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 explore the public perception of the coronavirus disease (COVID-19) and preventive behaviors by age group. Methods: Focus group interviews were conducted, and response contents were classified by age group and analyzed. A semantic network analysis was employed to investigate relationships among keywords, and some sub-network characteristics were derived using a cohesive group analysis. Results: Given the keywords and sub-networks derived from the semantic network analysis, COVID-19 risks were classified into three levels: individual, group, international. Preventive behaviors were classified into two dimensions (personal hygiene, distancing). It was also possible to visualize and confirm connections among detailed concepts that constituted the public perception of COVID-19. Conclusion: This study shows that in establishing an effective crisis management response strategy to prevent emerging infectious diseases, biological risks and risk perception should be considered

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.

Background: Drinking norms and motives accumulate with drinking experience; thus, it is likely that related drinking behaviors will differ with age. This study aimed to predict drinking behaviors by age based on drinking norms and motives in a sample of Korean women. Methods: This exploratory study used a nationwide demographically stratified sample including 1057 women aged 19–59 years. Self-report questionnaires assessed participants’ general drinking frequency and quantity, two drinking norms, and five dimensional motives. The data were analyzed using Spss 26. Results: Descriptive and injunctive norms were the predictors that accounted for the greatest variance in drinking frequency, quantity, binge drinking, and high-risk drinking across all age groups (p < 0.001). Descriptive norms predicted all drinking behavior better than injunctive norms and all five motives for all age groups. The effects of each of the five motives differed with age. The enhancement motive was the strongest predictor of the motives for drinking frequency, binge drinking, and high-risk drinking across all age groups. Social and conformity motives predicted only binge drinking. Conclusions: These results suggest that descriptive norms, injunctive norms, and enhancement motives predict drinking behaviors across all age groups, although the relative predictive strength of those variables differed by age.

This study examined the impact of patient-centered care, satisfaction with the hospital environment, and maternal information-seeking on maternal healthcare satisfaction in Tanzania. A total of 707 mothers who delivered at Chanika Hospital in Tanzania were surveyed using a structured questionnaire. Multiple regression analyses were conducted to identify factors related to maternal satisfaction. Only 9.9% of the participants reported that they “usually” or “always” felt involved in treatment decisions. High levels of satisfaction were found for hospital cleanliness (93.6%) and safety (94.9%). However, there was a significant gap in satisfaction regarding the adequacy of water quality for medical services, with only 8.1% expressing satisfaction. Limited use of digital platforms was observed in terms of information-seeking behavior for fetal development, with only 19.5% of the participants using the internet and 14.3% using mobile apps. Patient-centered experiences with healthcare providers, especially midwives, had a significant positive impact on maternal satisfaction (β = 0.11, p = 0.021). Other significant variables were satisfaction with the hospital environment (β = 0.25, p < 0.001) and satisfaction with hospital water (β = 0.13, p < 0.001). It is recommended that healthcare improvements focus on patient-centered experiences and water quality for drinking and medical services to improve patient satisfaction.