Background: Mobile health (mHealth) services enable real-time measurement of information on individuals’ biosignals and environmental risk factors; accordingly, research on health management using mHealth is being actively conducted. Objective: The study aims to identify the predictors of older people’s intention to use mHealth in South Korea and verify whether chronic disease moderates the effect of the identified predictors on behavioral intentions. Methods: A cross-sectional questionnaire study was conducted among 500 participants aged 60 to 75 years. The research hypotheses were tested using structural equation modeling, and indirect effects were verified through bootstrapping. Bootstrapping was performed 10,000 times, and the significance of the indirect effects was confirmed through the bias-corrected percentile method. Results: Of 477 participants, 278 (58.3%) had at least 1 chronic disease. Performance expectancy (β=.453; P=.003) and social influence (β=.693; P<.001) were significant predictors of behavioral intention. Bootstrapping results showed that facilitating conditions (β=.325; P=.006; 95% CI 0.115-0.759) were found to have a significant indirect effect on behavioral intention. Multigroup structural equation modeling testing the presence or absence of chronic disease revealed a significant difference in the path of device trust to performance expectancy (critical ratio=–2.165). Bootstrapping also confirmed that device trust (β=.122; P=.039; 95% CI 0.007-0.346) had a significant indirect effect on behavioral intention in people with chronic disease. Conclusions: This study, which explored the predictors of the intention to use mHealth through a web-based survey of older people, suggests similar results to those of other studies that applied the unified theory of acceptance and use of technology model to the acceptance of mHealth. Performance expectancy, social influence, and facilitating conditions were revealed as predictors of accepting mHealth. In addition, trust in a wearable device for measuring biosignals was investigated as an additional predictor in people with chronic disease. This suggests that different strategies are needed, depending on the characteristics of users.

Jeong YW, et al. J Korean Med Sci. 2023 Apr;38(19):e145. https://doi.org/10.3346/jkms.2023.38.e145

OBJECTIVES: This study was conducted to elucidate the effects of an air quality warning system (AQWS) implemented in January 2015 in Korea by analyzing changes in the incidence and exacerbation rates of environmental diseases.METHODS: Data from patients with environmental diseases were extracted from the National Health Insurance Service-National Sample Cohort database from 2010 to 2019, and data on environmental risk factors were acquired from the AirKorea database. Patient and meteorological data were linked based on residential area. An interrupted time series analysis with Poisson segmented regression was used to compare the rates before and after AQWS introduction. Adjustment variables included seasonality, air pollutants (carbon monoxide, nitrogen dioxide, sulfur dioxide, particulate matter less than 10 μm in diameter, and ozone), temperature, and humidity.RESULTS: After AQWS implementation, the incidence of asthma gradually decreased by 20.5%. Cardiovascular disease and stroke incidence also significantly decreased (by 34.3 and 43.0%, respectively). However, no immediate or gradual decrease was identified in the exacerbation rate of any environmental disease after AQWS implementation. Sensitivity analyses were performed according to age, disability, and health insurance coverage type. Overall, the AQWS effectively mitigated the occurrence of most environmental diseases in Korea. However, the relationships between alarm system implementation and reduced incidence differed among diseases based on the characteristics of vulnerable and sensitive individuals.CONCLUSIONS: Our results suggest that by tailoring the AQWS to demographic and sociological characteristics and providing enhanced education about the warning system, interventions can become an efficient policy tool to decrease air pollution-related health risks.

According to a popular belief, private participation in infrastructure service improves overall service efficiency. However, empirical evidence is mixed. In particular, private participation likely creates a potential agency problem, which may adversely affect service efficiency. This implies that proper government regulations can control opportunistic misbehaviors of private participants and reduce their behavioral uncertainty. Therefore, the effects of private participation on the efficiency of the power service can be hypothesized to be positively augmented by the level of government regulations. We developed an empirical model based upon key institutional, political, and socio-economic variables. The results suggest that private participation is in fact negatively associated with the efficiency of the power service. However, the results also show that the overall effects of private participation on efficiency are positively augmented according to the level of government regulations.

Background: Catastrophic health expenditure (CHE) represents out-of-pocket payment as a share of household income. Most previous studies have focused on incidence aspects when assessing health policy effects. However, because CHE incidence is a binary variable, the effect of the health policy could not accurately be evaluated. On the contrary, the intensity of CHE is a continuous variable that can yield completely different results from previous studies. This study reassesses the coverage expansion plan for four serious diseases using the intensity of CHE in Korea. Methods: We used the Korea Health Panel Study from 2013 to 2015 to conduct the analysis. The study population is households with chronic diseases patients. We divided the population into two groups: the policy beneficiary group, i.e., households with a patient of any of the four serious diseases, and the non-beneficiary group. A difference-in-difference model was employed to compare the variation in the intensity and incidence of CHE between the two groups. We defined the incidence of CHE as when the ratio of out-of-pocket medical expenses to household income is more than a threshold of 10%, and the intensity of CHE is the height of the ratio subtracting the threshold 10%. Results: The increased rate of CHE intensity in households with four serious diseases was lower than that in households with other chronic diseases. The interaction term, which represents the effect of the policy, has a significant impact on the intensity but not on the incidence of CHE. Conclusions: CHE indicators should be applied differently according to the purpose of policy evaluation. The incidence of CHE should be used as the final achievement indicator, and the intensity of CHE should be used as the process indicator. Furthermore, because CHE has an inherent characteristic that is measured by the ratio of household income to medical expenses, to lower this, a differential out-of-pocket maximum policy for each income class is more appropriate than a policy for strengthening the coverage for specific diseases.

Examining technology flow patterns is the first step for spillover research. Drawing on patent and citation records from the U.S. Patent and Trademark Office, this article develops technology flow matrices among regions and industries. The examination of a regional technology flow matrix reveals California's dominant position in new knowledge creation. It also shows that disproportionately large technology flows exist among inventors located in the same state, which strongly implies the presence of localization of technology spillovers. The industry-by-industry technology flow matrix illustrates the importance of two industry groups in knowledge creation: Professional and Scientific Instruments (Standard Industrial Classification [SIC] 38, except 3825) and Electronic Components, Accessories, and Communication Equipment (SIC 366, 367). New technology flow matrices derived in this article have important implications for spillover research and technology policy.