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Falls in older adults have become a serious problem and a major cause of home injuries and even deaths. The increasing number of older people that will enter the “older adults” category in a few years’ time calls for an effective plan to mitigate the risk factors to falling. This article reported on our study of the relationship between living environment hazards and fall risk in older adults to reduce and prevent the risk of falling using a specific case of a rural area in Thailand. A site investigation together with a questionnaire survey were conducted in a total of 950 homes of older people who were interviewed in conjunction with authorities from Banphaeo district of Samutsakorn Province, Thailand. Using a multinomial logistic regression model, this research found the following risk of falls based on the categorizations of the calculated risk factors among socio-economic characteristics (sex, age, marital status, income), health status (congenital diseases), and living environment characteristics (toilet availability in bedroom). The analysis identified a multifactorial relationship involving intrinsic and extrinsic factors that determined fall risk among older adults. Based on the findings of the research, risk factors associated with socioeconomic determinants in term of poverty were found as a key barrier in promoting the health and well-being of older adults. We recommend interventions for fall prevention and fall risk-reduction strategies through improvement of the physical environment in the homes of older adults as a proactive measure to lessen the causes of home injuries from falls.

Older adults living alone present a vulnerable physical and mental health group with public health and service needs. This situation has risen and is therefore expected to increase calls for urgent attention from concerned authorities. This article focuses on the study of factors related to different living arrangements of older adults and also examines the extent to which baseline variables explained the association between living alone and social isolation characteristics. A questionnaire survey restricted to respondents aged 60 years and over, living in Ban Phaeo, Samutsakhon, Thailand, was scoped for data collection. Older adults living alone and in co-residence (living together) constitute a total of 1162 samples. The binary logistic regression model was applied to examine the association between living alone and social isolation characteristics. The result found that factors relating to older adults’ different living arrangements are marital status, household members numbers, level of dependency, and type of caregivers. An association was found between the characteristics of living alone and social isolation in three relative variables, which are age, activities of daily living (ADLs), and type of caregivers. In conclusion, household living arrangements have different related factors like marital status, where a single or divorced person is more likely to live alone. Furthermore, it is also influenced by the need for caregiving on the part of the older adult or family members; particularly, their children typically emerge as the unpaid assistance from families. When only a sample of older adults living alone with social isolation is considered, it was discovered that with the advancing age of older adults living alone, whether single or married, encountered problems with the activities of daily living (ADLs). This set of people rarely goes out to perform activities outside their home and seldom attend social and physical activities. This could lead to a risk of social isolation with a greater risk of physical and mental health problems, including the well-being of older adults living alone in later life. Thus, family caregivers play a key role as a primary source of support to prevent older adults from being socially isolated, which has become an integral part of our healthcare system in promoting physical, mental, and functional health among older adults in a positive way.

The problem of continuous increasing of carbon dioxide emissions in line with higher energy demand in Thailand has been called for attention under global warming conditions. In order to tackle with this problem, transportation was found as a major sector in an escalation of energy consumption which is the cause of carbon emission. As a developing country, infrastructure development has always been focused on an increasing of supply side, while less promote on public transportation and almost ignore for nonmotorization. The purpose of this study is to propose the method for measuring factors associated with pattern of walking behavior in connecting to public transportation usage by selecting Bangkok Mass Transit System as a case study. The results demonstrated different dimension of built environment aspects influence on different level of pedestrians' satisfaction. Thus, transportation planners should consider different context of urban area as a key parameter to provide future metropolitan transportation while allocate appropriate strategy and management policy to create walkable urban place to shift in travel mode from vehicles to transit or active transportation.

Understanding the quality of life related to transportation plays a crucial role in enhancing commuters’ quality of life, particularly in daily trips. This study explores the spatial effects of built environment on quality of life related to transportation (QoLT) through the combination of GIS application and deep learning based on a questionnaire survey by focusing on a case study in Sukhumvit district, Bangkok, Thailand. The Geographic Information System (GIS) was applied for spatial analysis and visualization among all variables through a grid cell (500 × 500 sq.m.). In regard to deep learning, the semantic segmentation process that the model used in this research was OCRNet, and the selected backbone was HRNet_W48. A quality-of-life-related transportation indicator (life satisfaction) was implemented through 500 face-to-face interviews and the data were collected by a questionnaire survey. Then, multinomial regression analysis was performed to demonstrate the significant in positive and negative aspects of independent variables (built environment) with QoLT variables at a 0.05 level of statistical significance. The results revealed the individuals’ satisfaction from a diverse group of people in distinct areas or environments who consequently perceived QoLT differently. Built environmental factors were gathered by application of GIS and deep learning, which provided a number of data sets to describe the clusters of physical scene characteristics related to QoLT. The perception of commuters could be translated to different clusters of the physical attributes through the indicated satisfaction level of QoLT. The findings are consistent with the physical characteristics of each typological site context, allowing for an understanding of differences in accessibility to transport systems, including safety and cost of transport. In conclusion, these findings highlight essential aspects of urban planning and transport systems that must consider discrepancies of physical characteristics in terms of social and economic needs from a holistic viewpoint. A better understanding of QoLT adds important value for transportation development to balance the social, economic, and environmental levels toward sustainable futures.