INTRODUCTION
Cardiovascular disease (CVD) is the leading causes of mortality and disability worldwide, placing a substantial strain on public health systems[1,2]. Despite the progress in diagnostic and therapeutic modalities, the quality of life (QoL) and long-term prognosis of patients continue to be adversely affected by suboptimal disease management. Self-management has emerged as a pivotal element in CVD care with the potential to markedly enhance health outcomes facilitated by patient self-efficacy[3]. Self-efficacy, characterized by an individual’s confidence in their capacity to successfully execute a specific behavior, is essential for behavioral modification and informed decision-making. In the context of CVD management, patients exhibiting high self-efficacy are more inclined to adopt health-promoting behaviors such as adherence to medication regimens, maintaining a nutritious diet, and engaging in regular physical activity, thereby exerting better control over disease progression. Self-efficacy system interventions (SESIs), grounded in Bandura’s self-efficacy theory, seek to bolster self-efficacy using methodologies such as goal setting, behavior monitoring, social support, and feedback mechanisms. SESIs have been extensively implemented in the management of various CVDs, including coronary heart disease (CHD), heart failure (HF), and hypertension[4,5]. This minireview aimed to summarize the progress of SESIs in CVD management, analyze its effectiveness and limitations, and explore future directions for clinical practice and research.
The role of self-efficacy and psychological factors in CVD management
Bandura’s theory of self-efficacy underscores the significance of an individual’s belief in their capability to execute specific behaviors that are central to managing CVD. Elevated self-efficacy is a crucial component of the effective self-management of chronic illnesses[6]. Empirical evidence from studies of type 2 diabetes confirms a strong correlation between self-efficacy and disease management outcomes, which extend to CVD. Self-management programs can assist patients in navigating disease-related challenges, and enhancing self-efficacy promotes adaptive health behaviors, leading to improved outcomes[7].
Concurrently, the psychological and behavioral profiles of patients with CVD significantly influences disease trajectory. Psychological factors are pivotal determinants of chronic disease outcomes[8]. Research indicates that a high proportion of patients experience comorbid anxiety and depression; however, treatment rates remain low[9]. Fortunately, psychosocial interventions have proven effective in mitigating psychological distresses[10], highlighting the need for integrated care approaches.
Design principles for SESIs
The design of a SESIs must adhere to principles that ensure efficacy and practicality. The core objective was to enhance self-efficacy and facilitate sustainable healthy behavioral changes. A pilot eHealth study of patients with CVD showed that personalized psychoeducation successfully improved self-efficacy and well-being[11]. An effective intervention design incorporates multiple elements such as skill enhancement and the provision of social support. Furthermore, leveraging group dynamics in self-management programs can bolster individual and collective self-efficacy, contributing to better health results[7].
Disease-specific nuances in CVD self-management
It is crucial to recognize that the application of SESI principles must be tailored to the specific pathophysiology and self-management demands of different CVDs. For instance, the cornerstone of self-management of HF often revolves around daily weight monitoring, strict fluid restriction, and vigilant symptom recognition to prevent acute decompensation. In contrast, key challenges in hypertension management may focus on long-term medication adherence and lifestyle modifications in the absence of immediate symptoms, requiring different strategies to sustain self-efficacy. Exercise adherence, risk factor control, and angina management are central to patients with CHD. Acknowledging these disease-specific nuances is essential for designing effective SESIs and is reflected in the following discussion of clinical practices.
CLINICAL PRACTICE IN CVD MANAGEMENT
The following sections review the effects and implementation of SESIs in various CVD contexts. The selected studies exemplify how interventions are, or should be, adapted to meet the distinct self-efficacy challenges posed by different cardiovascular conditions, as outlined above.
Effects and application of SESIs on patient outcomes in CVD
SESIs significantly improve key outcomes in patients with CVD, including QoL, rehabilitation metrics, and health behaviors. Evidence has consistently shown that self-management interventions enhance the QoL, self-care behaviors, and self-efficacy in CVD populations, contributing to better disease knowledge and reduced hospitalization rates[12,13].
Research has highlighted the nuanced relationships between specific self-efficacy dimensions and outcomes. For instance, a correlational study among patients with HF in Singapore found that while overall self-efficacy and QoL were at moderately favorable levels, a deeper analysis revealed a counterintuitive pattern: Higher self-efficacy, specifically in maintaining function, was associated with better physical and overall QoL scores (lower scores on the QoL scale denote better status)[13]. This seemingly paradoxical finding presents a compelling psychological nuance: It may reflect a cognitive bias in which patients overestimate their functional capacity and underestimate the severity of their condition. Alternatively, higher self-efficacy in maintaining functioning could impose a psychological burden, what might be termed “effortful overconfidence”, where the pressure to sustain normalcy despite illness exacerbates stress and diminishes perceived well-being. This underscores the complexity of the role of self-efficacy, and suggests that interventions may need to target specific efficacy beliefs rather than global self-efficacy.
The benefits of structured SESIs have been demonstrated in rehabilitation settings. Studies comparing conventional cardiac rehabilitation with programs augmented by models, such as the Information-Motivation-Behavioral skills model, show that integrated interventions yield superior outcomes. For example, patients with CHD receiving Information-Motivation-Behavioral-based support demonstrated significantly greater improvements in cardiopulmonary function (e.g., left ventricular ejection fraction and peak oxygen uptake), physical capacity (e.g., 6-minute walk distance), and crucially, in self-efficacy across domains such as daily behavior and symptom management, along with higher satisfaction with care[14]. Furthermore, integrating self-efficacy theory into community-based programs, such as home-walking plans for patients with HF, successfully enhances exercise self-efficacy and self-management capabilities, indicating its utility beyond formal clinical settings[15].
Implementation strategies for SESIs in CVD management
The successful implementation of SESIs has utilized diverse strategies. Dinh et al[16] conducted a study in which six hospital wards were randomized into an intervention group and a control group involving 140 patients with HF. The intervention group received individualized teaching-back education, an HF booklet, a weighing scale, a diary, and a follow-up phone call two weeks after discharge, while the control group received only routine care and the booklet. Primary outcome measures included knowledge of HF (assessed using the Dutch HF Knowledge Scale) and self-care behaviors (assessed using the Self-care of HF Index) evaluated at baseline, 1 month, and 3 months. Results showed that the intervention group had significantly higher knowledge scores than the control group at 3 months (P = 0.002), and self-care maintenance scores were also significantly improved (coefficient, 10.8; 95% confidence interval: 4.7-16.9, P = 0.001). However, there were no significant differences between the two groups in terms of self-care management, confidence, or all-cause hospitalization rates. This study indicates that the teaching-back method can effectively enhance patients’ knowledge acquisition and self-care maintenance capabilities, is particularly suitable for populations with lower education levels, and is feasible for nurses to implement. This demonstrates that self-management interventions for chronic diseases (such as combining social identity to enhance self-efficacy in telemedicine) and specific educational techniques such as teaching back can significantly improve patients’ knowledge levels and self-care behaviors, providing a basis for promoting structured patient education in resource-limited settings. Additionally, mobile health applications represent a scalable strategy, as studies have demonstrated that CVD risk self-management via apps can substantially reduce key risk factors. Paz et al[17] studied 102475 adults with hypertension by using an artificial intelligence (AI)-driven mobile health application. Beyond basic self-monitoring, the app deployed specific behavior change techniques: Medication tracking/reminders, immediate feedback on readings, personalized daily tips, and a distinctive “Correlation Insights” feature that algorithmically linked user behavior (e.g., activity, weight) to changes in blood pressure (BP) or cholesterol, creating a reinforcement loop. Regression analyses showed that engagement with these specific modules, especially reading Correlation Insights, frequent measurements, and medication tracking, were significantly associated with greater improvements in BP, cholesterol, and weight. This indicates that the intervention’s effectiveness stems not from a single feature, but from an integrated mechanism combining real-time feedback, tailored education, and behavior-outcome reinforcement, offering actionable insights for replicable digital health design.
TECHNOLOGICAL ADVANCEMENTS IN SESIS
Digital and remote monitoring tools in SESI implementation
Digital tools play an increasingly significant role in SESIs for CVD. Mobile health technologies offer novel opportunities. For instance, an app-based cardiovascular risk self-management program significantly reduces BP and cholesterol levels in patients with hypertension or dyslipidemia[17]. Early digital formats, such as text message interventions, demonstrated the feasibility of remote reminders and basic support, effectively improving health-related self-efficacy in their time[18]. Today, these have evolved into or are complemented by more interactive and intelligent modalities, including chatbot dialogues, health games with adaptive challenges, and AI-powered conversational agents, which offer richer context-aware interactions to sustain patient engagement.
Remote monitoring technology extends these capabilities by enabling real-time data collection and timely intervention adjustments. The REMOTE-CR trial demonstrated that remote exercise monitoring could increase the accessibility to cardiac rehabilitation for patients who are unable to use standard services[19]. Furthermore, a web- and phone-based system for patients with HF improved their engagement with care plans, highlighting its potential for self-management[20].
The principle of personalized intervention design in SESIs
Personalized design is a critical principle for effective SESIs and requires strategies tailored to individual patient characteristics. This approach directly considers individual differences to enhance the intervention effectiveness. For example, one study stratified patients with CHD according to their self-efficacy levels; those with high self-efficacy received advisory interventions, whereas those with low self-efficacy received additional group-based reinforcement[21].
FUTURE PROSPECTS FOR SESIS
Future directions and innovative applications of SESIs
The future trajectory of SESIs in CVD management indicates greater integration, personalization, and technological sophistication. Development will focus on holistic models, such as mindfulness-based interventions, which have been shown to improve self-efficacy and reduce stress in older adults with CVD[22]. Digital transformation will advance through smarter applications that offer precise advice and monitoring, thereby optimizing targeting and effectiveness[23].
Innovation is driven by integrating emerging technologies. Virtual and augmented reality can provide immersive health management experiences, while digital phenotyping, the real-time, continuous analysis of behavioral and physiological data from digital devices, enables not only data collection, but also dynamic, adaptive intervention adjustments. This approach moves beyond static profiling toward a responsive intervention ecosystem that can anticipate and respond to patient needs in real time.
Looking ahead, we envision that SESIs will evolve into integrated AI-driven health coaching systems that function as 24/7 virtual health companions. Such systems would leverage multimodal data streams from wearables, mobile interactions, environmental sensors, and electronic health records to deliver personalized context-aware interventions. For instance, an AI coach could detect early signs of physiological stress or non-adherence, prompt just-in-time self-management strategies, and even facilitate telehealth consultations, if needed. This represents a shift from episodic clinician-led interventions to continuous patient-centered support systems that enhance self-efficacy through sustained engagement and intelligent feedback. Furthermore, expanding the reach of SESIs into non-clinical settings, such as workplaces, schools, and community centers, and tailoring them for special populations (e.g., the elderly, multimorbid patients, or low-health-literacy groups) will enhance their accessibility and public health impact.
Potential challenges and considerations
Despite its potential, the implementation and scaling of SESIs faces significant challenges. A primary concern is individual variation; personality traits and psychological states influence intervention engagement and outcomes, making one-size-fits-all approaches ineffective. For instance, interest in stress management apps varies with personality traits such as neuroticism and agreeableness[24]. Ensuring long-term sustainability is another major hurdle as studies have noted potential post-intervention rebound effects. Finally, demonstrating value through robust cost-benefit analyses is essential for securing support and ensuring efficient resource allocation[25].
CONTROVERSIAL ISSUES IN SESIS
The overall effectiveness of SESIs in CVD management remains debatable, with studies reporting significant improvements in patient outcomes alongside notable heterogeneity in intervention content and measurements, underscoring the need for more rigorous, high-quality research[26]. This variability may be influenced by factors such as sex, with some evidence suggesting that men derive greater benefits from certain interventions than women[27]. Beyond efficacy, the implementation of SESIs raises important ethical considerations, including the protection of patient privacy and data security, especially when digital tools are involved, and the imperative to ensure equitable access across different regions and socioeconomic backgrounds while upholding the principles of informed consent and patient autonomy[28]. A comprehensive evaluation of a SESIs must include rigorous cost-effectiveness analysis and consideration of reimbursement policies. A growing body of evidence suggests that digital interventions for CVD can yield favorable economic outcomes. For instance, a nationally implemented remote cardiac rehabilitation program in Australia that incorporated a mobile app significantly reduced per-patient hospitalization costs by 1488 Australian dollars over 12 months, while lowering mortality rates[29]. However, as noted in a previous systematic review, the cost-effectiveness of such interventions can vary depending on the intervention design, target population, and healthcare system context, necessitating specific evaluations[30].
CONCLUSION
Figure 1 shows the conceptual framework of SESI in CVD management. This minireview provides evidence that the design and implementation of SESIs must be tailored to the specific self-management demands of different CVDs to optimize patient outcomes. Generating evidence of disease-specific efficacy and addressing implementation challenges related to cost-effectiveness and equitable access are crucial for advancing SESIs in clinical practice. Furthermore, future research should strive to provide more granular, disease-specific evidence to clarify the SESI components that are most effective for particular CVD subtypes, moving beyond a “one-size-fits-all” approach.
Figure 1 Conceptual framework of self-efficacy system interventions in cardiovascular disease management.
SESI: Self-efficacy system intervention; AI: Artificial intelligence.
Peer review: Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Psychiatry
Country of origin: China
Peer-review report’s classification
Scientific quality: Grade B, Grade C
Novelty: Grade B, Grade C
Creativity or innovation: Grade B, Grade C
Scientific significance: Grade B, Grade C
P-Reviewer: Carnegie R, PhD, United Kingdom; Clifford BN, MD, United States S-Editor: Wu S L-Editor: A P-Editor: Yu HG
