Comparative effectiveness of different interventions on adherence to exercise-based CR among patients after percutaneous coronary intervention: a network meta-analysis of randomized controlled trials
verfasst von:
Chengyu Xia, Yingjun Zheng, Liuxia Ji, Hui Liu
Exercise-based phase II cardiac rehabilitation is critical for post-PCI patients, but adherence to exercise-based phase II cardiac rehabilitation remains low. Many studies aimed at improving adherence have been conducted in recent years, but the most effective interventions remain unclear. Hence, the objective of this study was to evaluate the effectiveness and ranks of various interventions in enhancing adherence to exercise-based phase II cardiac rehabilitation for post-PCI patients.
Methods
A network meta-analysis employing random effects was utilized to evaluate the effectiveness of different interventions. Bias evaluation was performed via the revised Cochrane risk of bias tool, with data analysis performed using STATA v15.0. The surface under the cumulative ranking was used to estimate the rankings among different interventions.
Results
In the final analysis, 30 RCTs with 4267 patients across 17 different interventions were included. The results showed that patients who received home-based cardiac rehabilitation combined with mobile health intervention had the best adherence to exercise-based phase II cardiac rehabilitation (83.8%), followed by hospital-based cardiac rehabilitation combined with mobile health intervention (79.9%).
Conclusions
This network meta-analysis identified home-based CR + mobile health intervention and hospital-based CR + mobile health intervention as the top two ranked interventions for improving adherence to exercise-based phase II CR in post-PCI patients. Healthcare providers may consider prioritizing the use of home-based cardiac rehabilitation combined with mobile health intervention in clinical practice, but still need to evaluate factors such as patient preference and Medicare reimbursement availability to develop customized interventions that are not only safe and effective but also satisfying to the patient.
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Abkürzungen
CHD
Coronary heart disease
PCI
Percutaneous coronary intervention
MACEs
Major adverse cardiovascular events
NWA
Network meta-analysis
RCTs
Randomized controlled trials
CR
Cardiac rehabilitation
PRISMA
Preferred reporting items for systematic reviews and Meta-analyses
OR
Odds ratio
CI
Confidence interval
SUCRA
Surface under the cumulative ranking
Introduction
Coronary heart disease (CHD) has long been acknowledged as the primary contributor to mortality and impairment, affecting populations worldwide [1]. Percutaneous coronary intervention (PCI) is a frequently utilized procedure in clinical settings to treat CHD [2]. Although previous research has demonstrated the significance of PCI in reducing mortality rates among CHD patients [3], cardiovascular risk factors cannot be eliminated, and the progression of coronary atherosclerosis cannot be slowed [4]. Comprehensive cardiac rehabilitation strategies are required to optimize recovery, reduce the risk of recurrent events, and enhance the overall quality of life [5, 6].
Cardiac rehabilitation (CR), as a crucial part of contemporary management of CHD, is recommended by the American Heart Association (AHA) and the American College of Cardiology (ACC) as a Class I intervention for treating CHD [7, 8]. CR can be divided into three phases, phase I (inpatient rehabilitation), phase II (early outpatient rehabilitation), and phase III (long-term rehabilitation) [9]. Phase II CR is the continuation of Phase I CR and the foundation of Phase III CR, which plays a key role in the recovery process after PCI and often lasts for 3–6 months after discharge [5, 10]. The components of CR mainly include exercise training, psychological intervention, pharmacological intervention, and risk factor control, of which exercise training is the most critical component [11].
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Exercise-based CR has proven benefits for patients after PCI, such as improvements in cardiopulmonary function, promotion of overall health, and enhancement of quality of life [12]. Additionally, research has demonstrated that exercise-based CR can lower the occurrence of cerebrovascular events, rates of rehospitalization, and mortality [13]. However, despite compelling evidence and recommendations, adherence to exercise-based phase II CR [14] continues to be suboptimal, with poor adherence undermining the potential benefits. Studies carried out in multiple countries indicate that only approximately 30% of patients adhere to phase II CR [15‐18].
Given the significance of exercise-based phase II CR for post-PCI patients and the prevalent issue of poor adherence, numerous interventions have been suggested to enhance adherence. These include but are not limited to cognitive-behavior interventions, nurse-led exercise management interventions, family-based self-management interventions, home-based intervention, and internet-based interventions [19, 20]. However, the rise in intervention options presents a challenge for health professionals and patients, as it complicates clinical decision-making [21, 22]. Identifying which intervention has the best effect on promoting adherence can guide discussions between doctors and patients [20]. Unfortunately, direct comparisons between different interventions are lacking, and traditional meta-analysis is unable to compare multiple intervention methods.
Network meta-analysis (NMA) is a novel analytical method that combines direct and indirect evidence, enabling the comparison of multiple interventions’ effects in a single analysis [23]. By simultaneously evaluating the efficacy of several interventions, NMA enables the ranking of alternative treatments for specific outcomes and provides insights into the relative effectiveness of each intervention, which can help guide treatment decisions [24]. Hence, to address the issue of low adherence to exercise-based phase II CR in post-PCI patients, this NMA was conducted to compare the efficacy of different interventions.
Methods
This research followed the guidelines outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) network statement [25] and the Cochrane Handbook for Systematic Review of interventions. The NMA was registered in PROSPERO (register number: CRD42024538435).
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Search strategy
Eleven electronic databases, including PubMed, EMBASE, Web of Science, CINAHL, Medline, Cochrane Central Register of Controlled Trials, Clinical Trials.gov, the Chinese National Knowledge Infrastructure (CNKI), the Wanfang database, VIP, and the Chinese Biomedicine Literature (CBM) were searched from inception to June 2024. Not only were the citations of all qualifying articles examined, but we also considered any pertinent reviews to uncover potential supplementary studies. The search strategy was outlined in Supplementary Table 1.
Inclusion and exclusion criteria
The criteria for selecting target trials were established based on the PICOS framework.
(a)
Population: Post-PCI patients who are aged 18 years or older. Only the participants in phase II CR were included.
(b)
Studies reporting the effects of any type of intervention on adherence to exercise-based phase II CR were included. The intervention must begin after the patient is discharged from the hospital, and the duration of the intervention should not exceed 6 months after discharge.
(c)
Comparisons: Control groups were defined as participants who received usual care.
(d)
Outcomes: The research must have assessed the adherence to exercise-based phase II CR (i.e., the extent to which a participant adheres in accordance with the recommended frequency, intensity, interval, and exercise prescription) [26] and the degree of adherence could be evaluated through the results of any questionnaires or scales. If a study only provided data on low, moderate, or high adherence levels, the adherence rate was calculated by dividing the number of high adherents by the total number of participants [27].
(e)
Study design: Only RCTs that were published in either English or Chinese language would be included in this study. Studies that have been repetitively published, or have been updated were excluded from the study.
Data selection and extraction
EndNote 20 was used to manage the literature and remove duplicate articles. Duplicate articles were removed, and then two reviewers scrutinized the titles and abstracts of the remaining papers, classifying them as “to search” (qualified or likely to be qualified/unclear) or “not to search”. The full text of the selected studies was then searched, and two authors independently reviewed the full text to determine which articles should be included, while also documenting the reasons for excluding studies that did not meet the criteria. Disagreements were resolved through discussion, and a third reviewer acted as an arbitrator for unresolved disagreements. To simplify the process of electronically comparing entries, a data extraction form was performed, focusing on extracting key study characteristics (i.e., author and year, country, sample size, characteristics of the participants, intervention and control group details, and outcomes).
Quality appraisal
The quality of each study was assessed by two independent reviewers using Cochrane’s risk-of-bias tool (RoB 2) [28]. Study quality was categorized as low, high, or unclear risk of bias based on five domains of RoB2, including randomization process, intended interventions, missing outcome data, outcome measurement, and reported results.
Data analysis
The data were analyzed using Stata version 17.0. Since all outcomes were categorical variables, network estimates were presented as odds ratios (ORs) and corresponding 95% confidence intervals (CIs). Statistical significance was determined by a P value of less than 0.05. Direct comparison data were then calculated and utilized for indirect comparison.
A traditional meta-analysis was conducted on all direct comparisons. Heterogeneity was evaluated using the Cochrane Q test (with a significance level of α = 0.1) along with I2. If heterogeneity was deemed acceptable (i.e., I2 < 50% and p > 0.05) [29], a fixed-effects model would be performed; otherwise, a random-effects model would be used and subgroup analyses would be performed to look for sources of heterogeneity. To perform a sensitivity analysis, each study was systematically excluded, and the meta-analysis was then rerun. Egger’s test and Begg’s test (α = 0.05) were used to assess the potential for publication bias.
The NMA was performed within the frequentist framework [30]. A network plot would be generated, with nodes representing various interventions and their sizes indicating the sample size. The lines connecting the nodes represent direct comparisons between intervention modalities, with the thickness of the lines indicating the number of studies that have compared those modalities. The transitivity assumption was assessed by analyzing the characteristics of the included studies. Additionally, by examining the complete network of available evidence, indirect evidence was then considered.
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To identify significant differences between direct and indirect comparisons for each intervention, the global inconsistency was evaluated through the use of the Wald test, and the local inconsistency was evaluated by employing the node-splitting approach [31]. A P value > 0.05 indicates consistency between direct and indirect evidence.
The surface under the cumulative ranking (SUCRA) values provide a summary of the probability that a particular intervention is the best among all treatments, indicating the level of certainty that one treatment is superior to another on average [32]. The values of SUCRA range from 0 to 1, with higher values indicating a greater likelihood of the intervention being the best.
To evaluate the potential for publication bias, a network funnel plot was generated and visually inspected for symmetry [33].
Results
Selection of literature
The search and selection process results were depicted in Fig. 1. Initially, 3131 articles were identified through 11 electronic databases. After eliminating 1468 duplicates, the titles and abstracts of the remaining articles were screened, leading to the exclusion of 892 articles that did not meet the criteria. Subsequently, 771 full-text articles were further assessed, with 741 articles failing to meet the criteria. Additionally, no relevant studies were found in the references of the included studies. Finally, a total of 30 articles were included in this study.
Fig. 1
Flow diagram for the search and selection of the included studies. Notes: CENTRAL: Cochrane Central Register of Controlled Trials; CINAHL: The Cumulative Index to Nursing and Allied Health Literature; WOS: Web of Science; CNKI: National Knowledge Infrastructure. CBM: Chinese Biomedicine Literature
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Characteristics of the included studies
The basic characteristics of the included studies are shown in Supplementary Table 2. 30 RCTs involving 4267 patients and 17 types of interventions were included in this analysis. The definitions of 17 types of interventions were shown in Supplementary Table 3. The included studies were conducted in 11 countries: South Korea (n = 1) [34], America (n = 6) [35‐40], Switzerland (n = 1) [41], Norway (n = 1) [42], Australia (n = 3) [43‐45], Chile (n = 1) [46], Finland (n = 1) [47], the Netherlands (n = 2) [48, 49], Japan (n = 1) [50], Canada (n = 1) [51], and China (n = 12) [52‐63]. 21 studies were published in English [34‐51, 55, 60, 63], and 9 studies were published in Chinese [52‐54, 56‐59, 61, 62]. The publication date of the report varies from 1999 to 2024. The mean age of the study participants was 57.2 ± 8.42, and the maximum follow-up period ranged from 3 weeks to 240 weeks. The duration of the interventions ranged from 1 week to 6 months after discharge.
Methodological quality of the studies
Evaluation of the quality of the 30 studies included in this NMA revealed a relatively low risk of bias and an acceptable outcome. Most trials described their randomization methods, but some lacked sufficient detail on allocation concealment, making the risk of bias unclear. The area with the lowest risk of bias was the “selection of reported results”, whereas the area with the highest risk of bias was “randomization process”. In terms of overall risk of bias, 19 studies were defined as a low risk of bias [34‐36, 38‐40, 44‐51, 53, 57, 58, 61, 63], 8 studies were considered to have some concerns [37, 41, 52, 54‐56, 59, 62], and 3 studies had a high risk of bias [42, 43, 60]. The risk of bias of the included studies is displayed in Fig. 2.
Fig. 2
Risk of bias of the selected literature assessed by RoB (version 2)
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Results of traditional meta-analysis
The overall findings of the traditional meta-analysis were shown in Supplementary Fig. 1. Compared to the control groups, the interventions significantly improved adherence to exercise-based phase II CR (OR = 2.90, 95% CI: 2.30 to 3.64). Substantial heterogeneity was observed through the I2 statistic (I2 = 57.9%, p < 0.000). The outcomes of the meta-analysis for direct pairwise comparisons between the different interventions can be found in Supplementary Table 4.
Subgroup analyses
Due to the heterogeneity existed with I2 of 57.9% (P < 0.000), the subgroup analyses based on the language of publication, the risk of bias, measures of the intervention group, and measures of the control group were subsequently stratified. The outcomes of the subgroup analyses were presented in Supplementary Table 5.
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Subgroup analyses revealed significant differences in measures of the intervention group (p = 0.04) and the language of publication (p = 0.042). Regarding the risk of bias, no significant differences were observed among the studies considered as “low risk of bias”, “some concerns” and “high risk of bias” (p > 0.05). Similarly, no between-group differences were found among measures of the control group (p > 0.05).
In the language of the publication subgroup analysis, lower heterogeneity was observed among studies published in Chinese (I2 = 0.00%, p = 0.755) compared to the overall heterogeneity (I2 = 57.9%, p < 0.000). Regarding the measures of the intervention group, higher heterogeneity was observed among studies involving individual intervention (I2 = 60.6%, p = 0.008).
Publication bias and sensitivity analysis
The Egger’s test and the Begg’s test revealed significant publication bias (p = 0.000), suggesting that potential publication bias could substantially influence the interpretation of the results. A sensitivity analysis was performed by sequentially excluding each study, and the results remained consistent with those obtained prior to the exclusion (Supplementary Fig. 2).
Results of network meta-analysis
The NMA included 17 interventions, encompassing a total of 4267 patients. Figure 3 shows the network weights of the eligible comparisons for treatment efficacy. The outcomes of the NMA are presented in Supplementary Table 6.
Fig. 3
Network plot. Notes: A = Usual care;B = Health education; C = hospital-based CR; D = home-based CR + Cognitive behavioral intervention; E = Hospital-based CR + Cognitive behavioral intervention; F = Patients’ diary combined with group meeting; G = Home-based CR; H = Psycho-educational intervention; I = Patient navigation; J = Cognitive behavioral intervention; K = Home-based CR + mobile health intervention; L = Hospital-based CR + mobile health intervention; M = Reduce hospital-based CR; N = Financial incentive; O = Progressive CR; P = Couple-based exercise program; Q = Hybrid CR
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Network geometry
A network plot was generated to visualize the relationships among the various interventions (Fig. 3), with a total of 17 interventions across all included studies, creating multiple closed loops. In the network plot, larger nodes indicate larger sample sizes, and the network plot shows that usual care has both the largest point and the greatest sample sizes. Thicker lines between points represent more comparisons between interventions. Usual care and home-based CR + mobile health intervention have the thickest lines, indicating that the largest number of studies comparing these two interventions. In detail, among the 30 studies, 22 used usual care as a control group, 7 used hospital-based CR, and 1 used health education. In the intervention group of 30 studies, 8 used home-based CR + mobile health intervention, 5 studies used cognitive behavioral intervention, 3 used hospital-based CR + mobile health intervention, 2 used home-based intervention,2 used psycho-educational intervention, 1 study used hospital-based CR, hospital-based CR + cognitive behavioral intervention, home-based CR + cognitive behavioral intervention, couple-based exercise program, progressive CR, patients’ diary combined with group meeting, financial incentive, hybrid CR, patient navigation, and reduce hospital-based CR. (see Supplementary Table 3).
Assessment of statistical consistency
The general consistency of each intervention aligned with the assumption of consistency (Chi square = 4.19, P = 0.1230). No significant global inconsistencies were found between direct and indirect estimates (P > 0.05), suggesting that the indirect estimates were comparable to the direct evidence (Supplementary Fig. 3). Node-split approach was used to analyze the inconsistency of each node. The results showed that the P value was greater than 0.05, there was no inconsistency, the results of direct and indirect comparisons could be combined (Supplementary Table 7).
Comparison of interventions
The interventions were listed based on their effectiveness ranking in the cumulative probability plots (Fig. 4) and SUCRA values (Table 1).
Fig. 4
Cumulative probability plots. Notes: A = Usual care; B = Health education; C = hospital-based CR; D = home-based CR + Cognitive behavioral intervention; E = Hospital-based CR + Cognitive behavioral intervention; F = Patients’ diary combined with group meeting; G = Home-based CR; H = Psycho-educational intervention; I = Patient navigation; J = Cognitive behavioral intervention; K = Home-based CR + mobile health intervention; L = Hospital-based CR + mobile health intervention; M = Reduce hospital-based CR; N = Financial incentive; O = Progressive CR; P = Couple-based exercise program; Q = Hybrid CR
Table 1
The SUCRA value
Treatment
SUCRA
PreBest
meanRank
K
83.8
34.3
3.6
L
79.9
11.4
4.2
N
71.2
2.6
5.6
M
70.2
14.7
5.6
Q
65.2
6.7
6.6
I
62.3
7.8
7
F
61
6.7
7.2
P
58.7
8.2
7.6
J
54.6
0.4
8.3
O
52
4.9
8.7
C
44.9
0
9.8
H
42.6
1.7
10.2
E
32.8
0.3
11.8
G
25.5
0.2
12.9
B
19.2
0.1
13.9
D
15.5
0
14.5
A
9.6
0
15.5
A = Usual care; B = Health education; C = hospital-based CR; D = home-based CR + Cognitive behavioral intervention; E = Hospital-based CR + Cognitive behavioral intervention; F = Patients’ diary combined with group meeting; G = Home-based CR; H = Psycho-educational intervention; I = Patient navigation; J = Cognitive behavioral intervention; K = Home-based CR + mobile health intervention; L = Hospital-based CR + mobile health intervention; M = Reduce hospital-based CR; N = Financial incentive; O = Progressive CR; P = Couple-based exercise program; Q = Hybrid CR
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Based on the results of SUCRA, home-based CR + mobile health intervention was identified as the top-ranked intervention in terms of effectiveness (83.8%), followed by hospital-based CR + mobile health intervention (79.9%), financial incentive (71.2%), reduce hospital-based CR (70.2%), hybrid CR (65.2%), patient navigation (62.3%), patients’ diary combined with group meeting (61%), couple-based exercise program (58.7%), cognitive behavioral intervention (54.6%), progressive CR (52%), hospital-based CR (44.9%), Psycho-educational intervention (42.6%), hospital-based CR + cognitive behavioral intervention (32.8%), home-based CR (25.5%), health education (19.2%), home-based CR + cognitive behavioral intervention (15.5%), and usual care (9.6%) (Table 1.).
Risk of bias across studies
The funnel plot presented in Fig. 5 displays nearly symmetrical scatter, indicating the absence of small effect sizes and a low risk of publication bias.
Fig. 5
The funnel plot. Notes: A = Usual care; B = Health education; C = hospital-based CR; D = home-based CR + Cognitive behavioral intervention; E = Hospital-based CR + Cognitive behavioral intervention; F = Patients’ diary combined with group meeting; G = Home-based CR; H = Psycho-educational intervention; I = Patient navigation; J = Cognitive behavioral intervention; K = Home-based CR + mobile health intervention; L = Hospital-based CR + mobile health intervention; M = Reduce hospital-based CR; N = Financial incentive; O = Progressive CR; P = Couple-based exercise program; Q = Hybrid CR
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Discussion
This study included 30 RCTs sourced from 11 databases. The included studies published from 1999 to 2024, during which time there has been some change in the type of interventions. As time has changed, with the development of technology, mobile health intervention is used in conjunction with the home-based or hospital-based CR. Additionally, in one of the earliest articles we included [50], hospital-based CR appeared as an intervention in the intervention group, and with the emergence of emerging interventions, it became an intervention in the control group. Notably, several new interventions emerged over time. Cognitive behavioral intervention emerged as an intervention in 2014 [48] and remained in use through 2022 [59]. Financial incentive was used as an intervention in 2019 [39], couple-based exercise programs appeared in 2022 [60], and hybrid CR appeared in 2024 [46].
The objective of this research was to evaluate the comparative effectiveness of various interventions in improving adherence to exercise-based phase II CR. To achieve this goal, this NMA was conducted to synthesize data from prior intervention studies for increased reliability in the evidence presented. However, due to heterogeneity and inconsistency in NWA, even when appropriate ranking statistics (e.g., SUCRA values) are used, the high-ranking interventions may still have modest or insignificant effects and therefore caution is still needed when selecting the best intervention [24].
Traditional meta-analysis
The findings of the meta-analysis indicated that, in comparison to control groups, all interventions demonstrated significant effectiveness in improving adherence rates to exercise-based CR. The total effect size was 2.90 (95% CI: 2.30 to 3.64). Subgroup analyses based on the publication of language, and measures of the intervention group revealed significant differences among groups, suggesting that varying interventions and publication of language may contribute to heterogeneity. Given the limited number of studies and considerable heterogeneity, these findings should be considered preliminary in nature.
Network meta-analysis
Top two interventions to improve adherence
The SUCRA results indicated that the home-based CR + mobile health intervention had an effectiveness of 83.8% while the second highest ranked intervention was hospital-based CR + mobile health intervention (79.9%).
A previous systematic review reported that both home-based CR and hospital-based CR appear to be equally effective in enhancing clinical outcomes and health-related quality of life for patients following myocardial infarction, revascularization or, heart failure [64]. And there may be several mechanisms for the higher adherence of patients exercising at home than in the hospital. Previous studies have reported that barriers emerged in phase II CR. Nowadays, most CR programs take place in CR centers or hospitals, issues like transportation and distance from CR centers further impede adherence [65]. Studies have also shown that barriers vary across populations, with older adults needing family members to accompany them to CR centers [66], housewives having many household chores to take care of [67], and working adults being able to rehabilitation centers only on weekends when many rehabilitation centers are closed [68]. Some studies have shown that certain patients feel discomfortable exercising in front of others [69], with some indicating embarrassment when men and women worked out together in a shared CR facility [66]. Home-based CR + mobile health intervention offers a solution for these barriers that typically occur in phase II CR, allowing patients to exercise in a familiar environment, not be influenced by commuting distance and transportation, have a more flexible schedule of activities, and provide a convenient and affordable option for patients in need of CR [70]. Because of the limited long-term follow-up in the home-based CR + mobile health intervention studies included, it might be uncertain whether these effects diminish over an extended period. But a previous systematic review focusing on the safety and long-term outcomes of remote cardiac rehabilitation indicates that home CR + mHealth can serve as a safe alternative for delivering cardiac rehabilitation [71]. Those might be the reasons that home-based CR + mobile health intervention can improve adherence better than hospital-based CR + mobile health intervention.
Both top two ranked interventions used mobile health intervention. Mobile health intervention is a medical and health management approach to provide remote CR guidance, doctor-patient communication and exchange, patient information collection and health promotion through data uploaded from smartphones, wearable sensors, or other online systems [72]. There are certain mechanisms that could help explain the influence of mobile health intervention on improving adherence to exercise-based phase II CR. With the development of mobile technology and its widespread adoption, mHealth and mobile apps are perceived as appealing and promising instruments for promoting behavioral changes [73]. According to previous studies, CR must be completed through exercise under medical supervision [74]. Advances in digital technology have provided a new platform for CR, especially for phase II, where healthcare professionals can conveniently and quickly connect with patients so that healthcare professionals can obtain feedback from patients while providing health education [71], and patients can communicate with healthcare professionals, which can help patients acquire knowledge in a timely manner and enhance the effectiveness of the interventions [75]. Several recent studies have explored the effectiveness of mobile health intervention in improving health habits and preventing cardiovascular disease in patients with CHD [76, 77]. They concluded that mobile health intervention positively impacts patients’ mobility, physical activity and quality of life, while also reducing readmissions for cardiovascular reasons. Previous studies have also shown that mobile health intervention has improved acceptance, adherence and completion of CR in patients with CHD [78]. Therefore, in future interventions, health care providers can first assess whether there are factors influencing the patient’s adherence, such as distance, transportation, etc. Depending on the patient’s specific situation and prefer, they can decide whether the mobile health intervention should be combined with home-based CR or hospital-based CR.
Other interventions to improve adherence
According to the SUCRA values, financial incentive (71.2%) also has significantly efficacy. Financial incentive has been shown to successfully change health behaviors in at-risk populations [39]. Additionally, financial incentive has proven beneficial in encouraging attendance at healthcare appointments, particularly among lower socioeconomic demographics [79]. Financial incentive can enhance adherence to exercise-based CR, which is crucial for achieving positive long-term health outcomes [39]. In the included study utilized financial incentive [39], participants in the incentive intervention group received financial incentives for completing CR, ranging from $4 to $50. Results indicated that this group completed more CR sessions (22.4 vs. 14.7; p = 0.013) and were nearly twice as likely to adhere to CR compared to the control group (55.4% vs. 29.2%; p = 0.002). However, the included study on financial incentive focus on low-income populations. Future research needs to evaluate the effectiveness of financial incentive in enhancing adherence among middle- and high-income populations. In addition, ethical issues should be considered when using financial incentive as an intervention.
Reducing hospital-based CR ranked fourth (70.2%), and there are some mechanisms that explain its effectiveness in improving adherence. Research indicates that some patients may not participate in CR due to transportation issues, financial burdens, and time limitations [80]. Additionally, hospital-based CR typically adopt a “one-size-fits-all” approach, treating a diverse range of cardiovascular patients without considering disease severity [51]. In contrast, reduce hospital-based CR is a comprehensive program that effectively improves exercise capacity and risk factors, achieving results comparable to hospital-based CR while requiring only one-third of the central CR course [51]. This makes reduce hospital-based CR a viable alternative for patients facing challenges related to time, distance, and travel.
Hybrid CR ranked fifth among all interventions. Hybrid CR begins with a hospital-based supervision phase followed by a second home-based phase of follow-up via mobile phone [46]. Hybrid CR is similar to the fourth-ranked reduced hospital-based CR in that it significantly improves patient adherence by customizing CR to overcome the limitations of hospital-based CR [81]. Hybrid CR has been widely implemented and advocated [82]. Considering resource availability and patient preferences, a hybrid CR may serve as an alternative intervention to a hospital-based CR program.
In the studies we included, cognitive-behavioral intervention emerged as an intervention in 2014 and were used until 2022. Cognitive-behavioral intervention enhanced patient adherence to some degree. It began with assessing patients’ perceptions of the disease and identifying reasons for their reluctance to adhere to exercise-based CR. Through discussions and educational sessions, patients learn that their irrational beliefs lack factual support. Finally, efforts were made to help them adjust these beliefs, enhancing treatment effectiveness and addressing incorrect disease-related thoughts [48, 52‐54, 59].
Interventions of uncertain effectiveness
Direct meta-analysis and NMA of this study (see Supplementary Table 6) showed that progressive CR, couple-based exercise program, home-based CR + cognitive behavioral intervention, hospital-based CR + cognitive behavioral intervention, psycho-educational intervention and health education did not have better adherence than usual care (p > 0.05). One of the main reasons is that there have been insufficient RCTs to confirm the difference in effectiveness between these interventions and usual care, only indirect comparisons could be made based on the result of SUCRA in NMA (i.e., couple-based exercise program > progressive CR > psycho-educational intervention > hospital-based CR + cognitive behavioral intervention > health education > hospital-based CR + cognitive behavioral intervention > usual care). The results need to be confirmed by further studies in the future.
Strengths and limitations
This study has several strengths and limitations. First, it is worth noting that this study represents the first NMA to investigate the comparative effectiveness of different interventions in enhancing adherence to exercise-based phase II CR among post-PCI patients. However, some trials lacked sufficient detail on allocation concealment, which may have some implementation and measurement bias. In addition, only studies published in Chinese or English were included in this study, which may have language bias. However, considering the strict control of paper quality by the peer-review process, the bias is likely to be minimal.
Conclusions
This network meta-analysis identified home-based CR + mobile health intervention and hospital-based CR + mobile health intervention as the top two ranked interventions for improving adherence to exercise-based phase II CR in post-PCI patients. However, interventions such as financial incentives, reducing hospital-based CR, hybrid CR, cognitive-behavioral intervention have been shown to improve exercise adherence significantly. Therefore, when determining the most appropriate interventions, health care providers should evaluate the efficacy of these interventions as well as the environmental factors available in CR, such as patient preference and the availability of health resource. Such a comprehensive assessment will enable the development of tailored interventions that are not only safe and effective but also aligned with patient satisfaction.
Acknowledgements
We are grateful to the included authors for their research.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
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Comparative effectiveness of different interventions on adherence to exercise-based CR among patients after percutaneous coronary intervention: a network meta-analysis of randomized controlled trials
