The Correlation and Mediating Effects Between Self-Efficacy and Quality of Life in Patients with Cardiovascular Diseases: A Systematic Review and Meta-Analysis

The Correlation and Mediating Effects Between Self-Efficacy and Quality of Life in Patients with Cardiovascular Diseases: A Systematic Review and Meta-Analysis | Research Square Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Correlation and Mediating Effects Between Self-Efficacy and Quality of Life in Patients with Cardiovascular Diseases: A Systematic Review and Meta-Analysis Chengfeng Lu, Ying Xiao, Yushuang Su, Li Xiao, Zhenzhen Feng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8293510/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Cardiovascular diseases (CVDs) are a leading cause of global morbidity, profoundly compromising patients' quality of life (QoL). Self-efficacy (SE)—an individual’s confidence in managing their health—is a modifiable psychosocial factor posited to enhance QoL. However, the aggregated magnitude of this association and the specific pathways through which SE operates remain unclear due to inconsistent primary findings. This study aimed to quantitatively synthesize the correlation between SE and QoL in CVD patients and to qualitatively elucidate the underlying mediating mechanisms.This provides evidence-based guidance for nursing interventions to improve the prognosis of cardiovascular disease (CVD) patients. Methods We conducted a systematic review and meta-analysis to quantitatively synthesize the correlation between SE and QoL in CVD patients and to qualitatively explore potential mediating pathways. We systematically searched Chinese and English databases from inception to September 20, 2025. Two reviewers independently screened studies, extracted data, and assessed quality. Pooled correlation coefficients were calculated using a random-effects model. Subgroup and sensitivity analyses were performed to explore heterogeneity, and mediating pathways were synthesized qualitatively. Results Twenty-six studies were included. Meta-analysis revealed a significant, moderate positive correlation between self-efficacy (SE) and quality of life (QoL) (r = 0.43, 95% CI : 0.38–0.48, p < 0.001). Subgroup analysis identified SE measure specificity (Q B = 17.4, p < 0.001) and study design (Q B = 9.11, p = 0.003) as significant moderators. Exercise task-specific SE instruments yielded the strongest correlation (r = 0.52), and cross-sectional studies demonstrated a stronger association (r = 0.45) than longitudinal studies (r = 0.28). The qualitative systematic review delineated multiple mediating pathways: self-management behaviors constituted the most robust and stable core mediator; psychoemotional factors represented complex yet distinct pathways; whereas physiological activity and physical function pathways exhibited greater heterogeneity, with their effects moderated by factors such as disease severity. Conclusions SE is closely associated with QoL in Cardiovascular disease patients, influenced by measurement tools and study design. SE primarily enhances QoL indirectly through self-management behaviors, supplemented by multiple pathways including psychological-emotional regulation and physical activity. Clinical interventions should prioritize self-management and tailor strategies to individual needs. Cardiovascular diseases self-efficacy quality of life Mediating Effect Systematic review Meta-analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide, placing a heavy burden on healthcare systems and patient health[1]. According to the Global Burden of Disease (GBD) 2023 data, the number of cardiovascular disease cases worldwide reached 626 million in 2023, more than doubling since 1990. Related deaths rose from 13.1 million in 1990 to 19.2 million in 2023, with disability-adjusted life years (DALYs) reaching 437 million, making it the leading cause of health loss[2]. Beyond these staggering morbidity and mortality figures, cardiovascular disease severely compromises patients’ health-related quality of life (HRQoL)—a multidimensional concept encompassing physical functioning, mental health, social engagement, and overall life satisfaction [3]. Persistent symptoms (e.g., fatigue, chest pain, dyspnea) limit daily activities and independent living [4], while the chronic, unpredictable nature of the disease elevates risks of anxiety, depression, and emotional distress [5]. Physical limitations and disease management burdens (e.g., frequent medical visits, medication regimens) may also cause social isolation, reduced work/leisure participation, and strained interpersonal relationships [6]. Notably, these HRQoL impairments correlate with poorer clinical outcomes, including higher readmission rates and mortality. Despite advances in surgical and medical interventions that effectively address anatomical and haemodynamic abnormalities, patients frequently experience prolonged recovery, complications such as arrhythmias and infections, and long-term risks including vascular restenosis[7, 8]. It is increasingly recognised that optimal disease management and outcomes extend beyond biomedical treatment, necessitating attention to psychosocial factors[9]. Among psychosocial factors, self-efficacy (SE)—a concept introduced by psychologist Albert Bandura referring to an individual’s belief in their capability to execute behaviors necessary to produce specific performance attainments—has emerged as a critical determinant of health behaviour and disease management[10]. In cardiac populations, higher SE is associated with improved adherence to medication, diet, and physical activity, thereby promoting better clinical outcomes[11–13]. Conversely, low SE is associated with lower participation in cardiac rehabilitation avoidance, functional decline, and a cycle of deteriorating health and well-being. In recent years, an in creasing number of studies have begun to focus on the association between SE and QoL in Cardiovascular disease patients. Most studies support a positive association between the two, but the consistency of research findings remains to be validated. Some studies indicate that the strength of the impact of SE on quality of life(QoL) varies, such as in patient populations of different ages, disease severity, or rehabilitation stages, where the correlation coefficients fluctuate significantly[14, 15]; Other studies suggest that, in addition to direct effects, SE may indirectly influence QoL through other variables, such as patients’ rehabilitation adherence, social support levels, and coping strategies, which act as potential mediating factors[16, 17]. The mechanisms underlying the roles of these variables in the relationship between SE and QoL have not yet reached a unified conclusion. Furthermore, existing studies vary in terms of sample size, research design, and measurement tools, which may lead to biases in research results, affecting the reliability and generalizability of conclusions. To address these critical gaps, this study employs a systematic review and meta-analysis to achieve three primary objectives: (1) to provide a definitive, quantitative estimate of the correlation between SE and QoL in CVD patients; (2) to investigate the moderating effects of key methodological and clinical variables (e.g., measurement tools, study design) on this relationship; and (3) to qualitatively synthesize mediating mechanisms into a hierarchical framework that informs targeted nursing strategies. Methods This systematic review and meta-analysis was conducted according to a pre-registered PROSPERO protocol (Registration Number: CRD420251169713). This research report adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 Statement[ 18 ]. As this study constitutes a comprehensive analysis of previously published research, no ethical review was required. All included original studies had obtained approval from their respective local ethics committees. Literature Search Strategy Computer-based searches were conducted in English-language databases such as PubMed, Embase, Web of Science, Cochrane Library, and CINAHL, as well as Chinese-language databases including China National Knowledge Infrastructure (CNKI), Wanfang Data Knowledge Service Platform, VIP Chinese Science and Technology Journal Database, and Chinese Biomedical Literature Database. Search terms included “Cardiovascular disease”, “self-efficacy”, “quality of life”, “correlation”, “mediating effect”, and their synonyms/related terms (e.g., “coronary heart disease”, “heart failure”, “health-related quality of life”). A combination of subject terms and free-text search ensured comprehensiveness. The search timeframe spanned from database inception to September 20, 2025. References of included studies were manually searched to supplement potentially omitted literature. For details on each database retrieval strategy, refer to Supplementary Table 1. Inclusion and exclusion criteria Studies were included if they met the following criteria: (i) participants were adult patients (≥ 18 years) with a diagnosed CVD; (ii) measured both self-efficacy and quality of life and reported their correlation coefficient, and/or explored mediating effects between them; (iii) All observational study designs (cross-sectional, cohort, case-control) were considered; (iv) were published in either Chinese or English. Exclusion criteria: (i) duplicate publications; (ii) non-original research types, including conference abstracts, reviews, commentaries, and case reports; (iii) study populations comprising non-CVD patients or individuals with conditions that could confound the results, such as severe mental disorders, cognitive impairment, or other serious physical comorbidities; and (iv) unavailability of the full-text article. Data Extraction and Risk of Bias Assessment Two trained researchers extracted data using standardized forms. Extracted information included study details (authors, publication year, study region, etc.), participant characteristics (sample size, age, gender, disease type, etc.), SE / QoL measurement tools and scores, correlation results, and mediation analysis findings. Disagreements were resolved via discussion with a third researcher. Risk of bias was assessed using the modified Newcastle-Ottawa Scale (NOS) for non-randomized observational studies[ 19 ], tailored for cross-sectional designs to enhance accuracy. The NOS evaluates studies across eight domains using a star-rating system: sample representativeness, sample size calculation, handling of non-respondents, use of validated measurement instruments, adjustment for confounding factors, use of validated outcome measures, ascertainment of the outcome, and appropriateness of statistical tests. Based on the total star score, studies were classified as having a high risk of bias (0–3 stars), an unclear risk (4–6 stars), or a low risk (7–9 stars)[ 20 ]. Variable Definition and Encoding Moderating variables were coded to examine their influence on the SE-QoL relationship: (i) type of self-efficacy measure (including general self-efficacy, cardiovascular symptom-specific self-efficacy, exercise self-efficacy, and other relevant self-efficacy measures); (ii) type of quality of life measure (including general QoL and cardiovascular-specific QoL). Table 1 details specific types, core definitions, coding parameters, and standardized measurement tools for SE and QoL. Table 1 List of Self-efficacy and Health-related Quality of Life Types, Definitions, Coding Parameters, and Measures. Outcome Studies(n) Coding Parameters Measures Self-efficacy Type:definition General:Applicable to multi-domain behaviors or complex scenarios[ 56 ] 14 (i) The items in the scale refer to efficacy in the general sense, (ii) and do not explicitly mention managing symptoms or performing exercise-related behaviors. GSES: General Self-efficacy Scale (GSES)[ 29 ] CDSS: Chronic Disease Self-efficacy Scale[ 23 ] PSEQ: Pain Self-Efficacy Questionnaire[ 24 ] CRF-SES: The cardiac risk factors self-efficacy scale[ 34 ] SESAMED: Self-Efficacy Scale for Adherence to the Mediterranean Diet[ 35 ] ARTSE: Adherence to Rehabilitation Therapy Self Efficacy[ 45 ] Cardiac symptom: The scale includes items focused on self-efficacy related to heart disease management (i.e., an individual's confidence in their ability to perform behaviors such as disease management and symptom control)[ 25 ] 9 (i)The items covers the assessment of individuals' self-management capabilities regarding their own cardiovascular disease symptoms. SESMs: Symptom Management Self-Efficacy Scale[ 25 ] SCSES: Sullivan’s cardiac self-efficacy scale[ 31 ] Cardiovascular Management Self-efficacy Scale HFSES: Heart Failure Self-Efficacy Scale[ 40 ] Exercise: Quantitative assessment tools specifically designed for dimensions related to sports/physical activity[ 44 ] 4 (i) Items focus on an individual's ability to perform actions related to physical activity or exercise. (ii ) do not address any aspects of managing symptoms of cardiovascular disease. CESS: Self-efficacy for Exercise Scale[ 38 ] Others: 1 Not falling under the above three categories, applicable to the doctor-patient communication environment. PEPPI−5: the 5-item perceived efficacy in patient-physician interaction scale[ 31 ] Quality of life type:definition General:Individuals’subjective perceptions of their physical and mental health status and functional capacity, which apply to all adult populations[ 30 , 31 ] 18 (i) This entry does not focus on cardiovascular disease and can be applied to both healthy individuals and patients with other conditions. (ii)Items did not refer to Cardiac specific symptoms SF−36: 36-item shot form health survey[ 27 ] EQ−5D: European Quality of Life 5-Dimensions Scale[ 23 ] SF−12: 12-item shot form health survey[ 32 ] SWLS: The Satisfaction With Life Scale[ 34 ] WHOQOL: Health-related Quality of Life[ 37 ] FPQLI: Ferrans and Powers Quality of Life Index[ 46 ] Cardiac-specific: Perceived physical and mental health status and function- ing is directly associated with cardiovascular disease[ 24 , 42 ] 8 (i)Entries focusing on cardiovascular disease or its consequences (ii)Entries directly addressing specific symptoms of cardiovascular disease CQQC: Quality of Life Assessment Questionnaire for Chinese Cardiovascular Patients[ 24 ] MLHFQ: Minnesota Living with Heart Failure Questionnaire[ 25 ] KCCQ: The Kansas City Cardiomyopathy Questionnaire[ 39 ] SAQ: the Seattle Angina Questionnaire[ 44 ] Notes:n: represents the“number of studies employing this health literacy measurement tool”; if a single study uses ≥ 2 health literacy measurement tools, it will be counted separately under the corresponding k for each tool (i.e., one study may correspond to multiple tool categories k). Statistical Analysis Descriptive analysis was conducted on the measurement tools for research countries, SE, and QoL, as well as the relationships between them. The correlation coefficients (r) from each study report underwent Fisher’s z-transformation for pooling[ 21 ], then were converted back to r values for result interpretation. When multiple subgroup results were reported within the same study, each result was treated as a separate study for analysis. Study heterogeneity was assessed using the Q test and I² statistic. If I² ≤ 50% and p ≥ 0.1, low heterogeneity was assumed, and a fixed-effects model was used for meta-analysis. If I²> 50% and p < 0.1, high heterogeneity was assumed. Sources of heterogeneity were explored through subgroup analysis and sensitivity analysis. If heterogeneity persisted, a random-effects model was employed. Publication bias was evaluated using funnel plot visualization and Egger’s regression test. Visual inspection of the funnel plot for symmetry, combined with an Egger’s test P-value > 0.05, indicated a low risk of publication bias; in contrast, a P-value < 0.05 suggested potential publication bias. For studies with suspected publication bias, the trim-and-fill method was applied to impute potential missing studies and adjust the pooled effect size, thereby enhancing the robustness of the results . Results An initial search yielded 2,579 articles. After removing 557 duplicates (EndNote 21.0), 2,022 articles proceeded to the initial screening phase. Following review of titles and abstracts, 102 articles were included. After further full-text reading, 26 articles were ultimately selected for qualitative analysis. Figure 1 presents the PRISMA flow chart. Basic Characteristics of Included Studies A total of 26 publication[ 22 – 47 ] (encompassing 33 independent analyses) were included in this review, with a combined sample size of 7,058 participants and individual study sample sizes ranging from 75 to 755. Geographic distribution revealed that China contributed 11 studies (42%); Iran and Italy each contributed 3 (11.5%) ; Spain and South Korea each contributed 2 studies (7.69% each); while Palestine, the United Kingdom, Switzerland, Singapore, and Poland each contributed one study (3.84% each). In terms of study design, cross-sectional studies predominated ( n = 23), with the remaining three being longitudinal studies. The total sample size reached 7,058 cases, with individual study sample sizes ranging from 75 to 755. The average patient age predominantly fell between 50 and 70 years, with a higher proportion of males (over 50% in most studies). Disease types were primarily coronary artery disease (13 studies), followed by heart failure (6 studies), and also included specific populations such as aortic valve replacement and cardiopulmonary bypass surgery. For measurement tools, self-efficacy (SE) was assessed using general SE scales ( n = 14, n refers to the number of studies using the tool type), cardiac symptom-specific SE scales ( n = 9), exercise-specific SE scales ( n = 4), and a doctor-patient interaction SE scale ( n = 1), where n represents the frequency of tool use—since some studies simultaneously employed more than one self-efficacy scale simultaneously (e.g., Barham et al. 2019 used PEPPI−5 、SEMCD−6 scales and 13-item SCSES); quality of life (QoL) was measured with general QoL scales ( n = 18) and cardiac-specific QoL scales ( n = 8), the total frequency of tool use exceeds the number of included studies. Detailed characteristics of all included studies are presented in Table 2 . Table 2 Characteristics of included studies Author, year, study country Research Design Sample Size /Males age Disease Type SE Tool(α) QoL Tool(α) Main r-value(SE-QoL) Risk of bias Li et al., 2009,China[ 22 ] cross-sectional 93/67 62.75 ± 7.62 CHD C-GSES(0.91) SF−36(>0.7) 0.330 4 Liu et al., 2019, China[ 23 ] cross-sectional 276/170 68.47 ± 10.45 CHD CDSS (0.894) EQ−5D (0.754) 0.347 6 Tang et al., 2024[ 24 ] China cross-sectional 381/231 65.54 ± 10.53 CHD PSEQ (0.819) CQQC (0.701) 0.531 6 Shen et al.,2019, China[ 25 ] China cross-sectional 220 /103 61.1 ± 13.7 CHF SESMs (0.919) MLHFQ (0.881) 0.453 7 Mai et al.,2025, China[ 26 ] cross-sectional 227/125 ≥ 60 CHF SEE(0.9) MLHFQ(0.881) 0.713 6 Piao et al.,2023, China[ 27 ] cross-sectional 400 / 290 18 ~ 75 CABG CESEI(0.949) SF−36(>0.7) 0.372 6 Zhang et al.,2021, China[ 28 ] cross-sectional 530/267 70.21 ± 8.13 CHD SCSES−16 items(0.82) SF−36(0.83) 0.612 6 Lu et al.,2024, China[ 29 ] cross-sectional 241 /190 18 ~ 59 CHD C-GSES(0.86–0.94) CQQC(0.90) 0.545 6 Du et al.,2022, China[ 30 ] cross-sectional 200/111 65.37 ± 10.37 CHD CDSS(0.94) SF−36(0.89) 0.734 7 Barham et al.,2019, Palestine[ 31 ] cross-sectional 275/154 59.51 ± 10.1 CHD PEPPI−5(0.799)SEMCD−6༈0.865༉ SCSES−13 items(0.848) EQ−5D(0.755) PEPPI−5: EQ−5D r = 0.419;SEMCD−6: EQ−5D r = 0.273; SCSES: EQ−5D r = 0.524 8 Thomson et al.,2018, UK[ 32 ] cross-sectional 168/143 64.5 ± 9.22 CABG SCSES-CS(0.75) SCSES-MF(0.79) SF−12(PCS: 0.77 MCS༚0.78) 0.39 5 Brink et al.,2014, Switzerland[ 33 ] Cohort Study 145/102 64.4 ± 9.4 CHD GSES(0.9) SF−36(0.89) PCS(r = 0.177) MCS(r = 0.246) 8 Steca et al.2014,Italy[ 34 ] cross-sectional 172 / 131 66.43 ± 9.99 CVD CRF-SES(0.8) SWLS(0.82) 0.28 5 Castillo-Mayén et al.,2020, Spain[35] Cohort Study 755/630 64.60 ±9.03 CHD SESAMED(0.93) SWLS(0.86) 0.26 8 Zhang et al.,2023, China[ 36 ] cross-sectional 283/123 52.00 ± 10.44 aortic valve replacement C-GSES(0.97) CQQC(0.79) 0.432 8 Author, year, study country Research Design Sample Size /Males Age Disease Type SE Tool(α) QoL Tool(α) Main r-value(SE-QoL) Risk of bias Janjani et al.,2025,Iran[ 37 ] cross-sectional 303/152 52(20–75) Coronary artery disease SEMCD (Cardiac risk: 0.717; Treatment compliance: 0.715; Symptom recognition: 0.708) WHOQOL (Physical health: 0.701; Psychological health: 0.718; Social relations: 0.712; Environmental conditions: 0.520) 0.352 6 Won et al.,2022, KR[ 38 ] cross-sectional 489/299 51.30 ± 9.00 CVD SCESS−13 items(0.90) EQ−5D(0.755) 0.49 8 Lee et al.,2017, KR[ 39 ] cross-sectional 116/93 62.15 ± 9.06 CHF (LVEF) ≤ 40% 9-items ESE-Q(0.92) KCCQ(0.75) 0.503 8 Zanatta et al., 2024, Spain[ 40 ] Cohort Study 410/357 63.2 ± 9.2 CHD HFSES(0.76) SF−12(0.82) 0.397 8 Fuochi & Foà.,2017, Italy[ 41 ] cross-sectional 250/0 53 CHD GSES(0.92) WHOQOL Physical health: 0.81 Psychological health: 0.81 Social relations: 0.68 Environmental conditions: 0.74 Physical QoL:= 0.45 Psychological QoL: = 0.49 Environmental QoL: = 0.43 Social QoL: = 0.37 5 Suresh et al.,2017, Singapore[ 42 ] cross-sectional 91/75 63 ± 13.02 HF SCSES(0.90) MLHFQ(0.93) 0.16 7 Nekouei Z et al., 2014,Iran[ 43 ] cross-sectional 398 / 231 25–70 CHD GSES(0.82) WHOQOL(0.90) 0.35 8 Li et al.,2024, China[ 44 ] cross-sectional 212 / 145 59.82 ± 10.33 CHD SCSES(0.893) SAQ(0.885) 0.372 8 Greco et al., 2015, Italy[ 45 ] cross-sectional 75 / 60 65.44 CVD ARTSE(0.76) OCR(Not reported) 0.29 5 Baradaranfard et al., 2018, Iran[ 46 ] cross-sectional 227 / 150 59.47 ± 13.01 HF SCSES(0.9) FPQLI(0.76) Satisfaction: 0.409 Importance: 0.465 8 Milaniak et al., 2014,Poland[ 47 ] cross-sectional 121 /91 55.01 ± 13.23 Heart Transplant- tion GSES(0.87) WHOQOL Physical: 0.82 Psychological: 0.81 Social : 0.68 Environment: 0.8 Total > 0.7 0.62 5 Notes: α values in parentheses represent Cronbach’s α coefficients of the scales; “Not reported” indicates that neither the original study nor the scale manual provided reliable α data; CHD:Coronary Heart Disease; HF:Heart Failure; CPB: Cardiopulmonary Bypass;CABG:Coronary Artery Bypass Graft HSV:CVD:Cardiovascular Disease;CS:controlling symptoms; MF: maintaining functioning༛PCS:physical component score༛MCS༚mental component score; UK:United Kingdom; KR:South Korea Methodological Quality Assessment A risk of bias assessment was conducted for 26 studies (33 independent investigations) using eight dimensions, including sample representativeness and sample size calculation. Results indicated no high-risk studies (all met meta-analysis inclusion criteria), with 13 studies (50%) [ 25 , 30 , 31 , 33 , 35 , 36 , 38 – 40 , 42 – 44 , 46 ]classified as low risk (7–9 stars) and 13 studies (50%)[ 22 – 24 , 26 – 29 , 32 , 34 , 37 , 41 , 45 , 47 ] as unclear risk (4–6 stars). Regarding sample representativeness, 17 papers[ 22 – 32 , 34 , 36 , 37 , 44 , 45 , 47 ] involved specific populations with overall poor representativeness. Concerning sample size calculation, 16 papers[ 22 – 29 , 32 , 34 , 35 , 38 , 41 , 42 , 45 , 47 ] failed to clearly report the rationale for sample size determination. Regarding non-response information, 9 papers[ 26 , 32 , 34 , 39 , 41 – 43 , 45 , 46 ] did not mention the number and proportion of non-respondents. Regarding confounding factor control, 4 papers[ 22 , 37 , 41 , 47 ] did not specify whether confounding factors were controlled, while 8 papers[ 23 , 24 , 27 – 30 , 32 , 34 ] had inadequate control of confounding factors. Regarding outcome measurement tools, 1 papers[ 45 ] did not use instruments or scales validated for reliability and validity; only 1 papers[ 40 ] utilized secondary data; Regarding the description of statistical tests, 2 papers[ 22 , 33 ] provided incomplete descriptions of key statistical information. These issues may impact the universality, authenticity, and credibility of the research findings to varying degrees.See Supplementary Table 2 for details. Qualitative Analysis of SE and QoL Among the 26 included studies, SE consistently demonstrated a positive association with QoL, though the specific strength of this association varied significantly. This variation was closely related to the type of measurement tool used and its degree of alignment with the construct. Studies using specific SE scales generally reported stronger correlations. For example: 4 studies[ 26 , 27 , 38 , 39 ] using the “Exercise SE Scale” reported correlation coefficients ranging from 0.372 to 0.713, while most of the 10 studies[ 25 , 28 , 31 , 32 , 37 , 40 , 42 , 44 , 46 ] applying Cardiac-Specific SE scales showed moderate to strong associations. In contrast, the 18 studies[ 22 – 24 , 29 – 31 , 33 – 36 , 41 , 43 , 45 , 47 ] using the “General SE Scale” showed greater variability, with correlation coefficients ranging widely from 0.177 to 0.734. One study[ 31 ] using a Doctor-Patient Interaction Effectiveness Scale reported a moderate correlation of 0.419. Regarding QoL measurement tools, among the 8 studies[ 24 – 26 , 29 , 36 , 39 , 42 , 44 ] using the “Cardiac-specific Scale,” all but one[ 42 ] reported a correlation coefficient of 0.16, the remaining studies all achieved coefficients ≥ 0.352. Similarly, among the 25 studies[ 22 , 23 , 27 , 28 , 30 – 35 , 37 , 38 , 40 , 41 , 43 , 45 – 47 ] using the “General QoL Scale,” correlation coefficients also spanned a wide range (0.177–0.734). Notably, conceptual alignment between SE and QoL instruments yielded more stable and significant correlations. Combinations of cardiac-specific scales or domain-specific SE with generic QoL tools consistently showed stronger associations. In contrast, pairings of generic instruments displayed greater variability, likely influenced by confounding factors such as disease severity and sample characteristics—as illustrated by Brink et al.'s report of lower correlations (r = 0.177 and 0.246), potentially reflecting methodological limitations. Correlation Analysis Between SE and QoL The meta-analysis synthesizing data from 26 studies demonstrated a statistically significant, moderate positive correlation between self-efficacy and quality of life in patients with cardiovascular disease ( r = 0.43, 95% CI : 0.38–0.48, p < 0.001). Considerable heterogeneity was observed across studies (I² = 89%), justifying the use of a random-effects model and subsequent exploration of potential moderators through subgroup analyses (Fig. 2 ). Subgroup Analysis To further explore the influence of latent variables on the relationship between SE and QoL, subgroup analyses were conducted based on the key characteristics of these studies across the following dimensions, as shown in Table 3 . SE and QoL showed a moderate positive association, with an overall pooled correlation coefficient of 0.43 (95% CI [0.38, 0.48]). However, high heterogeneity existed between studies (I² = 89.0%, Q B = 292.22), indicating potential moderating variables. Regarding measurement tools, the type of SE measurement tool showed a significant moderating effect (Q B = 1.74, p < 0.001). Specifically, studies using“exercise-task-specific SE tools” yielded the highest pooled effect size (r = 0.52, 95% CI [0.36, 0.68], k = 4), followed by “cardiac symptom-specific SE tools” (r = 0.43, 95% CI [0.35, 0.50], k = 8), and other types of tools (r = 0.42, 95% CI [0.32, 0.52], k = 1). The lowest pooled effect size was observed for “general SE tools” (r = 0.40, 95% CI [0.33, 0.48], k = 13). In contrast, the moderating effects of QoL measurement tool type (cardiovascular-specific: r = 0.48, k = 8; general: r = 0.41, k = 18) did not reach statistical significance (Q B = 1.38, p = 0.23). Regarding study characteristics, study design type showed a significant moderating effect (Q B = 9.11, p = 0.003), with Cardiovascular diseaseoss-sectional studies exhibiting significantly higher pooled correlation coefficients (r = 0.45, 95% CI [0.40, 0.50], k = 23) was significantly higher than that of longitudinal studies (r = 0.28, 95% CI [0.19, 0.38], k = 3). Research region showed a marginally significant moderating effect (Q B = 3.06, p = 0.08), with effect sizes higher in Asian countries (r = 0.46, 95% CI [0.39, 0.52], k = 18) than in European countries (r = 0.37, 95% CI [0.31, 0.44], k = 8). Additionally, analyses revealed that disease subtype (heart failure: r = 0.49, k = 6; coronary heart disease: r = 0.42, k = 13; other: r = 0.39, k = 7) failed to reach statistical significance ( p > 0.05). These findings systematically elucidate the sources of variation in the strength of the SE-QoL association, underscoring the need for future research and clinical practice to pay particular attention to the specificity of measurement tools, the selection of study designs, and the potential influence of cultural and regional factors on this relationship. Table 3 Subgroup Analysis Results Extraction Table Characteristic r(95% CI ) Total Sample Size( n ) Studies( k ) Heterogeneity Test for moderating effects Q I 2 (100%) Q B p Overall effect 0.43(0.38,0.48) 7058 26 292.22 89.0% Subgroup of Sample size 0.00 0.949 Sample size < 300 0.43((0.36,0.49) 3392 18 88.0% Sample size>300 0.42(0.33, 0.52) 3666 8 89.0% Subgroup of Male Gender Composition 0.19 0.659 <50% 0.42(0.36, 0.48) 753 3 0.0% ≥ 50% 0.44(0.40, 0.48) 6305 23 90.9% Subgroup of QoL 1.38 0.23 General QoL tools 0.41(0.35, 0.47) 5287 18 88.8% Cardiovascular specific 0.48(0.38, 0.58) 1771 8 87.8% Subgroup of SE 17.4 <0.001 General SE tools 0.40(0.33, 0.48) 3390 14 90.2% Cardiac symptom 0.43(0.35, 0.50) 2161 9 82.6% Exercise task 0.52(0.36, 0.68) 1232 4 93.3% others 0.42(0.32, 0.52) 275 1 0.0% Subgroup of Disease Subgroup 2.05 0.358 CHD 0.42(0.35, 0.49) 4166 13 90.9% HF 0.49(0.37, 0.61) 1002 6 88.7% others 0.39(0.33, 0.45) 1890 7 51.8% Subgroup of Study design 9.11 0.003 cross-sectional 0.45(0.40, 0.50) 5748 23 87.4%, Longitudinal 0.28(0.19, 0.38) 1310 3 68.7% Subgroup of Region where the study was conducted 3.06 0.08 Asian countries 0.46(0.39, 0.52) 4962 18 89.6% European countries 0.37(0.31, 0.44) 2096 8 78.3%, Notes :n:Total Sample Size; k: the number of studies (with special note: k for the corresponding research tool refers to “the number of studies that adopted this specific health literacy measurement tool”; if one study used two or more health literacy measurement tools simultaneously, it would be separately counted under the k for each of these tools (i.e., one study may correspond to multiple tool-specific k values)). Sensitivity Analysis Results To assess the stability of the pooled effect size and identify potential sources of heterogeneity, our study employed a leave-one-out method for sensitivity analysis of the correlation between quality of life (QoL) and self-efficacy (SE) in cardiovascular disease patients. Exclude studies with high heterogeneity and recalculate the pooled effect size to obtain a more precise effect estimate. Sensitivity analysis results (Fig. 3 a) demonstrated robust validity of the core findings. Upon sequentially excluding any single included study, the pooled correlation coefficient (r) estimates fluctuated between 0.33 and 0.46, with most values clustering around 0.39. All recalculated 95% confidence intervals substantially overlapped with the overall effect interval, with no reversal in effect direction. This indicates that the primary findings were not unduly influenced by any single study, and the pooled effect size demonstrated reliable stability. The initial meta-analysis revealed substantial heterogeneity (I² > 85%) among included studies, potentially compromising result reliability. To identify sources of heterogeneity, we combined sensitivity analysis plots showing effect size variability and excluded 10 studies likely contributing most to heterogeneity. The analysis after exclusion (Fig. 3 b) showed significantly reduced heterogeneity to an acceptable moderate level (I² = 48.6%, p = 0.005), yielding a more precise pooled effect size: a stable moderate positive correlation (r = 0.42, 95% CI : 0.39–0.45) between quality of life and self-efficacy in cardiovascular disease patients. In summary, the sensitivity analysis confirmed the robustness of the core findings. The subgroup analysis after addressing heterogeneity effectively reduced bias from inter-study differences, yielding more homogeneous and precise effect estimates. This further supports the reliability and clinical relevance of the study conclusions. Publication Bias Multiple methods were employed to assess the risk of publication bias. Visually, the funnel plot (Fig. 4 ) exhibited slight asymmetry, suggesting potential small-sample effects or non-publication of negative results. However, Egger’s linear regression test revealed no significant asymmetry (intercept =−0.1656604, p = 0.932). Overall, there was insufficient evidence of substantial publication bias in the Fisher's z-transformed correlation between SE and QoL. Qualitative Systematic Review of Mediating Effects Among the included studies, 8 reported analyses of mediating effects involving 12 mediating variables. 6 studies employed the Bootstrap method for mediating effect testing; detailed information is presented in Table 4 . Due to two key limitations in the original studies, quantitative meta-analysis of mediation effects was not feasible: First, mediation models varied significantly, encompassing single mediation, chain mediation, and parallel dual mediation. Second, effect size reporting was inconsistent, with some studies failing to fully present critical information such as path coefficient standard errors or confidence intervals. Therefore, this study employs a qualitative analysis approach, examining three dimensions: mediation path testing methods, effect strength, and result consistency. All mediation models can be categorized into the following three major types. Table 4 Summary Table of Key Findings from the Study on the Mediating Effect of SE on QoL in Cardiovascular disease Patients Study-id Mediating Variable Measurement Instrument / cronbach’s α Quantitative Result Analysis Method Standardized Effect Size (β; SE ; P) Mediating Effect Size (Point Estimate) Signif icance Liu et al., 2019[ 23 ] SM CSMS/0.739 81.49 ± 13.83 SEM (X→M→Y) SE → Self-Management → QoL: a1(X→M): β=0.642;SE-;P- b1(M→Y): β=0.644;SE-;P- c(X→Y):β=-;SE-;P- 0.414 Yes Won et al., 2022[ 38 ] Physical activity GPAQ/0.73 Physical activity: 2098 ± 38.06 Serial multiple mediation analysis + B (X→M1→Y)ESE → Physical Activity→QoL a1(X1→M1):β=0.011༈0.008– 0.015); SE = 0.002; P < 0.001 b1(M1→Y): β=0.145༈0.125, 0.165༉; SE = 0.010; P < 0.001 c:(X→Y): β=0.002༈0.001, 0.003༉; SE = 0.004; P < 0.001 −0.076 Yes Piao et al., 2023[ 27 ] Exercise Fear Exercise Fear Scale/0.949 50.20 ± 14.71 B 1、(X→M→Y) SE → Exercise Fear→ QoL a1(X→M): β =−0.160(−0.242,−0.078); SE = 0.042; P < 0.01 (M→Y):β=7.036(4.623, 9.449༉; SE = 1.235; P < 0.01 c(X→Y): β =3.795(2.112, 5.478); SE = 0.856; P < 0.01 1.12 Yes Janjani et al.2025[ 37 ] problem oriented 、excitement oriented CSQ/0.79 4.77 ± 1.54 SEM + B 1、(X→M1→Y)SE→Problem-oriented Coping →QoL a1(X→M):β=0.226; SE- ; p < 0.001 b1(M→Y):β=0.192; SE-; p < 0.001 c(X→Y): β=0.352; SE- ; p < 0.001 2、 (X→M2→Y)SE→excitement oriented→QoL a2(X→M2):β=− 0.074; SE- ; p = 0.444 b2(M2→Y):β=− 0.047; SE-; p = 0.418 Problem-oriented Coping:0.043 excitement oriented:0.0038 Yes Zanatta et al., 2024[ 40 ] 1Anxiety、depression 2、Goal Setting、3Emotions Regulation Anxiety、depression: HADS/0.88 Goal Setting:Independently developed /0.81 Emotions Regulation:STARTS/0.76 Anxiety:2.4 ± 1.2 Goal Setting: 2.9 ± 1.1 SEM + B 1、(X→M1→M2→Y)Cardiac SE → Anxiety → Physical Activity → QoL a1X→M1: β=−0.25 (−0.34,−0.16); SE = 0.043; P < 0.001 d1:M1→M2: β=−0.20 (−0.29,−0.11); SE = 0.045; P < 0.001 b1:M2→Y: β=0.12 (0.03, 0.21); SE = 0.045; P = 0.034 2. (X→M3→M2→ Y)Cardiac SE → Goal Setting → Physical Activity → QoL a2(X→M3): β=0.21 (0.12,0.30); SE = 0.045; P < 0.001 d2(M3→M2): β=0.33 (0.24,0.42); SE = 0.045; P < 0.001 b2(M2→Y): β=0.12 (0.03,0.21); SE = 0.045; P = 0.034 1. X→M1→M2→Y:0.006 2. X→M3→M2→Y:0.008 Yes Study-id Mediating Variable Measurement Instrument / cronbach’s α Quantitative Result Analysis Method Standardized Effect Size (β; SE ; P) Mediating Effect Size (Point Estimate) Signif- icance Mai et al., 2025[ 26 ] 1. Cardiopulmonary function 2. Psychological resilience CD-RISC/0.91 355.26 ± 20.27 45.26 ± 3.62 SEM + B 1、 (X→M1→Y) ESE → Cardiopulmonary function→ QoL: a1(X→M1): a1 = 0.325; SE-; P- b1(M1→Y):b1=−0.337; SE-,P- c(X→Y):β=−0.482; SE-; P- 2、(X→M2→Y)ESE → Psychological resilience → QoL a2(X→M2): β=0.346; SE-; P- b2(M2→Y): β=−0.354; SE-; P- c(X→Y):β=−0.482; SE-; P- 3. Direct effect (X→Y)(ESE → QoL): β =−0.482(−0.569 ,−0.354); SE = 0.053 ;P < 0.001 4. Total effect β =−0.715(−0.837,−0.675); SE-; P < 0.001 (X→M1→Y):−0.110(−0.175 ~ 0.063) (X→M2→Y): −0.123(−0.194, -0.086) Yes Zhang et al., 2021[ 28 ] SM CSCM/0.913 54.64 ± 15.08 SEM + B - 0.150 Yes Brink et al., 2014[ 33 ] 1、Fatigue, 2、Disturbed sleep 1、Fatigue: SHCQ/0.86 2、Disturbed sleep: Karolinska Sleep Questionnaire/0.87 Fatigue:9.80 ± 4.56 .Disturbed sleep:11.51 ± 5.48 SEM PCS:(X→M1→Y)GSE→Fatigue→PCS: a1(X→M): β =−0.264; SE-; P < 0.001 b1(M→Y): β =−0.574; SE-; P < 0.001 d1(Disturbed sleep → Fatigue): β = 0.435; SE-; P < 0.001 MCS: :(X1→M2→Y1)GSE→Fatigue→MCS a2(X1→M2): β=−0.264; SE-; P < 0.001 (M2→Y1): β =−0.659; SE-; P < 0.001 d2(Disturbed sleep → Fatigue): β = 0.435; SE-; P < 0.001 PCS: M1:0.152 MCS: M2:0.174 Notes : For scales with unreported reliability in the study, we supplemented the commonly accepted reliability range of standardized scales (referenced from the mainstream literature); -:not reported;SE = Standard Error;β:Beta Coefficient; SM:Self-management; SEM:Structural Equation Modeling;B:Bootstrap method; CI: Confidence Interval; X:Independent Variable;M:Mediator;Y:Dependent Variable;a:represents the standardized path coefficient of “X→M” ; b:represents the standardized path coefficient of “M→Y”; c:represents the standardized path coefficient of “X→Y”; d:represents the standardized path coefficient of “M1→M2”; CSMS:Coronary Artery Disease Self-management Scale; GPAQ: Global Physical Activity Questionnaire; CSQ:Coping Strategies Questionnaire; HADS:Hospital Anxiety and Depression Scale;STARTS:STress and Anxiety Regulation Strategies Scale༛CD-RISC༚Connor-Davidson Resilience Scale༛SHCQ༚Somatic Health Complaints Questionnaire Self-Management-Related Path This represents the core mediating pathway with the strongest effect and most stable results. For example, research by Liu et al. [ 23 ]demonstrated that self-management significantly mediates the relationship between SE and QoL, with relatively high path coefficients (a₁=0.642, b₁=0.644) and an indirect effect size of 0.413. Although one study did not provide specific data, the overall evidence supports good consistency for this pathway, indicating that self-management is a key behavioral mechanism through which SE improves QoL. Psychological and Emotional Pathways This was the most frequently reported pathway category, characterized by complex mechanisms and variable effect directions. Regarding core mechanisms, self-efficacy primarily enhances QoL indirectly by “alleviating negative psychological states”: Brink et al.[ 33 ] identified fatigue as a central mediator (effect size = 0.174 on QoL physical dimension), whereas sleep disturbance had a weaker effect (0.032). Their parallel dual-mediation model further revealed synergistic effects among psychological factors. Concerning coping and emotion regulation, Janjani et al.[ 37 ] found problem-focused coping was a positive mediator (indirect effect = 0.043), enhancing self-efficacy’s effect on QoL, while emotion-focused coping had a negligible role (indirect effect = 0.003). Zanatta et al.[ 40 ] used a chain mediation model to clarify the psychological causal sequence: self-efficacy influences physical activity via improved anxiety/depression or enhanced goal setting/emotion regulation, ultimately affecting QoL, illustrating the multi-level function of psychological and emotional factors. Physiological Activity and Physical Function Pathway This pathway demonstrated the greatest heterogeneity in findings, indicating the potential influence of unmeasured moderators—such as disease severity, rehabilitation phase, or specific intervention components—or differences in model specification. Regarding physical activity as a mediator, conclusions diverged substantially across studies. For instance, Won et al.[ 38 ] reported a minimal indirect effect (0.0016), whereas Zanatta et al.[ 40 ], using a chain mediation model with physical activity positioned later in the pathway, obtained more interpretable coefficients (b₂ = 0.12). In terms of exercise-related fear and physical function, Piao et al.[ 27 ]identified a pronounced negative indirect effect for exercise fear (indirect effect =−1.12576). Meanwhile, Mai et al.[ 26 ] also observed negative indirect effects for psychological resilience and cardiopulmonary fitness (−0.110 and−0.122, respectively). It is important to note that these negative estimates are consistent and expected in the case of Mai et al., as the QoL scoring was reversed in their measurement approach, thus aligning with the hypothesized direction of effects. Overall, variability across studies underscores the context-dependent nature of this pathway and the need for careful interpretation of effect directions based on measurement characteristics. In summary, Self-efficacy affects QoL in cardiovascular disease patients through a complex mediating network. Current evidence identifies self-management-related pathways as the strongest and most stable, warranting priority as clinical intervention targets. Psychological and emotional pathways are well-defined, primarily via “alleviating negative states and optimizing coping,” but require personalization based on specific patient psychological profiles (e.g., fatigue, anxiety). Physiological activity and physical function pathways exhibit significant heterogeneity due to multiple moderators, necessitating further validation of their precise mechanisms and applicable populations. Future research should investigate interactions among mediators (e.g., self-management and psychological resilience synergy) and employ large-scale multicenter designs to reduce heterogeneity, thereby refining the mechanistic foundation for clinical interventions. Discussion Principal Findings and Interpretation within the Hierarchical Mediation Model Our study provides robust evidence through meta-analysis that self-efficacy (SE) is significantly and positively correlated with quality of life (QoL) in patients with cardiovascular diseases (CVDs), with a pooled effect size (r = 0.43) that is both statistically significant and clinically meaningful. More importantly, by moving beyond a simple direct association, our qualitative systematic review of mediation evidence enables us to construct a hierarchical mediation framework (Fig. 5). This model posits that SE influences QoL not through a single pathway, but through a structured network where self-management behaviors form the central, most potent pathway; psycho-emotional factors act as critical upstream regulators and parallel pathways; and physical activity/function serve as downstream, context-dependent executors. This framework effectively reconciles previously disparate findings and provides a strategic blueprint for clinical intervention. A Confirmatory and Explanatory Synthesis of Existing Evidence Our findings resonate with, and substantially extend, the existing body of literature on psychosocial determinants in cardiovascular care. Numerous individual studies have reported a positive association between SE and QoL in various cardiac populations[32, 36, 40]. However, the reported strength of this association has been inconsistent, leading to uncertainty about its clinical significance. Our meta-analysis resolves this inconsistency by providing a precise aggregate estimate, confirming that the relationship is not only statistically significant but also of a moderate magnitude that is widely considered clinically meaningful in behavioral sciences. More importantly, this study addresses a critical gap by systematically integrating evidence on the mechanisms underlying this relationship. Previous research has often examined potential mediators like self-management or depression in isolation. Our hierarchical model synthesizes these disparate mediators into a coherent framework, explaining why certain pathways may appear more salient in some patient subgroups than others. For instance, the variable role of physical activity as a mediator can be understood through its position in the model as a downstream factor, whose impact is likely moderated by contextual variables such as disease severity and psychological status, a finding that aligns with other studies on cardiovascular disease[48, 49] . Multidimensional Mechanism Pathways of Self-Efficacy Influencing Quality of Life in Cardiovascular Disease Patients The beneficial effect of self-efficacy on quality of life can be explained through multiple, interlinked mechanistic pathways. At the behavioral level, self-management emerged as the most consistent and powerful mediator. Patients with higher SE are more confident and proficient in performing essential self-care behaviors, including medication adherence, dietary modification, physical activity, and symptom monitoring[50, 51].. These behaviors directly improve cardiovascular risk profiles, reduce symptom burden and hospital readmissions, and prevent functional decline, thereby forming the bedrock for sustained physical and mental well-being[52, 53]. On a psychological level, SE acts as a critical buffer against distress and a facilitator of adaptive coping. It mitigates the perception of stress and helplessness, potentially dampening maladaptive physiological responses. This is supported by psychoneuroimmunological research suggesting that SE may buffer hypothalamic-pituitary-adrenal axis overactivation, leading to better control of cortisol release, heart rate variability, and systemic inflammation—factors known to accelerate atherosclerosis and impair well-being[54, 55]. Our qualitative review identified that the alleviation of negative states, particularly fatigue and the use of problem-focused coping strategies, are key psychological pathways through which SE exerts its positive influence on QoL. In contrast, the physiological pathway, encompassing physical activity and cardiorespiratory fitness, demonstrated the greatest heterogeneity. This suggests that while improving physical capacity is a valid goal, its role as a universal mediator is less straightforward and highly dependent on individual patient circumstances, such as the presence of kinesiophobia (fear of movement) or specific cardiac limitations[27, 38] . Clinical Implications for Person-Centered Cardiovascular Care The implications of our findings for clinical nursing practice and cardiovascular care are substantial. Firstly, there is a compelling case for the routine integration of SE assessment into standard nursing evaluations for CVD patients. To ensure accuracy and clinical utility, we recommend using disease-specific SE scales (e.g., the Cardiac Self-Efficacy Scale) rather than generic instruments, as our subgroup analysis confirmed their stronger correlation with QoL outcomes. Secondly, interventions designed to improve patient QoL should be strategically prioritized based on our hierarchical model. Self-management support should be considered the foundational component of any SE-enhancing intervention. Nurses are uniquely positioned to provide structured self-management support through skill-building workshops, collaborative goal-setting, and proactive follow-up. Complementing this, routine psychological screening for issues like fatigue, anxiety, and depression should be conducted. For patients with significant psychological distress, integrating targeted strategies such as cognitive-behavioral techniques or mindfulness training can amplify the benefits of SE by addressing these potent mediating factors. For the physiological pathway, an individualized approach is essential, tailoring physical activity recommendations to the patient's functional capacity and phase of recovery. Limitations of the study Several limitations of this study must be acknowledged when interpreting its conclusions. First and foremost, the predominance of cross-sectional studies within the included literature precludes definitive causal inferences. While it is theoretically sound that SE influences QoL, the possibility of bidirectional relationships or reverse causality (where improved QoL boosts SE) cannot be ruled out. Second, the quantitative meta-analysis of mediating effects was not feasible due to inconsistent reporting of effect sizes (e.g., lack of standard errors or confidence intervals for path coefficients) in the primary studies. This restricted our analysis to a qualitative synthesis, limiting our ability to statistically compare the strength of different mediating pathways. Third, our search strategy was limited to Chinese and English publications, which may have introduced a language bias and omitted relevant studies in other languages. Finally, despite extensive subgroup and sensitivity analyses, a high degree of heterogeneity persisted, indicating that other unmeasured moderators, such as cultural factors, social support systems, or specific treatment modalities, may influence the SE-QoL relationship. Future Directions to Advance Theory and Practice Future research should aim to address these limitations and build upon our findings. Priority should be given to conducting longitudinal cohort studies and well-designed randomized controlled trials (RCTs) that explicitly test the causal relationships and the validity of the proposed hierarchical mediation model. There is an urgent need for standardization in the reporting of mediation analyses in primary research to enable future quantitative synthesis (meta-analytic structural equation modeling). Furthermore, the development and validation of brief, culturally adapted, CVD-specific SE instruments would greatly enhance clinical utility and research consistency across diverse populations. Finally, investigators should explore the potential interactive or chain-mediated effects between different pathways (e.g., how improved self-management might enhance psychological resilience, creating a positive feedback loop), which would further refine our understanding of the mechanistic network and inform the development of more sophisticated, multi-targeted interventions. Conclusions This systematic review and meta-analysis confirms a moderate positive correlation between self-efficacy and QoL in CVD patients, mediated primarily through self-management behaviors. Nurses should prioritize self-management support as a core component of cardiovascular nursing, supplemented by tailored psychological and physical activity interventions. Integrating disease-specific self-efficacy assessment into routine nursing evaluations will enhance the effectiveness of person-centered care. Abbreviations Related to the crowd CVD: Cardiovascular Disease CHD: Coronary Heart Disease HF: Heart Failure CABG: Coronary Artery Bypass Graft CPB: Cardiopulmonary Bypass PCI: Percutaneous Coronary Intervention LVEF: Left Ventricular Ejection Fraction Research Design and Statistics SE: Self-Efficacy QoL: Quality of Life HRQoL: Health-Related Quality of Life RCT: Randomized Controlled Trial NOS: Newcastle-Ottawa Scale CI: Confidence Interval β: Beta Coefficient SEM: Structural Equation Modeling B: Bootstrap Method QB: Between-Group Heterogeneity Statistic I²: I-squared Statistic PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses PROSPERO: International Prospective Register of Systematic Reviews Self-Efficacy Scale GSES: General Self-Efficacy Scale CDSS: Chronic Disease Self-Efficacy Scale PSEQ: Pain Self-Efficacy Questionnaire CRF-SES: Cardiac Risk Factors Self-Efficacy Scale SESAMED: Self-Efficacy Scale for Adherence to the Mediterranean Diet ARTSE: Adherence to Rehabilitation Therapy Self-Efficacy SESMs: Symptom Management Self-Efficacy Scale SCSES: Sullivan's Cardiac Self-Efficacy Scale HFSES: Heart Failure Self-Efficacy Scale CESS / ESE-Q: Self-Efficacy for Exercise Scale / Exercise Self-Efficacy Questionnaire PEPPI-5: Perceived Efficacy in Patient-Physician Interaction Scale Quality of Life Scale SF-36: 36-Item Short Form Health Survey SF-12: 12-Item Short Form Health Survey EQ-5D: European Quality of Life 5-Dimensions Scale EQ-VAS: EQ Visual Analogue Scale SWLS: Satisfaction With Life Scale WHOQOL: World Health Organization Quality of Life FPQLI: Ferrans and Powers Quality of Life Index CQQC: Quality of Life Assessment Questionnaire for Chinese Cardiovascular Patients MLHFQ: Minnesota Living with Heart Failure Questionnaire KCCQ: Kansas City Cardiomyopathy Questionnaire SAQ: Seattle Angina Questionnaire Instrument for Measuring Mediating Variables CSMS: Coronary Artery Disease Self-management Scale GPAQ: Global Physical Activity Questionnaire CSQ: Coping Strategies Questionnaire HADS: Hospital Anxiety and Depression Scale STARTS: Stress and Anxiety Regulation Strategies Scale CD-RISC: Connor-Davidson Resilience Scale SHCQ: Somatic Health Complaints Questionnaire Declarations Acknowledgements : Not applicable Author contributions : C.L.: Conceptualization, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing, Visualization; Y.X.: Conceptualization, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing; Y.S.: Investigation, Data Curation; L.X.: Investigation, Data Curation; Z.F.: Formal Analysis, Validation; Z.H.: Formal Analysis, Validation;C.P.: Supervision, Project Administration, Resources; Q.Y.: Writing – Review & Editing, Supervision, Project Administration. Funding :The authors declare that no funding was received for this study. Data Availability : Data Availability Statement: All data generated or analysed during this study are included in this published article and its supplementary information files. Ethics approval and consent to participate : Not applicable. Disclosure statement : No conflicts of interest. Acknowledgements : We would like to thank all the authors of the included studies for their valuable research contributions. Consent for publication : Not applicable. Competing interests : The authors declare no competing interests. Author details: 1.School of Medicine, University of Electronic Science and Technology of China 2.Department of Thoracic Surgery, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China 3.Department of Geriatric Cardiology, Sichuan Academy of Medical Sciences/Sichuan Provincial People’s Hospital, Chengdu, 610072, P. R. 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