Abstract
Background
High-intensity interval training (HIIT) is increasingly used in cardiac rehabilitation. Its effects on cardiopulmonary function and quality of life in patients with myocardial infarction (MI) require systematic evaluation.
Methods
A systematic search was conducted in PubMed, Embase, Web of Science, Cochrane Central, and CNKI from inception to April 1, 2025. Randomized controlled trials assessing the effects of HIIT on peak heart rate (HRpeak), peak oxygen uptake (VO₂peak), respiratory exchange ratio (RER), and quality of life (QOL) in post-MI patients were included. Data were synthesized using a random-effects model. Standardized mean differences (SMDs) and 95% confidence intervals (CIs) were calculated. Heterogeneity was assessed with the I² statistic, and sensitivity analyses were performed.
Results
Nine trials with 508 participants were included. HIIT did not significantly affect HRpeak (SMD = −0.20; 95% CI: −1.10 to 0.70; I2 = 62.4%) or VO₂peak (SMD = 0.32; 95% CI: −0.07 to 0.71; I2 = 69.1%). RER was significantly reduced in the HIIT group (SMD = −1.26; 95% CI: −1.54 to −0.98; I2 = 0%). No significant improvement was observed in QOL (SMD = 0.07; 95% CI: −0.37 to 0.50; I2 = 0%). Sensitivity analyses suggested that individual studies contributed to heterogeneity in HRpeak and VO₂peak outcomes.
Conclusions
HIIT may influence selected cardiopulmonary indicators in post-MI patients, particularly metabolic efficiency. Its effects on exercise capacity and quality of life remain uncertain. Further research with standardized protocols is needed.
Keywords: high-intensity interval training, myocardial infarction, peak oxygen uptake, respiratory exchange ratio, quality of life, meta-analysis
Introduction
Myocardial infarction (MI) remains one of the leading causes of morbidity and mortality worldwide, posing a substantial burden on healthcare systems and adversely affecting patients' functional capacity and quality of life (1–3). Despite advancements in reperfusion therapies, pharmacological interventions, and secondary prevention strategies, a significant proportion of post-MI patients continue to experience reduced cardiopulmonary function, persistent symptoms, and impaired psychosocial well-being (4, 5). These limitations not only diminish patients' ability to resume daily activities but are also closely associated with long-term prognosis and cardiovascular event recurrence (6, 7).
Cardiac rehabilitation (CR) has been firmly established as a cornerstone of secondary prevention in patients with coronary artery disease, with robust evidence supporting its role in improving exercise tolerance, modulating cardiovascular risk factors, and reducing mortality. Traditionally, moderate-intensity continuous training (MICT) has been the predominant exercise modality employed in CR programs. However, recent years have witnessed growing interest in high-intensity interval training (HIIT), an alternative exercise paradigm characterized by brief bursts of vigorous activity interspersed with periods of active recovery (8). HIIT has been proposed to elicit superior physiological adaptations through greater mechanical and metabolic stimulus, potentially leading to more pronounced improvements in maximal oxygen uptake (VO₂peak), endothelial function, and autonomic regulation (9, 10).
Emerging clinical studies suggest that HIIT may confer greater cardiopulmonary benefits than MICT in post-MI populations, with improvements in functional capacity translating into enhanced health-related quality of life (HRQoL) (11, 12). Moreover, the time efficiency and motivational appeal of HIIT may improve adherence among patients who are often reluctant to engage in prolonged exercise sessions (13). Nonetheless, findings across randomized trials remain heterogeneous, and concerns regarding the safety, feasibility, and generalizability of HIIT in high-risk cardiac populations persist. Given these uncertainties, there is a critical need to systematically synthesize available evidence to clarify the clinical value of HIIT in this context.
This meta-analysis aims to comprehensively evaluate the effects of high-intensity interval training on cardiopulmonary function and quality of life in patients recovering from myocardial infarction. By integrating data from randomized controlled trials and assessing the magnitude, consistency, and clinical significance of outcomes, this study seeks to inform evidence-based exercise prescription and guide future research directions in cardiac rehabilitation.
Methods
Search strategy
A systematic and comprehensive search of the literature was undertaken in five major databases: PubMed, Embase, Web of Science, Cochrane Library, and China National Knowledge Infrastructure (CNKI), from inception to April 1, 2025. The search strategy combined both Medical Subject Headings (MeSH) and free-text terms related to myocardial infarction, high-intensity interval training, aerobic capacity, cardiopulmonary rehabilitation, and quality of life. Boolean operators (“AND,” “OR”) were applied to combine terms appropriately. The search strategy was peer-reviewed and tailored to each database. No language restriction was applied during the search. A complete list of search terms and strategies is provided in Supplementary Table S1. Reference lists of included studies and relevant reviews were also manually screened to ensure comprehensiveness.
Inclusion and exclusion criteria
Studies were eligible for inclusion if they met the following criteria: (1) Randomized controlled trials (RCTs) comparing HIIT with usual care. (2) Adult patients (≥18 years) with a confirmed diagnosis of MI (by clinical, electrocardiographic, or biomarker criteria), regardless of sex, ethnicity, or infarct location. (3) At least one of the following was reported post-intervention: peak oxygen uptake (VO₂peak), peak heart rate (HR peak), peak respiratory exchange ratio (peak RER) or validated scales of health-related quality of life (14).
Studies were excluded if they met any of the following criteria: (1) Non-randomized, observational, or quasi-experimental designs; (2) Duplicate publications from the same cohort without additional information; (3) Conference abstracts, reviews, editorials, or animal studies.
Data extraction and management
Two reviewers independently extracted data from each eligible study using a standardized form. Extracted information included basic study characteristics such as author, year of publication, sample size, and participant demographics, as well as outcome indicators. When essential data were not directly reported, efforts were made to contact study authors for clarification. Any discrepancies in the extraction process were resolved through consensus or consultation with a third reviewer.
Risk of bias assessment
The risk of bias in each included study was evaluated independently by two reviewers using the Cochrane Risk of Bias 2.0 tool, which assesses five key domains: (1) bias arising from the randomization process, (2) deviations from intended interventions, (3) missing outcome data, (4) measurement of the outcome, and (5) selection of the reported result. Each domain was rated as “low risk,” “some concerns,” or “high risk.” An overall risk of bias judgment was generated for each study. Discrepancies were resolved by consensus or third-party arbitration.
Statistical analysis
All meta-analyses were conducted using R (version 4.3.3). For continuous variables, standardized mean differences with corresponding 95% confidence intervals were calculated. When analyzing dichotomous outcomes, risk ratios with 95% confidence intervals were used. Given the anticipated variability in study populations and intervention protocols, a random-effects model was applied. Statistical heterogeneity was evaluated using the I² statistic, with values of 25%, 50%, and 75% considered indicative of low, moderate, and high heterogeneity, respectively. To examine the robustness of the findings, sensitivity analyses were performed by sequentially excluding studies with methodological limitations or small sample sizes.
Result
Study selection and characteristics
A total of 638 articles were initially retrieved from PubMed, Cochrane Central, Embase, Web of Science, and CNKI. After removing duplicates and screening titles, abstracts, and full texts according to predefined criteria, nine randomized controlled trials (15–23) were ultimately included in the meta-analysis. The selection process is outlined in the PRISMA flow diagram (Figure 1), and the key characteristics of the included studies are summarized in Table 1.
Figure 1.
PRISMA flow diagram.
Table 1.
Baseline characteristics.
| First author | Publication year | Number of cases | Age (year) | Gender | |||
|---|---|---|---|---|---|---|---|
| (Male/Female) | |||||||
| Experimental group | Control group | Experimental group | Control group | Experimental group | Control group | ||
| Eser et al. (15) | 2022 | 34 | 35 | 53 (49–66) | 59 (52–62) | / | / |
| JonAnder et al. (16) | 2022 | 28 | 14 | / | / | 23/5 | 12/2 |
| Marcin et al. (17) | 2022 | 35 | 34 | 55 (50–66) | 59 (50–62) | / | / |
| Moholdt et al. (18) | 2012 | 30 | 59 | 56.7 ± 10.4 | 57.7 ± 9.3 | 25/5 | 49/10 |
| Ji et al. (19) | 2017 | 28 | 40 | 50.9 ± 6.6 | 50.8 ± 6.5 | 61/35 | 26/14 |
| Nam et al. (20) | 2024 | 29 | 32 | 58.69 ± 12.38 | 56.66 ± 9.50 | 25/4 | 28/4 |
| Trachsel et al. (21) | 2019 | 9 | 10 | 60 ± 10 | 57 ± 13 | 6/3 | 7/3 |
| Yakut et al. (22) | 2022 | 11 | 10 | 59.6 ± 4.5 | 58.5 ± 5.6 | 10/1 | 8/2 |
| Yi et al. (23) | 2021 | 39 | 31 | 55.63 ± 6.37 | 56.38 ± 7.06 | 21/18 | 18/13 |
Risk of bias assessment
The 9 included studies were all RCTs. Most of these studies demonstrated high methodological quality, particularly in terms of random sequence generation, blinding, allocation concealment, and completeness of outcome reporting. However, a few studies showed uncertainty regarding allocation concealment (Figures 2, 3).
Figure 2.
Risk of bias summary.
Figure 3.
Risk of bias graph.
Effect of HIIT on peak heart rate
The impact of high-intensity interval training (HIIT) on peak heart rate in patients with myocardial infarction was evaluated across six studies. The pooled analysis using a random-effects model indicated that HIIT did not significantly improve peak heart rate compared with conventional interventions, with a standardized mean difference of −0.11 and a 95% confidence interval from −0.48 to 0.25 (p > 0.05). Moderate heterogeneity was observed across studies (I² = 62.4%), potentially attributable to variations in training intensity, frequency, and duration. Sensitivity analysis revealed that exclusion of the study by Marcin et al. (2022) substantially reduced heterogeneity to 31.7%, although the direction and magnitude of the effect remained stable (SMD = 0.04, 95% CI: −0.20 to 0.28, p > 0.05) (Figures 4, 5).
Figure 4.
Meta analysis of peak heart rate.
Figure 5.
Sensitive analysis of peak heart rate.
Effect of HIIT on peak oxygen uptake
Analysis of Peak VO₂, a key marker of aerobic capacity, showed a positive trend associated with HIIT. The pooled effect size was 0.32 (95% CI: −0.07 to 0.71), p > 0.05, indicating that the result did not meet the conventional threshold for statistical significance. Substantial heterogeneity was detected (I² = 69.1%). Sensitivity analysis identified the study by Yi et al. (2021) as a major source of heterogeneity; exclusion of this study reduced heterogeneity to 0%, but concurrently decreased the effect size to 0.13 (95% CI: −0.10 to 0.36, p = 0.2743) (Figures 6, 7).
Figure 6.
Meta analysis of peak oxygen uptake.
Figure 7.
Sensitive analysis of peak oxygen uptake.
Effect of HIIT on respiratory exchange ratio
The RER showed a clear and consistent improvement following HIIT. The meta-analysis demonstrated a significant reduction in RER among patients undergoing HIIT compared to controls, with a standardized mean difference of −1.26 (95% CI: −1.54 to −0.98, p < 0.0001). No heterogeneity was observed across studies (I² = 0%), and sensitivity analyses confirmed the robustness of the findings, with minimal variation in effect size (±0.05) upon exclusion of individual studies (Figures 8, 9).
Figure 8.
Meta analysis of respiratory exchange ratio.
Figure 9.
Sensitive analysis of respiratory exchange ratio.
Effect of HIIT on quality of life
Evidence regarding the impact of HIIT on QOL remains limited. Only two studies met inclusion criteria for this outcome. The combined effect was not statistically significant (SMD = 0.07, 95% CI: −0.37 to 0.50, p = 0.7562), and no heterogeneity was detected (I² = 0%). Closer examination revealed that the study by Nam et al. (2024) reported a neutral effect, while Yakut et al. (2022) demonstrated a slight but non-significant negative trend. Although these findings suggest that HIIT may have a minimal impact on QOL in this population, the limited number of trials and variability in assessment tools underscore the need for future studies employing standardized instruments and longer follow-up durations to clarify this relationship (Figure 10).
Figure 10.
![Forest plot displaying the standardized mean differences from two studies: \"Nam 2024\" and \"Yakut 2022.\" The SMD for Nam is -0.03 with a 95% confidence interval of [-0.53, 0.48] and a weight of 74.7%. The SMD for Yakut is 0.34 with a confidence interval of [-0.52, 1.21] and a weight of 25.3%. Both common and random effects models show a combined SMD of 0.07 with a confidence interval of [-0.37, 0.50]. Heterogeneity is reported as I-squared being 0%.](https://www.ncbi.nlm.nih.gov/core/lw/2.0/html/tileshop_pmc/tileshop_pmc_inline.html?title=Click%20on%20image%20to%20zoom&p=PMC3&id=12745437_fcvm-12-1666325-g010.jpg)
Meta analysis of quality of life.
Discussion
This meta-analysis investigated the impact of HIIT on cardiopulmonary function and quality of life in patients following MI. The findings suggest that HIIT may induce favorable adaptations in respiratory metabolism, while evidence for its effects on heart rate dynamics and subjective quality of life remains limited and heterogeneous (24, 25). These variations likely reflect differences in the physiological mechanisms underlying each outcome, as well as the diversity in intervention protocols, patient characteristics, and assessment methods across studies (26).
The consistent improvement in RER observed in this analysis indicates enhanced metabolic flexibility following HIIT (27). RER reflects the balance between carbohydrate and fat oxidation during exercise, and its reduction suggests a shift toward greater reliance on fat metabolism (28). In the context of MI, where patients often exhibit impaired mitochondrial function and reduced oxidative capacity, HIIT may stimulate mitochondrial biogenesis and improve efficiency through repeated bouts of high-intensity stress, thereby restoring energy homeostasis (29). This metabolic shift is clinically meaningful, as it may enhance myocardial energy supply and reduce the burden on the ischemic myocardium during physical activity.
While improvements in RER were robust, changes in VO₂peak were more variable. VO₂peak is a multifactorial parameter influenced by central cardiac output, peripheral oxygen extraction, and muscular oxidative capacity (30). Although HIIT has the potential to improve all these components, the extent of adaptation depends on the training volume, intensity, and duration. Studies included in this meta-analysis differed markedly in terms of exercise intensity prescription, interval structure, and total training load, and the type of exercise ergometer used, which likely contributed to the observed heterogeneity (31). In addition, evidence from patients with heart failure and reduced ejection fraction suggests that patient characteristics may further influence VO2 peak responses, as highlighted in a recent systematic review and meta-analysis (32, 33). VO₂ at ventilatory threshold (VO₂-VT1) represents a valuable complementary parameter, as it reflects submaximal aerobic efficiency, integrates early metabolic and ventilatory adaptations, and is less influenced by patient motivation or the ability to reach true maximal effort (34). VO₂-VT1 may therefore offer a more stable and physiologically informative indicator of training-induced improvements, particularly in populations with cardiovascular limitations.
In contrast, the effects of HIIT on HR peak appeared inconsistent. Peak heart rate reflects autonomic nervous system regulation and chronotropic competence, both of which are frequently altered in post-MI patients. The widespread use of beta-blockers and other rate-limiting medications likely blunted the responsiveness of HRpeak to training. Moreover, short-duration interventions may not provide sufficient stimulus to improve autonomic balance or vagal tone. These pharmacological and physiological constraints may explain the limited observed benefit in this parameter, despite trends toward improvement in several studies (35).
The evidence regarding quality of life remains inconclusive, primarily due to the limited number of trials addressing this outcome. Quality of life is a complex construct influenced by physical, psychological, and social domains. Notably, improvements in cardiorespiratory endurance have been shown to exert a direct and meaningful impact on quality of life by enhancing functional capacity, reducing fatigue, and promoting psychological well-being (36). However, such physiological benefits may not be immediately reflected in subjective perceptions of well-being, especially in the early stages of recovery when psychological distress and uncertainty about prognosis are common (29). Therefore, future studies should adopt standardized and longitudinal assessments to better elucidate the causal and dynamic relationship between cardiorespiratory endurance and health-related quality of life.
Mechanistically, HIIT may exert its benefits through multiple pathways. On a cellular level, it activates key metabolic regulators such as AMP-activated protein kinase (AMPK), enhancing fatty acid oxidation and mitochondrial efficiency. Vascular adaptations, driven by intermittent shear stress during high-intensity bouts, may improve endothelial function and peripheral perfusion (22). Moreover, HIIT has been associated with favorable modulation of autonomic tone and systemic inflammation, both of which are implicated in post-MI recovery (37). These integrated physiological responses may collectively contribute to improved functional capacity and, potentially, long-term cardiovascular outcomes.
This meta-analysis has several strengths, including a systematic literature search, adherence to PRISMA guidelines, the use of robust random-effects models, and a specific focus on peak RER as a key metabolic outcome in post-MI patients undergoing HIIT. However, the findings must be interpreted in the context of several limitations. First, the overall number of included studies was relatively small, which may limit the generalizability of the findings and the statistical power for some outcomes. Second, we observed moderate-to-high heterogeneity in the analyses of HRpeak and VO2peak, which, despite our sensitivity analyses, could not be fully resolved. This is likely attributable to the variability in HIIT protocols and patient characteristics across studies. Third, as noted earlier, the assessment of QOL was limited by the small number of studies and the use of different measurement tools. Fourth, although VO2 at ventilatory threshold (VO2-VT₁) and post-training peak RER are potentially informative indicators of submaximal functional capacity and exercise-induced adaptations, limited reporting and inconsistency across studies prevented us from performing subgroup analyses or meta-regression to formally evaluate these effect modifiers. Future meta-analyses with a larger pool of primary studies and more comprehensive reporting are warranted to investigate these effect modifiers. Future meta-analyses with a larger pool of primary studies are warranted to investigate these effect modifiers.
In conclusion, this meta-analysis suggests that HIIT can significantly improve peak respiratory exchange ratio in patients recovering from myocardial infarction, indicating a potential positive effect on metabolic efficiency. However, its effects on peak heart rate, peak oxygen uptake, and quality of life remain inconclusive, partly due to heterogeneity and limited data. Future high-quality RCTs with standardized HIIT protocols, consistent outcome assessments, and longer follow-up durations are needed to definitively establish the role of HIIT in improving cardiopulmonary function and overall well-being in this patient population.
Funding Statement
The author(s) declared that financial support was not received for this work and/or its publication.
Footnotes
Edited by: Georgios A. Christou, University of Ioannina, Greece
Reviewed by: Nikolaos Koutlianos, Aristotle University, Greece
Guopeng Hu, Huaqiao University, China
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.
Author contributions
XX: Conceptualization, Data curation, Writing – original draft, Writing – review & editing. YS: Conceptualization, Data curation, Writing – original draft, Writing – review & editing. YL: Data curation, Formal analysis, Writing – original draft, Writing – review & editing.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fcvm.2025.1666325/full#supplementary-material
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Data Availability Statement
The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.