Technological advances have increased the availability of diverse digital health services. However, digital health benefits are not equally accessible. Recent studies have focused on digital health equity. Researchers are progressively identifying digital determinants of health (DDoH) to address potential health disparities stemming from digital health. This study investigated the determinants of disparities in app-based digital health within the framework of an ecological model. The method proposed by Arksey and O’Malley was adopted in this review. The PubMed, Embase, Scopus, and Google Scholar databases were searched from January 2016 to December 2021. Two reviewers independently screened and selected topics according to the guidelines for the scope of the topic. A consensus was reached to reconcile the differences, and the findings were collated, synthesized, summarized, and reported. This study identified 21 studies pertaining to health equity in app-based digital health. Seven countries were included in this study. Health inequities caused by the adoption of app-based digital health can be reflected in the following three levels according to the ecological model. At the individual level (N = 20), it was influenced by sociodemographic characteristics and digital literacy factors. At the interpersonal level (N = 10), factors such as feedback mechanisms, monitoring, communication modalities, technology-sharing practices, and standardized design were observed. At the community or social level (N = 7), disparities were noted in residential locality, integrated network infrastructure, and Internet accessibility. Finally, digital health policies should consider determinants of digital health inequalities. Ensuring health equity in digital health requires the equitable implementation and measurement of health outcomes through an equity lens. Based on the findings of this study, it is essential to maintain a continued focus on digital health to prevent the further widening of health disparities.

Objectives: This study aimed to identify the differences and influencing factors in the use rates of healthcare apps according to eHealth literacy. Methods: Data were collected using an online survey, with eight evaluation items of eHealth literacy, for 1,000 Seoul citizens aged 19 through 64. The data were analyzed through a chi-square (χ²) test on the general characteristics and eHealth literacy of two groups: the users’ group and the non-users’ group mobile apps. Multiple logistic regression analysis was performed to understand the effect of eHealth literacy on the users’ group. Results: Of the respondents, 58.9% had experience using healthcare apps. Women and the middle-aged (50-64 years old) were used healthcare apps more. As for the type of apps used, physical activity apps were used the most at 85.4%, and the period of use was the highest for them at 49.4%. Most respondents (70.0%) agreed with the statement, ‘I know how to use the Internet to answer my health questions.’ Respondents who agreed that ‘I can distinguish high-quality health information from the Internet’ were the lowest at 40.0%. On the other hand, there was a significant difference between the users’ group and the non-users’ group in all eight evaluation items of eHealth literacy. The effect of eHealth literacy on the experience of using apps was that those who had access to information to help their health were most likely to use the apps (adjusted OR 2.328); 95% CI 1.782-3.040). Conclusion: eHealth literacy significantly increases the experience of using digital healthcare. With the development of digital technology, digital health policies and designs are required that do not alienate groups with weak eHealth literacy.

Chronic diseases contribute to 68% of global mortality, highlighting the importance of early detection and management of conditions such as metabolic syndrome. Effective lifestyle interventions, particularly through mobile health (mHealth), have shown potential in promoting health and reducing cardiometabolic risk. This study utilized mHealth data from public health centers in South Korea, targeting adults with risk factors for metabolic syndrome. The Intervention-Motivation-Behavioral skills (IMB) theoretical model was applied to categorize participants’ practice patterns over time using the Group-Based Trend Model (GBTM). And the Generalized Estimating Equations (GEE) methodology was applied to confirm the effective practice patterns for improving metabolic syndrome. Data were collected over 24 weeks. The dataset encompasses life-log data capable of capturing changes in intervention, self-report surveys, and clinical measurements, all linked to personal identification keys and thereby integrated. Participants demonstrated improved health behaviors, with the healthy eating score increasing from 5.0 to 6.4 and physical activity rates rising from 41.5% to 59%. Health risk factors decreased significantly, with the mean number of risk factors dropping from 2.4 to 1.4. The percentage of subjects with three or more metabolic syndrome components decreased from 42.3% in the initial period to 19.2% in the final period. Practice patterns by IMB components were classified into three categories: continuous type, late decline type, and early decline type. Improvements in health behavior and metabolic syndrome were observed in the continuous type of each IMB component. The mHealth interventions were confirmed to be positively associated with improved health behavior and management of metabolic syndrome in the continuous practice patterns of IMB.

Objectives: This study investigated the impact of structural isolation within peer relationships on physical activity levels among Korean adolescents, exploring how different types of social isolation within classroom networks influence physical activity. Methods: This study utilized cross-sectional survey data. Specifically, using data from the 8th Korean Children and Youth Happiness Index survey (2016), which included 3356 middle and high school students in Korea, the study employed binary logistic regression and social network analysis to assess the relationship between social isolation and physical activity. Based on types of isolation, adolescents were categorized into social avoidant, actively isolated and socially indifferent groups. Results: The analysis demonstrated significant differences in physical activity based on social isolation status (χ² = 13.0, p < 0.001) and types of social isolation (χ² = 18.3, p < 0.001). Key variables such as gender, household subjective economic status, and self-rated health significantly influenced physical activity. The number of physically active friends had a considerable impact on both non-isolated groups (OR = 1.31, p < 0.001) and the actively isolated group (OR = 1.42, p < 0.05). The actively isolated group exhibited the highest explanatory power in the logistic regression models (Nagelkerke R² = 0.230). Conclusions: This study has highlighted that not only social isolation but also the type of isolation significantly influences physical activity among adolescents. Understanding these distinctions and tailoring interventions accordingly are crucial for promoting physical activity among socially isolated adolescents.

Objectives: With a rapid aging population and the expansion of digital health strategies after the COVID-19 pandemic, AI․IoT-based health services for older adults in the community were introduced. This study aimed to analyze the impact of digital health strategies on the health of older adults. Methods: The study was conducted with 9,566 older adult participants who enrolled in a health management program at 24 public health centers from November 10, 2020, to December 10, 2021, and completed the post-evaluation after using the services for over six months. Paired t-tests were performed to compare changes in frailty levels and health practices before and after the program. Results: Participants improved frailty levels, walking practices, and healthy eating practices. Positive changes were observed in all aspects of frailty, including overall, social, and cognitive frailty, as well as in the detailed evaluation items. Conclusion: The digital health strategies improved accessibility to healthcare services for older adults, allowing participation without time and space limitations. This contributed to an increase in the scale and sustainability of the program.