343766.
  92. 92. Hamlin MJ, Lizamore CA, Hopkins WG. The Effect of Natural or Simulated Altitude Training on High-Intensity Intermittent Running Performance in Team-Sport Athletes: A Meta-Analysis. Sports Med. 2018;48(2):431–46. Epub 2017/11/13. pmid:29129021.
  93. 93. Feng X, Zhao L, Chen Y, Wang Z, Lu H, Wang C. Optimal type and dose of hypoxic training for improving maximal aerobic capacity in athletes: a systematic review and Bayesian model-based network meta-analysis. Frontiers in Physiology. 2023;14. pmid:37745240
  94. 94. Kasai N, Mizuno S, Ishimoto S, Sakamoto E, Maruta M, Goto K. Effect of training in hypoxia on repeated sprint performance in female athletes. Springerplus. 2015;4:310. Epub 2015/07/15. pmid:26155449; PubMed Central PMCID: PMC4488237.
  95. 95. Pramkratok W, Songsupap T, Yimlamai T. Repeated sprint training under hypoxia improves aerobic performance and repeated sprint ability by enhancing muscle deoxygenation and markers of angiogenesis in rugby sevens. Eur J Appl Physiol. 2022;122(3):611–22. Epub 2022/01/04. pmid:34977961.
  96. 96. Montero D, Lundby C. Repeated sprint training in hypoxia versus normoxia does not improve performance: a double-blind and cross-over study. International Journal of Sports Physiology and Performance. 2017;12(2):161–7.
  97. 97. Gatterer H, Philippe M, Menz V, Mosbach F, Faulhaber M, Burtscher M. Shuttle-run sprint training in hypoxia for youth elite soccer players: a pilot study. J Sports Sci Med. 2014;13(4):731–5. Epub 2014/12/02. pmid:25435763; PubMed Central PMCID: PMC4234940.
  98. 98. Wang R, Fukuda DH, Hoffman JR, La Monica MB, Starling TM, Stout JR, et al. Distinct Effects of Repeated-Sprint Training in Normobaric Hypoxia and β-Alanine Supplementation. J Am Coll Nutr. 2019;38(2):149–61. Epub 2018/10/03. pmid:30277420.
  99. 99. Billaut F, Smith K. Sex alters impact of repeated bouts of sprint exercise on neuromuscular activity in trained athletes. Appl Physiol Nutr Metab. 2009;34(4):689–99. Epub 2009/09/22. pmid:19767805.
  100. 100. Hou YP, Wu JL, Tan C, Chen Y, Guo R, Luo YJ. Sex-based differences in the prevalence of acute mountain sickness: a meta-analysis. Mil Med Res. 2019;6(1):38. Epub 2019/12/10. pmid:31813379; PubMed Central PMCID: PMC6900850.