Efficacy of different modes of exercise-based cardiac rehabilitation delivery for patients with heart failure: a systematic review and network meta-analysis

Introduction

Heart failure (HF) is a clinical syndrome characterized by structural or functional abnormalities of the heart, leading to elevated intracardial pressure and insufficient cardiac output either at rest or during exercise (1), and it represents the common terminal stage of various cardiovascular diseases. Globally, more than 64 million individuals are afflicted by HF (2), and in developed countries, the incidence of diagnosed HF is estimated to be 1–2% of the general adult population (3). The prognosis of HF is even more dismal, as a recent meta-analysis that examined over 1.5 million patients diagnosed with full-form HF indicated that the 5- and 10-year survival rates were 57% and 35%, respectively (4). HF has emerged as one of the most severe diseases threatening human health and life worldwide (5). Therefore, considerable efforts have been devoted to improving the treatment and management of HF. Although medical therapy for HF has been demonstrated to be effective and widely utilized, it is not devoid of drawbacks. Adverse reactions and contraindications, along with high financial burdens and low adherence rates, have sparked increasing interest and anticipation in nondrug treatment options within this population. Among them, exercise has been recognized as a relatively safe and cost-effective treatment with minimal side effects and risks (6). Accumulating evidence from studies and clinical trials suggests that prescribed exercise regimens can reduce risk factors, decrease mortality, and improve quality of life (QoL) in patients with HF (7-10). Several meta-analyses have consistently shown exercise can provide significant prognostic benefits for HF patients (11,12). An optimal exercise prescription can mitigate left ventricular remodelling, increase exercise tolerance and QoL, and decrease mortality and rehospitalization rates in HF patients and this approach has been internationally recognized as a first-level recommendation for HF management (13). Although the benefits of exercise training for HF patients are well documented, there is no universal consensus on the optimal exercise prescription. A “one-size-fits-all” approach to exercise training is not appropriate for HF patients (14). It is crucial to tailor exercise recommendations on the basis of the specific type of HF and individual patient characteristics, including functional status and QoL (15). In this study, pairwise comparisons and network meta-analyses (NMAs) were conducted to evaluate different exercise regimens. We objectively assessed the impact of various exercise types on HF patients using multiple comprehensive indicators. Furthermore, we compared the responsiveness and efficacy of different exercise regimens across HF patients on the basis of their individual characteristics, such as age and ejection fraction. Our study aims to establish targeted and personalized exercise prescriptions for different HF patients, providing evidence-based recommendations to address significant clinical challenges in HF management. We present this article in accordance with the PRISMA-NMA reporting checklist (available at https://cdt.amegroups.com/article/view/10.21037/cdt-2024-698/rc).

Methods

Protocol and guidance

The protocol for this review was registered with PROSPERO (CRD42022346011).

Search strategy and inclusion criteria

The systematic search was performed in PubMed (Medline), the Cochrane Library and Web of Science between 2000 and October 2023 using a combination of relevant Medical Subject Heading (MeSH) terms and text words, including exercise, physical activity, HF and treatment, with the Boolean search terms ‘OR’ and ‘AND’. No search filters or limits were applied. The following search terms were used: (heart failure) AND((treatment) OR(treat) OR(prognosis)) AND((motion) OR(sports) OR(exercise) OR(rehabilitation) OR(running) OR(walking) OR(training) OR(HIIT) OR(aerobic) OR(dance)).

• PubMed: 26,544;
• Science of the Web: 19,404;
• Cochrane Library: 5,680.

Two reviewers (Y.T.L. and C.J.D.) scanned titles and abstracts independently to determine article eligibility. If there was a contradiction in the study inclusion and exclusion criteria, the results were determined by a third person (X.Y.). All the articles with strict randomized controlled trial (RCT) designs were included. All duplicate records and some special types of research, such as conference proceedings, guidelines etc. were removed and the search was restricted to English language studies of humans. Full text reports were subsequently obtained and screened to ensure that they met predefined eligibility criteria: studies reporting well-defined or accessible exposures and presenting clear data. All participants in the studies had been diagnosed with HF and completed a minimum of 4 weeks of exercise-based cardiac rehabilitation (CR). This process is depicted in Figure 1. Authors of research manuscripts were contacted when the full text was not available or information needed for analysis was unavailable.

Figure 1 Flowchart of the review process of potential papers. RCT, randomized controlled trial.

Data extraction

Two investigators (Y.T.L. and H.Y.Y.) independently extracted the related data from the included studies using a custom-made data extraction form. The following baseline characteristics for the study populations were collected: first author’s name and year of publication, sex and population included, age at baseline, New York Heart Association (NYHA) grading and LVEF at baseline, mean and standard deviation (SD) of pre- and postintervention indicators (peak VO₂, 6MWT, max_workload, LVEF, peak HR, MLHFQ score), movement type, pattern and total duration of CR. According to Chapter 6 of the Cochrane Handbook for Systematic Reviews of Interventions (16), the change in SD was computed using standard errors, 95% confidence intervals (CIs), P values, or t statistics if they were available.

Risk of bias and quality of research

Two review authors (F.L.Y. and H.Y.Y.) independently evaluated the methodological quality of each included study in accordance with the relevant criteria (17). A risk of bias table was constructed for each study, and incorporated the description and judgement (low, high or unclear risk of bias) for each of the seven types of potential bias. Studies with three or more items of high or unclear risk of bias were regarded as having low methodological quality. We summarize the risk of bias in Table S1.

CR, group classification and outcomes

Definition of the control group and exercise type

The control group included people who routinely used HF medications but did not receive exercise interventions and patients with HF who were sedentary or who only received health education and exercise advice. For the experimental group, the classification of exercise types was based on the description of exercise prescriptions in the original article, including the type, time, intensity, and frequency of exercise. The exercise classification and specific exercise prescriptions for each study are detailed in Table S2. On the basis of one form of gross movement, we distinguish 5 types of movement: ‘aerobic training’ (AT), ‘combined training’ (CT), ‘high-intensity interval training’ (HIIT), ’strength training’ (ST) and ‘inspiratory muscle training’ (IMT). Each category was then further explored for appropriate subgroups, allowing for the analysis of walking, running, cycling, and combining aerobic and other aerobic agents (Baduanjin, Tai Chi, dance) as aerobic exercise training (AET) subgroups; AT + ST, AT + IMT, ST + IMT, and ST + HIIT were further categorized in the CT group.

Definition of outcome

To evaluate the effects of different exercises systematically and comprehensively, we selected the following six indicators for comparison: ‘left ventricular ejection fraction’ (LVEF) (%), ’six-minute walk test’ (6MWT) (m), ‘the Minnesota Living with Heart Failure Questionnaire’ (MLHFQ) (score), ‘peak oxygen uptake’ (peak VO₂) (mL/kg/min), ‘maximum heart rate’ (MHR) (beats/min) and ‘max workload’ (W).

The above six indicators can be used to comprehensively measure a patient’s cardiopulmonary function, exercise capacity, and QoL. The degree of improvement in the index can predict the prognosis of a patient to a certain extent. We carefully reviewed the measures used in the included articles to determine that there were no large differences in outcomes.

Others

We performed a series of grouping and analyses on the basis of the patient’s baseline age, LVEF, and number of weeks of exercise. The duration of exercise was divided into short (<3 months), moderate (3–6 months) and long (>6 months) according to the number of weeks of exercise. The patients were divided into older (≥65 years) and younger (<65 years) groups. Patients with severely reduced ejection fraction (<30%), generally reduced ejection fraction (30–40%), and preserved ejection fraction (>40%) were categorized on the basis of baseline LVEF.

Statistical analysis

Pairwise meta-analyses

A pairwise meta-analysis was performed to analyse the data. The heterogeneity, both clinical and methodological, was evaluated by scrutinizing the baseline characteristics of the patients, the methodologies employed, the interventions implemented, and the outcomes reported within the studies. The weighted mean difference (WMD) and the standardized mean difference (SMD) were ascertained for our results, each accompanied by a 95% CI. The SMD is a relative indicator that is not affected by baseline risk and has good consistency. We used the results of the SMD as the primary outcome for all indicators. In addition, since WMD is a true reflection of the experimental effect and is easy to understand when applied, we also used WMD as the result, especially in the subgroup analysis of training duration and the subgroup analysis of training and outcome sections. Furthermore, we evaluated the I² statistic to assess the statistical heterogeneity present among the studies. If the I² value exceeds 50%, it indicates the presence of significant heterogeneity. When statistical heterogeneity was significant (P<0.10 or I²>50%), the random effects model was used. When statistical heterogeneity was not significant (P≥0.10 or I²≤50%), the fixed-effects model was selected.

Network meta-analysis (NMA)

NMAs were executed on the basis of primary categorizations of exercise modes. Subsequent analyses were performed using secondary subgroup categorizations. Network diagrams were generated to graphically represent the direct and indirect comparisons among the various exercise modes. Ranking probability analyses were conducted, with the surface under the cumulative ranking curve (SUCRA) values (18) being calculated for each exercise mode and submode. Furthermore, node splitting and inconsistency testing, which utilize STATASE 16 with the network pack, were employed to assess the consistency between direct and indirect evidence within the network.

Publication bias analysis and sensitivity analysis

Funnel plots were employed to detect publication bias within the incorporated literature and to perform bias analysis leveraging the obtained outcomes. To ascertain the robustness of the study’s findings, a sensitivity analysis of the primary outcomes was conducted, especially for analyses with high heterogeneity.

Subgroup analysis

Subgroup analysis was implemented to discern potential sources of variation and heterogeneity across variables. We performed subgroup analyses on the basis of patient characteristics and exercise characteristics, including patient age, LVEF, specific exercise type, and duration of exercise.

Results

Literature search and study characteristics

The titles and abstracts of 51,628 articles were screened. Most of them were excluded because they were irrelevant or did not meet the inclusion criteria. After the full texts of the 568 remaining articles were evaluated, 30 studies were excluded for incomplete data, 243 studies were excluded for inappropriate and unclear exercise reports, 75 studies were excluded for non-HF participants and 98 studies were excluded for no relevant outcomes. Finally, 122 studies were included. The reference of these studies is shown in Additional Reference. The detailed process of study inclusion or exclusion is shown in Figure 1. The analysis involved 122 effect sizes, including 111 two-arm studies and 11 multiarm trials. Categorizing by type of training, 81 studies reported AT [36 bicycle, 10 walking, 7 running, 26 combined aerobic training (CAT), 3 other aerobic training (OAT)], 38 studies reported CT (30 AT + ST, 4 AT + IMT, 2 ST + IMT, 2 ST + HIIT), 30 studies reported HIIT, and 4 studies reported ST and IMT. Characteristics and original data of all 122 trials are presented in Tables S2,S3. Most of the studies involved both men and women and were conducted in North America and Europe.

Sensitivity analysis and publication bias analysis

Publication bias analysis are presented in Figure S1A-S1F, which showed some publication bias among the studies. Most funnel charts are symmetrical and there were some pairwise comparisons with fewer than 10 studies, and the results should be treated with caution. Sensitivity analysis showed that nearly all analyses were stable, except for LVEF in ST and MHR in CT in pairwise analysis (due to only 4 studies included).

Pairwise analyses

Figure 2 displays the overall outcome following each exercise mode compared with the control group. Peak VO₂ intake can objectively reflect a patient’s cardiopulmonary function and can also predict the prognosis of a patient. We found that 3.91 mL/kg/min increased after HIIT (WMD: 3.91, 95% CI: 2.58 to 5.23, I²=84.3%, random) and that 2.49 mL/kg/min increased after AT (WMD: 2.49, 95% CI: 1.93 to 3.04, I²=93.2%, random). CT and ST can also increase peak oxygen uptake in people with HF. CT led to a 2.99 mL/kg/min improvement (WMD: 2.99, 95% CI: 1.89 to 4.10, I²=90.1%, random), and ST led to a 1.84 mL/kg/min improvement (WMD: 1.84, 95% CI: 0.16 to 3.51, I²=74.7%, random).

Figure 2 Pairwise analysis of training and outcome (SMD). 6MWT, six-minute walk test; CI, confidence interval; HIIT, high-intensity interval training; LVEF, left ventricular ejection fraction; MHR, maximum heart rate; MLHFQ, Minnesota Living with Heart Failure Questionnaire; SMD, standardized mean difference.

For other outcome, HIIT showed a 6.68% improvement in LVEF after training (WMD: 6.68, 95% CI: 0.57 to 12.80, I²=88.5%, random), with an overall increase of 2.97% in AT (WMD: 2.97, 95% CI: 1.00 to 4.93, I²=93.3%, random). In addition, patients who performed HIIT had an average 65.3 m increase in the 6MWT distance (WMD: 65.30, 95% CI: 40.16 to 90.45, I²=0, fix), and those who performed CT with AT had 49.02 m (WMD: 49.02, 95% CI: 1.91 to 96.12, I²=78.4%, random) increases compared to controls. As for MLHFQ score, HIIT exercise was able to reduce the MLHFQ score by 10.34 (WMD: −10.34, 95% CI: −15.50 to −5.18; I²=84.7%, random), and aerobic exercise combined exercise improved the QoL of patients with HF to varying degrees (WMD: −7.52, 95% CI: −12.09 to −2.95; I²=95.8%, random) and CT (WMD: −7.26, 95% CI: −11.55 to −2.97; I²: 67.8%, random). Furthermore, HIIT (WMD: 5.65, 95% CI: 2.39 to 8.91, I²=0, fix) and AT (WMD: 6.72, 95% CI: 3.50 to 9.94, I²=67.7%, random) can significantly increase the MHR during training, but CT was not significantly different. Finally, HIIT AT and CT reported that direct comparisons of exercises improved the patient’s external maximum work power: The specific analysis results of WMD and SMD are shown in Table S4 and the detailed studies included in the analysis can be seen in Table S5.

Subgroup analysis of training duration and effects

Table S6 shows as the duration of CR increased, the results improved, especially for 6MWT (WMD <3 months: 16.8, 95% CI: −2.37 to 35.96, I²=69.6%, random; WMD ≥3 months: 53.76, 95% CI: 29.33 to 78.19, I²=84.5%, random), peak VO₂ (WMD <3 months: 1.82, 95% CI: 0.87 to 2.78, I²=89.9%, random; WMD >6 months: 4.07, 95% CI: 3.20 to 4.94, I²=88.6%, random), LVEF (WMD <3 months: 1.57, 95% CI: −2.99 to 6.14, I²=95.9%, random; WMD >6 months: 4.83, 95% CI: 1.99 to 7.67, I²=85%, random) and the MLHFQ score (WMD <3 months: −6.53, 95% CI: −11.04 to −2.03, I²=70.1%, random; WMD >6 months: −11.95, 95% CI: −19.06 to −4.83, I²=94.3%, random) in AT.

Subgroup analysis of exercise modes and effects

Among the multiple exercise subgroup types, the aerobic exercise group performed best, especially cycling and combined aerobic exercise (Table S7). Bicycles significantly improved various metrics, such as peak VO₂, MHR, the MLHFQ score and Max_workload. Walking and running also significantly increased peak VO₂ capacity. For some indicators, such as Max_workload, aerobic + ST also significantly improved the indicators (WMD: 15.48, 95% CI: 7.47 to 23.48).

NMAs

Figure 3A,3B depict the primary and secondary training network diagrams for the peak VO₂ outcome. Network diagrams for other outcomes are shown in Figure S2A-S2E.

Figure 3 Network diagrams depicting the direct and indirect comparisons for the network meta-analyses. (A) Network diagrams for the primary network meta-analyses in peak VO₂ outcome. (B) Network diagrams for the secondary network meta-analyses in peak VO₂ outcome. HIIT, high-intensity interval training; IMT, inspiratory muscle training.

The comparison results of primary training can be clearly seen in Figure 4, and the Bayesian table of rank probabilities and surface under the cumulative ranking of primary training is shown in Table S8. Similar to the aforementioned pairwise meta-analysis, aerobic exercise and HIIT have been shown to be better in HF patients than other types of exercise. HIIT was significantly more effective at improving peak VO₂ than was CT (SMD: 0.68, 95% CI: 0.06 to 1.30); HIIT (SMD: 1.46, 95% CI: 0.33 to 2.60) and AT (SMD: 1.20, 95% CI: 0.28 to 2.13) were significantly more effective at improving the peak VO₂ than was ST. There were no other significant differences between the primary exercise modes for any of the outcomes.

Figure 4 Network meta-analysis for different training and outcome (SMD). The font is bolded when P<0.05. SMD, standardized mean difference; 6MWT, six-minute walk test; LVEF, left ventricular ejection fraction; MLHFQ, Minnesota Living with Heart Failure Questionnaire; HIIT, high-intensity interval training; IMT, inspiratory muscle training.

Figure 5 shows the SCURA rankings for six outcomes and different training methods. For peak VO₂, the order of effectiveness based on SUCRA values was HIIT (SUCRA: 90.8%), ST (74.7%), AT (64.4%), CT (41.7%) and IMT (18.3%). This ranking shows differences in other outcomes, such as LVEF, and the order of effectiveness for LVEF based on SCURA values was HIIT (90.5%), AT (77.8%), CT (50.3%), ST (49.9%) and IMT (7.7%). The data for all outcomes can be found in Table S9.

Figure 5 The comparison of training and outcome in SCURA%. 6MWT, six-minute walk test; HIIT, high-intensity interval training; HR, heart rate; IMT, inspiratory muscle training; LVEF, left ventricular ejection fraction; MAX, maximum; MLHFQ, Minnesota Living with Heart Failure Questionnaire; SUCRA, surface under the cumulative ranking curve.

For the secondary exercise mode NMA, the order of effectiveness in improving peak VO₂ based on SUCRA values was HIIT (80.3%), bicycle (72.3%), running (72.2%), ST (68.0%), CAT (61.0%), ST + HIIT (57.9%), AT + IMT (57.5%), walking (47.3%), ST + IMT (44.2%), AT + ST (32.4%), OAT (33.2%) and IMT (13.9%). Table S10 presents the SCURA values for the subgroup exercise mode. The Bayesian ranking panel plots for training and peak VO₂ are shown in Figure 6 (detailed data in Table S11). Figure 7 shows the overall NMA for the peak VO₂ in subgroup training modes. Cycling (SMD: 0.74, 95% CI: 0.08 to 1.40) and HIIT (SMD: 0.87, 95% CI: 0.20 to 1.55) were significantly more effective than ST + AT. There were no other significant peak VO₂ differences between the subgroup training modes.

Figure 6 The Bayesian ranking panel plots for training and peak VO₂ in subgroup exercise mode. HIIT, high-intensity interval training; IMT, inspiratory muscle training; SUCRA, surface under the cumulative ranking curve.

Figure 7 Comparative network meta-analysis for the peak VO₂ in secondary exercise modes. The font is bolded when P<0.05. AT, aerobic training; CAT, combined aerobic training; CT, combined training; HIIT, high-intensity interval training; IMT, inspiratory muscle training; ST, strength training; OAT, other aerobic training.

Subgroup analysis of baseline LVEF, age and effects in NMA

We found that participants under the age of 65 years may have had better responsiveness to exercise training, with more significant improvements in various outcomes, such as the 6MWT, MLHFQ score and Max_workload. In terms of baseline LVEF, although most of the results were not statistically significant, we found that patients with a lower ejection fraction had better responsiveness to exercise, particularly in terms of the Max_workload outcome of aerobic exercise (LVEF <30%, SMD: 2.18, 95% CI: 0.36 to 4.00; LVEF >40%, SMD: 0.82, 95% CI: 0.41 to 1.24). Seen in Table S12.

Inconsistency analysis for NMA

We tested for inconsistency across the outcomes of each primary (Table S13) and secondary exercise (Table S14) intervention. In the global and local inconsistency test, none of the training analyses showed clear consistency (P>0.05). Loop inconsistency analysis shows only control-CT-ST rings of the Max_workload in the original exercise (P=0.04) exist inconsistencies.

Discussion

Inconsistent with the prevailing view that engaging in physical activity can drastically enhance heart health and improve survival and prognosis (19). In our study, we found that exercise rehabilitation can provide varying degrees of benefit to patients with HF, especially HIIT benefits most.

HIIT is a highly effective exercise mode. By alternating short periods of high-intensity exercise with short periods of rest or low-intensity exercise, HIIT can significantly increase maximal oxygen uptake, enhance myocardial contractility, reduce resting heart rate, and enhance cardiopulmonary function (20,21). HIIT can also reduce body fat (22), enhance muscle strength and endurance (23), significantly reduce anxiety and depressive symptoms (24), increase bone mineral density, and improve bone metabolism (25). Several studies have also found that HIIT can also improve glycemic control, reduce lipid levels, and improve insulin sensitivity (26). These benefits make HIIT an ideal exercise option for people of different age groups and fitness levels. Long-term adherence to HIIT can not only effectively improve the level of physical health, but also help to prevent and treat a variety of chronic diseases, such as obesity, diabetes, cardiovascular disease, etc. (20,22,26,27). Although studies have systematically discussed the safety of HIIT (28,29), and exercise-related adverse events were not reported the studies we included, we found that this mode of exercise was widely used only in patients with NYHA II–III, and few articles investigated the safety of HIIT alone in patients with NYHA IV. The vast majority of the articles provided only a general description of the patient’s NYHA grade rather than a strict stratification. However, HIIT has a greater intensity than other types of exercise. Therefore, considering the diversity and complexity of HF patients, it is necessary to conduct more extensive research on the safety of HIIT, particularly for patients with advanced HF or comorbidities.

Our analysis also revealed that aerobic workouts can significantly improve symptoms, cardiorespiratory function, and QoL in people with HF. As the main method used in heart rehabilitation training, aerobic exercise is intimately linked to athletic characteristics and benefits. Aerobic workouts can offer measurable elements to enhance heart performance (such as peak VO₂) (30), and increase endothelial health by increasing mitochondrial oxygen absorption and use (31). Due to their broad scope, practicality, and operability in real-world clinical rehabilitation training which ensure patients’ acceptance and adherence, aerobic workouts did a really good job in improving HF. Beside, some other sports such as ST (resistance training) , respiratory muscle training and respiratory muscle training also can improved clinical parameters in patients with HF (32-34) and showed a clear advantage_, however due to the limited studies included, the benefit evidences were not significant in our analysis.

Last but not least, we found that participants under the age of 65 years may have had better responsiveness to exercise training, with more significant improvements in various outcomes, such as the 6MWT, MLHFQ score and Max_workload, which means the effective values varied across different populations. Research findings indicate that older patients face specific challenges in both the psychological executive ability of active participation in sports prognosis and the ability to engage in physical activity in practice (35). More refined and personalized indicators need to be used to more accurately evaluate the characteristics and advantages of these exercises in the future.

Limitations

This study has several limitations that should be acknowledged. First, a certain degree of publication bias existed due to the inclusion and exclusion criteria of the literature. Second, inadequate monitoring of the control group’s activities, a lack of reports of exercise safety and adverse effects, poor participant and investigator awareness of group allocation and baseline data for participants, strategies for exercise rehabilitation, timing, and assessment methods for each outcome varied widely across studies. Additionally, it was difficult to draw convincing evidence and conclusions for some exercise modes because of limited sample sizes. It is imperative that we incorporate a multitude of pertinent factors in our future endeavors and conduct comprehensive, large-sample, multicentre RCTs to examine the safety and impact of exercise rehabilitation training on individuals suffering from HF.

Conclusions

In this study, we reported that various exercise types, especially HIIT, can improve QoL, cardiac function, LVEF, and exercise tolerance in patients with HF. The results of this analysis should inform future exercise guideline personalized recommendations and prescriptions for HF patients.

Acknowledgments

The language editing of this manuscript was supported by American Journal Experts (AJE). The editing process adhered strictly to international academic publishing ethics. It solely focused on refining language expression and did not involve any changes to the academic arguments, research data, or conclusions.

Footnote

Reporting Checklist: The authors have completed the PRISMA-NMA reporting checklist. Available at https://cdt.amegroups.com/article/view/10.21037/cdt-2024-698/rc

Peer Review File: Available at https://cdt.amegroups.com/article/view/10.21037/cdt-2024-698/prf

Funding: The study was supported by Xinjiang Science and Technology, Tian shan yingcai Lingjun Project (No. 2022TSYCLJ0029); Special Funds Program for Central Guiding Local Science and Technology Development (No. ZYYD2024CG07); and Open project of State Key Laboratory of Pathogenesis, Prevention and Treatment of High Incidence Diseases in Central Asia (No. SKLHIDCA-2024-42).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://cdt.amegroups.com/article/view/10.21037/cdt-2024-698/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/.

References

1. Khan MS, Shahid I, Bennis A, et al. Global epidemiology of heart failure. Nat Rev Cardiol 2024;21:717-34. [Crossref] [PubMed]
2. Heidenreich PA, Bozkurt B, Aguilar D, et al. 2022 AHA/ACC/HFSA Guideline for the Management of Heart Failure: Executive Summary: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 2022;145:e876-94. [Crossref] [PubMed]
3. McDonagh TA, Metra M, Adamo M, et al. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J 2021;42:3599-726. [Crossref] [PubMed]
4. Savarese G, Becher PM, Lund LH, et al. Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc Res 2023;118:3272-87. [Crossref] [PubMed]
5. Groenewegen A, Rutten FH, Mosterd A, et al. Epidemiology of heart failure. Eur J Heart Fail 2020;22:1342-56. [Crossref] [PubMed]
6. Jones NR, Roalfe AK, Adoki I, et al. Survival of patients with chronic heart failure in the community: a systematic review and meta-analysis. Eur J Heart Fail 2019;21:1306-25. [Crossref] [PubMed]
7. Zhan Q, Peng W, Wang S, et al. Heart Failure with Preserved Ejection Fraction: Pathogenesis, Diagnosis, Exercise, and Medical Therapies. J Cardiovasc Transl Res 2023;16:310-26. [Crossref] [PubMed]
8. Mascolo A, di Mauro G, Cappetta D, et al. Current and future therapeutic perspective in chronic heart failure. Pharmacol Res 2022;175:106035. [Crossref] [PubMed]
9. Eleyan L, Gonnah AR, Farhad I, et al. Exercise Training in Heart Failure: Current Evidence and Future Directions. J Clin Med 2025;14:359. [Crossref] [PubMed]
10. Pelliccia A, Sharma S, Gati S, et al. 2020 ESC Guidelines on sports cardiology and exercise in patients with cardiovascular disease. Eur Heart J 2021;42:17-96. Erratum in: Eur Heart J 2021;42:548-9. [Crossref] [PubMed]
11. Al Hennawi H, Bedi A, Khan MK, et al. Impact of exercise training on clinical outcomes and quality of life in chronic congestive heart failure: A systematic review and meta-analysis. Curr Probl Cardiol 2024;49:102756. [Crossref] [PubMed]
12. Molloy CD, Long L, Mordi IR, et al. Exercise-based cardiac rehabilitation for adults with heart failure - 2023 Cochrane systematic review and meta-analysis. Eur J Heart Fail 2023;25:2263-73. [Crossref] [PubMed]
13. Bjarnason-Wehrens B, Nebel R, Jensen K, et al. Exercise-based cardiac rehabilitation in patients with reduced left ventricular ejection fraction: The Cardiac Rehabilitation Outcome Study in Heart Failure (CROS-HF): A systematic review and meta-analysis. Eur J Prev Cardiol 2020;27:929-52. [Crossref] [PubMed]
14. Taylor RS, Walker S, Smart NA, et al. Impact of Exercise Rehabilitation on Exercise Capacity and Quality-of-Life in Heart Failure: Individual Participant Meta-Analysis. J Am Coll Cardiol 2019;73:1430-43. [Crossref] [PubMed]
15. Imran HM, Baig M, Erqou S, et al. Home-Based Cardiac Rehabilitation Alone and Hybrid With Center-Based Cardiac Rehabilitation in Heart Failure: A Systematic Review and Meta-Analysis. J Am Heart Assoc 2019;8:e012779. [Crossref] [PubMed]
16. Higgins JPT, Li T, Deeks JJ. Choosing effect measures and computing estimates of effect. Cochrane Handbook for Systematic Reviews of Interventions; 2019:143-76.
17. Higgins JP, Altman DG, Gøtzsche PC, et al. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ 2011;343:d5928. [Crossref] [PubMed]
18. Nevill CR, Cooper NJ, Sutton AJ. A multifaceted graphical display, including treatment ranking, was developed to aid interpretation of network meta-analysis. J Clin Epidemiol 2023;157:83-91. [Crossref] [PubMed]
19. Harris E. Cardiac Rehabilitation Associated With Improved Survival. JAMA 2024;331:466. [PubMed]
20. Dun Y, Smith JR, Liu S, et al. High-Intensity Interval Training in Cardiac Rehabilitation. Clin Geriatr Med 2019;35:469-87. [Crossref] [PubMed]
21. MacInnis MJ, Gibala MJ. Physiological adaptations to interval training and the role of exercise intensity. J Physiol 2017;595:2915-30. [Crossref] [PubMed]
22. Su L, Fu J, Sun S, et al. Effects of HIIT and MICT on cardiovascular risk factors in adults with overweight and/or obesity: A meta-analysis. PLoS One 2019;14:e0210644. [Crossref] [PubMed]
23. Yin H, Zhang J, Lian M, et al. A systematic review and meta-analysis of the effectiveness of high-intensity interval training for physical fitness in university students. BMC Public Health 2025;25:1601. [Crossref] [PubMed]
24. Wang Y, Zhang X, Zhang Y, et al. The impact of high-intensity interval training on anxiety: a scoping review. Front Psychiatry 2025;16:1515266. [Crossref] [PubMed]
25. Amato A, Proia P, Alioto A, et al. High-intensity interval training improves bone remodeling, lipid profile, and physical function in multiple sclerosis patients. Sci Rep 2024;14:16195. [Crossref] [PubMed]
26. Poon ET, Wongpipit W, Li HY, et al. High-intensity interval training for cardiometabolic health in adults with metabolic syndrome: a systematic review and meta-analysis of randomised controlled trials. Br J Sports Med 2024;58:1267-84. [Crossref] [PubMed]
27. Sabag A, Way KL, Sultana RN, et al. The Effect of a Novel Low-Volume Aerobic Exercise Intervention on Liver Fat in Type 2 Diabetes: A Randomized Controlled Trial. Diabetes Care 2020;43:2371-8. [Crossref] [PubMed]
28. Taylor JL, Holland DJ, Keating SE, et al. Short-term and Long-term Feasibility, Safety, and Efficacy of High-Intensity Interval Training in Cardiac Rehabilitation: The FITR Heart Study Randomized Clinical Trial. JAMA Cardiol 2020;5:1382-9. [Crossref] [PubMed]
29. Callum K, Swinton P, Gorely T, et al. Physiological and psychological outcomes of high intensity interval training in patients with heart failure compared to moderate continuous training and usual care: A systematic review with meta analysis. Heart Lung 2024;64:117-27. [Crossref] [PubMed]
30. Hua C, Huang W, Chen Z, et al. Effects of exercise based cardiac rehabilitation delivery modes on chronic heart failure: a systematic review and network meta-analysis. Sci Rep 2024;14:31246. [Crossref] [PubMed]
31. Mølmen KS, Almquist NW, Skattebo Ø. Effects of Exercise Training on Mitochondrial and Capillary Growth in Human Skeletal Muscle: A Systematic Review and Meta-Regression. Sports Med 2025;55:115-44. [Crossref] [PubMed]
32. Morris BA, Sinaei R, Smart NA. Resistance is not futile: a systematic review of the benefits, mechanisms and safety of resistance training in people with heart failure. Heart Fail Rev 2024;29:827-39. [Crossref] [PubMed]
33. Edelmann F, Wachter R, Duvinage A, et al. Combined endurance and resistance exercise training in heart failure with preserved ejection fraction: a randomized controlled trial. Nat Med 2025;31:306-14. [Crossref] [PubMed]
34. Takahashi R, Yokota J, Matsukawa Y, et al. Influence of inspiratory muscle strength on 6-minute walk distance in patients with acute heart failure. PLoS One 2025;20:e0317679. [Crossref] [PubMed]
35. Pavasini R, Biscaglia S, Kunadian V, et al. Coronary artery disease management in older adults: revascularization and exercise training. Eur Heart J 2024;45:2811-23. [Crossref] [PubMed]