Rehabilitation effects of high-intensity interval training on asthma: a systematic review and meta-analysis of randomized controlled trials

Introduction

Asthma is defined as a chronic, heterogeneous inflammatory disorder of the airways. According to Global Burden of Disease Study, its prevalence has continued to rise over the past three decades (1). Currently, it affects approximately 339 million people worldwide, including more than 48 million in China, and results in an estimated 460,000 deaths and 23 million disability-adjusted life years lost each year (2). The pathological core of the disease lies in T-helper type 2 cell 2 (Th2)-type inflammation-driven airway hyperresponsiveness (AHR), mucus hypersecretion, and bronchial smooth muscle spasm (3,4). Its clinical manifestations include recurrent wheezing, chest tightness and coughing (5). The variability of its symptoms is closely related to environmental exposures (such as allergens, pollutants) and exercise stimulation (3). The Global Initiative for Asthma Prevention and Treatment guidelines recommend stepwise pharmacological intervention as the core management strategy: from the first level of on-demand use of short-acting β2 receptor agonists (SABA) to the fifth level of biological targeted therapy (such as anti-IgE monoclonal antibodies), but real-world data show that only 45–60% of patients achieve complete symptom control (6). Although the combination of long-acting bronchodilators (LABA) and inhaled corticosteroids (ICS) can significantly reduce the risk of acute attacks, long-term pharmacotherapy still presents multiple challenges. Approximately 35% of pediatric patients experience growth inhibition, 20–30% of adults report dysphonia and oral candidiasis, and the high cost of biologics further limits their accessibility (7).

Traditional rehabilitation programs for asthma are mainly based on low- to medium-intensity aerobic exercise. Although such interventions have been proven effective in improving lung function and quality of life, they are limited by slow onset of effect and poor patient adherence (8). In recent years, high-intensity interval training (HIIT) has shown notable benefits in cardiovascular disease and metabolic syndrome due to its time-efficient and high-impact nature, but its application in asthma management remains controversial (9,10). On the one hand, theory suggests that HIIT may enhance peak oxygen uptake (VO₂peak) more efficiently by upregulating antioxidant enzyme activity, reducing airway inflammatory markers, and improving respiratory muscle endurance (11,12). On the other hand, clinical concerns remain that high-intensity exercise may trigger bronchospasm and worsen asthma symptoms (13). Current evidence from randomized controlled trials (RCTs) remains highly contradictory. For example, the RCT conducted by Pitzner-Fabricius et al. in 2023 found a significantly greater rise in VO₂peak in the HIIT cohort than in the conventional training group (14), whereas the study by Türk et al. found no statistically significant variation when comparing HIIT with the control group (15). This disagreement stems from the heterogeneity of previous studies. There is a lack of standardization in the baseline population characteristics (such as age ranging from children to postmenopausal women), intervention programs (4 weeks to 6 months), control measures (from no intervention to combined drug training), and outcome indicator selection (16-18). Therefore, there is an urgent need to integrate the body of evidence through a systematic review.

A 2022 meta-analysis by Wang et al. covered three RCTs investigating HIIT as an add-on therapy for asthma (12). Given the small number of studies included and limited outcome measures related to HIIT, their study was unable to draw definitive conclusions about its benefits. Since the publication of that meta-analysis, several new RCTs have emerged to evaluate the effectiveness of HIIT in asthma rehabilitation, which may provide new insights into its effectiveness. The present work was designed as an updated review of rigorously conducted RCTs to clarify how high-intensity exercise influences asthma rehabilitation. We present this article in accordance with the PRISMA reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1702/rc).

Methods

Literature search

Following the PRISMA 2020 recommendations, we conducted a meta-analysis (19) registered with PROSPERO (CRD420251114650). A literature search across PubMed, Embase, Web of Science, and the Cochrane Library up to June 2025 retrieved RCTs examining high-intensity exercise interventions with conventional rehabilitation in individuals with asthma. The search strategy combined keywords such as ‘exercise’, ‘asthma’, and ‘high-intensity’, and reference lists from the included RCTs were hand-searched for other potentially relevant trials. Two investigators separately evaluated titles, abstracts, and full texts; conflicts were addressed by consensus. The complete search strategy is detailed in Table S1.

Inclusion and exclusion criteria

Eligibility criteria:

• P (population): participants were individuals with asthma.
• I (intervention): the intervention of interest was high-intensity exercise, either as a standalone treatment or together with conventional rehabilitation.
• C (comparison): control measures included conventional treatment methods, which involved moderate/low-intensity exercise or without any exercise intervention.
• O (outcomes): the main outcome measures included the five-question Asthma Control Questionnaire (5-item version) (ACQ-5), VO₂peak, fractional exhaled nitric oxide (FeNO), forced expiratory volume in 1 second as percentage of the predicted value (FEV1%), and the Mini Asthma Quality of Life Questionnaire (MiniAQLQ), etc.
• S (study design): RCTs.

Exclusion criteria:

• Reviews; unpublished work; studies without adequate data; non-randomized research; study protocols; and other non-original publications (such as meeting abstracts, replies, or corrections).

Data abstraction

Two investigators independently extracted the data, and any conflicts were addressed through adjudication by a third author. For every eligible RCT, the following details were retrieved: first author, publication year, design, study location, registration number, details of the intervention and control, sample size, participants’ age and sex, intervention duration, and outcome measures including ACQ-5, VO₂peak, FEV1%, FeNO, and MiniAQLQ. When necessary data were missing, we contacted the corresponding authors to request the missing information.

Quality evaluation

Quality evaluation of the RCTs adhered to the Cochrane Handbook for Systematic Reviews of Interventions (v5.1.0), assessing seven domains: allocation concealment, selective reporting, completeness of outcome data, blinding of outcome assessment, random sequence generation, blinding of participants and personnel, and other biases (20). The risk for each was graded as low, high, or unclear. Trials with more domains rated low risk were classified as higher quality. Two reviewers carried out the assessment independently and settled any discrepancies through consensus.

Statistical analysis

Data synthesis was performed using Review Manager 5.4.1. For continuous variables, standardized mean differences (SMDs) with 95% confidence intervals (CIs) were calculated. The Chi-squared (χ²) test (Cochran’s Q) together with the I² index was applied to determine heterogeneity, with substantial heterogeneity defined as a χ² P value <0.10 or I²>50%. Using a random-effects approach, pooled SMDs were determined. When an outcome was reported by more than two studies, a leave-one-out sensitivity analysis was carried to examine the influence of individual RCTs on the overall results. Egger’s regression test, implemented in Stata 15.1 (StataCorp, College Station, TX, USA), was employed to determine potential publication bias, with P<0.05 defined as significant. We planned to extract safety outcome data such as adverse events, exercise-induced bronchospasm (EIB), or acute exacerbations, but since none of the included original studies systematically reported this information, quantitative analysis was not possible.

Results

Literature retrieval, study characteristics, and baseline

Figure 1 depicts the process of literature identification and study selection. A total of 131 records were retrieved from PubMed (n=20), Embase (n=47), Web of Science (n=22), and Cochrane Library (n=42). After duplicate removal, 86 titles and abstracts were screened, resulting in the inclusion of 7 RCTs (14-18,20,21) encompassing 518 participants in the meta-analysis. The key features of the trials included are summarized in Table 1, while Figure 2 provides a detailed assessment of their methodological quality. It is worth noting that none of the RCTs included in this systematic review reported systemic safety outcomes (such as serious adverse events or the incidence of EIB) associated with HIIT.

Figure 1 Flowchart of the systematic search and selection process.

Figure 2 Details of the quality evaluation for included RCTs. RCT, randomized controlled trial.

Change in ACQ-5

Data from five RCTs were combined to evaluate ACQ-5 outcomes. The meta-analysis showed a similar change in ACQ-5 between the two groups (SMD: −1.19; 95% CI: −2.69 to 0.32; P=0.12), with significant heterogeneity (I²=98%, P<0.001) (Figure 3A).

Figure 3 Forest plots of (A) ACQ-5, (B) VO₂peak. ACQ-5, Asthma Control Questionnaire (5-item version); CI, confidence interval; HIIT, high-intensity interval training; IV, inverse variance; SE, standard error; Std, standard deviation; VO₂peak, peak oxygen uptake.

Change in VO₂peak

Data from five RCTs were combined to evaluate changes in VO₂peak. The meta-analysis showed a similar change in VO₂peak between the two groups (SMD: 1.41; 95% CI: −0.25 to 3.08; P=0.10), with significant heterogeneity (I²=97%, P<0.001) (Figure 3B).

Change in MiniAQLQ

Data from four RCTs were combined to examine changes in MiniAQLQ. The meta-analysis indicated a similar change in MiniAQLQ between the two groups (SMD: 1.44; 95% CI: −0.71 to 3.60; P=0.19), with significant heterogeneity (I²=98%, P<0.001) (Figure 4A).

Figure 4 Forest plots of (A) MiniAQLQ, (B) FeNO, (C) FEV1%. CI, confidence interval; FeNO, fractional exhaled nitric oxide; FEV1%, forced expiratory volume in 1 second as percentage of the predicted value; HIIT, high-intensity interval training; IV, inverse variance; MiniAQLQ, Mini Asthma Quality of Life Questionnaire; SE, standard error; Std, standard deviation.

Change in FeNO

Data from four RCTs were combined to evaluate changes in FeNO. The meta-analysis indicated a similar change in FeNO between the two groups (SMD: 0.15; 95% CI: −0.46 to 0.76; P=0.63), with significant heterogeneity (I²=84%, P<0.001) (Figure 4B).

Change in FEV1%

Data from six RCTs were combined to evaluate changes in FEV1%. The meta-analysis indicated a similar change in FEV1% between the two groups (SMD: 0.45; 95% CI: −0.09 to 0.98; P=0.10), with significant heterogeneity (I²=92%, P<0.001) (Figure 4C).

Sensitivity analysis and publication bias

Sensitivity analyses were performed for ACQ-5, VO₂peak, MiniAQLQ, FeNO, and FEV1% by removing each study one at a time to evaluate its influence on the overall SMD. Sensitivity analyses indicated that the overall SMD remained consistent after sequentially excluding individual RCTs for ACQ-5 (Figure 5A), VO₂peak (Figure 5B), MiniAQLQ (Figure 5C), FeNO (Figure 5D), and FEV1% (Figure 5E). In addition, Egger’s tests for ACQ-5 (P=0.30, Figure 6A), VO₂peak (P=0.55, Figure 6B), MiniAQLQ (P=0.48, Figure 6C), FeNO (P=0.52, Figure 6D), and FEV1% (P=0.47, Figure 6E) showed no evidence of publication bias. Furthermore, it is noteworthy that after excluding data from Pitzner-Fabricius 2023, the heterogeneity of the VO₂peak decreased from 97% to 53%; the heterogeneity of ACQ-5 decreased from 98% to 77%; the heterogeneity of FeNO decreased from 84% to 0%; and the heterogeneity of Mini AQLQ decreased from 98% to 0%, suggesting that this study was the main reason for the significant heterogeneity in this meta-analysis.

Figure 5 Sensitivity analysis of (A) ACQ-5, (B) VO₂peak, (C) MiniAQLQ, (D) FeNO, (E) FEV1%. ACQ-5, Asthma Control Questionnaire (5-item version); CI, confidence interval; FeNO, fractional exhaled nitric oxide; FEV1%, forced expiratory volume in 1 second as percentage of the predicted value; MiniAQLQ, Mini Asthma Quality of Life Questionnaire; VO₂peak, peak oxygen uptake.

Figure 6 Egger’s test plots of (A) ACQ-5, (B) VO₂peak, (C) MiniAQLQ, (D) FeNO, (E) FEV1%. ACQ-5, Asthma Control Questionnaire (5-item version); FeNO, fractional exhaled nitric oxide; FEV1%, forced expiratory volume in 1 second as percentage of the predicted value; MiniAQLQ, Mini Asthma Quality of Life Questionnaire; VO₂peak, peak oxygen uptake.

Discussion

This analysis systematically integrated clinical evidence from seven RCTs with a total of 518 asthma patients by strictly following the PRISMA guidelines for systematic review methods. It comprehensively evaluated the effectiveness of HIIT in the overall management of asthma compared to conventional rehabilitation programs (including moderate-intensity continuous training, low-intensity exercise, and no-exercise control conditions). The core findings revealed that the combined effect size of all pre-set primary endpoints—ACQ-5, VO₂peak, MiniAQLQ, FeNO, and FEV1% did not reach the statistical significance threshold (P value range, 0.10–0.63). This result presents a substantial challenge to the current mainstream theoretical framework in the field of respiratory rehabilitation. Although the point estimates of the SMD of VO₂peak (SMD =1.41) and MiniAQLQ (SMD =1.44) showed a clinically significant positive trend, the wide range of their 95% CIs (VO₂peak: −0.25 to 3.08; MiniAQLQ: −0.71 to 3.60) indicated that these effects were highly uncertain. This uncertainty may stem from the complex pathophysiological characteristics of asthma itself: bronchial hyperresponsiveness, as a core pathological feature, significantly limits the patient’s physiological tolerance to high-intensity exercise (22). These implementation-level barriers directly affect the fidelity of the HIIT program, resulting in the actual training intensity failing to reach the theoretical preset goal, thereby weakening the physiological adaptation effect of high-intensity exercise. The deeper problem lies in the profound heterogeneity of asthma phenotypes—ranging from EIB to eosinophilic asthma—each of which exhibits fundamentally different inflammatory response mechanisms to exercise stimuli. However, existing studies have failed to establish an effective hierarchical analysis framework.

In addition, the substantial heterogeneity observed in this study (I²>80% for all outcome indicators, P<0.001) requires in-depth deconstruction from four dimensions: intervention design, control selection, population characteristics, and methodology. At the intervention level, there are fundamental differences in the HIIT protocols included in the study. These differences are not only reflected in the absolute value of exercise intensity, but more importantly, the huge differences in the work/rest ratio and the amount of single training directly affect the signaling pathways that produce adaptive responses in the body. The effects of high-intensity exercise-induced mitochondrial biogenesis and capillary angiogenesis have clear dose-dependent characteristics (23). The diversity of control group settings further amplifies the heterogeneity, including no exercise intervention control, moderate-intensity continuous training, and conventional care (drug management + health education). This difference in control strategy leads to confusion in the reference system when calculating the effect size. For example, when HIIT is compared with a blank control, it may show significant benefits, while the difference is diluted when compared with an active control. The population heterogeneity is even more significant: the age spectrum of the subjects extends from childhood to old age; the severity of asthma covers Global Initiative for Asthma (GINA) grade II (mild) to grade IV (severe); and the distribution of comorbidities ranges from simple asthma to obese asthma and geriatric comorbidities. Notably, there are fundamental differences in the pathophysiological characteristics of different age groups: childhood asthma is mainly characterized by reversible AHR, while elderly patients are often accompanied by fixed airflow limitation and lung parenchymal destruction (24-26). Methodological variability must also be acknowledged: intervention durations ranges from short-term to long-term, and the timing of outcome assessment (immediately post-intervention vs. follow-up period) was inconsistent. This heterogeneity strongly suggests that future studies should establish standardized reporting protocols for HIIT interventions, including clarifying intensity calibration methods, work/rest ratios, and implementing stratified designs based on key prognostic factors (age, asthma phenotype, baseline lung function) during the randomization stage.

In the symptom control domain, although the combined effect size of ACQ-5 (SMD =−1.19) did not reach the significance threshold of P<0.05, its point estimate substantially exceeded the minimum clinically important difference (MCID =0.5), with the lower limit of the 95% CI reaching −2.69. This phenomenon may be related to the regulatory effect of high-intensity exercise on neuroimmune pathways. HIIT has been shown to activate the cholinergic anti-inflammatory pathway, promoting the release of acetylcholine from vagus nerve endings, which subsequently inhibits the secretion of tumor necrosis factor alpha (TNF-α) from splenic macrophages, and ultimately downregulate airway inflammatory response (27,28). However, the FeNO results of this study (SMD =0.15, P=0.63) did not show improvement in airway eosinophilic inflammation, suggesting that the anti-inflammatory effect of HIIT may be independent of the classic Th2 pathway, but is achieved by regulating the function of innate immune cells (28,29). In terms of exercise tolerance, although the combined analysis of VO₂peak did not reach statistical significance, the RCT conducted by Pitzner-Fabricius et al. [2023] found a significantly greater improvement in VO₂peak in the HIIT group compared to the conventional training group (14). This discrepancy may be due to the specific training effect of HIIT on respiratory muscle function: the high ventilation demand during high-intensity exercise forces the diaphragm, intercostal muscles and other respiratory muscles to work above the fatigue threshold, inducing muscle fiber type transformation and increased capillary density (30,31). However, this hypothesis requires simultaneous verification of electromyography and diaphragm ultrasound.

The systematic application of the Cochrane Risk of Bias assessment tool revealed three key methodological flaws that collectively weakened the reliability of this study’s conclusions. First, the risk of bias assessment used in this study reveals a methodological limitation prevalent and difficult to avoid in exercise intervention RCTs—the systematic lack of blinding for participants and intervention implementers. This limitation can affect different types of outcome measures to varying degrees. For patient-reported outcomes like ACQ-5, which are highly subjective, subjects who are aware of their grouping may overestimate or underestimate the degree of symptom improvement due to expectation effect (HIIT group) or disappointment bias (control group), leading to an artificially exaggerated effect size. In contrast, for objectively measured indicators like FEV1%, which are measured by pulmonary function testing, the lack of blinding may have a smaller impact on the measurement results themselves. Therefore, the uncertainty in the pooled effect size of ACQ-5 in this meta-analysis may partly stem from such performance and detection biases, while the FEV1% result is relatively less susceptible to these influences. Furthermore, the small sample sizes in some studies may limit the statistical power of the tests. In addition, the insufficient sample size directly limits the statistical power. The sample size of the included studies was as low as 12 cases, which may lead to an unavoidable small sample size effect. The cumulative effect of these methodological flaws led to a significantly increased risk of type II error—that is, the actual clinical benefit was masked by the statistical method, which was confirmed by the 95% CI upper limit of VO₂peak of 3.08.

Sensitivity analysis of this study indicates that the Pitzner-Fabricius 2023 study was the main outlier contributing to the high heterogeneity of multiple outcome measures. This impact likely stems from significant differences between this study and other included studies across several key dimensions. First, its sample size was significantly larger than other studies, giving it a higher weight in the meta-analysis, yet its effect estimates differed from other studies, thus amplifying heterogeneity. Second, the intervention duration (6 months) was significantly longer than most other studies (typically 12 weeks), potentially leading to different physiological adaptations and response patterns. Furthermore, this study targeted a broad, “untrained” asthma population, whose baseline characteristics, disease severity, and potential physical activity levels may differ systematically from studies targeting specific subgroups (such as postmenopausal women or patients with moderate to severe asthma). Additionally, the passive control group used in this study (“continued daily activities”) differs from most studies using active controls (such as low-intensity exercise), and this difference in control design may directly affect the estimation of effect size. These clinical and methodological heterogeneities, coupled with potential variations in geographical location, cultural background, and specific intervention implementation details (such as supervision intensity and adherence management) across different studies, highlight the core issue of the current lack of standardized protocols and reporting standards in the field of exercise intervention research. Therefore, future research urgently needs to reach broader consensus on protocol design, population definition, outcome measurement, and outcome reporting, and strengthen the standardized description of patient baseline characteristics, intervention details, and control settings to enhance comparability between studies, reduce heterogeneity, and thus provide a more reliable and generalizable evidence base for evidence-based decision-making.

Based on a detailed analysis of current evidence, the application of HIIT in asthma rehabilitation should follow an individualized framework of “accurate assessment-step-by-step advancement-dynamic monitoring”. The first step is to implement exercise risk stratification: patients with suspected EIB need to undergo standardized bronchial provocation testing, as unverified high-intensity training may induce bronchospasm events. In designing an exercise prescription, a three-stage step-by-step progressive strategy is recommended. The initial adaptation phase (weeks 0–4) focuses on building basic aerobic capacity through continuous training at 50–60% of maximal heart rate (HRmax) for 20 minutes, combined with threshold-load respiratory muscle training [30% of maximum inspiratory pressure (MIP), three times per week]. The goal of this stage is to establish a safe exercise threshold. The mid-term strengthening period (5–8 weeks) introduces a modified HIIT program (high-intensity period 70–80% HRmax for 2 minutes, recovery period extended to 4 minutes, work/rest ratio 1:2), focusing on improving ventilation efficiency. The advanced optimization period (≥9 weeks) transitions to standard HIIT (85–95% HRmax for 4 minutes, recovery period 3 minutes, work/rest ratio 1:1), and ultimately achieves adaptive reconstruction of cardiopulmonary function (11,32). The monitoring system should extend beyond traditional lung function tests. During each HIIT training, real-time monitoring of oxygen pulse (VO₂/HR) is recommended to assess stroke-by-stroke oxygen uptake efficiency. Monthly cardiopulmonary exercise testing (CPET) should be conducted to evaluate the dynamic evolution of ventilatory efficiency, using the VE/VCO₂ slope as a key indicator. Regarding biomarkers, it is recommended to jointly test FeNO and serum periostin levels, the former reflects Th2 inflammatory activity, and the latter indicates the process of airway remodeling (33,34). Customized intervention plans are needed for special populations: adolescent groups can use gamification design to improve training compliance; obese asthma patients are recommended to do high-intensity intermittent weight-bearing training such as kettlebell swings to simultaneously improve metabolic and ventilation functions; elderly patients need to extend warm-up and cool-down activities to 20 minutes, and prepare for salbutamol inhalation to prevent delayed bronchospasm.

The limitations of this study accurately map out several scientific frontiers that urgently require further exploration. Firstly, one of the most significant limitations of this review is the lack of standardized reporting of safety outcomes (such as EIB and acute exacerbations) in all included studies. This greatly limits a comprehensive assessment of the risk-benefit ratio of HIIT in the asthmatic population. Given that high-intensity exercise is a known trigger for bronchospasm, future studies must incorporate prospective, standardized safety monitoring as a core design element to establish the safety boundaries of HIIT in clinical practice. Secondly, the short intervention cycle makes it difficult to capture the long-term effects of exercise intervention on airway remodeling. It is recommended to design a 24–52-week extension study, focusing on the dynamic evolution of small airway function and airway resistance. Thirdly, because of the inherent limitations of exercise intervention trials, none of the seven RCTs achieved adequate blinding of participants or investigators, which could compromise the reliability of the findings. Furthermore, the intervention methods (type, duration and frequency of high-intensity exercise) of the RCTs varied, introducing potential heterogeneity. In addition, with small number of available studies, it was not possible to evaluate the impact of high-intensity exercise on outcomes such as patients’ psychological status, which remains an area for future research. Moreover, data constraints hindered subgroup analyses by intervention type, training intensity, frequency, age, or ethnicity, leaving uncertainty about the influence of these variables on the results. Although limitations remain, this updated meta-analysis addresses the small sample sizes of earlier reviews and provides additional evidence for the non-inferiority of high-intensity exercise in cardiopulmonary rehabilitation for asthma patients. Innovative directions for future research include: developing phenotype-targeted exercise prescriptions—designing anti-inflammatory synergistic programs for eosinophilic asthma; building a digital therapy integration platform to automatically trigger HIIT intensity reduction when the peak expiratory flow (PEF) is <80% of the personal best through the Internet of Things linkage between smart inhalers and sports bracelets; promoting pharmacoeconomic indicators as primary endpoints; and finally establishing a predictive model for asthma exercise response through cross-scale effect evaluation. These paradigm changes will drive HIIT from the current “fuzzy benefits” to a new era of “precision prescriptions”.

Conclusions

This meta-analysis synthesized evidence from seven RCTs involving a total of 518 asthma patients and found that HIIT did not achieve statistical significance in improving asthma control (ACQ-5), cardiopulmonary function (VO₂peak), quality of life (MiniAQLQ), airway inflammation (FeNO), and lung function (FEV1%) compared to conventional rehabilitation programs. The extremely high heterogeneity of the results was mainly due to the lack of standardization of HIIT programs, mixed control group settings, diverse population characteristics, and methodological limitations. In the future, it is necessary to optimize individualized interventions through phenotypic stratification studies, unified HIIT programs, and combined with digital monitoring technology. In clinical practice, a stepwise progression strategy combined with dynamic biomarker assessment is recommended to ensure both safety and efficacy.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1702/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1702/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1702/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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