Seoul (SEOV) and Hantaan (HTNV) orthohantaviruses are significant zoonotic pathogens responsible for hemorrhagic fever with renal syndrome. Here, we investigated the molecular evolution of SEOV and HTNV through phylogenetic and bioinformatic analyses using complete genome sequences of their large (L), medium (M), and small (S) gene segments. Despite similar epizootic cycles and clinical symptoms, SEOV and HTNV exhibited distinct genetic and evolutionary dynamics. The phylogenetic trees of each segment consistently showed major genetic clades associated with the geographical distribution of both viruses. Remarkably, SEOV M and S segments exhibit higher evolutionary rates, rapidly increasing genetic diversity, and a more recent origin in contrast to HTNV. Reassortment events were infrequent, but both viruses appear to utilize the M gene segment in genetic exchanges. SEOV favors the L or M segment reassortment, while HTNV prefers the M or S segment exchange. Purifying selection dominates in all three gene segments of both viruses, yet SEOV experiences an elevated positive selection in its glycoprotein Gc ectodomain. Key amino acid differences, including a positive ‘lysine fence’ (through residues K77, K82, K231, K307, and K310) located at the tip of the Gn, alongside the physical stability around an RGD-like motif through M108-F334 interaction, may contribute to the unique antigenic properties of SEOV. With the increasing global dispersion and potential implications of SEOV for the global public health landscape, this study highlights the unique evolutionary dynamics and antigenic properties of SEOV and HTNV in informing vaccine design and public health preparedness.

Background: Prevention of the risk factors for metabolic syndrome (MetS) in middle-aged individuals is an important public health issue. Technology-mediated interventions, such as wearable health devices, can aid in lifestyle modification, but they require habitual use to sustain healthy behavior. However, the underlying mechanisms and predictors of habitual use of wearable health devices among middle-aged individuals remain unclear. Objective: We investigated the predictors of habitual use of wearable health devices among middle-aged individuals with risk factors for MetS. Methods: We proposed a combined theoretical model based on the health belief model, the Unified Technology of Acceptance and Use of Technology 2, and perceived risk. We conducted a web-based survey of 300 middle-aged individuals with MetS between September 3 and 7, 2021. We validated the model using structural equation modeling. Results: The model explained 86.6% of the variance in the habitual use of wearable health devices. The goodness-of-fit indices revealed that the proposed model has a desirable fit with the data. Performance expectancy was the core variable explaining the habitual use of wearable devices. The direct effect of the performance expectancy on habitual use of wearable devices was greater (β=.537, P<.001) than that of intention to continue use (β=.439, P<.001), and the total effect estimate of the performance expectancy was 0.909 (P<.001), including the indirect effect (β=.372, P=.03) on habitual use of wearable devices via intention to continue use. Furthermore, performance expectancy was influenced by health motivation (β=.497, P<.001), effort expectancy (β=.558, P<.001), and risk perception (β=.137, P=.02). Perceived vulnerability (β=.562, P<.001) and perceived severity (β=.243, P=.008) contributed to health motivation. Conclusions: The results suggest the importance of the users’ performance expectations for wearable health devices for the intention of continued use for self-health management and habituation. Based on our results, developers and health care practitioners should find better ways to meet the performance expectations of middle-aged individuals with MetS risk factors. They also should generate device use easier and find a way to encourage users’ health motivation, thereby reducing users’ effort expectancy and resulting in a reasonable performance expectancy of the wearable health device, to induce users’ habitual use behaviors.

Background: Physical activity plays a crucial role in maintaining a healthy lifestyle, and wrist-worn wearables, such as smartwatches and smart bands, have become popular tools for measuring activity levels in daily life. However, studies on physical activity using wearable devices have limitations; for example, these studies often rely on a single device model or use improper clustering methods to analyze the wearable data that are extracted from wearable devices. Objective: This study aimed to identify methods suitable for analyzing wearable data and determining daily physical activity patterns. This study also explored the association between these physical activity patterns and health risk factors. Methods: People aged >30 years who had metabolic syndrome risk factors and were using their own wrist-worn devices were included in this study. We collected personal health data through a web-based survey and measured physical activity levels using wrist-worn wearables over the course of 1 week. The Time-Series Anytime Density Peak (TADPole) clustering method, which is a novel time-series method proposed recently, was used to identify the physical activity patterns of study participants. Additionally, we defined physical activity pattern groups based on the similarity of physical activity patterns between weekdays and weekends. We used the χ² or Fisher exact test for categorical variables and the 2-tailed t test for numerical variables to find significant differences between physical activity pattern groups. Logistic regression models were used to analyze the relationship between activity patterns and health risk factors. Results: A total of 47 participants were included in the analysis, generating a total of 329 person-days of data. We identified 2 different types of physical activity patterns (early bird pattern and night owl pattern) for weekdays and weekends. The physical activity levels of early birds were less than that of night owls on both weekdays and weekends. Additionally, participants were categorized into stable and shifting groups based on the similarity of physical activity patterns between weekdays and weekends. The physical activity pattern groups showed significant differences depending on age (P=.004) and daily energy expenditure (P<.001 for weekdays; P=.003 for weekends). Logistic regression analysis revealed a significant association between older age (≥40 y) and shifting physical activity patterns (odds ratio 8.68, 95% CI 1.95-48.85; P=.007). Conclusions: This study overcomes the limitations of previous studies by using various models of wrist-worn wearables and a novel time-series clustering method. Our findings suggested that age significantly influenced physical activity patterns. It also suggests a potential role of the TADPole clustering method in the analysis of large and multidimensional data, such as wearable data.

Following the global emergence of the SARS-CoV-2 Alpha variant of concern (VOC) in 2020, the Delta variant triggered another wave in 2021. The AY.69 lineage, a Delta VOC, was particularly prevalent in Republic of Korea (South Korea) from May 2021 to January 2022, despite the synchronized implementation of vaccination programmes and non-pharmaceutical interventions (NPIs) such as social distancing. In this study, we used phylogeographic analysis combined with a generalized linear model (GLM) to examine the impact of human movement and vaccination on viral transmission. Our findings indicated that transmission primarily originated in South Korea’s metropolitan areas, and a positive correlation was observed between total human mobility (tracked by GPS on mobile phones and estimated through credit card consumption) and viral spread. The phylodynamic analysis further revealed that non-vaccinated individuals were the primary transmitters of the virus during the study period, even though vaccination programmes had commenced three months prior to the AY.69 outbreak. Our study emphasizes the need to focus on controlling SARS-CoV-2 transmission in metropolitan regions and among unvaccinated populations. Furthermore, the positive correlation between mobility data and viral dissemination could contribute to the development of more accurate predictive models for local spread of pandemics.

BACKGROUND Prevention of the risk factors for metabolic syndrome (MetS) in middle-aged individuals is an important public health issue. Technology-mediated interventions, such as wearable health devices, can aid in lifestyle modification, but they require habitual use to sustain healthy behavior. However, the underlying mechanisms and predictors of habitual use of wearable health devices among middle-aged individuals remain unclear. OBJECTIVE We investigated the predictors of habitual use of wearable health devices among middle-aged individuals with risk factors for MetS. METHODS We proposed a combined theoretical model based on the health belief model, the Unified Technology of Acceptance and Use of Technology 2, and perceived risk. We conducted a web-based survey of 300 middle-aged individuals with MetS between September 3 and 7, 2021. We validated the model using structural equation modeling. RESULTS The model explained 86.6% of the variance in the habitual use of wearable health devices. The goodness-of-fit indices revealed that the proposed model has a desirable fit with the data. Performance expectancy was the core variable explaining the habitual use of wearable devices. The direct effect of the performance expectancy on habitual use of wearable devices was greater (β=.537, P<.001) than that of intention to continue use (β=.439, P<.001), and the total effect estimate of the performance expectancy was 0.909 (P<.001), including the indirect effect (β=.372, P=.03) on habitual use of wearable devices via intention to continue use. Furthermore, performance expectancy was influenced by health motivation (β=.497, P<.001), effort expectancy (β=.558, P<.001), and risk perception (β=.137, P=.02). Perceived vulnerability (β=.562, P<.001) and perceived severity (β=.243, P=.008) contributed to health motivation. CONCLUSIONS The results suggest the importance of the users’ performance expectations for wearable health devices for the intention of continued use for self-health management and habituation. Based on our results, developers and health care practitioners should find better ways to meet the performance expectations of middle-aged individuals with MetS risk factors. They also should generate device use easier and find a way to encourage users’ health motivation, thereby reducing users’ effort expectancy and resulting in a reasonable performance expectancy of the wearable health device, to induce users’ habitual use behaviors.

Lee S, et al. J Vet Sci. 2026 ;27:e65. https://doi.org/10.4142/jvs.26088