Eun-Jung Kim

Background: With expansion of technology-enabled care, digital health literacy (DHL) has become integral to effective cardiovascular disease (CVD) management. However, quantitative evidence regarding determinants and health outcomes of DHL in CVD remains limited and heterogeneous, necessitating comprehensive evidence synthesis. Objective: This study aimed to (1) estimate DHL levels, (2) synthesize DHL-associated factors, and (3) examine DHL-related health outcomes in CVD. Methods: A systematic review and meta-analysis of DHL in adults with CVD was conducted per PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. PubMed, Embase, Cochrane CENTRAL, CINAHL, Scopus, Web of Science, and Google Scholar were searched for peer-reviewed studies published between 2006 and January 31, 2026. Quantitative studies enrolling adults with CVD, which reported a measure of DHL were included. Studies focusing exclusively on primary cerebrovascular disease and non–peer-reviewed articles were excluded. Risk of bias (ROB) was assessed using the Appraisal Tool for Cross-Sectional Studies tool, the Newcastle-Ottawa Scale, the Revised Cochrane Risk-of-Bias Tool for Randomized Trials, and the Risk of Bias in Nonrandomized Studies of Interventions. Certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation approach. Pooled mean eHealth Literacy Scale (eHEALS) scores were synthesized using a random-effects meta-analysis. Heterogeneity was quantified using the I2 statistic and 95% prediction intervals. Results: Twenty studies involving 8581 adults with CVD were included. The overall pooled mean eHEALS score was 24.26 (95% CI 21.19-27.32), with substantial heterogeneity (I2=98.4%; τ2=15.55; τ=3.94) and a wide 95% prediction interval (14.66-33.85). Lower DHL was consistently associated with older age, lower educational attainment, female sex, limited social support, and less experience with digital technologies. Higher DHL was associated with more favorable health-related outcomes, including health behaviors, better quality of life, and greater use and acceptance of digital health technologies. Subgroup analyses showed no statistically significant differences in DHL by region, disease type, or age group. The certainty of evidence was rated as low to very low, and substantial heterogeneity persisted across analyses. Conclusions: Our findings underscore DHL as a foundational capability for digitally supported self-management in CVD care and reveal disparities associated with age and socioeconomic factors. By integrating evidence on DHL levels, associated factors, and DHL-related health outcomes in CVD populations, this review provides a more comprehensive, clinically relevant understanding of DHL beyond studies relying on a single instrument (eg, eHEALS) or examining isolated domains. DHL appears to be a context-dependent competency shaped by broader structural and social determinants. From a clinical and health system perspective, digital health interventions should be accompanied by structured digital inclusion strategies, including routine assessment of DHL and care delivery to patients’ digital capacities. Further longitudinal and interventional studies are warranted to clarify the causal pathways linking DHL to health outcomes in adults with CVD and to incorporate provider- and system-level perspectives beyond individual-level assessments. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD420251068000; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251068000

Background: Stroke is a complex, multidimensional disorder influenced by interacting inflammatory, immune, coagulation, endothelial, and metabolic pathways. Single-omics approaches seldom capture this complexity, whereas multiomics techniques provide complementary insights but generate high-dimensional and correlated feature spaces. Machine learning (ML) offers strategies to manage these challenges; however, the predictive accuracy and reproducibility of multiomics-based ML models for stroke remain poorly characterized. Objective: This review aimed to conduct a systematic evaluation of ML models using multiomics data for stroke risk stratification and comprehensive patterns in discriminatory performance, integration strategies, and validation and reporting practices to inform future methodological development. Methods: We conducted a comprehensive literature search following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 recommendations. Studies published from January 2000 to July 2025 were identified across 9 databases, including PubMed, MEDLINE Ultimate, EMBASE, CINAHL, Web of Science, Scopus, Cochrane CENTRAL, ACM Digital Library, and IEEE Xplore. Eligible studies included adults with ischemic, hemorrhagic, or unspecified stroke as the prediction target; applied at least 2 omics layers; and reported ML performance metrics. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool, while reporting quality was evaluated using Minimum Information for Medical AI Reporting. The primary outcome was the area under the receiver operating characteristic curve. Results: A total of 7 studies (n=40,274) published between 2022 and 2025 fulfilled the inclusion criteria. All studies combined 2 omics layers, most often using middle-level integration with dyads such as metabolomics-proteomics and metabolomics-lipidomics. Supervised ML algorithms across studies included support vector machines, tree-based ensembles, generalized linear models, and deep learning architectures. Three studies reported external validation of the integrated multiomics model, while 1 study conducted only an external assessment of a single marker rather than validation of the integrated model. Three studies reported an assessment of calibration, and clinically prespecified operating points were rarely described. Reported areas under the receiver operating characteristic curve varied by prediction task, ranging from 0.75 to 0.96 for acute diagnosis models and from 0.75 to 0.97 for onset risk prediction models; the highest externally validated performance was achieved by a support vector machine trained on a metabolomics-proteomics dyad in mixed stroke types (ischemic and hemorrhagic). Conclusions: Multiomics ML models showed high apparent discrimination for stroke risk stratification, but current evidence remains methodologically limited. Small sample sizes, heterogeneous designs, and incomplete reporting currently hinder the reproducibility and generalizability of multiomics ML models for stroke risk prediction. To advance the field, future studies should adopt leakage-resistant evaluation frameworks, conduct site-specific external validations, and benchmark against both single-omics and clinical baselines to demonstrate incremental value. Well-designed, transparently reported investigations will be essential to move multiomics ML models from exploratory promise toward clinically actionable tools in precision stroke care.

BACKGROUND With expansion of technology-enabled care, digital health literacy (DHL) has become integral to effective cardiovascular disease (CVD) management. However, quantitative evidence regarding determinants and health outcomes of DHL in CVD remains limited and heterogeneous, necessitating comprehensive evidence synthesis. OBJECTIVE This study aimed to (1) estimate DHL levels, (2) synthesize DHL-associated factors, and (3) examine DHL-related health outcomes in CVD. METHODS A systematic review and meta-analysis of DHL in adults with CVD was conducted per PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. PubMed, Embase, Cochrane CENTRAL, CINAHL, Scopus, Web of Science, and Google Scholar were searched for peer-reviewed studies published between 2006 and January 31, 2026. Quantitative studies enrolling adults with CVD, which reported a measure of DHL were included. Studies focusing exclusively on primary cerebrovascular disease and non–peer-reviewed articles were excluded. Risk of bias (ROB) was assessed using the Appraisal Tool for Cross-Sectional Studies tool, the Newcastle-Ottawa Scale, the Revised Cochrane Risk-of-Bias Tool for Randomized Trials, and the Risk of Bias in Nonrandomized Studies of Interventions. Certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation approach. Pooled mean eHealth Literacy Scale (eHEALS) scores were synthesized using a random-effects meta-analysis. Heterogeneity was quantified using the I2 statistic and 95% prediction intervals. RESULTS Twenty studies involving 8581 adults with CVD were included. The overall pooled mean eHEALS score was 24.26 (95% CI 21.19-27.32), with substantial heterogeneity (I2=98.4%; τ2=15.55; τ=3.94) and a wide 95% prediction interval (14.66-33.85). Lower DHL was consistently associated with older age, lower educational attainment, female sex, limited social support, and less experience with digital technologies. Higher DHL was associated with more favorable health-related outcomes, including health behaviors, better quality of life, and greater use and acceptance of digital health technologies. Subgroup analyses showed no statistically significant differences in DHL by region, disease type, or age group. The certainty of evidence was rated as low to very low, and substantial heterogeneity persisted across analyses. CONCLUSIONS Our findings underscore DHL as a foundational capability for digitally supported self-management in CVD care and reveal disparities associated with age and socioeconomic factors. By integrating evidence on DHL levels, associated factors, and DHL-related health outcomes in CVD populations, this review provides a more comprehensive, clinically relevant understanding of DHL beyond studies relying on a single instrument (eg, eHEALS) or examining isolated domains. DHL appears to be a context-dependent competency shaped by broader structural and social determinants. From a clinical and health system perspective, digital health interventions should be accompanied by structured digital inclusion strategies, including routine assessment of DHL and care delivery to patients’ digital capacities. Further longitudinal and interventional studies are warranted to clarify the causal pathways linking DHL to health outcomes in adults with CVD and to incorporate provider- and system-level perspectives beyond individual-level assessments. CLINICALTRIAL PROSPERO International Prospective Register of Systematic Reviews CRD420251068000; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251068000

Heart failure (HF) can limit the capacity of patients to perform daily activities, increasing their dependence on caregivers and contributing to significant caregiver burden. Accurate assessment of this burden is crucial for elucidating its impact and guiding the development of evidence-based strategies to support both patients and their caregivers. This study aims to translate, culturally adapt, and evaluate the psychometric properties of the heart failure caregiver burden questionnaire (HF-CQ) for application among Korean caregivers of individuals with HF. The HF-CQ was translated into Korean and culturally adapted in accordance with the World Health Organization guidelines. A total of 328 caregivers of patients with HF were enrolled between June 2021 and December 2023. To evaluate construct validity, a confirmatory factor analysis was used, including the assessments of convergent and discriminant validity of the instrument. Criterion validity and known-group validity were also examined. Reliability was determined by calculating Cronbach’s α for internal consistency and the intraclass correlation coefficient (ICC) for test–retest stability. The Korean version of the HF-CQ confirmed a three-factor structure—physical wellbeing, emotional wellbeing, and lifestyle—comprising 20 items. The instrument demonstrated excellent content validity. Construct validity was supported using acceptable model fit, standardized factor loadings and average variance extracted values > 0.50, composite reliability values > 0.70, and heterotrait–monotrait ratios < 0.85. Criterion validity was verified via significant correlations with depressive symptoms (r = 0.44, p < 0.001) and with health-related quality of life (r = -0.23, p < 0.001). Known-group validity was not demonstrated, with no significant differences in caregiver burden scores caregivers of patients in NYHA Class II and those in Class III/IV (t = -1.680, p = 0.094). Internal consistency was excellent (Cronbach’s α = 0.95), and test–retest reliability was good (ICC = 0.73). The 20-item Korean HF-CQ demonstrated sound psychometric properties, offering a culturally adapted tool for evaluating caregiver burden in HF. Implementation in clinical and community settings can facilitate early detection of caregivers experiencing high levels of burden, thereby allowing prompt recognition and the development of individualized support strategies.

Background and Objectives: The Sarcopenia and Calf Circumference (SARC-CalF) tool has been proposed to enhance the detection of sarcopenia. A comprehensive systematic review evaluating the diagnostic performance of the SARC-CalF could support its broader adoption as a valid screening instrument. Thus, this study aimed to assess the diagnostic accuracy of the SARC-CalF in detecting sarcopenia risk among older adults. Research Design and Methods: A systemic review was conducted in six electronic databases and Google Scholar. The values of sensitivity, specificity, and area under the curve were estimated simultaneously using a bivariate model. Evaluation of the quality of individual studies included in this review was conducted with the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Results: Our systematic review included 18 studies, with 10,367 older adults. The pooled sensitivity and specificity of the SARC-CalF were 53.3% (95% confidence interval [CI]: 0.46–0.60) and 87.3% (95% CI: 0.84–0.90), respectively. The pooled diagnostic odds ratio was 7.34 (95% CI: 5.87–9.19), and the area under the curve was 0.78 (95% CI: 0.73–0.81), indicating moderate diagnostic accuracy. Subgroup analyses revealed variations based on reference standards and calf circumference cutoff values. Discussion and Implications: This study highlights the SARC-CalF as a practical, cost-effective screening scale for sarcopenia of older people in community settings. Further research is required to establish optimal calf circumference cutoff values and validate its utility in various clinical environments. Combining the SARC-CalF scale with other functional measures may be useful and optimal tools for sarcopenia among older adults.

BACKGROUND Stroke is a complex, multidimensional disorder influenced by interacting inflammatory, immune, coagulation, endothelial, and metabolic pathways. Single-omics approaches seldom capture this complexity, whereas multiomics techniques provide complementary insights but generate high-dimensional and correlated feature spaces. Machine learning (ML) offers strategies to manage these challenges; however, the predictive accuracy and reproducibility of multiomics-based ML models for stroke remain poorly characterized. OBJECTIVE This review aimed to conduct a systematic evaluation of ML models using multiomics data for stroke risk stratification and comprehensive patterns in discriminatory performance, integration strategies, and validation and reporting practices to inform future methodological development. METHODS We conducted a comprehensive literature search following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 recommendations. Studies published from January 2000 to July 2025 were identified across 9 databases, including PubMed, MEDLINE Ultimate, EMBASE, CINAHL, Web of Science, Scopus, Cochrane CENTRAL, ACM Digital Library, and IEEE Xplore. Eligible studies included adults with ischemic, hemorrhagic, or unspecified stroke as the prediction target; applied at least 2 omics layers; and reported ML performance metrics. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool, while reporting quality was evaluated using Minimum Information for Medical AI Reporting. The primary outcome was the area under the receiver operating characteristic curve. RESULTS A total of 7 studies (n=40,274) published between 2022 and 2025 fulfilled the inclusion criteria. All studies combined 2 omics layers, most often using middle-level integration with dyads such as metabolomics-proteomics and metabolomics-lipidomics. Supervised ML algorithms across studies included support vector machines, tree-based ensembles, generalized linear models, and deep learning architectures. Three studies reported external validation of the integrated multiomics model, while 1 study conducted only an external assessment of a single marker rather than validation of the integrated model. Three studies reported an assessment of calibration, and clinically prespecified operating points were rarely described. Reported areas under the receiver operating characteristic curve varied by prediction task, ranging from 0.75 to 0.96 for acute diagnosis models and from 0.75 to 0.97 for onset risk prediction models; the highest externally validated performance was achieved by a support vector machine trained on a metabolomics-proteomics dyad in mixed stroke types (ischemic and hemorrhagic). CONCLUSIONS Multiomics ML models showed high apparent discrimination for stroke risk stratification, but current evidence remains methodologically limited. Small sample sizes, heterogeneous designs, and incomplete reporting currently hinder the reproducibility and generalizability of multiomics ML models for stroke risk prediction. To advance the field, future studies should adopt leakage-resistant evaluation frameworks, conduct site-specific external validations, and benchmark against both single-omics and clinical baselines to demonstrate incremental value. Well-designed, transparently reported investigations will be essential to move multiomics ML models from exploratory promise toward clinically actionable tools in precision stroke care.

Background: Machine learning (ML) models may offer greater clinical utility than conventional risk scores, such as the Thrombolysis in Myocardial Infarction (TIMI) and Global Registry of Acute Coronary Events (GRACE) risk scores. However, there is a lack of knowledge on whether ML or traditional models are better at predicting the risk of major adverse cardiovascular and cerebrovascular events (MACCEs) in patients with acute myocardial infarction (AMI) who have undergone percutaneous coronary interventions (PCI). Objective: The aim of this study is to systematically review and critically appraise studies comparing the performance of ML models and conventional risk scores for predicting MACCEs in patients with AMI who have undergone PCI. Methods: Nine academic and electronic databases including PubMed, CINAHL, Embase, Web of Science, Scopus, ACM, IEEE, Cochrane, and Google Scholar were systematically searched from January 1, 2010, to December 31, 2024. We included studies of patients with AMI who underwent PCI, and predicted MACCE risk using ML algorithms or conventional risk scores. We excluded conference abstracts, gray literature, reviews, case reports, editorials, qualitative studies, secondary data analyses, and non-English publications. Our systematic search yielded 10 retrospective studies, with a total sample size of 89,702 individuals. Three validation tools were used to assess the validity of the published prediction models. Most included studies were assessed as having a low overall risk of bias. Results: The most frequently used ML algorithms were random forest (n=8) and logistic regression (n=6), while the most used conventional risk scores were GRACE (n=8) and TIMI (n=4). The most common MACCEs component was 1-year mortality (n=3), followed by 30-day mortality (n=2) and in-hospital mortality (n=2). Our meta-analysis demonstrated that ML-based models (area under the receiver operating characteristic curve: 0.88, 95% CI 0.86‐0.90; I²=97.8%; P<.001) outperformed conventional risk scores (area under the receiver operating characteristic curve: 0.79, 95% CI 0.75‐0.84; I²=99.6%; P<.001) in predicting mortality risk among patients with AMI who underwent PCI. Heterogeneity across studies was high. Publication bias was assessed using a funnel plot. The top-ranked predictors of mortality in both ML and conventional risk scores were age, systolic blood pressure, and Killip class. Conclusions: This review demonstrated that ML-based models had superior discriminatory performance compared to conventional risk scores for predicting MACCEs in patients with AMI who had undergone PCI. The most commonly used predictors were confined to nonmodifiable clinical characteristics. Therefore, health care professionals should understand the advantages and limitations of ML algorithms and conventional risk scores before applying them in clinical practice. We highlight the importance of incorporating modifiable factors—including psychosocial and behavioral variables—into prediction models for MACCEs following PCI in patients with AMI. In addition, further multicenter prospective studies with external validation are required to address validation limitations.

Purpose: The purpose of this study was to examine health literacy, walking practice, sarcopenia, and health-related quality of life among rural older adults, and to identify the factors influencing health-related quality of life. Methods: This was a cross-sectional descriptive study. A total of 360 rural older adults participated in the study, and data collection was conducted from August 28 to September 30, 2023. The data were analyzed using descriptive statistics, independent t-tests, one-way ANOVA, Scheffé test, Pearson correlation coefficients, and multiple regression analysis. Results: The results of this study show that sarcopenia (β=-.37, p<.001), working status (β=-.29, p<.001), walking practice (β=-.14, p=.001), and education level (β=-.11, p=.020) were significant factors affecting health-related quality of life among rural older adults. Conclusion: It is important to identify the factors affecting the health-related quality of life among rural older adults who often experience health disparities. This suggests the need to develop a program that promotes walking practice and prevents sarcopenia tailored to the characteristics of rural older adults.

This cross‐sectional study aimed to identify the impact of multimorbidity on self‐reported sarcopenia and compare the differences in the prevalence and associated factors of sarcopenia in community‐dwelling older adults with and without multimorbidity. We enrolled 876 community‐dwelling older adults in South Korea. Multimorbidity was defined as the coexistence of two or more chronic diseases, and sarcopenia was evaluated using the Strength, Assistance in walking, Rising from a chair, Climbing stairs, and Falling Questionnaire. Binomial logistic regression analyses were performed. Our study revealed that the prevalence of sarcopenia was significantly higher (43.5%) in older adults with multimorbidity than in those without multimorbidity (20.8%). This finding underscores the significant impact of multimorbidity on sarcopenia. We also found that poor perceived health status was the strongest predictor of sarcopenia in older adults with multimorbidity, while oldest‐old age (≥ 85 years) was the strongest predictor of sarcopenia in those without multimorbidity. Large prospective cohort studies using objective sarcopenia screening tools are needed to demonstrate the synergistic effects of multimorbidity and sarcopenia on health outcomes among community‐dwelling older adults.