Older adults’ out-of-home behaviors (OOHBs) are critical for maintaining health and quality of life. Taking Singapore’s Yuhua East as a case, this study applied a qualitative approach to explore what neighborhood environmental factors influence older adults’ OOHBs. Twelve older adults were recruited for walk-along interviews through the use of purposeful convenience sampling. A content analysis was conducted using NVivo 11 via an inductive approach. Research results revealed 12 categories of environmental factors that affected older adults’ OOHBs: access to facilities (shops and services, public transit, and connectivity), pedestrian infrastructure (sidewalk quality, sheltered walkways, universal design, crossings, benches, and public toilets), aesthetics (natural elements, buildings, noise, and cleanliness), traffic safety (behavior of other road users and road width), safety from crime, wayfinding, familiarity (long-term residency and routine activities), weather, social contact, high-rise, high-density (lifts, population density, flat size, and privacy), affordability (shops and services, as well as transportation), and maintenance and upgrading. This analysis concluded that access to facilities and pedestrian infrastructure are important for older adults’ OOHBs. Considering Singapore’s weather, sheltered walkways, the proximity of facilities and connectivity should be given serious emphasis. In addition to physical factors, social contacts and the affordability of shops and services are also important.

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Background and Objectives Various aspects of the neighborhood environment have been shown to correlate with older adults’ health. Socio-ecological models of health posit that interventions in the living environment can influence population health. Yet, there are no scales to comprehensively measure older people’s experiences of their neighborhoods especially in dense urban contexts. This study analyzes the psychometric properties and factor structure of a holistic measure of Older People’s Neighborhood Experience (OpenX) to understand constituent factors of residential satisfaction and well-being in dense urban contexts. Research Design and Methods Participants were 1,011 community-dwelling older adults aged 50 and older in Singapore. Face-to-face interviews were conducted. Questions were drawn to measure physical and social aspects of the neighborhood as well as sociodemographic variables. Exploratory and confirmatory factor analyses were conducted to obtain a shorter version; content validity, internal consistency, and external validity were assessed. Results The OpenX has a 4-dimensional structure, explaining 45.5% of the variance of neighborhood experience. They are communal affordance, embeddedness, environment pleasantness, and time outdoors. Good reliability and validity were found, including Cronbach’s alpha of 0.827. The correlation between neighborhood experience and objectively measured proximity to parks and fitness corners approached significance (p = .082). Discussion and Implications The 16-item OpenX demonstrated good psychometric properties. With reference to the transdisciplinary neighborhood health framework, it is useful for assessing older adults’ neighborhood environment, identifying neighborhoods for pilot population health interventions, and understanding how the neighborhood environment affects older adults’ health.

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Given reduced life spaces, the neighborhood often functions as a social venue for older adults. Yet how these everyday social spaces affect older adults’ psychosocial wellbeing remains largely unknown. Drawing on the GRP-CARE Survey data, this paper examined the relation between neighborhood experiences and positive mental health. Participants were 601 community-dwelling Singaporeans aged 50+ who lived in public housing neighborhoods. Neighborhood experiences were measured using the four-factorial, 16-item OpenX scale (Gan, Fung, Cho, 2019); positive mental health was measured using a six-factorial, 19-item scale (Vaingankar et al., 2011). Both scales have good psychometric properties and had been validated. Path analysis between relevant factors of both scales was conducted using Stata, within a theorized model of causation from neighborhood environment to social factors to psychosocial health. Age, education, ethnicity and sex were controlled for. Multiple linear regression analysis showed a strong, positive association between neighborhood experiences and mental health (p=0.000) even after controlling for personal traits (operationalized as depressive symptoms, GDS) in addition to sociodemographic variables. Path analysis showed that two distinct neighborhood health processes mediated this association. These were (1) the potential for a sense of community in the neighborhood improved emotional support, and (2) having better neighborly friendships improved interpersonal skills. These neighborhood health processes provide us with new lenses to understand older adults’ everyday experiences of their neighborhoods. Community-based interventions to improve older adults’ psychosocial wellbeing may be developed to facilitate these processes. Spatial and programmatic implications will be discussed in relation to age-friendly cities and communities (AFCC).