Social Prescription of Nature, Physical Activity (PA), and healthy living initiatives in cardiovascular health offer the potential for non-clinical interventions to enhance cardiovascular outcomes through the mediation of the social support pathway. It is vital to connect people with community-based activities, such as exposure to nature, group exercise, and lifestyle programs, through social prescribing to encourage health enhancement (16). This model integrates the Biophilia Hypothesis (17), Self-Determination Theory (SDT) (18), and Social Cure Theory (19) to demonstrate how interventions lead to cardiovascular benefits through social support. The choice of theories was based on their direct relationship to nature-based interventions, motivational forces of healthy behavior, social processes, and health promotion (20). The model posits that the interventions (independent variables) and cardiovascular health outcomes (dependent variables) are mediated by social support, and the association is reciprocal, such that improved health, in turn, enhances participation in these interventions. The Biophilia Hypothesis advocates for the effectiveness of social prescribing that employs nature as a remedy, presuming that humans have a natural affinity for nature that leads to physiological and psychological benefits. Natural settings, such as green spaces for walking and community gardening, help lower stress biomarkers (e.g., cortisol), improve air quality, and reduce CVD. The situation is further enhanced by these beneficial effects, as group situations contribute to community cohesion and amplify the mediating effect of social support (17). Self-Determination Theory (SDT) defines the success of PA and healthy living programs as intrinsic motivation that relies on autonomy, competence, and relatedness. Prescribed activities, such as group exercises or nutritional workshops, enable participants to make informed choices, learn new skills, and establish social connections. These provisions promote adherence to healthy behaviors, which reduces cardiovascular risk factors, including obesity and sedentary lifestyles, and results in lower blood pressure and lipid profiles. It is the relatedness dimension, in particular, that reinforces social support, as the activities carried out in groups build positive interpersonal relationships that strengthen healthy behaviors (19). The Social Cure Theory views social support as a mediator, positing that participation in an activity group fosters social identity, reduces isolation, and mitigates cardiovascular risk. Social support encompasses emotive, productive, and informational support and emerges within the collective experience of nature-based or PA programs. Robust social networks decrease the risk of cardiovascular death by reducing stress and enhancing adherence to interventions. One key fact is that group nature walks cultivate trust and reduce feelings of loneliness, which in turn lead to improved cardiovascular health through long-term participation (Figure 1) (19, 20).

Social prescribing is gaining increasing prominence. Healthcare professionals recommend non-drug treatments for the social determinants of a patient's health. Several social-prescribing methods have been recognized as effective in enhancing physical activity and social engagement among older adults. Cardiovascular diseases (CVDs) are one of the primary causes of mortality in the global population, indicating the need to develop efficient strategies to prevent and treat these diseases. Social prescriptions, particularly when healthcare providers recommend nature-based activities and exercise to patients, are a new and promising method for managing patients' health. Promoting a healthy lifestyle has been established as an essential component of social prescribing, including active living programs and natural prescriptions for health. These programs enable people to incorporate exercise and outdoor activities into their daily lives, positively impacting community health (14). Therefore, physical activity is a well-established determinant of cardiovascular health, as exercise helps individuals reduce their risk of cardiovascular diseases. There has been a development of nature-based social prescriptions that promote the well-being of people by linking them to nature. These initiatives enable humans to connect with the natural environment, ultimately leading to improved cardiovascular health, increased physical activity, and reduced stress levels. Healthy living interventions encompass a range of activities and programs designed to enhance overall quality of life. It encompasses physical, cultural, health, educational, and social dimensions to provide a comprehensive approach to well-being (15). The effectiveness of such interventions in creating positive and sustained shifts in the social determinants of health depends on a more profound understanding of the extent to which contextual factors engage and support individuals undergoing therapy and other prescribed modalities (21). Caregiver support, which encompasses support offered by social networks, is vital for effective health interventions. The essential role of social support in promoting the adoption of appropriate hygiene and health practices in various community contexts has been widely discussed in the literature. Among the antecedents of positive health behaviors, such as exercising, maintaining cleanliness, and practicing personal hygiene, family support stands out as the one that has been consistently identified as influential (22). Consequently, based on the limited knowledge in the literature and exploratory studies by scholars, there is a need to address the growing research gap concerning the vital role of social support in promoting nature, physical activity, and other healthy living initiatives as aspects of social prescriptions that support cardiovascular health. This study examined the relationships between exposure to nature, physical activity levels, participation in healthy lifestyle programs, and cardiovascular health, with social support serving as a moderating factor (Figure 2).

Nature-based social prescribing is a relatively new area of focus in healthcare that involves prescribing nature-related activities to improve health (23). Nature prescriptions have been developed to manage diseases and promote physical exercise through participation in activities such as gardening and nature walking (24, 25). These initiatives enhance health and functionality by exposing people to the natural environment and objects, such as plants and gardens (26, 27). Nature-based social prescribing, such as community gardening, requires individuals to engage directly with nature and participate in social activities that improve health outcomes (28). An analysis of the impact of nature-based social prescriptions revealed a positive correlation with improved cardiovascular health. The support of green spaces for exercise, particularly for older individuals, enhances their cardiovascular strength and overall health (29). Nature-based social prescriptions offer substantial cardiovascular health benefits through access to nature, physical activity, and social connectedness. Blue nature-based social prescriptions have been recognized as having strong positive effects on individuals' health and are beneficial for those with chronic diseases (30). The following hypothesis was developed based on the relevant literature.

Social prescribing is defined as a model in which general practitioners (GPs) and other health professionals refer patients to additional sources of support to improve their health and well-being (31). Walking, biking, and physical exercise in parks have been found to impact cardiovascular well-being (32). Physical activity has been prescribed more frequently as a social intervention that positively affects cardiovascular health. Exercise training influences the prevention and treatment of cardiovascular diseases, thereby reducing mortality rates (33). Promoting physical activity as part of social prescription aligns well with the social concept of natural prescriptions, wherein green spaces and the natural environment have a positive influence on community health (34). Health promotion or disease prevention models, such as social prescribing programs, have been shown to lower cardiovascular disease risk factors and, consequently, cardiovascular disease incidence, morbidity, and mortality (35). By incorporating physical activity into these programs, individuals can achieve better aerobic fitness levels and other health improvements. Physical activity prescriptions in social prescribing initiatives can also help narrow healthcare access and outcome differences. Personalized exercise prescriptions benefit the objective indexes of cardiovascular health (36). The formulation of the following hypothesis is based on the above literature.

Several healthy living interventions under social prescriptions have been highlighted for their effectiveness in enhancing cardiovascular health. These innovations aim to promote modifications in diet, exercise, and other stress-related factors to prevent and reduce cardiovascular diseases (37). The American Heart Association focuses on adopting healthy physical behaviors in clinics to improve cardiovascular health (33). Community programs are designed to encourage individuals to make healthier choices and adopt socially and culturally acceptable healthy practices (38). Lifestyle medicine is a sub-discipline that focuses on therapeutic lifestyle changes to manage diseases such as cardiovascular diseases. It focuses on adopting healthy patterns and schedules of exercise and healthy eating, which implies preventing and managing several health issues (39). Moreover, providing free or low-cost food and nutritional information through this intervention also helps promote and enhance cardiovascular health among individuals with diet-related conditions through social prescribing (40). Social prescribing initiatives have immense potential for combating food insecurity and improving dietary practices, which are crucial for maintaining healthy heart function. Increasing the availability of healthy foods leads to improved health standards and reduces the cost of treating heart-related diseases (41, 42). In the population health domain, as seen in the Kuwait National Programme for Healthy Living, promoting healthy living to mitigate the impact of chronic diseases, including cardiovascular diseases (CVDs), is crucial (43). Lifestyle medicine and other community interventions, collectively referred to as healthy living initiatives under social prescriptions, can potentially improve cardiovascular health in diverse populations. The formulation of the following hypothesis is based upon the above pertinent literature.

Social support reduces cardiac reactivity to stressors and lowers the risk of developing cardiovascular diseases. Perceived social support has been found to reduce cardiovascular reactivity to stress in older adults, a sign of cardiovascular health (44). Social support has been linked to health behaviors, including smoking cessation, exercise, and dietary changes, which impact physical functions, such as the cardiovascular and immune systems (45). Higher social support is associated with lower blood pressure, decreased cardiovascular reactivity, and a more effective functional immune response, whereas loneliness is linked to increased cortisol levels and compromised immunity (46). As applied to cardiovascular health in specific populations, including Black persons and Latinos aged ≥ 45 years and older adults with type 2 diabetes, promoting social support interventions has led to changes in several measures of cardiovascular health (47). Research has shown that social prescribing initiatives, particularly those involving natural environment interventions, can enhance mental health and help manage various medical conditions. Such programs offer individualized services and recreational opportunities that enhance social inclusion, improve psychological and physical well-being, and reduce reliance on medical interventions (48). Social support is beneficial in reducing cardiovascular risk factors, as it offers an opportunity to integrate into society, maintain a good mental state, and influence health-related behavior. Incorporating social support into natural resource-based interventions could enhance cardiovascular health and well-being. The following hypothesis was developed based on the relevant literature.

People need to be physically active to support their cardiovascular needs, and exercise is one of the most effective ways to manage and prevent cardiovascular diseases (49). Exercise is generally safe for nearly everyone and offers numerous health and fitness benefits; the risks of exercise decrease as fitness levels improve (2). Coupled with aerobic training, it is recommended for individuals with no cardiovascular disease or those with pre-existing cardiovascular disease to enhance cardiovascular fitness and reduce mortality (50). Vigorous activity that involves endurance training improves endurance and the heart and arteries, whereas high-intensity activities of short and intermittent durations increase muscle strength. The cardiovascular stress response during physical exercise is an early index of cardiovascular fitness, and exercise stress tests are used to identify cardiovascular problems in individuals with cardiac disorders (51, 52). This finding demonstrates that social support is a crucial factor in maintaining cardiovascular health, as numerous studies have shown that it reduces the risk of cardiovascular disease (CVD) and enhances patients' quality of life (53). Healthy living initiatives contribute to this protective mechanism through social support, stress management, exercise, a balanced diet, physiological processes, and improvements in cardiovascular health. Research on social support among clients with chronic diseases, such as coronary artery disease, reveals that self-care is bolstered by peer and family support, indicating the importance of social relationships in addressing cardiovascular diseases (54). As noted above, the positive link between social support, most notably in patients with CVD, has significant implications for the quality and quantity of social networks in enhancing cardiovascular health (55). This study also revealed a positive link between social support and well-being among patients with different CVDs, thus highlighting the importance of social support as a mediator of health in the population (56). Social interactions and cardiovascular health are equally complex, involving social integration, contact, and support, as well as their impacts on health-related behaviors (57). Engaging social networks to publicize and encourage physical activity and assistance can dramatically enhance outcomes for individuals with cardiovascular diseases or those at risk of developing them. The following hypothesis was developed based on the relevant literature.

Social support positively impacts self-care, stress management, and quality of life in patients with cardiovascular diseases (58). Individuals with social support exhibit improved coping styles and adhere to healthy living standards, including moderate exercise and a balanced diet, which serve as protective factors for cardiovascular health (59). Furthermore, social support has been proven to cause a decrease in CVD in response to stress, meaning that having a group of people to support you has a physiological advantage. Social support draws more attention to promoting healthy living measures, beginning in childhood. These childhood assets, which are social support, have been associated with favorable cardiovascular health in middle-aged adults (60, 61). This implies that efforts to improve cardiovascular health should include social support approaches in childhood to establish correct preventive values. In addition, peer and family support has been found to increase compliance with self-care measures in patients with chronic illnesses, including CVD, indicating the importance of social connections in managing cardiovascular disease (62, 63). Virtual communities and online support groups are valuable sources of social support for patients with cardiovascular disease. Notably, the support that patients obtain from social health networks regarding information and emotional support can positively affect their health (64). These educational interventions are instrumental in prevention strategies designed to reduce the occurrence of cardiovascular diseases by promoting appropriate and healthy behaviors (65). The most suitable approach for improving cardiovascular health at the clinical level involves training health professionals to deliver healthy living interventions. Lifestyle medicine interventions acknowledge that addressing the social determinants of health is crucial, as life-course approaches emphasize (41, 66). These metrics offer a basis for observing health indicators and outcomes alongside healthy behavioral patterns, with a focus on cardiovascular health and related behavioral risk factors. The following hypothesis was formulated based on the literature:

China occupies a vast area within the Asia–Pacific region and is endowed with rich geographical and demographic diversity. The study participants were sourced through general practitioners who recommended their patients to non-pharmacological approaches to promote cardiovascular health. A screening criterion was used to identify the target population for the study, ensuring that only qualified participants were included. The inclusion criterion was a predefined age of 33 to 80+ years with cardiovascular health problems who were visiting non-healthcare facilities as part of social prescriptions by general practitioners. A stratified sampling method was adopted to gather primary data from the target populations. Using stratified samples means that each stratum in the population is well represented in the sample. Of the target population, 5,600 participants were selected for the study (See Figure 3).

Based on a thorough analysis of the available literature, the instrument was developed as a self-report questionnaire using a five-point Likert scale, consisting of 24 items. Such a scale has been routinely used in the social sciences to measure participants' attitudes from completely disagree to agree. The questionnaire was formulated based on theoretical perspectives and empirical methods, leading to improved content validity and research goal utility. A strict validation protocol has documentary evidence showing that there are no biases to invalidate responses and that the content domain coverage is extensive for the target population. The semi-structured interview technique was employed under the Brislin approach to discuss the questionnaire with ten field experts (67). Two bilingual translators met to jointly change the items, adding the perspectives of ten subject matter experts. Consequently, the need for professional input to ensure that the questionnaires reflect relevant themes in the population under study and consider cultural and contextual differences was considered. After completing the validation stage, a pilot study was conducted in the pre-testing context, involving 27 cases for the application of the questionnaire testing. The exploratory stage was critical in identifying pragmatic problems, such as ambiguous wording, respondent fatigue, and various other logistical issues that might arise during the deployment. The constructs in the questionnaire were checked for internal consistency using Cronbach's alpha, which measures the reliability of individual items based on correlations between them. Alpha values above the conventional threshold of 0.70 were obtained, indicating minimal redundancy/inconsistency between the items (68). Thus, these satisfactory reliability values instilled confidence in using the questionnaire in the main study.

The data collection tools included demographic information, social prescription of physical activity, healthy living initiatives, social support, and cardiovascular health.

The collected data were analyzed using structural equation modeling (SEM) and artificial neural network (ANN) techniques. SEM is a popular method for analyzing multiple relationships between variables, especially for testing the causal relationships among variables. This enables researchers to consider direct and indirect effects in light of theory.

Table 1 shows the demographic information of the study participants. The gender distribution shows that the majority of participants were female (38.7 %), followed by males (25.7 %), with a significant portion (35.6 %) preferring not to disclose their gender. In terms of age, the largest group was between 73–82 years old, comprising 29.3% of the total, while the youngest group, aged 33–42, makes up 10.4%. The educational background is diverse, with the highest percentage of participants having a college degree (28.7 %) and the lowest percentage having primary education (26.5 %). Living status reveals that a slight majority, 39.8%, live in other unspecified arrangements, while 31.4% live alone and 28.8% live with family. Geographically, the participants were spread across China, with the highest concentration in the Eastern region at 21.8%. Regarding marital status, a significant number of participants were single (35.2 %), and a considerable portion were divorced (28.2 %). Regarding occupation, the most represented group was retirees at 20.4%, with government employees and the unemployed following closely at 15.3% and 17.4%, respectively.

Hence, we opted for the SEM-ANN method, as factor-based and composite-based SEMs cannot handle non-linear relationships, given the evidence of non-linearity (see Table 2). The positive and negative non-linear correlations in our model were as follows:

The heterotrait-monotrait (HTMT) and internal variance inflation factor (VIF) were applied during the examination of CMB. CMB occurs when the significant constructs are highly inter-correlated (correlation coefficient > 0.90). All correlation values for the study constructs were <0.90. Therefore, there is no CMB because the maximum correlation value is zero. The maximum VIF for the current study is 1.870, which is less than the threshold value of 3.30, suggesting no concern regarding CMB (71).

A measurement model was established using Smart-PLS software 4.0, which revealed the factor loadings (FL) of all items and found that all were within the interval of 0. After confirming that all threshold values had been reached, we proceeded to the second phase of the structural model to analyze whether the proposed hypotheses were true (69). The average variance extracted (AVE) values obtained were >0. The convergent validity of all items regarding their respective constructs was confirmed. The multicollinearity of all items was assessed using the variation influence factor (VIF) value. The Cronbach's alpha (CA) and composite reliability (CR) were >0.70, indicating good internal consistency (see Table 3 and Figure 4).

Discriminant validity was further tested using the Fornell-Larcker test and the HTMT ratios of herbivores and mono-traits (Tables 4, 5). The findings of the Fornell-Larcker (72) analysis consist of two approximately square matrices below the diagonal matrix of the constructs' correlations. The square root of its AVE measures that each construct's reliability is greater than the correlations below its diagonal, establishing discriminant validity. All correlation values were <0.9, confirming discriminant validity.

When examining the structural model, we first analyzed the inner variance inflation factor (VIF) to check for collinearity. Once the VIF was computed again, we found that the VIF value was below 3, which is less than 5; hence, it was agreed that there was no collinearity issue. Nevertheless, the test analysis is in the form of the magnitude of the effect size, which is (F²), and the coefficient of determination (R²⁾. All these values are greater than or equal to the threshold values listed in Table 6.

The model fitness in this study was examined using the standardized root mean square residual (SRMR), normed fit index (NFI), and chi-square (χ2) values. SRMR value is a standardized-residual index that was developed among observed covariance and hypothesized matrices, which shows the measurement of model fitness (see Table 7). The acceptable range of the SRMR value is less than or equal to 0.08. According to the results, the estimated SRMR value was 0.033, which is acceptable as a good model fit. The NFI value is 0.946, and chi-square (χ2) shows the value of 4335.985.

We utilized both 95% confidence intervals and t-statistics to determine the significance of these associations. The results of the structural model bootstrapping (see Figure 5, Table 8) shows that each path is significant (H1: β = 0.27, CI = 95%, LL = 0.250, UL = 0.290, t = 26.81, p = 0.00); (H2: β = 0.27, CI = 95%, LL = 0.250, UL = 0.290, t = 27.64, p = 0.00); (H3: β = 0.29, CI = 95%, LL = 0.270, UL = 0.320, t = 26.94, p = 0.00); (H4: β = 0.21, CI = 95%, LL = 0.190, UL = 0.230, t = 19.88, p = 0.00); (H5: β = 0.22, CI = 95%, LL = 0.200, UL = 0.240, t =21.29, p = 0.00); (H6: β = 0.17, CI = 95%, LL = 0.150, UL = 0.1900, t = 17.68, p = 0.00).

Multilayer artificial neural networks with input, hidden, and output layers were selected for the applications. During the analysis process, we verified the data fitness and calculated the root mean square of the errors. One of the relevant features of the employed ANN architecture is the complexity and structure of the output neuron, which has two hidden layers. The sample usage was divided into training and testing sets, with 90% of the samples allocated for training and the remaining 10% for testing (Table 9). The accuracy of the ANN prediction model was verified using the root mean square error (RMSE) in both the training and testing datasets (73). The average RMSEs of the training and testing data are 0.5878 and 0.5838, respectively, as shown in the last two columns of Table 8. Thus, to confirm the previous fact, it can be concluded that the model fits the data well.

Consequently, the relative importance of the above results was obtained by dividing them by their maximum significance. The results are presented as percentages to determine the predictive power of each input neuron desirability factor (Table 10). The most significant predictors are HLISP (Healthy Living Initiative Social Prescription (HLISP) at 100%, Nature Social Prescription (NSP) at 117.73%, and Physical Activity Social Prescription (Pa SP) at 102.50%. The normalized importance percentages for different Artificial Neural Network (ANN) configurations, including ANN (i) through ANN (x), with NSP demonstrating the highest normalized importance at 117.73%, followed by Pa SP at 102.50%, and HLISP at 100%. This indicates that NSP has the greatest influence on the predictive model, followed closely by Pa SP and HLISP, providing a comprehensive understanding of the key variables driving this analysis.

The results of this study are helpful for public health practitioners interested in the health of the CVDs population and the use of nonmedical integrative interventions for health promotion. Social prescriptions (i.e., nature-based physical activity and mass healthy living programmes) should be offered as standard-of-care for clinical and community-based practice. They must be included in a broader health policy. Interventions like these should be focused on people who are at high risk of developing CVD or who have problems coping with their disease. Thus, structural components of interventions associated with social networks, such as the formation of community circles for training and peer support, are recommended to act as mediators for increased efficiency of prescriptions and compliance by long-term patients. Some health professionals are encouraged to work with primary care physicians and community development regarding social determinants of health to decrease health disparities and increase referrals and access to interventions. Furthermore, medical professionals' training in the practice of lifestyle medicine and social prescribing in countries beyond China should also benefit from the establishment of appropriate policy and practice frameworks to enable and disseminate mainstream social evidence-based interventions. To optimize these strategies for decreasing CV morbidity and mortality at the population level, outcomes and participation rates must be monitored.

The current investigation has several limitations: the cross-sectional nature of the design and the data gathered through self-administered questionnaires. Additionally, the study sample had certain restrictions in terms of demographics and geographical location, which may make the findings less applicable to other demographic groups or cultural settings. The inclusion of perceived social support as a mediator meant that other possible moderating variables, such as gender and comorbid conditions, were not explored. Future studies are needed to examine these moderators to detect the limits of these relationships.