Abstract
Background
Older adults who have undergone percutaneous coronary intervention (PCI) face a heightened risk of major adverse cardiovascular and cerebrovascular events (MACCE). While physical activity is known to lower MACCE risk, improve long-term prognosis, and enhance quality of life, studies consistently report inadequate physical activity levels in this population. The relationships and potential mechanisms linking physical activity with its influencing factors remain incompletely understood, limiting the development of effective interventions. Therefore, this study aimed to identify key factors affecting physical activity and examine their underlying pathways in elderly post-PCI patients.
Methods
A cross-sectional study was conducted in Zhengzhou, China, from December 2024 to May 2025. Four hundred and eighty elderly individuals aged over 60 years who had undergone PCI completed the International Physical Activity Questionnaire-Long Form, Coronary Heart Disease Patients’ Rehabilitation Exercise Questionnaire, Cardiac Rehabilitation Exercise Self-efficacy Scale, 7-item Generalized Anxiety Disorder Scale, Patient Health Questionnaire-9, and a socio-demographic data sheet. Data analysis was performed using SPSS 25.0 for descriptive statistics, Spearman correlation, and binary logistic regression. Mediation analysis was conducted via structural equation modeling (SEM) in MPLUS 8.3.
Results
52.90% of participants did not meet the recommended physical activity guidelines. Physical activity self-efficacy significantly mediated the relationship between physical activity knowledge and physical activity (β = 0.730, P < 0.001). Although anxiety and depression symptoms were not directly associated with physical activity (P > 0.05), they exerted significant indirect negative effects through reduced physical activity self-efficacy(anxiety: β=-0.287, 95% confidence interval [-0.553,-0.118]; depression: β=-0.394, 95% confidence interval [-0.683,-0.204]).
Conclusion
This study found that the prevalence of being physically inactive was high in elderly adults after PCI. The present study’s findings suggested that healthcare providers should try to enhance physical activity self-efficacy, knowledge, and positive emotions of elderly adults after PCI to improve their physical activity with a focus on physical activity self-efficacy.
Trial registration number
Registry: The Central China Cardiovascular Hospital in Fuwai, TRN: 2025 No. 29, Registration date: 12 March 2025. Registry: The First Affiliated Hospital of Henan University of Traditional Chinese Medicine’s Ethics Committee, TRN:2025HL-030-01, Registration date: 13 February 2025.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-025-06785-w.
Keywords: Older adults, Physical activity self-efficacy, Percutaneous coronary intervention, Physical activity, Cardiac rehabilitation
Background
Cardiovascular diseases, especially coronary heart disease (CHD), remain the leading cause of death globally [1] and represent a major public health challenge [2]. The incidence, mortality, and comorbidity burden of CHD rise with age, disproportionately affecting individuals over 65 [3, 4]. Percutaneous coronary intervention (PCI) is commonly used for coronary revascularization [5]. However, this technique primarily addresses vascular patency issues but does not halt the progression of underlying atherosclerosis. Consequently, major adverse cardiac and cerebrovascular events (MACCE) occur in up to 29.09% of patients after PCI [6]. These events include recurrent angina and myocardial infarction. Older patients face a higher risk due to an age-related decline in physiological reserves [7].
To reduce MACCE risk and improve prognosis, physical activity(PA) is established as a central component of cardiac rehabilitation in the management of coronary heart disease [8]. Cardiac rehabilitation centered on physical activity offers distinct benefits in the management of CHD, including reducing MACCE—such as the risk of myocardial infarction, all-cause mortality, and hospital readmissions—alleviating healthcare burdens, and generating substantial economic value in CHD care [9]. Therefore, according to the European Society of Cardiology [10]and the Chinese Guideline for Cardiac Rehabilitation and Secondary Prevention [11], adults are recommended to achieve 150–300 min of moderate-intensity aerobic exercise weekly, or 75–150 min of vigorous-intensity aerobic exercise weekly. However, physical activity remains insufficient in most older patients with CHD following PCI. Their activity levels are consistently below the recommended guidelines [12].
The existing studies indicate that physical activity knowledge, self-efficacy, anxiety, and depression are key factors associated with physical activity in patients with CHD [13, 14]. Physical activity knowledge facilitates engagement in physical activity [15, 16] and is positively correlated with self-efficacy and physical activity participation [17]. However, knowledge alone is often insufficient to drive behavioral change, which suggests the involvement of intermediary mechanisms [17, 18]. Among these, self-efficacy for physical activity is considered a central element [19]. Furthermore, psychological factors such as anxiety and depression—affecting nearly 50% of CHD patients [20]are linked to reduced PA motivation [21–23]. Thus, there is a need for an integrated model that examines how PA knowledge, self-efficacy, and emotional symptoms jointly influence PA behavior.
A better understanding of these mediating pathways can help tailor interventions [24]. Without formal mediation analysis, the interrelationships among these predictors remain inadequately elucidated. Therefore, this study aimed to examine the potential mediating role of physical activity self-efficacy in the relationships between physical activity knowledge, anxiety, depressive symptoms, and physical activity among elderly individuals in the early post-PCI period.
Methods
Study design
This cross-sectional study was conducted and reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE). This study examines physical activity as the outcome variable, while physical activity knowledge, anxiety, and depressive symptoms serve as independent variables, and physical activity self-efficacy acts as a mediating variable. The conceptual framework of this study is grounded in Bandura’s self-efficacy theory [19]. According to this theory, an individual’s belief in their ability to perform a specific behavior (known as self-efficacy) is a key determinant of behavior change. Knowledge serves as one source for building self-efficacy [25]. Meanwhile, emotional states (such as anxiety and depression) are both influenced by and can, in turn, influence self-efficacy [26, 27]. This perspective has been supported by multiple empirical studies. Therefore, to test the direct and indirect effects of these factors on insufficient physical activity, we propose the following hypotheses (Fig. 1) based on self-efficacy theory and a review of the literature.
Fig. 1.
Hypothesis model for relationships among study variables
Hypothesis
Hypothesis 1 (H1)
Knowledge, anxiety symptoms, depressive symptoms, and physical activity self-efficacy are significantly correlated.
Hypothesis 2 (H2)
Knowledge, anxiety symptoms, depressive symptoms, and physical activity self-efficacy are all significantly correlated with physical activity.
Hypothesis 3 (H3)
The association between physical activity and knowledge, anxiety, and depression was mediated by physical activity self-efficacy.
Setting and participants
Eligible senior patients were recruited from December 2024 to May 2025 at two regional teaching hospitals in Zhengzhou, Henan Province, China. The following are the requirements for inclusion: (1) Age 60 years or older; (2) PCI treatment for coronary atherosclerotic heart disease for more than one week [22]; (3) Heart function levels I to III [28]; (4) Low-risk or moderate-risk exercise risk classification in coronary heart disease patients [29]; (5) Stable vital signs (heart rate 60–100 beats/minute, systolic blood pressure 90–140 mmHg, and diastolic blood pressure 60–90 mmHg, oxygen saturation >95%, and normal body temperature); (6) Voluntary participation in this study and signing of the informed consent form. Patients were excluded if they had any contraindications to physical activity, including severe arrhythmia, unstable angina, uncontrolled hypertension or hypotension, severe valvular heart disease, severe heart failure, or concurrent coronary artery bypass graft surgery or other cardiac surgical procedures. Additionally, patients were excluded if the attending physician determined they had impaired consciousness (e.g., coma or dementia) or communication barriers that would prevent them from completing the questionnaire.
Measurement
Outcome
The Chinese version of the International Physical Activity Questionnaire (IPAQ) has been certified by the World Health Organization (WHO) as a valid tool [30]. It can be used to determine whether surveyed populations meet the recommended levels of physical activity [30]. The long-form questionnaire has 27 items that cover four categories of physical activities: domestic, transportation, leisure, and occupational [30]. It also covers walking, moderate-intensity, and high-intensity activities, as well as sitting. Every kind of activity has a corresponding metabolic equivalent of task value (MET). Walking has a MET value of 3.3, moderate-intensity activities vary from 3.0 to 6.0, high-intensity activities range from 5.5 to 8.0, and low-intensity activities (such as sitting and sleeping) range from 1.0 to 1.5.
Weekly physical activity level (MET-h/w) was calculated as follows: activity MET coefficient × daily activity duration (h/d) × weekly frequency (d/w). The amount of physical activity increases with a higher score [31]. The total of the three weekly intensities of physical activity is the overall level of physical activity. The IPAQ’s Chinese version has a test-retest reliability of 0.75. This study adopted the recommended standards from physical activity guidelines, which specify at least 150 min of moderate-intensity activity per week, or 7.5 metabolic equivalent hours per week [11]. In this study, physical inactivity is defined as failing to meet the guideline-recommended level (< 7.5 MET-h/w).
Mediator
This study assessed physical activity self-efficacy using the Cardiac Rehabilitation Exercise Self-Efficacy Scale (CESES) [32]. The CESES consists of 16 items, each rated on a 5-point Likert scale, with 5 indicating“strongly agree”and 1 indicating “strongly disagree.”The total score ranges from 16 to 80. A higher total score indicates a stronger sense of self-efficacy in patients’ cardiac rehabilitation exercises. Following similar literature [33], we used the tertile method to categorize scores into three levels: low (16–33.33), medium (33.34–66.66), and high (66.67–80.67). The Chinese version of the CESES has a Cronbach’s α coefficient of 0.941, demonstrating high psychometric properties [34]. The Cronbach’s α coefficient of the CESES in this study is 0.951.
Independent variables
The Cardiac Rehabilitation Exercise Knowledge Questionnaire (CREKQ) was used to measure physical activity knowledge [35].The CREKQ consists of 11 items, with a higher total score indicating a more thorough understanding. In this study, participants were categorized based on their coronary heart disease rehabilitation knowledge score. Those with a total score below the mean of 9.12 were classified as having low knowledge, while those scoring above or equal to the mean were classified as having high knowledge. In this study, the Cronbach’s α coefficient of the CREKQ was 0.917. Another study reported that the CREKQ had a content validity of 0.950 and a Cronbach’s α coefficient of 0.733 [35].
The simplified Chinese version of the Generalized Anxiety Disorder-7 (GAD-7) for Mainland China was used [36]. The GAD-7 consists of 7 symptom items. Each item offers 4 response options, with scores ranging from 0 to 3 points. The total score for the seven items ranges from 0 to 21 points. Based on the total score of GAD-7, anxiety symptoms were divided into severe anxiety (15–21 points), moderate anxiety (10–14 points), mild anxiety (5–9 points), and no anxiety (0–4 points). The Chinese version of the GAD-7 has demonstrated good reliability and validity in both domestic and international clinical applications [37]. Its internal consistency coefficient is 0.867, and the test-retest reliability coefficient is 0.861 [36]. In this study, the Cronbach’s alpha coefficient is 0.851.
The 9 items on the Patient Health Questionnaire-9 (PHQ-9) were used to assess depressive symptoms [38]. Each item is scored on a 4-point Likert scale ranging from 0 to 3. The total score can range from 0 to 27. Higher scores indicate a higher level of depressive symptoms [39]. Based on previous research [39], depressive symptoms were classified as: no depression (0–4), mild (5–9), moderate (10–14), moderately severe (15–19), or severe (20–27). The Chinese version of the PHQ-9 depression screening has been shown to have good reliability and validity, with an internal consistency coefficient of 0.809 and a test-retest reliability coefficient of 0.813 [37], and the Cronbach’s α of the PHQ-9 is 0.906. All questionnaires used in this study were validated Chinese versions. In addition to the long-form IPAQ, we also performed confirmatory factor analyses for all questionnaires (see Supplementary Table 7 for details).
Sociodemographic information was collected, including age, registered residence address, type of medical insurance, educational attainment, monthly household income, and occupational category. Clinical data covered a range of indicators, including the degree of coronary artery stenosis, left ventricular ejection fraction, mean arterial pressure, glycated hemoglobin, low-density lipoprotein cholesterol, history of coronary heart disease, number of hospitalizations, frequency of PCI, quantity of diseased coronary arteries, and cardiac function classification. Data on physical activity included patients’ exercise habits before hospitalization.
Ethics approval
This study was approved by the Central China Cardiovascular Hospital in Fuwai and the First Affiliated Hospital of Henan University of Traditional Chinese Medicine’s Ethics Committee (ethical batch numbers: 2025HL-030-01 and 2025 No. 29), and it closely complies with the Helsinki Declaration’s ethical standards. All participants were assured that their participation was voluntary and their data would be kept confidential, and that they could withdraw from the study at any time. The research team will suggest that participants be sent to a doctor or psychiatrist for additional assessment if they receive a score of at least 15 on the GAD-7 or at least 20 on the PHQ-9 [36, 39].
Data collection
The data was collected by the first author, who worked as a research assistant (RA) at at the two study hospitals. The RA collected the data through a standardized review of electronic medical records. All patients who fulfilled the study criteria were invited to participate. The RA explained the purpose of the study to the participants and their families, and obtained informed consent forms by the principle of voluntariness. The RA stayed nearby to answer questions, if any, and received the completed questionnaires.
Data analysis
Data were analyzed by using MPLUS 8.3 software (Muthén & Muthén, 1998–2019) and SPSS 25.0 software (IBM Corp, Armonk, NY, USA). Descriptive statistics were used to analyze the study variables and demographic characteristics of elderly participants. Spearman correlation and binary logistic regression analyses were performed to investigate variable associations and identify factors influencing physical inactivity in elderly patients post-PCI. The structural equation model (SEM) is a method to examine pathways linking physical activity to knowledge level, anxiety symptoms, depression symptoms, and physical activity self-efficacy, testing both direct and indirect effects. Indirect effects were assessed using the bias-corrected Bootstrap method with 2000 resamples to calculate 95% confidence intervals (CIs). The indirect effects were deemed statistically significant if the bootstrapped 95% CIs excluded zero [40]. Model fit was evaluated through multiple indices: root mean square error of approximation (RMSEA; <0.05), standardized root mean square residual (SRMR; <0.08), comparative fit index (CFI; >0.90), Tucker-Lewis index (TLI; >0.90), and chi-square to degrees of freedom ratio (χ²/df < 3) [41].
Results
Participant characteristics
Of the 496 eligible elderly patients at one week post-PCI, 10 refused to participate in the study, 5 withdrew midway, and 1 questionnaire was invalid. Therefore, 480 elderly patients post-PCI were included in this study, with a response rate of 96.8%. Table 1 presents the clinical data and sociodemographic data. All participants were elderly individuals aged 60 years or older, with a mean age of 68.76 ± 6.53 years. The majority (69.6%) had rural household registration, and most (75.0%) were covered by urban-rural residents’ medical insurance. In terms of educational attainment, 72.7% of participants had an education level of junior high school or below, and 53.75% were primarily engaged in physical labor. The average monthly personal income was concentrated in the range of 3,000–5,000 RMB (84.2%), which was consistent with the local basic wage level. Regarding disease-related characteristics, 81% of participants had a history of coronary heart disease ranging from 1 to 10 years, and 61.7% (296 cases) underwent PCI for the first time. The proportions of participants with more than 3 diseased coronary arteries and those with coronary artery stenosis exceeding 80% were 60.8% and 64.4%, respectively. Most participants had a cardiac function classification of Grade II (61.9%). The mean values of left ventricular ejection fraction, mean arterial pressure, and low-density lipoprotein cholesterol were at relatively favorable levels, while the glycosylated hemoglobin level was elevated. Notably, over half (61.7%) of the elderly patients had a physical activity duration of ≤ 60 min before admission.
Table 1.
Demographic and clinical data characteristics (N = 480)
| Characteristics | [60, 70) (n = 276) |
[70, 80) (n = 182) |
[80, ་∞) (n = 22) |
Overal (n = 480) |
|---|---|---|---|---|
| Gender | ||||
| Male | 183(66.3) | 107(58.8) | 13(59.1) | 303(63.1) |
| Female | 93(33.7) | 75(41.2) | 9(40.9) | 177(36.9) |
| Registered residence | ||||
| Urban areas | 94(34.1) | 45(24.7) | 7(31.8) | 146(30.4) |
| Rural areas | 182(65.9) | 137(75.3) | 15(68.2) | 334(69.6) |
| Medical insurance type | ||||
| Urban-Rural Resident Basic Medical Insurance | 198(71.7) | 148(81.3) | 14(63.6) | 360(75.0) |
| Municipal Medical Insurance | 64(23.2) | 27(14.8) | 5(22.7) | 96(20.0) |
| Provincial Medical Insurance | 8(2.9) | 3(1.6) | 1(4.5) | 12(2.5) |
| Self-Paid | 6(2.2) | 4(2.2) | 2(9.1) | 12(2.5) |
| Educational level | ||||
| Junior high school or below | 177(64.1) | 155(85.2) | 17(77.3) | 349(72.7) |
| Senior high school or above | 99(35.9) | 27(14.8) | 5(22.7) | 131(27.3) |
| Monthly household income (per person per month) | ||||
| ≤ ¥3000 (~ US$418.80) | 4(1.4) | 7(3.8) | 4(18.2) | 15(3.1) |
| ¥ 3000–5000 (~ US$418.80-US$698) | 232(84.1) | 158(86.8) | 14(63.6) | 404(84.2) |
| ≥ ¥5000 (~ US$698) | 40(14.5) | 17(9.3) | 4(18.2) | 61(12.7) |
| Occupational category | ||||
| Physical Labor-Intensive Occupations | 143(51.8) | 102(56.0) | 13(59.1) | 258(53.75) |
| Mixed Mental-Physical Occupations | 133(48.2) | 80(44.0) | 9(40.9) | 222(46.25) |
| History of coronary heart disease (years) | ||||
| (0, 10] | 232(84.1) | 140(76.9) | 17(77.3) | 389(81.0) |
| (10, 20] | 39(14.1) | 32(17.6) | 2(9.1) | 73(15.2) |
| (20, ་∞) | 5(1.8) | 10(5.5) | 3(13.6) | 18(3.8) |
| Number of hospitalizations | ||||
| ≤ 2 | 193(69.9) | 136(74.7) | 17(77.3) | 346(72.1) |
| >2 | 83(30.1) | 46(25.3) | 5(22.7) | 134(27.9) |
| Number of Percutaneous Coronary Interventions(PCI) | ||||
| ≤ 1 | 165(59.8) | 118(64.8) | 13(59.1) | 296(61.7) |
| ≥ 2 | 111(40.2) | 64(35.2) | 9(40.9) | 184(38.3) |
| Number of diseased coronary arteries | ||||
| ≤ 2 | 104(37.7) | 75(41.2) | 9(40.9) | 188(39.2) |
| ≥ 3 | 172(62.3) | 107(58.8) | 13(59.1) | 292(60.8) |
| Cardiac functional classification(NYHA) | ||||
| Class Ⅰ | 44(15.9) | 19(10.4) | 4(18.2) | 67(14.0) |
| Class Ⅱ | 170(61.6) | 114(62.6) | 13(59.1) | 297(61.9) |
| Class Ⅲ | 62(22.5) | 49(26.9) | 5(22.7) | 116(24.2) |
| Degree of coronary artery stenosis | ||||
| 50%−80% | 105(38.0) | 57(31.3) | 9(40.9) | 171(35.6) |
| ≥ 80% | 171(62.0) | 125(68.7) | 13(59.1) | 309(64.4) |
| Left ventricular ejection fraction(%) | 60.21 ± 7.54 | |||
| Mean arterial pressure | 97.81 ± 11.89 | |||
| Glycosylated hemoglobin(%) | 6.82 ± 2.26 | |||
| Low-density lipoprotein cholesterol(mmol/L) | 2.09 ± 0.89 | |||
| Pre-hospitalization physical activity(PHY)(min) | ||||
| <30 | 80(29.0) | 65(35.7) | 10(45.5) | 155(32.3) |
| 30–60 | 82(29.7) | 53(29.1) | 6(27.3) | 141(29.4) |
| ≥ 60 | 114(41.3) | 64(35.2) | 6(27.3) | 184(38.3) |
Main variable description
Table 2 presents the mean, standard deviation, and incidence of the research variables. Of the 480 elderly patients at one week post-PCI, 254(52.90%) reported physical inactivity; 131(27.29%), 330 (68.75%), and 19 (3.96%) participants reported low, moderate, and high levels of physical activity self-efficacy, respectively; more than half of the older adults (n = 302, 62.9%) had low knowledge awareness; and the incidences of anxiety symptoms and depressive symptoms post-PCI were 39.6% and 52.5%, respectively.
Table 2.
Physical activity, physical activity Self-efficacy, physical activity Knowledge, anxiety and depression symptoms (N = 480)
| Variable | Mean (SD) | n (%) | ||
|---|---|---|---|---|
| Physical activity (MET-h/wk) | 8.94(7.38) | |||
| Meet the minimum standards recommended by the guidelines (≥ 7.5 MET-h/w) | 226(47.10) | |||
| Does not meet the minimum standards recommended by the guidelines (< 7.5 MET-h/w) | 254(52.90) | |||
| Knowledge of Physical Activity | 9.12(5.43) | |||
| Low (< Mean) | 302(62.90) | |||
| High (>Mean) | 178(37.10) | |||
| Physical activity self-efficacy | 44.72(13.10) | |||
| Low level (16–33.33 scores) | 131(27.29) | |||
| Medium level (33.34–66.66 scores) | 330(68.75) | |||
| High level (66.67–80.67 scores) | 19 (3.96) | |||
| Anxiety symptoms | 4.76(3.73) | |||
| No (0–4 scores) | 290(60.40) | |||
| Mild (5–9 scores) | 107(22.30) | |||
| Moderate (10–14 scores) | 76(15.80) | |||
| Severe (15–21 scores) | 7(1.50) | |||
| Depressive symptoms | 5.40(4.32) | |||
| No (0–4 scores) | 228(47.50) | |||
| Mild (5–9 scores) | 163(33.96) | |||
| Moderate (10–14 scores) | 82(17.08) | |||
| Moderate to severe (15–19 scores) | 6(1.25) | |||
| Severe (20–27 scores) | 1(0.21) | |||
Hypothesis 1 (H1)
Knowledge, anxiety symptoms, depressive symptoms, and physical activity self-efficacy are significantly correlated.
Table 3 presents the results of the correlation analysis among variables. Physical activity self-efficacy shows a significant positive correlation with knowledge level (r = 0.430, P < 0.01), and significant negative correlations with anxiety (r = −0.282, P < 0.01) and depression (r = −0.400, P < 0.01). Additionally, physical activity knowledge level exhibits significant negative correlations with anxiety (r = −0.256, P < 0.01) and depression (r = −0.364, P < 0.01); meanwhile, a significant positive correlation is observed between anxiety and depression symptoms (r = 0.266, P < 0.01).
Table 3.
Correlation between variables (N = 480)
| Variable | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| 1. Knowledge of Physical Activity | 1 | ||||
| 2. Physical activity self-efficacy | 0.430c | 1 | |||
| 3. Anxiety symptoms | −0.256c | −0.282c | 1 | ||
| 4. Depressive symptoms | −0.364c | −0.400c | 0.266c | 1 | |
| 5. Physical Activity | 0.280c | 0.301c | −0.139c | −0.198c | 1 |
cP<0.01
Hypothesis 2 (H2)
Knowledge, anxiety symptoms, depressive symptoms, and physical activity self-efficacy are all significantly correlated with physical activity.
Table 3 results also indicate that physical activity was significantly positively correlated with knowledge (r = 0.280, P < 0.01) and self-efficacy (r = 0.301, P < 0.01), while significantly negatively correlated with anxiety (r=−0.139, P < 0.01) and depression (r=−0.198, P < 0.01), with the correlation with depressive symptoms being relatively strong. These findings are consistent with the univariate analysis results of binary logistic regression presented in Tables 4 and 5. In the multivariate analysis shown in Tables 4 and 5, the forward method was employed to screen variables influencing physical activity. Two variables entered the final equation in the optimal model: knowledge and self-efficacy. The results demonstrate that physical activity level was independently and positively associated with the degree of knowledge mastery and self-efficacy, with self-efficacy exerting the most significant impact.
Table 4.
Multiple regression analysis on physical activity (N = 480). Univariate analysis
| Variables | β | SE | Wald | t | P | Exp(β) | The 95% confidence interval of Exp(β) | |
|---|---|---|---|---|---|---|---|---|
| Lower | Upper | |||||||
| Knowledge | 0.066c | 0.017 | 14.623 | 3.882 | 0.000 | 1.068 | 1.033 | 1.105 |
| Self-efficacy | 0.030c | 0.007 | 16.778 | 4.286 | 0.000 | 1.030 | 1.016 | 1.045 |
| Anxiety symptoms | −0.061b | 0.150 | 5.912 | −0.407 | 0.015 | 0.941 | 0.895 | 0.988 |
| Depressive symptoms | −0.060c | 0.022 | 7.629 | −2.727 | 0.006 | 0.942 | 0.902 | 0.983 |
VIF:1.125 ~ 1.397
Table 5.
Multiple regression analysis on physical activity (N = 480). Multivariate analysis
| Variables | β | SE | Wald | t | P | Exp(β) | The 95% confidence interval of Exp(β) | |
|---|---|---|---|---|---|---|---|---|
| Lower | Upper | |||||||
| Knowledge | 0.042b | 0.020 | 4.682 | 2.100 | 0.030 | 1.043 | 1.004 | 1.084 |
| Self-efficacy | 0.021c | 0.008 | 6.932 | 2.625 | 0.008 | 1.022 | 1.005 | 1.038 |
| Constant | −1.462 | 0.339 | 18.621 | −4.180 | 0.000 | 0.232 | ||
Model evaluation index: Model 2 is the best. Chi-square=22.023; P＜0.001
−2 Loglikelihood=641.764; Cox & snell R Square=0.045； Nagelkerke R Square=0.060;Hosmer-Lemeshow：P=0.210（P＞0.05）
β Regression coefficient, SE Standard error, Wald Regression coefficient hypothesis test Wald Chi-square value, Exp(β) OR value, CI Confidence interval
cP＜0.01
bP＜0.05
Hypothesis 3 (H3)
The association between physical activity and knowledge is mediated by anxiety, depression, and physical activity self-efficacy.
Hypothesis 3 was partially supported. The data fit of the SEM model (RMSEA = 0.024, SRMR = 0.070, χ²/df = 1.280, CFI = 0.968, TLI = 0.966) was acceptable. As shown in Fig. 2, physical activity self-efficacy exhibited a positive correlation with physical activity knowledge (β = 0.366; 95% CI: 0.294 to 0.445), while demonstrating negative correlations with anxiety symptoms (β = −0.130; 95% CI: −0.214 to −0.049) and depressive symptoms (β = −0.194; 95% CI: −0.274 to −0.110). Furthermore, physical activity knowledge was negatively associated with anxiety (β = −0.302; 95% CI: −0.381 to −0.215) and depressive symptoms (β = −0.295; 95% CI: −0.366 to −0.211). A positive correlation was observed between anxiety and depressive symptoms (β = 0.179; 95% CI: 0.083 to 0.267).
Fig. 2.
The mediation model of physical activity self-efficacy with standardized coefficients lines indicated significant lines, and dashed lines indicate nonsignificant paths. cP<0.001; bP<0.01
As shown in Table 6, physical activity self-efficacy emerged as a significant mediating variable in the model. No direct associations were observed between anxiety symptoms, depressive symptoms, and physical activity. Among the elderly population who underwent PCI, physical activity self-efficacy mediated the relationships between physical activity and knowledge level (β = 0.730; 95% CI: 0.463 to 1.068), anxiety symptoms (β=−0.287; 95% CI: −0.553 to −0.118), and depressive symptoms (β=−0.394; 95% CI: −0.683 to −0.204). Additionally, the combined effects of physical activity self-efficacy with anxiety symptoms (β = 0.078; 95% CI: 0.031 to 0.161) and with depressive symptoms (β = 0.114; 95% CI: 0.055 to 0.210) jointly mediated the association between physical activity knowledge and physical activity.
Table 6.
Bootstrap test for indirect effects (N = 480)
| Path | β | SE | 95%CI | P |
|---|---|---|---|---|
| Knowledge → Physical Activity | ||||
| Total effect | 3.756c | 0.553 | [2.857,4.693] | <0.001 |
| Total indirect effects | 1.195c | 0.251 | [0.809,1.630] | <0.001 |
| Knowledge → Anxiety symptoms → Physical activity | 0.151 | 0.185 | [−0.086,0.371] | 0.273 |
| Knowledge → Self-efficacy → Physical activity | 0.730c | 0.213 | [0.463,1.068] | <0.001 |
| Knowledge → Anxiety symptoms → Self-efficacy → Physical activity | 0.078a | 0.038 | [0.031,0.161] | <0.05 |
| Knowledge → Depressive symptoms → Physical activity | 0.121 | 0.123 | [−0.062,0.337] | 0.323 |
| Knowledge → Depressive symptoms → Self-efficacy → Physical activity | 0.114a | 0.048 | [0.055,0.210] | <0.05 |
| Direct effect | 0.257c | 0.040 | [0.185,0.318] | <0.001 |
| Anxiety symptoms→ Physical activity | ||||
| Total effect | −0.922 | 0.494 | [−1.700,0.093] | 0.062 |
| Total indirect effects | −0.368b | 0.154 | [−0.675,−0.156] | <0.05 |
| Anxiety symptoms→ Self-efficacy → Physical activity | −0.287b | 0.125 | [−0.553,−0.118] | <0.05 |
| Anxiety symptoms→ Depressive symptoms→ Physical activity | −0.081 | 0.078 | [−0.262,0.029] | 0.358 |
| Direct effect | −0.050 | 0.045 | [−0.120,0.029] | 0.267 |
| Depressive symptoms→ Physical activity | ||||
| Total effect | −0.812 | 0.416 | [−1.477,0.136] | 0.051 |
| Total indirect effects | −0.394b | 0.145 | [−1.089,0.409] | <0.01 |
| Depressive symptoms→ Self-efficacy → Physical activity | −0.394b | 0.145 | [−0.683,−0.204] | <0.01 |
| Direct effect | −0.041 | 0.041 | [−0.106,0.026] | 0.311 |
Data are reported as standardized coefficients
Β Beta (Standardized path coefficient), SE Standard error, CI Confidence interval
cP＜0.001
bP＜0.01
aP＜0.05
Discussion
The primary aim of this study was to examine the role of physical activity self-efficacy in mediating the relationship between physical activity knowledge, anxiety symptoms, depressive symptoms, and physical activity. To our knowledge, this is the first study in the Chinese Mainland to explore the impact of these complex factors on the physical activity of elderly patients with heart disease after PCI. Our analyses revealed two main findings. First, nearly half (52.9%) of elderly patients with CHD exhibited insufficient physical activity one week after PCI (Table 3), failing to meet the minimum recommendations outlined in cardiac rehabilitation guidelines [11, 42, 43]. Second, a lack of physical activity knowledge and the emergence of negative emotions hinder physical activity primarily by undermining confidence in physical activity [44, 45].
Principal findings
Correlation among Variables (H1). The present study found that physical activity knowledge, anxiety symptoms, depressive symptoms, and self-efficacy are all significantly correlated (Table 3). Specifically, a higher level of physical activity knowledge is associated with stronger self-efficacy, which aligns with the perspective of the “Knowledge-Attitude-Behavior (KAB) Model” [46] and Bandura’s Self-Efficacy Theory [19]. An adequate knowledge base is the cornerstone for building confidence in physical activity. A cross-sectional study [47]and a systematic review [48] have indicated that patients post-PCI have a poor level of knowledge regarding physical exercise and low confidence in participating in physical activity. In this study, more than half (62.9%) of elderly patients post-PCI had a low level of knowledge regarding cardiac rehabilitation. This may be related to the fact that the cardiac rehabilitation system in local hospitals is still in the exploratory stage and lacks standardized development. A survey of 25 hospitals in Guangdong Province [49] showed that only 8% of them provide Phase I cardiac rehabilitation knowledge education. This finding indicates that hospitals pay insufficient attention to cardiac rehabilitation knowledge education, which directly leads to patients’ difficulty in accessing such knowledge and consequently results in a low level of physical activity participation.
This study also found that self-efficacy is negatively correlated with anxiety and depressive symptoms. Self-efficacy can influence an individual’s emotional state and thinking patterns [50]. Specifically, individuals with high self-efficacy are more likely to show a positive and confident attitude when facing difficulties. This can effectively prevent and reduce the occurrence of negative emotion [51]. In this study, self-efficacy acts as a bridge between negative emotions such as anxiety and depressive symptoms and physical activity. This suggests that improving self-efficacy may help enhance patients’ mental health and physical activity levels. Furthermore, anxiety symptoms are significantly positively correlated with depressive symptoms [52]. This indicates that psychological interventions should adopt a comprehensive rather than isolated approach.
The findings indicate shortcomings in patient guidance on physical exercise, disease knowledge, and emotional management within healthcare settings [53]. These elements are not yet systematic or routine in clinical practice. Therefore, lack of knowledge, negative emotions like anxiety and depression, and the resulting low self-efficacy act as major barriers to physical activity [54–56]. Phase I cardiac rehabilitation is the first key window for addressing factors limiting physical activity [57, 58]. It lays the foundation for patients to develop healthy habits after discharge [59]. In this context, nurses, as primary providers of health education and patient communication, are crucial for improving physical activity adherence in this population [60]. Based on this cross-sectional study, future research should explore forming specialized teams focused on cardiac rehabilitation education. In clinical practice, integrating exercise knowledge, personalized education, and psychological support could effectively promote healthier patient behaviors [61–63].
These findings reveal that hospitals pay insufficient attention to physical exercise, knowledge education, and negative emotions. Physical exercise, knowledge, and psychological support have not yet been integrated into the clinical practice of healthcare providers. Consequently, knowledge deficit, negative emotions, and low self-efficacy are barriers to physical activity. Nurses, as implementers of health education and communication, play a critical role in improving physical activity adherence in this population [64]. Future studies should establish professional healthcare teams specializing in cardiac rehabilitation education. It is necessary to integrate physical exercise knowledge, educational approaches, and psychological management strategies into clinical practice to promote the transformation of patients’ health behaviors.
To support Hypothesis 2 (H2), this study found significant correlations between physical activity and knowledge, anxiety symptoms, depressive symptoms, and physical activity self-efficacy. This is consistent with previous research results [45, 65]. Specifically, improved knowledge level and self-efficacy can promote physical activity. However, a randomized controlled trial [66]combined scales (subjective indicators) and accelerometers (objective indicators), found that the facilitating effect of improved knowledge on physical activity may be moderated by anxiety and depression. Enhanced knowledge in individuals with high levels of anxiety and depression may reduce physical activity [66].
Interestingly, in the multivariate logistic regression analysis, neither anxiety nor depressive symptoms were retained in the final regression equation (Tables 4 and 5). We hypothesize that the high intercorrelations among physical activity self-efficacy, knowledge, anxiety symptoms, and depressive symptoms may have led to a competitive explanation of the dependent variable (physical activity). Given that physical activity self-efficacy and knowledge demonstrated the most substantial contributions to the model, statistical software algorithms likely prioritized these variables while excluding anxiety and depressive symptoms through automatic variable selection procedures [67].
SEM (Fig. 2) has provided partial support for Hypothesis 3 (H3). The present study found that physical activity self-efficacy mediated between knowledge and physical activity. This finding was consistent with the core tenets of Bandura’s Self-Efficacy Theory (1977) [46]. According to this theory, Self-efficacy, or confidence in one’s capacity to achieve certain goals, motivates positive behavior change. Previous studies [68, 69] have focused on the mediating role of self-efficacy in the relationships between social support, emotions, and physical activity. However, empirical studies [17, 45] directly investigating the “knowledge-self-efficacy-physical activity” pathway remain limited. Specifically, physical activity knowledge may enhance physical activity levels when physical activity self-efficacy is high [70]. In this study, elderly rural coronary heart disease patients exhibited low self-efficacy, which stemmed from both cognitive and emotional factors. Limited education hindered their acquisition of knowledge [71]. It also impaired the building of self-efficacy through “mastery experiences.” Furthermore, over half of the patients experienced fear of movement, laziness, and low self-esteem during hospitalization [72], often triggered by the risk of adverse cardiovascular events. These issues easily led to anxiety and depressive symptoms. Together, these two factors contributed to impaired self-efficacy. These two factors jointly reduce self-efficacy, which, to a certain extent, explains the prevalent physical activity barriers among the elderly population.
Notably, the present study found that anxiety and depressive symptoms are predictive factors that can indirectly affect physical activity through self-efficacy. This result was consistent with the previous studies [73]. Currently, most mediation models focus on the pathway where physical exercise enhances individuals’ emotional regulation self-efficacy, alleviates their anxiety and depression, and thereby promotes mental health [44, 74]. In contrast, models examining the impact of negative emotions on physical activity behavior through self-efficacy remain relatively scarce. However, some literature has confirmed that higher depressive symptoms are associated with lower self-efficacy [75], and lower self-efficacy increases barriers to engaging in physical activity [76]. In a longitudinal study on chronic obstructive pulmonary disease (COPD) [66], it was found that in patients with low levels of anxiety and depression, improved knowledge of physical activity could enhance confidence in physical activity, thereby indirectly promoting physical activity. In contrast, severe anxiety and depression could hinder the processing of knowledge information [77]. In such cases, increased knowledge might impede physical activity [78]. This, to a certain extent, illustrates such a relationship. In the present study, the total indirect effect of anxiety and depressive symptoms on physical activity was significant (P < 0.01).
Furthermore, this study unveiled a dual detrimental impact of knowledge cognition deficits in this population: Firstly, patients exhibited activity avoidance due to inadequate access to scientific exercise guidance (e.g., intensity, duration, frequency) [79, 80]; secondly, misconceptions exacerbated anxiety symptoms [81], creating a vicious cycle of “knowledge deficiency → cognitive distortion → anxiety → reduced self-efficacy”. Notably, the National Center for Cardiovascular Diseases points out that patient education in the secondary prevention of cardiovascular diseases is a core component of cardiac rehabilitation, and educational interventions have been proven effective in promoting changes in physical activity behavior [82].
Limitations
This study indicates that in the early post-PCI period among the elderly population, physical activity self-efficacy is significantly correlated with actual physical activity levels. For future physical activity interventions targeting this group, enhancing self-efficacy should be prioritized as a core strategy to improve physical activity participation.
There are several limitations in the present study. Firstly, the present study adopted a cross-sectional design, which could not determine the causal relationships between variables or observe the changing trends of physical activity. Future studies may employ longitudinal research to provide more evidence. Secondly, participants in the present study were recruited from two local teaching hospitals in China, with a majority originating from rural areas and having low educational attainment. This may limit the generalizability of our findings to different regions or populations. Future studies need to conduct multi-center research with diverse samples to validate our results. Thirdly, all measurement tools were self-reported, which may have led to recall errors and subjective bias. Future studies could employ objective assessment tools, such as the ActiGraph GT3X + triaxial accelerometer (ActiGraph LLC, Fort Walton Beach, Florida, USA), to quantify physical activity [83]. Fourth, this study was conducted in two distinct hospital settings: a traditional Chinese medicine hospital and a Western medicine hospital. The hospital environment, including physical aspects such as ward layout, space size, and activity areas [84], as well as cultural and resource factors like facilities [85] and staff attitudes [86], can all influence patients’ PA levels. This study did not quantify the specific impact of the hospital environment on PA. Future research should consider evaluating the role of such environmental factors in PA behavior. Finally, this study did not explore contextual factors such as social support, family support, and comorbidity burden, which may interact with self-efficacy and subsequently influence physical activity behavior. Subsequent studies should incorporate these factors into the analytical model to refine the theoretical framework.
Conclusion
This study examined the mediating mechanisms among complex factors influencing physical activity in elderly patients with CHD following PCI. The findings demonstrated that physical activity self-efficacy mediated the association between physical activity and knowledge. Additionally, anxiety and depressive symptoms served as partial mediators in the association between physical activity knowledge and physical activity. In clinical practice, health education guidance can be an effective measure to improve knowledge levels and promote physical activity. However, attention should also be paid to the level of negative emotions. As educators of physical activity knowledge, healthcare providers should not only focus on strategies to enhance self-efficacy but also assess patients’ mental health. This can further promote the transformation of health behaviors, reduce the risk of MACCE, and improve quality of life.
Supplementary Information
Acknowledgements
We would like to thank all the older people for participating in the study.
Authors’ contributions
**Haoran Gao** : Writing - review & editing, Writing - original draft, Data curation, Formal analysis, Project administration. **Jun Liu** : Writing - review & editing, Resources, Methodology, Investigation. **Jiuxin Zhou** : Writing - review & editing, Software, Methodology, Data curation, Conceptualization. **Yujie Zhou** : Writing - review & editing, Methodology, Conceptualization. **Ke Song and Su Lyu** : Writing - review & editing, Methodology, Data curation, Conceptualization. **Xiao Yang** : Writing - review & editing, Conceptualization, Supervision, Funding acquisition. **Yuanmei Qin** : Writing - review & editing, Conceptualization, Supervision.
Funding
This study was supported by the Henan Province Science and Technology Research Project (Grant number 252102311130), Henan Provincial Health Commission TCM Culture and Management Project (Grant number TCM2025025), and Henan Federation of Social Sciences(Grant number SKL-2025-946).
Data availability
Additional supporting information may be found online in the Supporting Information section at the end of the article.
Declarations
Ethics approval and consent to participate
Ethical approval has been obtained from the First Affiliated Hospital of Henan University of Chinese Medicine and Fuwai Central China Cardiovascular Hospital (No. 2025HL-030-01 and 2025 No. 29). Written informed consent was obtained from all individual participants. All clinical trial procedures were performed in accordance with relevant guidelines and regulations. (Addendum: A pilot survey was conducted prior to obtaining ethical approval for this study. The two required ethical approvals were obtained sequentially following review by the relevant medical ethics committees.)
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Xiao Yang, Email: yxiao1025@163.com.
Yuanmei Qin, Email: qinyuanmei69@163.com.
References
- 1.Jingtao L, He S, Xiaojin Q, et al. Guidelines on cardiac rehabilitation in patients with coronary heart disease: a systematic Review[J]. Chin Gen Pract. 2023;26(19):2323– 31. 10.12114/j.issn.1007-9572.2022.0700.
- 2.Safiri S, Karamzad N, Singh K, et al. Burden of ischemic heart disease and its attributable risk factors in 204 countries and territories, 1990–2019. Eur J Prev Cardiol. 2022;29(2):420– 31. 10.1093/eurjpc/zwab213. [DOI] [PubMed]
- 3.Carter HE, Schofield D, Shrestha R. Productivity costs of cardiovascular disease mortality across disease types and socioeconomic groups. Open Heart. 2019;6(1):e000939. 10.1136/openhrt-2018-000939. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Li FR, Wang S, Li X, et al. Multimorbidity and mortality among older patients with coronary heart disease in Shenzhen, China. J Geriatr Cardiol. 2024;21(1):81–9. 10.26599/1671-5411.2024.01.005. [DOI] [PMC free article] [PubMed]
- 5.Shoji S, Yamaji K, A.T. Sandhu, et al. Regional variations in the process of care for patients undergoing percutaneous coronary intervention in Japan[J].Lancet Reg Health West Pac. 2022;22100425. 10.1016/j.lanwpc.2022.100425. [DOI] [PMC free article] [PubMed]
- 6.Zhixiu S, Xunsong D, Jie C. Risk factors of major adverse cardiovascular and cerebrovascularevents after PCI in elderly patients with coronary heart diseaseafter PCI in elderly patients with coronary heart disease. J Clin Cardiol (China). 2022;38(02):132–6. 10.13201/j.issn.1001-1439.2022.02.010.
- 7.Dongmei Z. Influencing factors of severe bleeding after coronary intervention in elderly patients with coronary heart disease. Chin J Gerontol. 2020;40(09):1799–801. 10.3969/j.issn.1005-9202.2020.09.003.
- 8.Kim C, Choi I, Cho S, et al. Do cardiac rehabilitation affect clinical prognoses such as recurrence, readmission, revascularization, and mortality after AMI?: Systematic review and meta-analysis. Ann Rehabil Med. 2021;45(1):57–70. 10.5535/arm.20080. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Oldridge N, Taylor RS. Cost-effectiveness of exercise therapy in patients with coronary heart disease, chronic heart failure and associated risk factors: a systematic review of economic evaluations of randomized clinical trials. Eur J Prev Cardiol. 2020;27(10):1045–55. 10.1177/2047487319881839. [DOI] [PubMed]
- 10.Ambrosetti M, Abreu A, Corrà U, et al. Secondary prevention through comprehensive cardiovascular rehabilitation: from knowledge to implementation. 2020 update. A position paper from the Secondary Prevention and Rehabilitation Section of the European Association of Preventive Cardiology. Eur J Prev Cardiol. 2021;28(5):460–95. 10.1177/2047487320913379. [DOI] [PubMed]
- 11.Lixia Y, Rongjing D. Interpretation of the Chinese guidelines for cardiac rehabilitation and secondary Prevention[J]. Chinese circulation J. 2019;34(S1):86–90. 10.3969/j.issn.1000-3614.
- 12.Hallal PC, Andersen LB, Bull FC. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380(9838):247–57. 10.1016/s0140-6736(12)60646-1. [DOI] [PubMed] [Google Scholar]
- 13.Zanatta F, Tabernero C, Steca P. Predicting physical activity and quality of life in coronary heart disease patients: an 18-month path analysis of motivational and emotional factors. Health Psychol. 2024;43(5):352–64. 10.1037/hea0001348. [DOI] [PubMed]
- 14.Zhang Q, Han H, Yang S. Facilitators and barriers of initiation and maintenance of physical activity among people with coronary heart disease: a qualitative study. Disabil Rehabil. 2024;46(23):5521–30. 10.1080/09638288.2024.2309512. [DOI] [PubMed]
- 15.Ghisi GLM, Rouleau F, Ross MK et al. Effectiveness of an education intervention among cardiac rehabilitation patients in canada: A Multi-Site Study[J]. CJC Open. 2020;2(4):214–21. 10.1016/j.cjco.2020.02.008. [DOI] [PMC free article] [PubMed]
- 16.Wasilewski M, Vijayakumar A, Szigeti Z et al. Barriers and facilitators to delivering inpatient cardiac rehabilitation: A scoping Review[J].J Multidiscip Healthc. 2023;(16);2361-76. 10.2147/jmdh.S418803. [DOI] [PMC free article] [PubMed]
- 17.Chen L, Han RR, Chen X. Evaluation of the mediating role of physical activity self-efficacy in the relationship between knowledge, social support, and physical activity in pregnant women with a high risk for gestational diabetes. BMC Pregnancy Childbirth. 2024;24(1):857. 10.1186/s12884-024-07068-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Wu M, Wang W, He H et al. Mediating effects of health Literacy, Self-Efficacy, and social support on the relationship between disease knowledge and patient participation behavior among chronic ill patients: A Cross-Sectional study based on the Capability-Opportunity-Motivation and behavior (COM-B) Model[J]. Patient Prefer Adherence. 2025;(19):1337–50. 10.2147/ppa.S513375. [DOI] [PMC free article] [PubMed]
- 19.Bandura A. Self-efficacy: toward a unifying theory of behavioral change[J]. Psychol Rev. 1977;84(2):191–215. 10.1037/0033-295X.84.2.191. [DOI] [PubMed]
- 20.Wirtz PH, von Känel R. Psychological stress, inflammation, and coronary heart disease. Curr Cardiol Rep. 2017;19(11):111. 10.1007/s11886-017-0919-x. [DOI] [PubMed] [Google Scholar]
- 21.Marshall PWM, Schabrun S, Knox MF. Physical activity and the mediating effect of fear, depression, anxiety, and catastrophizing on pain related disability in people with chronic low back pain. PLoS One. 2017;12(7):e0180788. 10.1371/journal.pone.0180788. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Wenqing X, Jianhua Y, Bo L et al. Correlation analysis of frailty and physical exercise function indicators in patients with coronary artery disease[J]. Acad J Naval Med Univ. 2023;44(12):1459–65. 10.16781/j.CN31-2187/R.20230284.
- 23.Bei T, Yaqiong F, Xiaoxi J. Investigation on the current situation and influencing factors of anxiety and depression in outpatients of cardiovascular medicine based on structural equation Model[J]. Chin J Health Stat. 2024;41(1):95–8. 10.11783/j.issn.1002-3674.2024.01.021.
- 24.Bauman AE, Sallis JF, Dzewaltowski DA et al. Toward a better Understanding of the influences on physical activity: the role of determinants, correlates, causal variables, mediators, moderators, and confounders[J].Am J prev Med,2002,23(2 Suppl):5–14. 10.1016/s0749-3797(02)00469-5 [DOI] [PubMed]
- 25.Dzerounian J, Pirrie M, AlShenaiber L, et al. Health knowledge and self-efficacy to make health behaviour changes: a survey of older adults living in Ontario social housing. BMC Geriatr. 2022;22(1):473. 10.1186/s12877-022-03116-1. [DOI] [PMC free article] [PubMed]
- 26.Burić I, Slišković A, Sorić I. Teachers’ Emotions and Self-Efficacy: A Test of Reciprocal Relations[J]. Front Psychol. 2020;(11):1650. 10.3389/fpsyg.2020.01650. [DOI] [PMC free article] [PubMed]
- 27.Sharma PK, Kumra R. Relationship between mindfulness, depression, anxiety and stress: mediating role of self-efficacy. Pers Individ Differ. 2022. 10.1016/j.paid.2021.111363.
- 28.Caraballo C, Desai NR, Mulder H, et al. Clinical implications of the New York Heart Association classification. J Am Heart Assoc. 2019;8(23):e014240. 10.1161/jaha.119.014240. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Cardiology, C. S. o., C. D. P. Committee, P. C. o. C.-C. Diseases. Chinese expert consensus on cardiac rehabilitation and secondary prevention of coronary heart disease. Chin J Gen Pract. 2014;(05):340–8. 10.3760/cma.j.issn.1671-7368.2014.05.006.
- 30.Ningning Q, Keji L. Study on the reliability and validity of international physical activity questionnaire (Chinese Vision, IPAQ)[J].Chinese journal of Epidemiology,2004,(03):87–90. 10.3760/j.issn:0254-6450.2004.03.021. [PubMed]
- 31.Mengyu F, Jun L, Pingping H. Chinese guidelines for data processing and analysis concerning the International Physical Activity Questionnaire[J]. Chin J Epidemiol. 2014;35(08). 10.3760/cma.j.issn.0254-6450.2014.08.019. [PubMed]
- 32.Hickey ML, Owen SV, Froman RD. Instrument development: cardiac diet and exercise self-efficacy[J]. Nurs Res. 1992;41(6):347–51. 10.1097/00006199-199211000-00006. [PubMed]
- 33.Altman DG, Bland JM. Statistics notes: quartiles, quintiles, centiles, and other quantiles. BMJ. 1994;309(6960):996. 10.1136/bmj.309.6960.996. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Yuxiao S, Chunyan Z, Yan Z et al. The chinesization and reliability and validity test of the cardiac exercise Self-Efficacy Instrument[J]. Chin J Mod Nurs. 2021;(32):4441–6. 10.3760/cma.j.cn115682-20210506-01940.
- 35.Mengli Z, Huiqiao H, Pingyue T et al. Development and validation of rehabilitation exercise knowledge-belief-practice scale for patients with coronary heart disease[J]. J Nurs Sci. 2020;35(7). 10.3870/i.issn.1001-4152.2020.07.087.
- 36.Spitzer RL, Kroenke K, Williams JB. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092-7. 10.1001/archinte.166.10.1092. [DOI] [PubMed]
- 37.Li W. Validation of anxiety-depression self-rating scales in cardiovascular patients and prevalence and factors associated anxiety-depression in acute coronary symptom patients. Chongqing Medical University; 2015. 10.7666/d.D791729.
- 38.Shanjie Z. Screening and reliability analysis of PHQ-9 and GAD-7 for anxiety and depression in hospitalized patients with cardiovascular diseases. Lanzhou University. 2021. 10.27204/d.cnki.glzhu.2021.003044.
- 39.Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13. 10.1046/j.1525-1497.2001.016009606.x. [DOI] [PMC free article] [PubMed]
- 40.Macho S, Ledermann T. Estimating, testing, and comparing specific effects in structural equation models: the phantom model approach. Psychol Methods. 2011;16(1):34–43. 10.1037/a0021763. [DOI] [PubMed] [Google Scholar]
- 41.Babin B, Hair J, Anderson R, et al. Multivariate data analysis 8th Edition[M]. Cengage Learning EMEA. 2018.
- 42.Smith SC Jr., Benjamin EJ, Bonow RO et al. AHA/ACCF secondary prevention and risk reduction therapy for patients with coronary and other atherosclerotic vascular disease: 2011 update: a guideline from the American Heart Association and American College of Cardiology Foundation endorsed by the World Heart Federation and the Preventive Cardiovascular Nurses Association[J]. J Am Coll Cardiol. 2011;58(23):2432–46. 10.1016/j.jacc.2011.10.824. [DOI] [PubMed]
- 43.Knuuti J, Wijns W, Saraste A, et al. 2019 ESC guidelines for the diagnosis and management of chronic coronary syndromes. Eur Heart J. 2020;41(3):407–77. 10.1093/eurheartj/ehz425. [DOI] [PubMed]
- 44.Deng J, Liu Y, Wang T et al. The association between physical activity and anxiety in college students: parallel mediation of life satisfaction and self-efficacy[J].Front Public Health,2024,121453892. 10.3389/fpubh.2024.1453892. [DOI] [PMC free article] [PubMed]
- 45.Yang X, Song Y, Zhang J et al. Physical activity Self-Efficacy among pregnant women at high risk for gestational diabetes mellitus in china: A Cross-Sectional Study[J]. J Multidiscip Healthc. 2024;(17):5725-36. 10.2147/jmdh.S489118. [DOI] [PMC free article] [PubMed]
- 46.A B. Social learning theory[M]. Engleweod Cliffs: Prentice-Hall.; 1997. [Google Scholar]
- 47.Wenyan D, Demei C, Runping N. Status quo and influencing factors of self-efficacy of cardiac rehabilitation exercise in patients after percutaneous coronary intervention. Chinese Nursing Management. 2024;24(1):52–6. 10.3969/j.issn.1672-1756.2024.01.011.
- 48.Xie T, Zhang Q, Zhang S et al. Effects of health education on Self-efficacy, negative Emotions, and life quality in breast cancer patients: A systematic review and Meta-analysis[J].Cancer Nurs,2025.10.1097/ncc.0000000000001472. [DOI] [PubMed]
- 49.Xiaocui C. Survey and analysis on the implementation status of phase I cardiac rehabilitation in 25 hospitals in Guangdong Province. Journal of Nursing(China). 2012;19(6):28–30. 10.3969/j.issn.1008-9969.2012.06.010. [Google Scholar]
- 50.Doménech P, Tur-Porcar AM, Mestre-Escrivá V. Emotion regulation and self-efficacy: the mediating role of emotional stability and extraversion in adolescence. Behav Sci (Basel). 2024;14(3). 10.3390/bs14030206. [DOI] [PMC free article] [PubMed]
- 51.Zhao-hui L. Effects of physical exercise on negative emotion for university students ——the mediating and moderating effects of self-efficacy and mental resilience. J Phys Educ. 2020;27(5):102–8.
- 52.Jacobson NC, Newman MG. Anxiety and depression as bidirectional risk factors for one another: a meta-analysis of longitudinal studies. Psychol Bull. 2017;143(11):1155–200. 10.1037/bul0000111. [DOI] [PubMed]
- 53.Beibei L, Zhaoli M. Research on the exercise intervention path of chronic diseases under the mode of“combination of sports and medicine. Bull Sports Sci Technol Lit. 2020;28(11):41–3. 10.19379/j.cnki.issn.1005-0256.2020.11.017.
- 54.Zelle DM, Corpeleijn E, Klaassen G, et al. Fear of movement and low self-efficacy are important barriers in physical activity after renal transplantation. PLoS One. 2016;11(2):e0147609. 10.1371/journal.pone.0147609. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Huang X, Wang X, Hu J et al. Inadequate mental health literacy and insufficient physical activity potentially increase the risks of anxiety and depressive symptoms in Chinese college Students[J].Front Psychiatry,2021. 10.3389/fpsyt.2021.753695. [DOI] [PMC free article] [PubMed]
- 56.Gertz G, Ketels G, Klotz SG. Facilitators and barriers to physical activity in patients in an acute care hospital setting from an interprofessional perspective: a qualitative study. Clin Rehabil. 2025;39(5):668–78. 10.1177/02692155251325614. [DOI] [PMC free article] [PubMed]
- 57.Ghlichi Moghaddam N, Namazinia M, Hajiabadi F, et al. The efficacy of phase I cardiac rehabilitation training based on augmented reality on the self-efficacy of patients undergoing coronary artery bypass graft surgery: a randomized clinical trial. BMC Sports Sci Med Rehabil. 2023;15(1):156. 10.1186/s13102-023-00770-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Chai Z, Fan Y, Gong X et al. Adherence to phase I cardiac rehabilitation in post-PCI patients: a latent class analysis[J].Front Cardiovasc Med,2025,121460855. 10.3389/fcvm.2025.1460855. [DOI] [PMC free article] [PubMed]
- 59.Izawa KP, Ishihara K, Kanejima Y, et al. Sedentary behaviour may cause differences in physical outcomes and activities of daily living in older cardiovascular disease patients participating in phase I cardiac rehabilitation. Sci Rep. 2024;14(1):14037. 10.1038/s41598-024-65001-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Karataş T, Polat Ü. Effect of nurse-led program on the exercise behavior of coronary artery patients: Pender’s Health Promotion Model. Patient Educ Couns. 2021;104(5):1183–92. 10.1016/j.pec.2020.10.003. [DOI] [PubMed]
- 61.Barrett S, Begg S, O’Halloran P et al. Integrated motivational interviewing and cognitive behaviour therapy can increase physical activity and improve health of adult ambulatory care patients in a regional hospital: the Healthy4U randomised controlled trial[J]. BMC Public Health 2018;18(1):1166. 10.1186/s12889-018-6064-7. [DOI] [PMC free article] [PubMed]
- 62.Gallé F, Marte G, Cirella A, et al. An exercise-based educational and motivational intervention after surgery can improve behaviors, physical fitness and quality of life in bariatric patients. PLoS One. 2020;15(10):e0241336. 10.1371/journal.pone.0241336. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Bielecka-Dabrowa A, Lewek J, Sakowicz A, et al. Effects of implementing personalized health education in ambulatory care on cardiovascular risk factors, compliance and satisfaction with treatment. J Pers Med. 2022;12(10). 10.3390/jpm12101583. [DOI] [PMC free article] [PubMed]
- 64.Liu C, Chen Z, Qin H, et al. Health knowledge acquisition and health communication ability of nurses: a chain mediating model of health literacy and health education competence. J Adv Nurs. 2025. 10.1111/jan.16728. [DOI] [PubMed] [Google Scholar]
- 65.Li B, Jiang W, Han SS, et al. Influence of moderate-to-high intensity physical activity on depression levels: a study based on a health survey of Chinese university students. BMC Public Health. 2024;24(1):1023. 10.1186/s12889-024-18433-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Schüz N, Walters JA, Cameron-Tucker H, et al. Patient anxiety and depression moderate the effects of increased self-management knowledge on physical activity: a secondary analysis of a randomised controlled trial on health-mentoring in COPD. COPD: Journal of Chronic Obstructive Pulmonary Disease. 2015;12(5):502–9. 10.3109/15412555.2014.995289. [DOI] [PubMed]
- 67.Bo Z. Research progress on evaluation methods for the relative importance of independent variables in logistic regression model. Zhejiang Prev Med. 2012;24(9):17–9. 10.3969/j.issn.1007-0931.2012.09.006.
- 68.Guo S, Fu H, Guo K. Effects of physical activity on subjective well-being: the mediating role of social support and self-efficacy. Front Sports Act Living. 2024;6:1362816. 10.3389/fspor.2024.1362816. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 69.Ma Q, Luo J, Cao H et al. Social support, health behavior self-efficacy, and anxiety on physical activity levels among lung cancer survivors: a structural equation modeling[J].J Cancer Surviv,2024. 10.1007/s11764-024-01626-y [DOI] [PubMed]
- 70.Qin L, Ho WKY, Khoo S. The relationship between perceived quality physical education and 7-day physical activity among secondary school students in China: the mediating role of exercise self-efficacy. Arch Public Health. 2025;83(1):242. 10.1186/s13690-025-01731-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 71.Guerra-Carrillo B, Katovich K, Bunge SA. Does higher education hone cognitive functioning and learning efficacy? Findings from a large and diverse sample. PLoS One. 2017;12(8):e0182276. 10.1371/journal.pone.0182276. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 72.Chandra SS, Calvert F, Sui Z, et al. Perceived barriers and facilitators to healthy eating and physical activity in endoscopic bariatric patients: a qualitative study. Eat Weight Disord. 2022;27(5):1633–40. 10.1007/s40519-021-01299-x. [DOI] [PubMed] [Google Scholar]
- 73.Mittelmark MB, Psaty BM, Rautaharju PM et al. Prevalence of cardiovascular diseases among older adults. Cardiovasc Health Study[J] Am J Epidemiol 1993;137(3):311–7. 10.1093/oxfordjournals.aje.a116678. [DOI] [PubMed]
- 74.Tang S, Chen H, Wang L, et al. The relationship between physical exercise and negative emotions in college students in the post-epidemic era: the mediating role of emotion regulation self-efficacy. Int J Environ Res Public Health. 2022;19(19). 10.3390/ijerph191912166. [DOI] [PMC free article] [PubMed]
- 75.Siow E, Leung DYP, Wong EML, et al. Do depressive symptoms moderate the effects of exercise self-efficacy on physical activity among patients with coronary heart disease? J Cardiovasc Nurs. 2018;33(4):E26-e34. 10.1097/jcn.0000000000000491. [DOI] [PubMed] [Google Scholar]
- 76.Gold AK, Rabideau DJ, Katz D et al. Self-Efficacy for exercise in adults with lifetime depression and low physical Activity[J]. Psychiatry Res Commun. 2024;4(1). 10.1016/j.psycom.2024.100159. [DOI] [PMC free article] [PubMed]
- 77.Magallón-Botaya R, Méndez-López F, Oliván-Blázquez B, et al. Effectiveness of health literacy interventions on anxious and depressive symptomatology in primary health care: a systematic review and meta-analysis. Front Public Health. 2023;11:1007238. 10.3389/fpubh.2023.1007238. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 78.Sitot A, Workye H. Assessment of knowledge, attitude and practice towards ante natal exercise among pregnant women attending antenatal care at Health centers of Mekelle, Tigray Region, Ethiopia, 2020. PLoS One. 2022;17(8):e0262805. 10.1371/journal.pone.0262805. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 79.Bäck M, Caldenius V, Svensson L, et al. Perceptions of kinesiophobia in relation to physical activity and exercise after myocardial infarction: a qualitative study. Phys Ther. 2020;100(12):2110–9. 10.1093/ptj/pzaa159. [DOI] [PubMed]
- 80.Rong H, Mei D, Huahua H et al. Mediating effect of exercise self-efficacy between kinesiophobia and amount of physical activity in patients with coronary heart disease[J]. Chin J Nurs Educ. 2022;19(12):1119–23. 10.3761/j.issn.1672-9234.2022.12.013.
- 81.Mehra VM, Gaalema DE, Pakosh M, et al. Systematic review of cardiac rehabilitation guidelines: quality and scope. Eur J Prev Cardiol. 2020;27(9):912–28. 10.1177/2047487319878958. [DOI] [PMC free article] [PubMed]
- 82.Report on Cardiovascular Health and Diseases in China. 2023: an updated Summary[J]. Chin Circulation J. 2024;39(07):625–60. 10.3969/j.issn.1000-3614.2024.07.001. [DOI] [PubMed]
- 83.Simmons D, Devlieger R, van Assche A et al. Effect of physical activity and/or healthy eating on GDM risk: the DALI lifestyle Study[J].J clin endocrinol Metab. 2017;102(3):903–13. 10.1210/jc.2016-3455. [DOI] [PMC free article] [PubMed]
- 84.Shannon MM, Elf M, Churilov L, et al. Can the physical environment itself influence neurological patient activity? Disabil Rehabil. 2019;41(10):1177–89. 10.1080/09638288.2017.1423520. [DOI] [PubMed]
- 85.Geelen SJG, van Dijk-Huisman HC, de Bie RA, et al. Barriers and enablers to physical activity in patients during hospital stay: a scoping review. Syst Rev. 2021;10(1):293. 10.1186/s13643-021-01843-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 86.Alsop T, Woodforde J, Rosbergen I, et al. Perspectives of health professionals on physical activity and sedentary behaviour in hospitalised adults: a systematic review and thematic synthesis. Clin Rehabil. 2023;37(10):1386–405. 10.1177/02692155231170451. [DOI] [PMC free article] [PubMed]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Additional supporting information may be found online in the Supporting Information section at the end of the article.