Assessment of Cardiovascular Disease-Related Distress and Psychosocial Determinants: A Prospective Observational Study

Abstract

Introduction

Acute Coronary Syndrome (ACS) imposes substantial psychological and cardiovascular disease-related distress, influenced by socioeconomic and psychosocial determinants. Identifying these determinants is essential for improving patient outcomes and delivering holistic patient-centered care.

Objectives

This study aimed to assess cardiovascular disease-related distress among patients with Acute Coronary Syndrome (ACS) and examine its association with psychosocial and socioeconomic determinants.

Methodology

A prospective observational study was conducted over five months at a tertiary care hospital after obtaining ethical approval. A total of 105 adult patients diagnosed with ACS by a cardiologist were enrolled. Patients aged ≥ 18 years, of either gender, who provided written informed consent were included in the study. Socio-demographic characteristics, clinical parameters, and cardiovascular disease-related distress were assessed using a structured distress assessment tool comprising domains including Fear and Uncertainty, Changes to Role and Responsibilities, Economic Impact, and Overwhelm and Depletion. Data regarding psychosocial and socioeconomic determinants were collected and analyzed to evaluate their association with distress levels.

Results

The highest distress scores were observed in the domains of changes in roles and relationships, overwhelm and depletion, and fear and uncertainty. Significant associations were identified between cardiovascular distress and socioeconomic status, age, gender, and polypharmacy (p < 0.05). Patients from lower socioeconomic classes and rural areas demonstrated higher psychosocial and economic distress, while female participants reported significantly greater distress in role-related and medication-management domains. No significant association was observed between distress and ACS subtype or comorbidity burden.

Conclusion

Cardiovascular disease-related distress among ACS patients varied across specific psychosocial domains rather than uniformly affecting all aspects of patient well-being. Distinct patterns of distress were observed across socioeconomic groups, highlighting the influence of psychosocial and socioeconomic determinants on cardiovascular distress. These findings emphasize the importance of identifying domain-specific distress to facilitate targeted and individualized patient care.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12872-026-06329-z.

Introduction

Cardiovascular diseases (CVD) remain the leading cause of morbidity and mortality worldwide, accounting for approximately 32% of global deaths, or nearly 17.9 million deaths annually [1]. Among these conditions, Acute Coronary Syndrome (ACS) represents a major clinical emergency characterized by acute myocardial ischemia resulting from reduced coronary blood flow [2]. Despite advances in diagnostic and therapeutic strategies, ACS continues to impose a substantial burden due to high hospitalization rates, long-term disability, and significant mortality, particularly in low- and middle-income countries where rapid urbanization, lifestyle transitions, and limited healthcare access contribute to increasing incidence and poorer outcomes [3, 4]. In addition to traditional biological and lifestyle risk factors, psychological determinants such as emotional distress, depression, anxiety, and chronic stress play a significant role in the progression and prognosis of cardiovascular diseases [5]. These factors can also influence health behaviors, particularly medication adherence, which is essential for effective disease management and prevention of recurrent cardiovascular events [6]. Understanding the psychosocial and socioeconomic factors influencing distress and medication adherence among patients with ACS is therefore essential for improving holistic cardiovascular care. Accordingly, the present study aims to assess cardiovascular disease–related distress and its associated psychosocial determinants among patients with ACS, while also examining medication adherence patterns and relevant socioeconomic influences.

Methodology

This study was a prospective observational design conducted over a period of 5.5 months, from 15 October 2024 to 31 March 2025, at the B & M Patel Cardiac Centre and Shree Krishna Hospital, Karamsad, Anand, Gujarat, India. The study aimed to evaluate cardiovascular disease–related distress among patients diagnosed with Acute Coronary Syndrome (ACS) using Cardiac distress Inventory (CDI) and association between cardiovascular distress and psychosocial determinants, demographic variables, Socioeconomic status, and clinical characteristics. All the patients attending follow-up visits at the B & M Patel Cardiac Centre during the study duration were screened for eligibility and the patients aged ≥ 18 years, diagnosed with ACS (UA, NSTEMI, or STEMI), of either gender, and willing to provide informed consent were included in the study while the patients who underwent major surgery in the previous month or had a scheduled surgery, Pregnant or lactating women, and those with diagnosed psychiatric illness were excluded. A total of 250 patients were screened during the study period, of whom 105 fulfilled the eligibility criteria and were enrolled for final data collection and analysis, while two eligible patients declined participation. The study was Ethical approval was obtained from the Institutional Ethics Committee-2, Bhaikaka University, Karamsad, Anand, Gujarat (DHR Registration No. EC/NEW/INST/2021/592; OHRP Registration No. IRB00014178) under approval number IEC/BU/157/Faculty/9/352/2024 dated 14 October 2024. The study was registered with the Clinical Trials Registry of India (CTRI) under Trial Reference Number REF/2025/02/100177 and Registration Number CTRI/2025/03/082119.

Data collection was performed through face-to-face interviews in a confidential setting using a structured Case Report Form (CRF). The data collection included demographic details, clinical characteristics such as type and severity of ACS, duration of illness, comorbidities, cardiovascular risk factors, and relevant laboratory parameters, Socioeconomic status using updated Modified Kuppuswamy Socioeconomic Scale by Radhakrishnan et al. [21] and Cardiovascular distress was assessed using The Cardiac Distress Inventory (CDI), originally developed and validated by Le Grande et al. [20], was adapted to improve its sociocultural and contextual applicability for the Indian population. Two supplementary domains—Pharmacist and Medication Management (F-A) and Economic Impact (F-B)—were incorporated into the CDI. These domains were developed and sent for the content validation to a multidisciplinary team of 15 experts comprising of 3 cardiologists, 2 psychiatrists, 5 physicians, 3 clinical pharmacists, 1 emergency and trauma care specialist and 1 intensivist. The questions were revised based on the expert consensus and content validity index was calculated for each question and the items with ³ 0.78 CVI score were retained in the domain. Potential sources of bias were considered during study planning and data collection. Selection bias was minimised through consecutive recruitment of eligible ACS patients using predefined inclusion and exclusion criteria. Information bias was reduced through standardized data collection using a structured CRF and validated assessment tools. However, recall bias and response bias associated with self-reported distress measures could not be completely excluded.

All available completed case data were included in the final statistical analysis. Variables with missing or incomplete responses were not subjected to inferential analysis. Statistical analysis was performed using Jamovi software version Descriptive statistics including frequency, percentage, mean, and standard deviation were used to summarize demographic and clinical characteristics of the participants. Data were analyzed using descriptive statistics. As distress scores were ordinal in nature, non-parametric statistical tests were used. The Kruskal–Wallis test, Mann–Whitney U tests and Chi-square tests, pairwise Wilcoxon and Spearman’s correlation were applied.

The Cardiac Distress Inventory (CDI) responses were measured using a five-point Likert scale, where participants rated each statement according to the level of distress experienced. The Likert scale responses ranged from 1 (no distress) to 5 (severe distress). Domain scores were calculated by aggregating the responses for items belonging to each distress domain.

Because Likert-scale responses represent ordinal data, non-parametric statistical tests were applied for inferential analysis. The Kruskal–Wallis test was used to compare distress scores across multiple groups such as socio-economic classes and age groups. The Mann–Whitney U test was used for comparisons between two groups, including gender and locality. Associations between demographic or clinical variables (age group, gender, locality, diagnosis type, comorbidity status, socioeconomic class, and polypharmacy) were evaluated using the Chi-square test for categorical variables.

Statistical significance was considered at a p-value < 0.05 (fig. 1)

Fig. 1.

Study flow diagram

Result

Demographic and Clinical Characteristics of the Study Population

A total of 105 participants were enrolled in the study. Most of the participants, 42 (40.0%) belonged to the age bracket of 61–70 years with male predominance (n = 73; 69.5%). Regarding the burden of comorbidity, 82 (78.1%) had one or more comorbidity. More than half of the participants, 57 (54.2%) had the history of tobacco chewing or smoking or alcohol consumption and belonged to the upper middle or lower middle class. Myocardial Infarction (MI) was the most prevalent ACS subtype observed in 62 (59%) participants. Detailed demographic distribution is provided in Table 1.

Table 1 .

Sociodemographic & clinical characteristics of study population (N = 105)

Characteristic	Category	n (%)
Age (years)	≤ 50	17 (16.2)
	51–60	31 (29.5)
	61–70	42 (40.0)
	71–80	12 (11.4)
	81–90	3 (2.9)
Gender	Male	73 (69.5)
	Female	32 (30.5)
Comorbidity burden	None	23 (21.9)
	Single comorbidity	47 (44.8)
	> 1 comorbidity	35 (33.3)
Social history	Smoking	22 (20.9)
	Tobacco use	25 (23.8)
	Alcohol consumption	10 (9.5)
Residential locality	Rural	37 (35.2)
	Urban	68 (64.8)
ACS subtype	Unstable Angina (UA)	31 (29.5)
	Myocardial Infarction (MI)	62 (59.0)
	Chest pain, troponin-positive ACS	11 (10.5)
	Chest pain, troponin-negative ACS	1 (1.0)
Socioeconomic class (Kuppuswamy Scale)	Upper	5 (4.8)
	Upper Middle	27 (25.7)
	Lower Middle	38 (36.2)
	Upper Lower	35 (33.3)
	Lower	0 (0.0)
Prior surgical history	Yes	84 (80.0)
	No	21 (20.0)

Assessment of Cardiovascular Distress across CDI Psychosocial Factors

Distress was evaluated across ten CDI domains: F1 (Fear and Uncertainty), F2 (Disconnection and Hopelessness), F3 (Changes in Roles and Relationships), F4 (Overwhelm and Depletion), F5 (Cognitive Challenges), F6 (Physical Challenges), F7 (Health System Challenges), F8 (Death Concerns), FA (Pharmacist and Medication Management), and FB (Economic Impact). The lasttwo factors (FA and FB) are developed and validated with respect to the Indian population. The content validity index for all the items were > 0.78 and were considered for addition. A pilot study was carried out among _ patients and Exploratory Factor Analysis (EFA) was evaluated. EFA was selected as the primary framework based on superior Composite Reliability (CR = 0.89), R² value was 0.75 which was acceptable according to the COSMIN guidelines.

Friedman test analysis across all ten factors yielded a statistically significant overall difference (χ² = 830, df = 9, p < 0.001), confirming non-uniform distress contribution across psychosocial domains. The highest mean distress scores were recorded for F3 (Changes in Roles and Relationships; mean = 23.6, median = 23), followed by F4 (Overwhelm and Depletion; mean = 20.6, median = 20.5), and F1 (Fear and Uncertainty; mean = 20.3, median = 21). In contrast, the lowest burden was observed in F7 (Health System Challenges; mean = 5.3), FA (mean = 7.5), and F5 (Cognitive Challenges; mean = 9.2). Pairwise Wilcoxon signed-rank tests confirmed significant differences between most factor dyads (p < 0.05), validating the heterogeneity of distress burden across domains. Detailed factor-level statistics and inferential results are presented in Table 2.

Table 2 .

Summary of CDI distress scores and inferential test results across ten psychosocial factors (N = 105)

CDI Factor	Mean	Median	p-value	Post-hoc Significance
F1 – Fear and Uncertainty	20.3	21	< 0.001	Significantly higher vs. most factors
F2 – Disconnection and Hopelessness	12.1	12	< 0.05	Significant pairwise differences by SEC
F3 – Changes in Roles & Relationships	23.6	23	< 0.001	Significantly higher vs. most factors; significant in polypharmacy group
F4 – Overwhelm and Depletion	20.6	20.5	< 0.001	Significantly higher vs. most factors; significant in polypharmacy group
F5 – Cognitive Challenges	9.2	9	> 0.05	No significant pairwise differences
F6 – Physical Challenges	14.3	14	> 0.05	No significant pairwise differences
F7 – Health System Challenges	5.3	5	> 0.05	No significant pairwise differences
F8 – Death Concerns	8.2	8	< 0.05	61–70 yr group significantly higher (DSCF; χ2 = 18.43)
FA – Pharmacist & Medication Management	7.5	7	< 0.05	Significant gender difference (females higher); significant by SEC
FB – Economic Impact	11.4	11	< 0.05	Lower Middle & Upper Lower > Upper (DSCF post-hoc); rural > urban (p ≈ 0.05)

F1–F8 = original CDI factors; FA = Pharmacist and Medication Management (supplementary); FB = Economic Impact (supplementary). Overall Friedman test: χ² = 830, df = 9, p < 0.001. Pairwise comparisons by Wilcoxon signed-rank test. Post-hoc pairwise comparisons for subgroup analyses by Dwass-Steel-Critchlow-Fligner (DSCF) test. SEC = socioeconomic class. '—' in Friedman χ² column denotes that the statistic is reported collectively for the omnibus test

^*p < 0.05

Association between Distress and ACS Diagnostic Subtype

Kruskal–Wallis analysis revealed no statistically significant differences in distress across ACS subtypes (UA, MI, troponin-positive ACS, and troponin-negative ACS) for any of the ten CDI factors (all p > 0.05). Although MI patients exhibited numerically higher median scores—notably in F3 (median = 24), F4 (median = 22), and F6 (median = 18)—these differences did not attain statistical significance, indicating that ACS subtype per se is not a primary driver of psychosocial distress.

Association between Distress and Socioeconomic Class

Significant associations were identified between socioeconomic class (SEC) and distress in F2 (Disconnection and Hopelessness), FA (Pharmacist and Medication Management), and FB (Economic Impact) (all p < 0.05). Post-hoc DSCF tests revealed that FB was significantly elevated in the Lower Middle and Upper Lower classes relative to the Upper class, while the Upper Middle class reported lower distress than the Upper Lower class—highlighting the protective role of financial stability. No significant pairwise differences were found between Lower Middle and Upper Lower classes for F2 and FA despite overall group-level significance. These findings underscore the bidirectional influence of financial insecurity and limited healthcare access on psychosocial distress burden in lower SEC strata.

Association between Distress and Age Group

Distress scores were highest among participants aged 61–70 years across factors F4, F3, F6, and F1, while those aged ≤ 50 and 51–60 years reported comparatively lower scores. a statistically significant age-group effect exclusively for F8 (Death Concerns; χ² = 18.43, p < 0.05) was observed. DSCF post-hoc tests confirmed that the 61–70 year cohort reported significantly greater F8 distress than those with < 50 years of age and 51–60 year age group, suggesting age specific distress due death concern (Table 3).

Table 3 .

Median CDI distress scores stratified by socioeconomic class and age group (N = 105)

Subgroup	F1	F2	F3	F4	F5	F6	F7	F8	FA	FB
Socioeconomic Class (Kuppuswamy Scale)
Upper	8	8	11	10	4	12	5	4	6	4
Upper Middle	16	10	19	21	8	14	5	8	6	8
Lower Middle	21	16	23.5	22.5	10	18	5	14	7	14.5
Upper Lower	16	12	19	20	4	16	5	11	6	15
Kruskal–Wallis p-value	> 0.05	< 0.05*	> 0.05	> 0.05	> 0.05	> 0.05	> 0.05	> 0.05	< 0.05*	< 0.05*
Age Group (years)
< 50	13	6	17.5	18.5	8	13	5	5	4	5
51–60	15	7	19	21	10	15	5	5	4.5	5
61–70	24	8	22	24	13	15	5	13	5	5
71–80	19	6.5	21	20	12	14	5	9	5	5
81–90	11	6	16	16	10	10	5	4	4	5
Kruskal–Wallis p-value	> 0.05	> 0.05	> 0.05	> 0.05	> 0.05	> 0.05	> 0.05	< 0.05*	> 0.05	> 0.05

Median scores reported for each CDI factor per subgroup. Kruskal–Wallis test applied across subgroups

^*p < 0.05. DSCF post-hoc test for significant factors. SEC = Socioeconomic Class (Kuppuswamy Scale). F8 showed significant age-group effect (χ² = 18.43, p < 0.05); FB and F2 showed significant SEC effect (p < 0.05). FA = Pharmacist and Medication Management; FB = Economic Impact

Gender-based Differences in Distress

Although Male participants demonstrated higher distress scores, statistically significant gender differences were observed in F3 (Changes in Roles and Relationships), F4 (Overwhelm and Depletion), F6 (Physical Challenges), and FA (Pharmacist and Medication Management) (Mann–Whitney U test, all p < 0.05), with female participants reporting higher median distress in these domains.

Association between Distress and Residential Locality

Rural participants consistently reported higher distress scores across all ten CDI factors compared to urban counterparts, with the most pronounced differences in F3, F4, and F6. A borderline-significant difference was observed in FB (Economic Impact; p ≈ 0.05), with rural residents reporting greater economic distress, likely reflecting limited healthcare infrastructure and financial vulnerability. Only FB approached statistical significance; other factors showed directional but non-significant trends, indicating that rural residence selectively amplifies economic and role-related distress dimensions.

Association between Distress and Comorbidity Burden

Participants with one or more comorbidities consistently reported higher distress scores across most factors, with peak values in F3. Those without comorbidities demonstrated the lowest scores, particularly in F1 and F5. Despite these observable trends, Kruskal–Wallis analysis did not identify statistically significant differences across comorbidity groups for any CDI factor (all p > 0.05), suggesting that comorbidity count per se does not independently drive psychosocial distress, with broader psychosocial and contextual factors likely serving as more proximal determinants.

Association between Distress and Prior Surgical History

Participants with prior surgical history reported consistently higher distress scores across all CDI factors compared to those without such history. Notably elevated distress was observed in F3, F1, and F6, suggesting persistent psychosocial burden linked to prior operative experience. These findings highlight the need for targeted psychological assessment in post-surgical ACS patients as part of holistic follow-up care.

Association between Polypharmacy and Distress

Polypharmacy (concurrent use of ≥ 5 medications) was significantly associated with elevated distress in F3 (Changes in Roles and Relationships) and F4 (Overwhelm and Depletion) (Mann–Whitney U test, both p < 0.05). However, no statistically significant linear relationship was identified between polypharmacy status and overall cumulative distress (Spearman's rank correlation, p > 0.05). Strong intercorrelations among distress factors (p < 0.001) support a multifactorial model wherein polypharmacy acts as a contributing, though not exclusive, determinant of psychosocial distress.

Discussion

The present study evaluated cardiovascular disease–related distress among patients with Acute Coronary Syndrome (ACS) using the Cardiac Distress Inventory (CDI) and demonstrated that distress among ACS patients is multidimensional, extending beyond physical symptoms to include emotional, social, functional, and economic burdens. The findings identified Fear and Uncertainty (F-1), Economic Impact (F-B), Overwhelm and Depletion (F-4), and Changes in Roles and Responsibilities (F-3) as the predominant distress domains, highlighting the complex psychosocial challenges experienced following ACS.

Fear and Uncertainty (F-1) demonstrated the highest distress level (58.58%), suggesting persistent concerns regarding recurrence of cardiac events, uncertainty about long-term health stability, fear of physical exertion, and limitations in daily functioning. These findings are consistent with previous studies reporting that fear of recurrence and uncertainty are among the most significant contributors to psychological morbidity after ACS and other cardiovascular conditions [7, 8] The present findings further support existing evidence indicating that fear-related distress may negatively influence emotional recovery, lifestyle modification, and long-term adherence to cardiac rehabilitation [9]. In our cohort, women commonly expressed these fears in relation to caregiving responsibilities and family dependence, while rural participants highlighted concerns regarding access to emergency medical services and delayed treatment availability. Similar gender- and locality-related disparities have been documented in cardiovascular psychosocial research [10, 11].

Economic Impact (F-B) emerged as another major distress domain, particularly among lower socioeconomic groups. Patients frequently reported concerns related to direct treatment costs, reduced earning capacity, long-term medication expenses, and the burden of polypharmacy. These findings align with prior literature demonstrating that socioeconomic disadvantage significantly affects cardiovascular outcomes, healthcare accessibility, medication adherence, and quality of life [12]. Financial toxicity associated with chronic cardiovascular care has increasingly been recognized as a clinically relevant determinant of distress and treatment burden [13]. Compared with earlier studies primarily focusing on anxiety and depression, the present study expands current knowledge by specifically evaluating the economic dimension of cardiovascular distress using a modified CDI adapted for the local population.

Changes in Roles and Responsibilities (F-3) reflected the impact of ACS on occupational, familial, and social functioning. Younger participants commonly described fears regarding employment instability and loss of productivity, whereas elderly participants experienced dependency and withdrawal resulting from reduced physical functioning and overprotective family behavior. Similar findings have been observed in cardiac rehabilitation and quality-of-life studies, where disruptions in social functioning and autonomy significantly contributed to emotional distress and impaired recovery [14].

Overwhelm and Depletion (F-4) was especially prominent among elderly participants and those with multiple comorbidities. Emotional exhaustion, low energy levels, and reduced coping capacity were commonly reported, consistent with previous evidence linking emotional fatigue with poor rehabilitation adherence, decreased functional recovery, and lower health-related quality of life in cardiovascular populations [15]. The present findings further emphasize that psychosocial exhaustion in ACS patients may coexist with physical illness burden and should be routinely assessed during follow-up care.

Disconnection and Hopelessness (F-2), Cognitive Distress (F-5), and Physical Distress (F-6) showed comparatively lower distress scores. This observation may partly reflect the protective influence of extended family structures, community support, and cultural coping mechanisms commonly present in the Indian setting. Previous studies have similarly demonstrated that strong social support and spirituality may reduce emotional distress and improve resilience in cardiovascular patients [16]. Health system-related distress (F-7) and death-related concerns (F-8) were also relatively low, possibly reflecting improved healthcare accessibility, increasing awareness regarding cardiovascular treatment, and positive expectations toward recovery.

Distress associated with Pharmacist and Medication Management (F-A) was comparatively minimal. However, the limited frequency of pharmacist-led counseling and medication education observed during the study suggests substantial opportunity for improving medication literacy, adherence counseling, and multidisciplinary cardiovascular care. Existing evidence supports the role of pharmacist-led interventions in improving medication adherence, reducing adverse events, and enhancing patient understanding of long-term cardiovascular therapy [17, 18].

The study also demonstrated significant variations in distress patterns across gender, socioeconomic status, and locality. Female participants showed higher distress levels in emotional and physical domains, consistent with studies reporting that women with cardiovascular disease often experience greater psychosocial burden, anxiety, depressive symptoms, and reduced quality of life compared to men [10, 19]. Rural participants demonstrated higher distress across multiple domains, likely due to limitations in healthcare accessibility, transportation barriers, delayed emergency care, and reduced healthcare resources.

Compared with existing literature, the present study provides a broader multidimensional evaluation of cardiovascular distress by integrating psychosocial, functional, and economic determinants using the CDI framework. While many previous studies focused predominantly on depression and anxiety following ACS, this study highlights domain-specific distress patterns and demonstrates how socioeconomic and psychosocial determinants influence different aspects of cardiovascular distress. The inclusion of culturally adapted domains such as Economic Impact and Pharmacist and Medication Management further improves contextual relevance for the Indian population and contributes additional evidence regarding psychosocial distress assessment in low- and middle-income settings.

Overall, the findings emphasize that cardiovascular disease–related distress in ACS patients is heterogeneous and domain-specific rather than uniform across all psychosocial areas. These findings support the incorporation of structured psychosocial screening into routine cardiovascular care and highlight the need for individualized, multidisciplinary, and patient-centered interventions addressing emotional, financial, and social determinants of recovery.

Strengths and limitations

The present study has several notable strengths. It prospectively evaluated cardiovascular disease–related distress among a clinically diverse ACS population, including UA, NSTEMI, and STEMI patients, using the Cardiac Distress Inventory (CDI), a validated multidimensional assessment tool. In addition to established CDI domains, the incorporation of culturally relevant domains related to Economic Impact and Pharmacist and Medication Management improved contextual applicability for the Indian population. The study further explored associations between distress and demographic, socioeconomic, clinical, and treatment-related variables, contributing to the limited Indian literature on cardiovascular distress following ACS.

However, certain limitations should be considered. The study was conducted at a single center with a relatively modest sample size, which may limit generalizability. The study was prospective in nature which precludes evaluation of longitudinal changes and causal relationships. The findings were based on self-reported responses and may therefore be subject to reporting and recall bias.

Conclusion

Post-ACS distress arises from a combination of fear of recurrence, financial pressures, emotional exhaustion, and disruption of established social and occupational roles. Vulnerable groups—particularly women, rural residents, and those with comorbidities or limited financial resources—require targeted psychosocial and economic support. These findings highlight the potential importance of routine distress screening, patient counselling, and socioeconomic support in improving post-ACS recovery.

Abbreviations

ACCF

American College of Cardiology Foundation

ACDS

Adherence in Chronic Disease Scale

ACS

Acute Coronary Syndrome

AHA

American Heart Association

AI

Artificial Intelligence

AM

Angina Myocardium

BP

Blood Pressure

CAD

Coronary Artery Disease

CABG

Coronary Artery Bypass Grafting

CBT

Cognitive Behavioural Therapy

CDI

Cardiac Distress Inventory

CHW-Led Interventions

Community Health Worker–Led Interventions (programs in which trained community health workers provide patient education, counselling, and adherence support)

CVD

Cardiovascular Disease

ECG

Electrocardiogram

EF

Ejection Fraction

ESC

European Society of Cardiology

GCP

Good Clinical Practice

HF

Heart Failure

HOD

Head of Department

HR

Heart Rate

HRQoL

Health-Related Quality of Life

HTN

Hypertension

IEC

Institutional Ethics Committee

LMICs

Low- and Middle-Income Countries

mHealth

Mobile Health

MI

Myocardial Infarction

NCDs

Non-Communicable Diseases

NSTEMI

Non-ST-Elevation Myocardial Infarction

PCI

Percutaneous Coronary Intervention

PHQ

Patient Health Questionnaire

QoL

Quality of Life

RCT

Randomized Controlled Trial

SEC

Socioeconomic Class/Status

STEMI

ST-Elevation Myocardial Infarction

UA

Unstable Angina

WHO

World Health Organization

Authors’ contributions

Authors A, B, C, and D contributed to study conceptualization, study design, data collection, conduct of the study, data analysis, and manuscript drafting. Authors E, F, G, H, and I contributed to study design, critical revision of the manuscript, interpretation of results, and provided overall scientific guidance. All authors read and approved the final manuscript.

Funding

Not applicable. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request and with permission from the original author, in accordance with institutional and ethical guidelines.

Declarations

Ethics approval and consent to participate

Ethical approval for this study was obtained from the Institutional Ethics Committee-2, Bhaikaka University, Karamsad, Anand, Gujarat (DHR Registration No. EC/NEW/INST/2021/592; OHRP Registration No. IRB00014178) under approval number IEC/BU/157/Faculty/9/352/2024 dated 14 October 2024 prior to initiation of the study. The study was conducted in accordance with the International Council for Harmonisation–Good Clinical Practice (ICH-GCP) guidelines and the Declaration of Helsinki. Written informed consent was obtained from all participants before enrolment in the study.

Consent for publication

Written informed consent for publication was obtained from all participants. Participant anonymity was strictly maintained, and no personally identifiable information has been included in this manuscript.

Competing interests

The authors declare no competing interests.