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Mental health disorders have become a global problem, garnering considerable attention. However, the root causes of deteriorating mental health remain poorly understood, with existing literature predominantly concentrating on socioeconomic conditions and psychological factors. This study uses multi-linear and geographically weighted regressions (GWR) to examine the associations between built and natural environmental attributes and the prevalence of depression in US counties. The findings reveal that job sprawl and land mixed use are highly correlated with a lower risk of depression. Additionally, the presence of green spaces, especially in urban area, is associated with improved mental health. Conversely, higher concentrations of air pollutants, such as PM 2.5 and CO, along with increased precipitation, are linked to elevated depression rates. When considering spatial correlation through GWR, the impact of population density and social capital on mental health displays substantial spatial heterogeneity. Further analysis, focused on two high depression risk clustering regions (northwestern and southeastern counties), reveals nuanced determinants. In northwestern counties, depression rates are more influenced by factors like precipitation and socioeconomic conditions, including unemployment and income segregation. In southeastern counties, population demographic characteristics, particularly racial composition, are associated with high depression prevalence, followed by built environment factors. Interestingly, job growth and crime rates only emerge as significant factors in the context of high depression risks in southeastern counties. This study underscores the robust linkages and spatial variations between built and natural environments and mental health, emphasizing the need for effective depression treatment to incorporate these multifaceted factors., Competing Interests: The authors declare no conflicts of interest relevant to this study., (© 2024 The Author(s). GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union.)

The purpose of this study was to analyze the mediating role of digital health literacy and the moderating effect of parasocial relationships on the relationship between the viewing experience of health exercise-related YouTube content and the intention for health exercise behavior. Based on the health action process approach, this study established a foundational theoretical model to analyze how digital health literacy mediates the impact of media viewing experience on health exercise behavior intention. Additionally, this study examined the moderating effect of parasocial relationships with YouTube creators. For empirical analysis, variables were measured using a self-administration method among 409 randomly sampled consumers of YouTube health exercise content. The collected data were analyzed using a structural equation model incorporating mediation parameters, and a multigroup model analysis was conducted to understand differences based on parasocial relationships. The results revealed that increased YouTube viewing experience enhanced cognitive, skill, and evaluative components of digital health literacy, which were significant factors in increasing health exercise behavior intention. Notably, the mediating effect of cognition played a crucial role, and the strengthening effect of parasocial relationships on this relationship was confirmed. These findings can be utilized as practical foundational data for designing digital health communication strategies, particularly in developing motivational mechanisms that encourage consumers to engage voluntarily and consistently in health behaviors based on online health information.

Highly conductive nylon 6 nanofiber webs, incorporating poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) and dimethyl sulfoxide (DMSO), were prepared for textile-based signal transmission lines. To improve the electrical performance of the textiles, they were optimized by the number of coating cycles and the solvent treatment step. The nanofiber web coated four times with PEDOT:PSS showed a six-times reduction in sheet resistance compared to that of once. In addition, the sample treated with both adding and dipping of DMSO showed a significant decrease of 83 times in sheet resistance compared to the sample without treatment of DMSO. Using samples with excellent electrical conductivity, the waveforms of the signal in the time domain were analyzed and shown to have an amplitude and phase almost identical to that of the conventional copper wire. As a result of the S21 characteristic curve, selected textiles were available up to the 15 MHz frequency bandwidth. In the FE-SEM image, it was observed that the surface of the coated sample was generally covered with PEDOT:PSS, which was distinguished from the untreated sample. These results demonstrate that the nanofiber web treated with the optimized conditions of PEDOT:PSS and DMSO can be applied as promising textile-based signal transmission lines for smart clothing.