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BackgroundThe successful completion of medical practices often relies on information collection and analysis. Government agencies and medical institutions have encouraged people to use medical information technology (MIT) to manage their conditions and promote personal health. In 2014, Taiwan established the first electronic personal health record (PHR) platform, My Health Bank (MHB), which allows people to access and manage their PHRs at any time. In the face of the COVID-19 pandemic in 2020, Taiwan has used MIT to effectively prevent the spread of COVID-19 and undertaken various prevention measures before the onset of the outbreak. Using MHB to purchase masks in an efficient and orderly way and thoroughly implementing personal protection efforts is highly important to contain disease spread. ObjectiveThis study aims to understand people’s intention to use the electronic PHR platform MHB and to investigate the factors affecting their intention to use this platform. MethodsFrom March 31 to April 9, 2014, in a promotion via email and Facebook, participants were asked to fill out a structured questionnaire after watching an introductory video about MHB on YouTube. The questionnaire included seven dimensions: perceived usefulness, perceived ease of use, health literacy, privacy and security, computer self-efficacy, attitude toward use, and behavioral intention to use. Each question was measured on a 5-point Likert scale ranging from “strongly disagree” (1 point) to “strongly agree” (5 points). Descriptive statistics and structural equation analysis were performed using SPSS 21 and AMOS 21 software. ResultsA total of 350 valid questionnaire responses were collected (female: 219/350, 62.6%; age: 21-30 years: 238/350, 68.0%; university-level education: 228/350, 65.1%; occupation as student: 195/350, 56.6%; average monthly income

BackgroundTraditional studies of health behaviors are typically conducted using one-shot, cross-sectional surveys. Thus, participants’ recall bias may undermine the reliability and validity of the data. To capture mood changes and health behaviors in everyday life, we designed an online survey platform, ClickDiary, which helped collect more complete information for comprehensive data analyses. ObjectiveWe aim to understand whether daily mood changes are related to one’s personal characteristics, demographic factors, and daily health behaviors. MethodsThe ClickDiary program uses a Web-based platform to collect data on participants’ health behaviors and their social-contact networks. The name ClickDiary comes from the platform’s interface, which is designed to allow the users to respond to most of the survey questions simply by clicking on the options provided. Participants were recruited from the general population and came from various backgrounds. To keep the participants motivated and interested, the ClickDiary program included a random drawing for rewards. We used descriptive statistics and the multilevel proportional-odds mixed model for our analysis. ResultsWe selected 130 participants who had completed at least 30 days of ClickDiary entries from May 1 to October 31, 2014 as our sample for the study. According to the results of the multilevel proportional-odds mixed model, a person tended to be in a better mood on a given day if he or she ate more fruits and vegetables, took in more sugary drinks, ate more fried foods, showed no cold symptoms, slept better, exercised longer, and traveled farther away from home. In addition, participants were generally in a better mood during the weekend than on weekdays. ConclusionsSleeping well, eating more fruits and vegetables, and exercising longer each day all appear to put one in a better mood. With the online ClickDiary survey, which reduces the recall biases that are common in traditional one-shot surveys, we were able to collect and analyze the daily variations of each subject’s health behaviors and mood status.

Understanding the causes of noise annoyance requires recognition of the factors that affect soundscape perceptions. This study aims to explore multilevel factors of acoustic comfort and the perceived health effects of sound exposure, including personal traits, sound characteristics, and spatiotemporal features of the surrounding environment. We designed the Soundmap chatbot to collect data from the participants during May 16-July 16, 2022. The participants completed two tasks including sound recording and questionnaires. Sound feature extraction and identification were computed. Two soundscape perception variables were used as outcome variables and measured, and cumulative link mixed models were applied for statistical analysis. Results showed that for female participants, lower acoustic comfort was associated with sound exposure at night, at the land use of transportation and built-up areas, as well as the sounds of machines, vehicles, and airplanes. Low-frequency sound exposure and sounds of nature, silence, music, and human activity were associated with higher acoustic comfort, and these exposures were also associated with positive perceptions of health in rural areas and on weekends. Individuals with extraversion had a higher tolerance to sound; those with high noise sensitivity had a lower tolerance to sound. Understanding sound perception is crucial in maintaining a sustainable urban acoustic environment.

Objectives: This study aimed to explore the relationship between multidimensional factors, such as environment, health status, behavior, social support, and the well-being of middle-aged and older adults. Methods: This study utilized data from 2 waves of the nationally representative Taiwan Longitudinal Study on Aging Survey Report (TLSA) conducted in 2015 and 2019. The TLSA assesses socioeconomic status, physical and health status, the 5-item World Health Organization Well-Being Index (WHO-5 index), and social support. Data regarding the degree of digital development were obtained from the 2020 Township Digital Development Report. We applied a generalized estimating equation (GEE) to analyze the influencing factors. Results: This study included 4796 participants. Residing in areas with a higher degree of digital development, having a higher socioeconomic status, and experiencing better physical and mental health were significantly associated with well-being. Furthermore, emotional and attentive support mediated the association between physical and mental status and well-being. Conclusion: People's awareness of searching for and receiving social support and medical resources is important for enhancing their well-being. It is also crucial to pay attention to the living environment and maintain one's health status to promote well-being. [ABSTRACT FROM AUTHOR]

Since long-term exposure to PM2.5 is a growing concern in Taiwan, therefore, our study aimed to investigate the association between different PM2.5 concentrations and cause-specific mortalities, as well as explore the influence of demographic, socio-economic, and geographic determinants on mortality. Utilizing a Distributed Lag Non-Linear Model (DLNM), we estimated the PM2.5-attributable mortality burden among the elderly population (above 65 years) in Taiwan from 2005 to 2018. Our analysis considered various thresholds defined by the World Health Organization (WHO) to assess the impact of PM2.5 concentrations, ranging from standard Air Quality Guidelines (AQG) levels (0–15 μg/m3) to higher interim targets (up to 80 μg/m3). Our study reveals that achieving AQG levels (< 15 μg/m3) in Taiwan could potentially prevent 5571 elderly deaths, including 889 deaths from CVDs, 934 deaths from RTIs, and 680 deaths from chronic diseases. We observed a rapid increase in attributable deaths at lower PM2.5 concentrations, with a subsequent marginal increase at higher levels of exposure. We also identified several factors that modify the effects of PM2.5 exposures among elderly. For example, we found that higher elderly population ratios and high-income households were associated with an elevated risk of PM2.5-related deaths. In contrast, higher social welfare expenditure, better availability of medical practitioners per square kilometer, and higher forest-to-land ratios were associated with positive effects, indicating their potential in reducing mortality risk. This study fills a knowledge gap by examining the health consequences of varying PM2.5 levels and explored the complex interplay between demographic, socio-economic, and geographic factors in relation to PM2.5-related mortality. [ABSTRACT FROM AUTHOR]

Urban heat islands will occur if city neighborhoods contain insufficient green spaces to create a comfortable environment, and residents' health will be adversely affected. Current satellite imagery can only effectively identify large-scale green spaces and cannot capture street trees or potted plants within three-dimensional building spaces. In this study, we used a deep convolutional neural network semantic segmentation model on Google Street View to extract environmental features at the neighborhood level in Taipei City, Taiwan, including the green vegetation index (GVI), building view factor, and sky view factor. Monthly temperature data from 2018 to 2021 with a 0.01° spatial resolution were used. We applied a linear mixed-effects model and geographically weighted regression to explore the association between pedestrian-level green spaces and ambient temperature, controlling for seasons, land use information, and traffic volume. Their results indicated that a higher GVI was significantly associated with lower ambient temperatures and temperature differences. Locations with higher traffic flows or specific land uses, such as religious or governmental, are associated with higher ambient temperatures. In conclusion, the GVI from street-view imagery at the community level can improve the understanding of urban green spaces and evaluate their effects in association with other social and environmental indicators. [ABSTRACT FROM AUTHOR]