Accessibility settings

This paper is in the following e-collection/theme issue:

Formative Evaluation of Digital Health Interventions (3588) eHealth Literacy / Digital Literacy (591) Methods and Feasibility Studies (571) User Needs and Competencies (515) Cross-Sectional Studies in Public Health (486) Health Care Quality and Health Services Research (370) Patient-Views on Cardiology Technology and Innovations (15) Patient Education in Cardiology (52)

Published on in Vol 10 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/71647, first published .
Man checking heart rate on smartphone app with cardiogram

The Role of eHealth Literacy and Patient Adherence in Mediating Health Consciousness and Perceived Severity in Quality of Life Among Young Patients With Ischemic Heart Disease: Cross-Sectional Study

The Role of eHealth Literacy and Patient Adherence in Mediating Health Consciousness and Perceived Severity in Quality of Life Among Young Patients With Ischemic Heart Disease: Cross-Sectional Study

Authors of this article:

Puteri Sofia Nadira Megat Kamaruddin1, 2 Author Orcid Image ;   Azmawati Mohammed Nawi2 Author Orcid Image ;   Mohamad Nurman Yaman3 Author Orcid Image ;   Rajiv Chowdhury4 Author Orcid Image ;   Mohd Shawal Faizal Mohamad5 Author Orcid Image ;   Abdul Muizz Abdul Malek6 Author Orcid Image ;   Ezarul Faradianna Lokman1 Author Orcid Image ;   Mohd Fairulnizal Md Noh1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    1Nutrition, Metabolism and Cardiovascular Research Centre, Institute for Medical Research, National Institutes of Health, Ministry of Health Malaysia, Shah Alam, Malaysia

    2Department of Public Health Medicine, Faculty of Medicine, University Kebangsaan Malaysia Medical Centre, Ministry of Higher Education Malaysia, Jalan Yaacob Latif, Kuala Lumpur, Malaysia

    3Department of Medical Education, Faculty of Medicine, Universiti Kebangsaan Malaysia, Ministry of Higher Education Malaysia, Kuala Lumpur, Malaysia

    4Robert Stempel College of Public Health and Social Work, Academic Health Center 5, Florida International University, Miami, United States

    5Cardiology Medical Department, University Kebangsaan Malaysia Medical Centre, Ministry of Higher Education Malaysia, Kuala Lumpur, Malaysia

    6Cardiology Department, Medical, Serdang Hospital, Ministry of Health Malaysia, Kuala Lumpur, Malaysia

    Corresponding Author:

    Azmawati Mohammed Nawi, MD, MCommMed, PhD


    Background: Ischemic heart disease (IHD) is becoming increasingly prevalent, with a rising trend significantly impacting the quality of life (QoL) of young Malaysians.

    Objective: This study aimed to investigate the direct and indirect relationships between eHealth literacy (eHealth) and patient adherence (PA), as well as their mediating effects on the associations of health consciousness (HC) and perceived severity to chronic disease (PS) with QoL among young patients with IHD.

    Methods: A cross-sectional study was conducted at 2 hospitals, recruiting eligible patients through consecutive sampling at outpatient cardiology clinics. Data were collected via a validated self-administered questionnaire encompassing sociodemographic and socioeconomic status, medical history, and PS, HC, eHealth, PA, and QoL data. Structural equation modeling analysis was used to evaluate the relationships. The ethics approval was obtained from the Ethical Committee of Universiti Kebangsaan Malaysia Medical Centre (FF-2021-117).

    Results: A total of 136 young patients with IHD participated in the study. Structural equation modeling analysis revealed that eHealth had the strongest positive effect on QoL (β=0.287, P=.002), followed by PA (β=0.245, P=.02), and HC (β=0.218, P=.02). Two significant mediation models were identified, aligning with the Transactional Model of eHealth Literacy theory. The first model demonstrated parallel mediation, where eHealth (β=0.125, P=.008) and PA (β=0.083, P=.046) significantly mediated the relationship between HC and QoL. The second model indicated serial mediation through eHealth and PA between HC and QoL (β=0.042, P=.049). The parallel mediation model exhibited medium predictive power and was deemed the best-fit model.

    Conclusions: The parallel model pathway showed significant direct and indirect associations between eHealth, PA, HC, and QoL, with eHealth demonstrating the strongest association. Higher eHealth and PA were associated with QoL among young patients with IHD; interventions to support eHealth warrant further investigation in longitudinal or interventional studies.

    JMIR Form Res 2026;10:e71647

    doi:10.2196/71647

    Keywords

    eHealth literacy; patient adherence; quality of life; young ischemic heart disease; chronic disease 


    Overview

    Cardiovascular diseases (CVDs) are the primary cause of mortality globally, with ischemic heart disease (IHD) serving as the largest contributor to this health burden. According to the World Health Organization (WHO), an estimated 19.8 million people died from CVDs in 2022, representing approximately 32% of all global deaths. Of these, about 85% were caused by heart attacks and strokes, and over three-quarters of CVD-related deaths occurred in low- and middle-income countries [1]. The Sustainable Development Goals established by the United Nations in 2015 comprise 17 interconnected objectives designed to tackle a broad range of social, economic, and environmental issues, with a target for achievement by 2030 [2]. A primary objective of Sustainable Development Goals 3 (good health and well-being) is to achieve a one-third reduction in premature deaths caused by noncommunicable diseases through effective prevention and treatment strategies [3].

    Although IHD predominantly affects older adults, its prevalence among younger populations is increasing, particularly in South Asia [4,5]. In Malaysia, research indicates a rising prevalence of IHD among individuals less than 45 years of age, a pattern similarly observed in other Southeast Asian nations. In a study, 6.1% of Malaysian patients with IHD were below 45 years old, with a mean age of 39 (SD 6) years [6]. Another study found that 16% of patients undergoing percutaneous coronary intervention for IHD were considered “young,” defined as men below 45 years of age and women below 55 years of age [6,7]. While IHD is less common in younger individuals, it can result in significant morbidity, emotional distress, and financial burdens, particularly because these individuals are the most productive.

    For young patients with IHD, managing the disease is particularly challenging, as they must balance health concerns with work, family, and social responsibilities [6,7]. The recurrence of IHD can drastically reduce quality of life (QoL), leading to physical, emotional, and social difficulties [8]. Understanding and improving patient adherence (PA) to medical recommendations is crucial for managing IHD and enhancing QoL within this demographic [9,10]. Research has shown that good adherence can improve outcomes, whereas nonadherence can lead to disease progression, reduced functionality, and diminished QoL [10,11].

    In the modern digital era, young people are increasingly dependent on technology for accessing health information. eHealth, as defined by the WHO, refers to the cost-effective and secure use of information and communication technologies in support of health and health-related fields, including health services, surveillance, and education. Correspondingly, eHealth literacy is the ability to seek, find, understand, and apply health information from electronic sources to address or solve a health problem, which has become vital for effectively managing chronic conditions such as IHD [12,13]. The effect of eHealth literacy on PA and overall QoL in young individuals with IHD remains unclear [14]. While some studies suggest that higher eHealth literacy can improve disease management and adherence, the literature does not provide a clear consensus on this relationship [15,16].

    Theoretical Framework

    Despite increasing global interest in eHealth literacy, there remains a theoretical gap in understanding how it translates into behavioral outcomes, such as PA, particularly among younger populations with chronic conditions such as IHD. Existing models often focus on general populations or older adults, leaving the mechanisms linking eHealth literacy to PA and QoL in young patients with IHD insufficiently explored. Moreover, the dynamic and context-sensitive interactions proposed by the Transactional Model of eHealth Literacy (TMeHL) have yet to be empirically tested among younger cohorts with IHD, underscoring the need to bridge this theoretical void.

    The TMeHL, proposed by Paige et al [17], conceptualizes eHealth literacy as a dynamic and reciprocal process influenced by personal, social, and technological contexts. It posits that individuals continuously interact with digital health information, shaping their knowledge, attitudes, and health behaviors through iterative exchanges. The model integrates individual determinants, such as health consciousness (HC) and perceived severity to chronic disease (PS), with behavioral outcomes, such as PA, ultimately influencing health and QoL. Guided by this framework, this study applies the TMeHL to examine both direct and indirect (mediated) pathways linking these constructs among young patients with IHD.

    Guided by TMeHL, this study sought to examine the connections between eHealth literacy, PA, and QoL in young individuals with IHD in the Klang Valley, an urban agglomeration in Malaysia. From a practical standpoint, there is a regional knowledge gap concerning how eHealth literacy and adherence interact within the sociocultural context of Malaysia and the broader Southeast Asian region. While global literature on digital health and adherence is expanding, few studies have investigated how regional variations, such as differences in health care accessibility, digital infrastructure, and cultural perceptions of health, affect these relationships. Understanding these contextual nuances is vital for developing tailored eHealth interventions and PA strategies relevant to young patients with IHD in Malaysia and neighboring countries. This study investigates how eHealth literacy and PA mediate the relationships between HC, PS, and QoL among young patients with IHD, thereby advancing the understanding of how digital health competencies translate into improved management and QoL. To achieve these objectives, a cross-sectional study design was adopted, using validated self-administered questionnaires to capture multidimensional constructs related to eHealth literacy, PA, and QoL among young patients with IHD.


    Study Design, Setting, and Reporting Standards

    This cross-sectional study was conducted between November 2021 and June 2022 at 2 tertiary cardiac referral centers in the Klang Valley, Malaysia: Hospital Serdang, Selangor, and Hospital Canselor Tuanku Muhriz, Kuala Lumpur. Both centers provide specialist cardiology services, including cardiac rehabilitation and structured outpatient follow-up, for patients diagnosed with IHD. The inclusion of a public specialist hospital and a university hospital was intended to ensure diversity while maintaining comparable clinical management across sites.

    The reporting of this study adhered to established international reporting guidelines to enhance transparency and reproducibility. The manuscript was prepared in accordance with the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement for cross-sectional research (Checklist 1) [18].

    Participants and Eligibility Criteria

    The study population comprised young adults aged 18 to 45 years diagnosed with IHD (unstable angina, ST-elevation myocardial infarction [STEMI], non-STEMI, or stable angina) who attended cardiology outpatient follow-up appointments during the study period. Eligible participants were Malaysian patients who were able to understand either English or Malay and had provided written informed consent.

    Patients were excluded if they had documented dementia or psychiatric illness, evidence of alcohol intoxication, or altered mental status (eg, lethargy, confusion, restlessness, or delirium) that could impair their ability to participate in study procedures. Eligibility was verified through documented case notes and clinical assessment prior to enrollment.

    Recruitment Procedures and Sampling

    Participants were recruited from cardiology outpatient clinics and follow-up services during routine clinical visits. Consecutive sampling was employed, whereby all eligible patients attending the clinics during the recruitment period were approached sequentially until the required sample size was achieved. This approach was selected due to the specialized nature of the young population with IHD and logistical constraints during the COVID-19 pandemic. Participants primarily consisted of patients previously discharged from cardiology wards who were attending scheduled outpatient follow-up or cardiac rehabilitation appointments.

    Clinic appointment lists and medical records were screened by the researchers to identify potentially eligible and clinically stable patients. Eligible individuals were approached during their outpatient visits and provided with a written participant information sheet outlining the study objectives and the voluntary nature of participation, and written informed consent was obtained prior to enrollment.

    Data Collection Procedures

    Following consent, participants completed a validated self-administered questionnaire that included sections on sociodemographic characteristics, medical history, PS, HC, eHealth literacy, PA, and QoL. Depending on participant preference and prevailing public health restrictions during the COVID-19 pandemic, questionnaires were administered either in printed format during clinic visits or electronically via an online platform. Participants were allowed to seek clarification from the researcher at any time during completion.

    Ethical Considerations

    Ethics Approval

    The ethical approval for this study was obtained prior to data collection from the Ethical Committee of Universiti Kebangsaan Malaysia Medical Centre (FF-2021‐117) and the National Medical Research Register, Ministry of Health Malaysia (NMRR-21-178-57942). Institutional permissions were also secured from the participating hospitals and cardiology departments before recruitment commenced.

    Informed Consent

    Written informed consent was obtained from all participants prior to enrollment. Participants were provided with a participation information sheet outlining the purpose of the study, procedures involved, potential risks and benefits, and their rights as research participants. Individuals were informed that participation was voluntary and that they could decline participation or withdraw from the study at any time without consequences to their clinical care or services received. Contact information for the researcher was provided should participants have any questions or require clarification before consenting.

    Privacy and Confidentiality

    All collected data were treated as confidential. Participants’ identities were protected through the use of unique identification codes, and identifiable information was accessible only to the research team for study purposes. Questionnaires were administered in either printed or secure online formats, and data were stored in password-protected systems accessible only to authorized researchers. Personal health information was handled in accordance with institutional [19] and national ethical guidelines [20] to ensure participant privacy and data security.

    Compensation

    No financial incentives or material compensation were provided to participants for their involvement in this study.

    Hypotheses Development

    Guided by the TMeHL and empirical evidence from prior research [17], this study proposed a series of hypotheses to examine both the direct and indirect relationships among HC, PS, eHealth literacy, PA, and QoL among young patients with IHD (Multimedia Appendices 1 and 2). Specifically, it was hypothesized that both HC and PS would have significant positive effects on QoL (H1-H2). Furthermore, higher levels of PA and eHealth literacy were expected to be directly associated with improved QoL (H3–H4).

    In addition to these direct associations, the study also tested the mediating roles of eHealth literacy and PA. It was posited that PA and eHealth literacy would each independently mediate the relationships between HC and QoL (H5-H6), as well as between PS and QoL (H7-H8). Beyond these parallel pathways, the study further hypothesized that eHealth literacy and PA would operate in a serial sequence, such that eHealth literacy enhances PA, which in turn improves QoL, when initiated by HC (H9) and by PS (H10).

    Finally, the study postulated that the overall structural model would demonstrate strong predictive capability in explaining the variance in QoL among young patients with IHD (H11).

    Study Tools

    Translation and cross-cultural adaptation were conducted to ensure the questionnaire’s content validity and to preserve its psychometric properties [21-25]. A validated and reliable self-administered questionnaire was utilized, as described in a previous study [26].

    Part A explored participants’ sociodemographic and socioeconomic characteristics, including age, sex, race, gross monthly income, occupation, and education level. These variables were categorized following the National Health and Morbidity Survey guidelines [27]. Part B collected information about participants’ medical history, such as comorbidities, coexisting medical conditions, and their diagnosis of IHD (unstable angina, non-STEMI, STEMI, or stable angina). The duration since the initial IHD diagnosis was also recorded.

    Part C focused on PS via a validated 4-item scale (Cronbach α=0.907) originally developed for South Koreans [28]. PS refers to how an individual personally evaluates the seriousness of a chronic health condition. Part D assessed HC through a 5-item scale addressing various dimensions of health awareness and concern (Cronbach α=0.720‐0.835) [29-31].

    Part E assessed eHealth literacy, which is the capacity to find, assess, and utilize eHealth information for managing health [32]. The original eHEALS (eHealth Literacy Scale) in English, which had a Cronbach α of 0.90, was modified for use in this study [33]. Part F examined PA to recommendations and behaviors via a modified English version of the Medical Outcomes Study Specific Adherence Scale. This validated scale (Cronbach α=0.58‐0.77) measures PA to medication regimens and healthy lifestyle practices [34-38].

    Part G employed the World Health Organization Quality of Life: Brief Version (WHOQOL-BREF) questionnaire, a widely used tool for evaluating QoL in patients with cardiovascular disease. This self-administered questionnaire consists of 26 questions that evaluate 4 areas: physical health, mental well-being, social connections, and the environment. The WHOQOL-BREF questionnaire (Malay) demonstrates high internal consistency, with a Cronbach α of 0.89 for 24 items [39].

    Sample Size

    Sample size determination was conducted using the F test of regression via G*Power software (Erdfelder, Faul, and Buchner) [40]. A power analysis for multiple regression with 9 predictors was performed to determine the appropriate sample size. The sample size was determined using a medium effect size, consistent with Cohen convention, to ensure adequate statistical power while maintaining a feasible and methodologically sound sample size in the absence of prior effect estimates [41]. The measure (f2=0.15) [42,43] was set with an α level of 0.05 and a statistical power of 80%. The minimum required sample size for this study was 114 participants.

    Data Analysis

    The analysis was performed using the SPSS software (IBM Corporation, 2019) and SmartPLS version 4.0.9.2 (SmartPLS GmbH) [44], a widely used tool for statistical analysis. Patient characteristics were summarized through the descriptive analysis. Continuous variables were reported as means and SDs, whereas categorical data were presented as frequencies and percentages.

    The study used partial least squares (PLS) analysis via SmartPLS version 4.0.9.2 [44] to explore the TMeHL theory. A 2-stage approach was used to examine direct and indirect effects (parallel and serial mediation) and to assess the model’s predictive power in relation to QoL among young patients with IHD [45]. This approach enabled the evaluation of the exploratory nature and applicability of the TMeHL theory within this context.


    Participant Characteristics

    A total of 136 patients with IHD, with a mean age of 39.2 (SD 5.22) years, took part in the study. The majority were of Malay ethnicity (n=98, 72.1%) and male (n=112, 82.4%). Socioeconomically, the participants reported an average monthly income of RM 2447.88 (SD RM 2186.11; approximately US $544.18 [SD US $485.99]), with most being private employees (n=62, 45.6%) and having attained secondary education (n=71, 52.2%). With respect to their medical background, most patients had multiple comorbid conditions (n=95, 69.9%) and were diagnosed with STEMI (n=59, 43.4%) within the past 1 to 5 years (n=79, 58.1%; Table 1).

    Table 1. Sociodemographic characteristics of young patients with ischemic heart disease (IHD) who participated in a cross-sectional observational study conducted at 2 tertiary cardiac referral centers in the Klang Valley, Malaysia, between November 2021 and June 2022 (N=136).
    Participant’s characteristicsValues
    Sociodemography
    Age (y), mean (SD)39.2 (5.22)
    Gender, n (%)
    Male112 (82.4)
    Female24 (17.6)
    Race, n (%)
    Malay98 (721)
    Chinese17 (12.5)
    Indian17 (12.5)
    Othersa4 (3)
    Socioeconomy
    Monthly income (RM), mean (SD)2447.88 (2186.11)b
    Occupation, n (%)
    Government employee16 (11.8)
    Private employee62 (45.6)
    Self-employed32 (23.5)
    Unpaid worker/housewife8 (5.9)
    Not working18 (13.2)
    Education level, n (%)
    Nil9 (6.6)
    Primary6 (4.4)
    Secondary71 (52.2)
    Tertiary50 (36.8)
    Medical history
    Comorbid, n (%)
    Nil41 (30.1)
    Yes95 (69.9)
    Diagnosis, n (%)
    STEMIc59 (43.4)
    NSTEMId41 (30.1)
    Unstable angina24 (17.6)
    Stable angina12 (8.8)
    Years diagnosed with ischemic heart disease, n (%)
    Less than 1 year50 (36.8)
    1‐5 years79 (58.1)
    6‐10 years7 (5.1)

    aBumiputera Sabah and Sarawak.

    bApproximately US $544.18 (SD US $485.99).

    cSTEMI: ST-elevation myocardial infarction.

    dNSTEMI: non–ST-elevation myocardial infarction.

    Measurement Model

    The multivariate normality assumption was tested and found to be violated, as reflected by Mardia multivariate skewness (β=11.567, P<.001) and kurtosis (β=73.321, P<.001) [46]. Consequently, nonparametric bootstrapping with 5000 samples was performed during the structural model assessment [47]. A full collinearity assessment confirmed the absence of bias from single-source data, with variance inflation factor values ranging from 1.032 to 1.636 (see Multimedia Appendix 3).

    The measurement model was evaluated for outer loadings (indicator reliability), convergent validity, and internal consistency reliability (see Multimedia Appendix 4). The outer loadings ranged from 0.668 to 0.937 for all the constructs, exceeding the acceptable threshold of 0.5, and no items were removed. Convergent validity was determined by examining the average variance extracted values, ranging from 0.632 to 0.823, and factor loadings, all above the recommended 0.50 threshold [48]. The values for composite reliability were between 0.792 and 0.965, reflecting strong internal consistency.

    The Heterotrait-Monotrait ratio of correlation was subsequently used to evaluate discriminant validity. Table 2 shows the Heterotrait-Monotrait ratio of correlation values ranging from 0.122 to 0.590, meeting the condition of ≤0.85 [47,49], confirming that all the constructs are distinct from each other.

    Table 2. Discriminant validity using the Heterotrait-Monotrait ratio of correlations among constructs derived from a cross-sectional study examining the relationship between eHealth literacy, patient adherence, health consciousness, perceived severity to chronic disease, and quality of life among young patients with ischemic heart disease attending tertiary cardiac referral centers in the Klang Valley, Malaysia (November 2021-June 2022; N=136), analyzed using partial least squares-structural equation modeling (PLS-SEM).
    VariableHealth consciousnessPatient adherencePerceived severity to chronic diseaseQuality of lifeeHealth literacy
    Health consciousness—a0.3700.5900.3760.540
    Patient adherence0.370—0.2280.4540.450
    Perceived severity to chronic disease0.5900.228—0.1220.425
    Quality of life0.3760.4540.122—0.410
    eHealth literacy0.5400.4500.4250.410—

    aNot applicable.

    Structural Model

    Direct Effects

    Table S3 in Multimedia Appendix 1 presents the hypothesis testing of direct effects, controlling for variables, such as age, comorbidities, education, sex, IHD diagnosis, years diagnosed with IHD, monthly income, occupation, and ethnicity. These control variables were identified due to their well-established influence on patients with IHD, as reported in previous studies [50,51].

    The analysis revealed that HC (β=0.218, t=2.114, P=.02), PA (β=0.245, t=2.123, P=.02), and eHealth literacy (β=0.287, t=2.887, P=.002) were positively associated with QoL, as confirmed by bias-corrected CIs that did not straddle 0. Conversely, PS (β=−.157, t=1.543, P=.06) was not significantly associated with QoL.

    Among the direct relationships with QoL, eHealth literacy exhibited the strongest association (β=0.287), followed by PA (β=0.245) and HC (β=0.218). Together, these variables explained 30.8% of the variance in QoL (R²=0.308), indicating that the model accounted for a substantial proportion of the factors influencing QoL in young patients with IHD.

    In terms of effect size, eHealth literacy (f²=0.075), PA (f²=0.066), and HC (f²=0.038) had weak but substantive effects on QoL, with eHealth literacy showing the largest effect size. In contrast, PS had the smallest effect size (f²=0.025) and was not significantly related to QoL.

    Parallel Mediation Model

    Table S4 in Multimedia Appendix 2 and Figure 1 depict that bootstrapping of the indirect effect was significant for both parallel pathways initiated by the construct of HC: (HC → PA → QoL [β=0.083, t=1.687, P=.046]) and (HC → eHealth literacy → QoL [β=0.125, t=2.403, P=.008]). The bias-corrected CIs for both the lower limits (BCI LLs) and upper limits (BCI ULs) did not straddle 0, confirming that mediation occurred. Overall, both hypotheses were supported, whereby H5 stated that PA positively mediated the relationship between HC and QoL, with almost no effect size (f2=0.0069), whereas H6, with a small effect size (f2=0.0156) of eHealth literacy, positively mediated the relationship between HC and QoL.

    ‎
    Figure 1. Parallel mediation model evaluated using partial least squares-structural equation modeling (PLS-SEM) in a cross-sectional study among young patients with ischemic heart disease at tertiary cardiac referral centers in Klang Valley, Malaysia (November 2021-June 2022), illustrating hypothesized pathways linking health consciousness and perceived severity to chronic disease with quality of life through eHealth literacy and patient adherence.

    Moreover, bootstrapping of the indirect effect for both parallel pathways initiated by PS was not significant, and the hypotheses were not supported. Both H7 (PS → PA → QoL [β=−0.014, t=0.461, P=.32]) and H8 (PS → eHealth literacy → QoL [β=0.046, t=1.219, P=.11]) were not positively related to QoL. Both the BCI LLs and BCI ULs straddled 0, confirming that mediation was absent. H7 and H8 both had no effect sizes.

    Serial Mediation Model

    Table S4 in Multimedia Appendix 2 and Figure 2 show that the bootstrapping of the indirect effect was significant for the pathway (HC → eHealth literacy → PA → QoL [β=0.042, t=1.653, P=.049]), but it was not significant for the pathway (PS → eHealth literacy → PA → QoL [β=0.016, t=1.037, P=.15]). Both the BCI LLs and BCI ULs did not straddle 0, confirming that mediation occurred for H9 but not H10. Overall, H9 supported the hypothesis that eHealth literacy and PA positively and serially mediated the relationship between HC and QoL but with almost no effect size (f2=0.0018). On the other hand, H10, with no effect size (f2=0.0003) of eHealth literacy and PA, did not positively mediate the serial relationship between HC and QoL.

    ‎
    Figure 2. Serial mediation model tested using partial least squares-structural equation modeling (PLS-SEM) in a cross-sectional study of young patients with ischemic heart disease in Klang Valley, Malaysia (November 2021-June 2022), demonstrating sequential mediation pathways involving eHealth literacy and patient adherence influencing quality-of-life outcomes.
    The Predictive Power of the Model (Out-of-Sample Prediction)

    Table 3 illustrates the predictive power of the model via PLS-predict. In this study, PLS-predict was applied exclusively to the construct of QoL, using a 5-fold procedure with 5 repetitions. The Q² values for QoL were 0.078 and 0.010 for the parallel and serial mediation models, respectively. The root mean square error or mean absolute error (MAE) was then compared with the values from a linear model (LM) for each construct item, according to the guidelines of Shmueli et al [52]. While the root mean square error is the default metric [53], the MAE was used in this analysis because the histogram indicated a nonnormal distribution.

    Table 3. PLS-predict model assessment demonstrating the predictive performance of parallel and serial mediation models derived from a cross-sectional study among young patients with ischemic heart disease attending tertiary cardiac referral centers in Klang Valley, Malaysia (November 2021-June 2022; N=136), using PLS-SEMa,b.
    Quality-of-life itemsQ² predictPLS-SEM_MAEcLMd_MAEPLS-LM MAE
    Parallel mediation modele
    Environment0.07711.89512.004−0.109
    Physical health0.03511.27711.0780.199
    Psychological0.03711.98412.413−0.429
    Social relationships0.07216.01516.042−0.027
    Serial mediation modelf
    Environment0.02012.37912.0040.375
    Physical health−0.01211.62111.0780.543
    Psychological0.00512.29312.413−0.120
    Social relationships0.00016.91416.0420.872

    a95% CI was used with a bootstrapping of 5000.

    bPLS-SEM: partial least squares-structural equation modeling.

    cMAE: mean absolute error.

    dLM: linear model.

    eThis model demonstrated medium predictive power.

    fThis model demonstrated low predictive power.

    The parallel mediation model demonstrated medium predictive power, considering that the MAE values from PLS structural equation modeling were lower compared to those of the LM for most construct items. In contrast, the serial mediation model exhibited low predictive power, given that the MAE values of PLS structural equation modeling were only lower than those of the LM for a limited number of construct items. Figures 3 and 4 present the final models, illustrating the relationships via parallel and serial mediation models.

    ‎
    Figure 3. Final parallel mediation pathway model derived from the partial least squares-structural equation modeling (PLS-SEM) analysis among young patients with ischemic heart disease attending tertiary cardiac referral centers in Klang Valley, Malaysia (November 2021-June 2022), showing statistically significant pathways predicting quality of life.
    ‎
    Figure 4. Final serial mediation pathway model using the partial least squares-structural equation modeling (PLS-SEM) analysis from a cross-sectional study among young patients with ischemic heart disease in Klang Valley, Malaysia (November 2021-June 2022), illustrating sequential mediation effects of eHealth literacy and patient adherence on quality-of-life outcomes.

    Findings

    The model examined in this study was grounded in the TMeHL [17], which focuses on the effects of eHealth literacy, PA, and other factors on QoL. An investigation of the model, which explored both direct and mediation effects, revealed that eHealth literacy was the most significant factor influencing QoL in young patients with IHD, exceeding the impact of HC and PA, whereas the PS had no considerable effect. This study highlights an important and previously underexplored aspect of eHealth literacy, examining its interplay with other factors influencing QoL. The TMeHL theory offers a robust framework for understanding the dynamic and complex interactions between individuals and eHealth technologies, providing valuable insights into this evolving domain.

    The role of HC in the direct pathway hypothesis, “H1: Health consciousness is positively and significantly related to quality of life,” was supported in this study. HC was significantly related to the QoL of young patients with IHD, influencing both direct and indirect pathways in the parallel and serial mediation models.

    The role of eHealth literacy was also supported by multiple hypotheses. The direct pathway hypothesis, “H4: eHealth literacy is positively and significantly related to quality of life,” the parallel mediation hypothesis, “H6: eHealth literacy positively mediates the relationship between health consciousness and quality of life in a parallel pathway,” and the serial mediation hypothesis, “H9: eHealth literacy and patient adherence positively serially mediate the relationship between health consciousness and quality of life,” were validated by the findings. The results highlight the key role eHealth literacy plays in enhancing the QoL of young individuals with IHD.

    The findings highlight the significant role of eHealth literacy in enhancing QoL, aligning with existing but limited research indicating its potential to empower individuals to make informed health decisions [54]. Notably, incorporating eHealth literacy as a mediator between HC and QoL adds considerable value, as demonstrated by the parallel mediation model. Individuals with higher eHEALS scores are often “information explorers” capable of identifying reliable information sources and resolving conflicting information with minimal assistance [55].

    The observed levels of eHealth literacy scores may reflect broader contextual factors during the study period, including reliance on digital health information; however, this study did not directly measure such influences [56]. Proficiency in eHealth literacy enables young patients with IHD to manage overwhelming health information effectively, facilitating better decision-making, social support, and access to health care services, thereby improving QoL [57]. The pandemic’s “infodemic” further underscores the critical need for reliable health information and the empowerment of patients with eHealth skills [56].

    The final models demonstrated that eHealth literacy significantly contributes, both directly and indirectly, to improving QoL. This underscores its importance as a foundation for enhancing self-care among young patients with IHD through the effective use of technology, ultimately fostering better health outcomes and a greater QoL.

    The study revealed a negative direct effect between PS, also referred to as illness perceptions, and QoL. Perceived severity influences distress levels, which ultimately affect QoL outcomes [58]. This construct is associated with various factors, including physical functioning, disability, depression, and anxiety. Notably, contemporary researchers applying the Health Belief Model often overlook emotional aspects when defining severity [59]. According to the Health Belief Model, perceived vulnerability and disease severity together form a “threat” that drives action [60]. Actions are further influenced by an individual’s beliefs about the availability and efficacy of treatments or solutions. When both perceived vulnerability and severity are absent, there is no motivation to act.

    Participants in this study demonstrated perceptions consistent with viewing IHD as a chronic condition; however, temporal changes in illness perception cannot be inferred due to the cross-sectional design. A deeper understanding of IHD often contributes to a prolonged perception of the illness and increased concern. The diagnosis of a chronic disease imposes significant physical, emotional, and social burdens on young patients, affecting their overall environment over time. In conclusion, as young patients with IHD transition from perceiving their illness as acute to viewing it as a chronic condition, they develop a more negative perception. This shift stems from the demands of lifelong treatment, regular follow-up, and preventive measures, which can exacerbate emotional and psychological challenges over time.

    Effective eHealth literacy is vital for accessing reliable health information, which can enhance patients’ adherence to medical recommendations among young patients with IHD. This adherence encompasses critical aspects, such as medication, lifestyle modifications, and dietary changes. The importance of PA in the model is underscored by its dual role, having both direct and mediating effects on QoL. Following medical advice and recommendations significantly contributes to a better QoL for young patients with IHD.

    Moreover, eHealth literacy plays a pivotal role in improving PA by enabling patients to access trustworthy health information and apply it effectively. By fostering patients’ adherence to health advice tailored to their condition, eHealth literacy supports young patients with IHD in managing their disease and enhancing their QoL.

    The final model of this study aligns with the theoretical foundation of the TMeHL, as the parallel mediation structure reflects the dynamic and transactional nature of eHealth [17]. The parallel model incorporates three key assumptions: (1) task-oriented and user-oriented factors, such as HC and individual perceptions; (2) eHealth literacy, which represents a multifaceted and hierarchical set of intrapersonal skills; and (3) patient engagement, demonstrated through PA, which results from being informed and empowered, ultimately affecting health outcomes.

    In practice, parallel mediation, where multiple tasks are addressed simultaneously, is more feasible and practical than serial mediation, where tasks are completed sequentially. Improving the QoL of young patients with IHD in the digital age necessitates promoting HC and eHealth literacy while fostering PA to medical recommendations. Limited health literacy has been associated with inadequate understanding of illnesses, poor disease management, increased hospitalization rates, and reduced use of preventive services.

    The COVID-19 pandemic has emphasized the crucial role of eHealth literacy in health care delivery, with eHealth resources serving as vital tools for linking care and enhancing resilience. Moreover, the concept of capability is key to striking a balance between autonomy and paternalism, empowering individuals to make informed health care decisions while safeguarding them from harm [54]. This approach highlights the need to equip young patients with IHD with the tools and knowledge necessary for optimal self-care and improved health outcomes.

    Strengths and Limitations

    This study’s novelty lies in its integration of the TMeHL theory [17] to explore eHealth literacy among young patients with IHD. This is the first study to utilize this model, highlighting the growing importance of eHealth literacy, as young patients increasingly engage with online health care resources. This study aims to provide a thorough analysis of the elements that influence eHealth literacy and how it impacts QoL. Another strength is the focus on young patients with IHD, a demographic experiencing a growing prevalence of IHD both locally and globally. This approach provides insights into unique factors influencing IHD in younger populations, including the potential role of eHealth literacy in early detection and disease management.

    One limitation of the study is its cross-sectional design; therefore, causal relationships and temporal directionality among constructs cannot be established. Additionally, the use of consecutive sampling, although practical, is nonrandom and may limit generalizability. Given that the sample size depended on patient accessibility and the order of enrollment, the sample might not provide a comprehensive representation of the wider young patient population with IHD. This study faced challenges, such as restricted in-person data collection, limited site access, and movement restrictions under the Movement Control Order during the COVID-19 pandemic. Additional obstacles included participants’ COVID-19 status (eg, positive cases and persons under investigation) and time constraints requiring a focus on stable patients. Online data collection methods, including virtual interviews, mitigated some challenges but introduced issues, such as unequal technology access, potential selection bias, and lower response rates. The pandemic’s dynamic nature has also had confounding effects on QoL assessment, necessitating cautious generalization of findings.

    Conclusion and Recommendations

    The study emphasized the importance of a parallel mediating relationship between eHealth literacy and PA, driven by HC, in predicting QoL improvements. These findings align with the TMeHL theory, confirming reliable and valid constructs with medium predictive power for understanding and enhancing QoL among young patients with IHD. Future research may explore whether digital tools, structured eHealth education, or provider-led interventions can strengthen these relationships.

    eHealth literacy is crucial for young patients with IHD in the digital health era, as it enables them to access trustworthy online resources, critically evaluate health information, and communicate effectively with health care providers via telemedicine. The incorporation of eHealth literacy into daily life facilitates self-management tasks, such as symptom tracking and medication adherence. In Malaysia, apps such as MyHealth and MySejahtera serve as credible platforms to promote health education and improve digital health skills. Certification programs could further validate and enhance eHealth literacy competencies.

    Integrating eHealth literacy with PA can greatly enhance QoL, particularly in areas such as physical health, where challenges, such as pain, often disrupt daily activities. Health care providers play a pivotal role by introducing digital tools and educating patients on their effective use for medication compliance, pain management, and lifestyle modifications.

    Interactive digital resources further empower patients by providing secure platforms for sharing information and communicating with health care teams. By adopting these approaches, young patients with IHD can achieve better disease management, improved health outcomes, and enhanced QoL.

    Acknowledgments

    The authors express their gratitude to the Department of Public Health Medicine, University Kebangsaan Malaysia, for providing the opportunity to conduct this research. The authors thank the Director General of Health Malaysia and the Director of the Institute for Medical Research, Malaysia, for giving permission to publish this article. Finally, the authors would like to thank all of the study participants. The authors declare the use of generative artificial intelligence (GAI) in the research and writing process. According to the GAIDeT taxonomy (2025), the following tasks were delegated to GAI tools under full human supervision: proofreading and editing. The GAI tool used was ChatGPT (OpenAI and GPT-5.2). The responsibility for the final manuscript lies entirely with the authors. GAI tools are not listed as authors and do not bear responsibility for the final outcomes. The declaration was submitted by PSNMK.

    Funding

    The authors declared no financial support was received for this work.

    Data Availability

    The datasets supporting the conclusions of this article are included within the article.

    Authors' Contributions

    Conceptualization: PSNMK, AMN, MNY

    Data curation: PSNMK, AMN, MNY, RC, EFL, MFMN

    Formal analysis: PSNMK, AMN, RC, MNY

    Investigation: PSNMK, MSFM, AMAM

    Writing – original draft: PSNMK, AMN, MNY

    Writing – review & editing: PSNMK, AMN, MNY, RC, MSFM, AMAM, EFL, MFMN

    All the authors have read and agreed to the published version of the manuscript.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Hypothesis testing direct effects.

    DOCX File, 18 KB

    Multimedia Appendix 2

    Hypothesis testing indirect effects.

    DOCX File, 17 KB

    Multimedia Appendix 3

    Assessment of full collinearity testing.

    DOCX File, 16 KB

    Multimedia Appendix 4

    Outer loadings, convergent validity, and internal consistency reliability of the constructs.

    DOCX File, 20 KB

    Checklist 1

    STROBE checklist.

    PDF File, 120 KB
    1. Cardiovascular diseases (CVDs). World Health Organization. 2025. URL: https:/​/www.​who.int/​news-room/​fact-sheets/​detail/​cardiovascular-diseases-(cvds)#:~:text=Key%20facts,38%25%20were%20caused%20by%20CVDs [Accessed 2025-10-18]
    2. The 17 goals. United Nations. 2025. URL: https://sdgs.un.org/goals [Accessed 2026-04-22]
    3. SDG target 3.4 non-communicable diseases and mental health. World Health Organization. 2025. URL: https://tinyurl.com/yd454jw7 [Accessed 2026-04-22]
    4. World Heart Report 2023 confronting the world’s number one killer. World Heart Federation; 2023. URL: https://world-heart-federation.org/wp-content/uploads/World-Heart-Report-2023.pdf [Accessed 2026-04-22]
    5. Goh RSJ, Chong B, Jayabaskaran J, et al. The burden of cardiovascular disease in Asia from 2025 to 2050: a forecast analysis for East Asia, South Asia, South-East Asia, Central Asia, and high-income Asia Pacific regions. Lancet Reg Health West Pac. Aug 2024;49:101138. [CrossRef] [Medline]
    6. Mohd Nor NS, Chua YA, Abdul Razak S, Ismail Z, Nawawi H, MyHEBAT-CRES investigators. Identification of cardiovascular risk factors among urban and rural Malaysian youths. BMC Cardiovasc Disord. Feb 23, 2022;22(1):70. [CrossRef] [Medline]
    7. Zuhdi AS, Mariapun J, Mohd Hairi NN, et al. Young coronary artery disease in patients undergoing percutaneous coronary intervention. Ann Saudi Med. 2013;33(6):572-578. [CrossRef] [Medline]
    8. Bahall M, Legall G, Khan K. Quality of life among patients with cardiac disease: the impact of comorbid depression. Health Qual Life Outcomes. Jun 17, 2020;18(1):189. [CrossRef] [Medline]
    9. Jarrah M, Khader Y, Alkouri O, et al. Medication adherence and its influencing factors among patients with heart failure: a cross sectional study. Medicina (Kaunas). May 16, 2023;59(5):960. [CrossRef] [Medline]
    10. Burnier M. The role of adherence in patients with chronic diseases. Eur J Intern Med. Jan 2024;119:1-5. [CrossRef] [Medline]
    11. Kardas P. From non-adherence to adherence: can innovative solutions resolve a longstanding problem? Eur J Intern Med. Jan 2024;119:6-12. [CrossRef] [Medline]
    12. Li Z, Lu F, Wu J, et al. Usability and effectiveness of eHealth and mHealth interventions that support self-management and health care transition in adolescents and young adults with chronic disease: systematic review. J Med Internet Res. Nov 26, 2024;26:e56556. [CrossRef] [Medline]
    13. Magi CE, Bambi S, Rasero L, et al. Health literacy and self-care in patients with chronic illness: a systematic review and meta-analysis protocol. Healthcare (Basel). Mar 31, 2024;12(7):762. [CrossRef] [Medline]
    14. Suhail M, Saeed H, Saleem Z, et al. Association of health literacy and medication adherence with health-related quality of life (HRQoL) in patients with ischemic heart disease. Health Qual Life Outcomes. Apr 13, 2021;19(1):118. [CrossRef] [Medline]
    15. van Boven JFM, Fonseca JA. Editorial: digital tools to measure and promote medication adherence. Front Med Technol. 2021;3:751976. [CrossRef] [Medline]
    16. Wubante SM, Tegegne MD, Melaku MS, et al. eHealth literacy and its associated factors in Ethiopia: systematic review and meta-analysis. PLoS ONE. 2023;18(3):e0282195. [CrossRef] [Medline]
    17. Paige SR, Stellefson M, Krieger JL, Anderson-Lewis C, Cheong J, Stopka C. Proposing a transactional model of eHealth literacy: concept analysis. J Med Internet Res. Oct 2, 2018;20(10):e10175. [CrossRef] [Medline]
    18. von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. Apr 2008;61(4):344-349. [CrossRef] [Medline]
    19. Universiti Kebangsaan Malaysia (UKM) - National University of Malaysia [Article in Malaysian]. URL: https://www.ukm.my/jepukm/ [Accessed 2023-07-25]
    20. National Medical Research Register. URL: https://nmrr.gov.my/ [Accessed 2023-07-25]
    21. Beaton DE, Bombardier C, Guillemin F, Ferraz MB. Guidelines for the process of cross-cultural adaptation of self-report measures. Spine (Phila Pa 1976). Dec 15, 2000;25(24):3186-3191. [CrossRef] [Medline]
    22. Herngren B, Stenmarker M, Enskär K. Barnhöft: a hip specific 6-item questionnaire for children. J Patient Rep Outcomes. 2017;1(1):16. [CrossRef] [Medline]
    23. Silva VZMD, Araújo JAD Neto, Cipriano Jr G Jr, et al. Brazilian version of the Functional Status Score for the ICU: translation and cross-cultural adaptation [Article in Portuguese, English]. Rev Bras Ter Intensiva. 2017;29(1):34-38. [CrossRef] [Medline]
    24. Tsang S, Royse CF, Terkawi AS. Guidelines for developing, translating, and validating a questionnaire in perioperative and pain medicine. Saudi J Anaesth. May 2017;11(Suppl 1):S80-S89. [CrossRef] [Medline]
    25. Zun AB, Ibrahim MI, Mokhtar AM, Halim AS, Wan Mansor WNA. Translation, cross-cultural adaptation, and validation of the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) into the Malay language. Int J Environ Res Public Health. Jun 10, 2019;16(11):2054. [CrossRef] [Medline]
    26. Kamaruddin PSNM, Nawi AM, Yaman MN, et al. Translation, cross-cultural adaptation, and developing Malay questionnaires to measure health empowerment for patients with ischaemic heart disease (IHD): a validation study. Malays J Public Health Med. 2023;23(2):112-123. [CrossRef]
    27. National health and morbidity survey 2019 non-communicable diseases, healthcare demand, and health literacy: key findings. Institute for Public Health, National Institutes of Health (NIH), Ministry of Health Malaysia; 2019. URL: https://iku.nih.gov.my/images/IKU/Document/REPORT/NHMS2019/Infographic_Booklet_NHMS_2019-English.pdf [Accessed 2026-04-22]
    28. Kim J, Park HA. Development of a health information technology acceptance model using consumers’ health behavior intention. J Med Internet Res. Oct 1, 2012;14(5):e133. [CrossRef] [Medline]
    29. Dutta-Bergman MJ. Primary sources of health information: comparisons in the domain of health attitudes, health cognitions, and health behaviors. Health Commun. 2004;16(3):273-288. [CrossRef] [Medline]
    30. Kaynak R, Eksi S. Ethnocentrism, religiosity, environmental and health consciousness: motivators for anti-consumers. Eurasian J Bus Econ. 2011;4(8):31-50. URL: https://www.ejbe.org/EJBE2011Vol04No08p031-KAYNAK-EKSI.pdf [Accessed 2026-04-22]
    31. Pu B, Zhang L, Tang Z, Qiu Y. The relationship between health consciousness and home-based exercise in China during the COVID-19 pandemic. Int J Environ Res Public Health. Aug 6, 2020;17(16):5693. [CrossRef] [Medline]
    32. Norman CD, Skinner HA. eHEALS: the eHealth literacy scale. J Med Internet Res. Nov 14, 2006;8(4):e27. [CrossRef] [Medline]
    33. Paige SR, Krieger JL, Stellefson M, Alber JM. eHealth literacy in chronic disease patients: an item response theory analysis of the eHealth literacy scale (eHEALS). Patient Educ Couns. Feb 2017;100(2):320-326. [CrossRef] [Medline]
    34. Measures of Patient Adherence Survey. RAND Corporation. 2020. URL: https://www.rand.org/health-care/surveys_tools/mos/patient-adherence.html [Accessed 2026-04-22]
    35. Lee WL, Abdullah KL, Bulgiba AM, Zainal Abidin I. Prevalence and predictors of patient adherence to health recommendations after acute coronary syndrome: data for targeted interventions? Eur J Cardiovasc Nurs. Dec 2013;12(6):512-520. [CrossRef] [Medline]
    36. Fogel J, Fauerbach JA, Ziegelstein RC, Bush DE. Quality of life in physical health domains predicts adherence among myocardial infarction patients even after adjusting for depressive symptoms. J Psychosom Res. Jan 2004;56(1):75-82. [CrossRef] [Medline]
    37. Kravitz RL, Hays RD, Sherbourne CD, et al. Recall of recommendations and adherence to advice among patients with chronic medical conditions. Arch Intern Med. Aug 23, 1993;153(16):1869-1878. [Medline]
    38. Spernak SM, Moore PJ, Hamm LF. Depression, constructive thinking and patient satisfaction in cardiac treatment adherence. Psychol Health Med. Mar 2007;12(2):172-189. [CrossRef] [Medline]
    39. Hasanah CI, Naing L, Rahman AR. World Health Organization quality of life assessment: brief version in Bahasa Malaysia. Med J Malaysia. Mar 2003;58(1):79-88. [Medline]
    40. Erdfelder E, Faul F, Buchner A. GPOWER: a general power analysis program. Behav Res Methods Instrum Comput. Mar 1996;28(1):1-11. [CrossRef]
    41. Sun Y, Shen L, Pan Z, Qian S. Toward a more powerful experimental communication science: an assessment of two decades’ research (2001–2023). Communic Res. Mar 2025;52(2):173-208. [CrossRef]
    42. Cohen J. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. Lawrence Erlbaum Associates, Publishers; 1988. URL: https://utstat.toronto.edu/~brunner/oldclass/378f16/readings/CohenPower.pdf [Accessed 2026-04-22]
    43. Mayr S, Erdfelder E, Buchner A, Faul F. A short tutorial of GPower. TQMP. 2007;3(2):51-59. [CrossRef]
    44. Ringle CM, Wende S, Becker JM. SmartPLS. URL: https://www.smartpls.com [Accessed 2023-07-25]
    45. Anderson JC, Gerbing DW. Structural equation modeling in practice: a review and recommended two-step approach. Psychol Bull. 1988;103(3):411-423. [CrossRef]
    46. Univariate and multivariate skewness and kurtosis calculation. Webpower. 2018. URL: https://webpower.psychstat.org/models/kurtosis/ [Accessed 2026-04-22]
    47. Hair Jr JF, Hult GTM, Ringle CM, Sarstedt M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 2nd ed. SAGE Publications, Inc; 2017. URL: https:/​/eli.​johogo.com/​Class/​CCU/​SEM/​_A%20Primer%20on%20Partial%20Least%20Squares%20Structural%20Equation%20Modeling_Hair.​pdf [Accessed 2026-04-22]
    48. Bagozzi RP, Yi Y. On the evaluation of structural equation models. JAMS. Mar 1988;16(1):74-94. [CrossRef]
    49. Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J of the Acad Mark Sci. Jan 2015;43(1):115-135. [CrossRef]
    50. Aggarwal A, Srivastava S, Velmurugan M. Newer perspectives of coronary artery disease in young. World J Cardiol. Dec 26, 2016;8(12):728-734. [CrossRef] [Medline]
    51. Richtering SS, Hyun K, Neubeck L, et al. eHealth literacy: predictors in a population with moderate-to-high cardiovascular risk. JMIR Hum Factors. Jan 27, 2017;4(1):e4. [CrossRef] [Medline]
    52. Shmueli G, Sarstedt M, Hair JF, et al. Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. Eur J Mark. Nov 11, 2019;53(11):2322-2347. [CrossRef]
    53. Chica M, Rand W. Building agent-based decision support systems for word-of-mouth programs: a freemium application. J Mark Res. Oct 2017;54(5):752-767. [CrossRef]
    54. Falk Erhag H, Lagerlöf Nilsson U, Rydberg Sterner T, Skoog I, editors. A Multidisciplinary Approach to Capability in Age and Ageing. Springer; 2022. ISBN: 978-3-030-78063-0
    55. Osborne RH, Batterham RW, Elsworth GR, Hawkins M, Buchbinder R. The grounded psychometric development and initial validation of the Health Literacy Questionnaire (HLQ). BMC Public Health. Jul 16, 2013;13(1):658. [CrossRef] [Medline]
    56. Corinti F, Pontillo D, Giansanti D. COVID-19 and the Infodemic: an overview of the role and impact of social media, the evolution of medical knowledge, and emerging problems. Healthcare (Basel). Apr 14, 2022;10(4):732. [CrossRef] [Medline]
    57. Soleimaninejad A, Valizadeh-Haghi S, Rahmatizadeh S. Assessing the eHealth literacy skills of family caregivers of medically ill elderly. Online J Public Health Inform. 2019;11(2):e12. [CrossRef] [Medline]
    58. Zhang M, Hong L, Zhang T, et al. Illness perceptions and stress: mediators between disease severity and psychological well-being and quality of life among patients with Crohn’s disease. Patient Prefer Adherence. 2016;10:2387-2396. [CrossRef] [Medline]
    59. Champion VL. Instrument development for health belief model constructs. ANS Adv Nurs Sci. Apr 1984;6(3):73-85. [CrossRef] [Medline]
    60. Anuar H, Shah SA, Gafor H, Mahmood MI, Ghazi HF. Usage of Health Belief Model (HBM) in health behavior: a systematic review. Mal J Med Health Sci. Nov 2020;16:201-209. URL: https://medic.upm.edu.my/upload/dokumen/2020112512435726_2020_0366.pdf [Accessed 2026-04-22]

    ‎
    BCI LL: bias-corrected CI for lower limit
    BCI UL: bias-corrected CI for upper limit
    CVD: cardiovascular disease
    eHEALS: eHealth Literacy Scale
    eHealth: eHealth literacy
    HC: health consciousness
    IHD: ischemic heart disease
    LM: linear model
    MAE: mean absolute error
    PA: patient adherence
    PLS: partial least squares
    PS: perceived severity to chronic disease
    QOL: quality of life
    STEMI: ST-elevation myocardial infarction
    STROBE: Strengthening the Reporting of Observational Studies in Epidemiology
    TMeHL: Transactional Model of eHealth Literacy
    WHO: World Health Organization

    Edited by Amaryllis Mavragani; submitted 23.Jan.2025; peer-reviewed by Aderonke Elizabeth Fakayode, Chenyu Zou, Raghad Elgamar; final revised version received 10.Mar.2026; accepted 10.Mar.2026; published 26.May.2026.

    Copyright

    © Puteri Sofia Nadira Megat Kamaruddin, Azmawati Mohammed Nawi, Mohamad Nurman Yaman, Rajiv Chowdhury, Mohd Shawal Faizal Mohamad, Abdul Muizz Abdul Malek, Ezarul Faradianna Lokman, Mohd Fairulnizal Md Noh. Originally published in JMIR Formative Research (https://formative.jmir.org), 26.May.2026.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as this copyright and license information must be included.