Associations of objectively-assessed neighborhood characteristics with older adults’ total physical activity and sedentary time in an ultra-dense urban environment: Findings from the ALECS study
Introduction
In the last decade, numerous studies have examined neighborhood environmental correlates of older adults’ physical activity (PA) (e.g., Carlson et al., 2012, Cerin et al., 2013a, Van Cauwenberg et al., 2011) and, to a much lesser extent, sedentary behavior (SB) (Barnett et al., 2015b, Van Cauwenberg et al., 2014). Aspects of transport-related walkability (i.e., neighborhood characteristics supportive of utilitarian walking, including residential density, street intersection, and access to services) and access to public transport, recreational facilities, and open public space (e.g., parks) have been associated with higher levels of PA (Carlson et al., 2012, Cerin et al., 2013a, Cerin et al., 2014, Van Cauwenberg et al., 2011, Van Holle et al., 2014). There is also some evidence that access to public transport, facilities, and greenery may be associated with less non-transport sitting (Barnett et al., 2015b, Van Cauwenberg et al., 2014).
Most of these studies used self-report measures of environmental exposures and/or PA (Cerin et al., 2013b, Cerin et al., 2013c, Van Cauwenberg et al., 2011) and SB (Barnett et al., 2015b, Van Cauwenberg et al., 2014). From a behavioral and socio-ecological perspective (Cerin et al., 2011, Giles-Corti et al., 2005), self-report measures are useful as they can easily provide contextual information on PA and SB - such as activity type (e.g., walking), domain (e.g., transport), and location (e.g., within the neighborhood) – which can then be linked to relevant neighborhood characteristics. However, from a public health viewpoint, it is critical to establish whether specific neighborhood characteristics may contribute to the total accumulation of PA and SB. Indeed, it is the total amount of activity that is deemed to affect health (Nelson et al., 2007). Total PA and SB are best measured objectively via accelerometry because estimates of total PA and SB based on self-reports are less reliable, valid, and comparable across populations (Cerin et al., 2016, Welk, 2002). The use of self-report measures of total PA and SB is especially problematic in older adults who may have difficulties recalling the amount and intensity of activities (Cerin et al., 2012, Matthews, 2002).
Most studies on neighborhood environmental correlates of older adults’ PA and SB focused on perceived neighborhood characteristics. Such studies cannot reliably inform urban planning practice for the creation of an activity-friendly environment because perceptions of aspects of the neighborhood environment may be determined by whether or not a person engages in a particular activity (McCormack et al., 2008). For example, neighborhood recreational facilities may be reported by residents who engage in leisure-time PA, while those who do not engage in leisure-time PA may be unaware of the presence of such facilities. In this case, using a measure of perceived recreational facilities would yield inflated estimates of the associations between the presence of recreational facilities and leisure-time PA. It is, thus, important to identify objectively-assessed neighborhood environmental correlates of PA and SB because they can more reliably inform urban planning practice (types of destinations needed to support an active lifestyle).
Only three studies have examined associations between objectively-assessed neighborhood characteristics and accelerometry-based estimates of PA in older adults (Carlson et al., 2012, Nathan et al., 2014, Van Holle et al., 2014), and none have identified correlates of accelerometry-based estimates of SB. Two of these studies (Carlson et al., 2012, Van Holle et al., 2014) examined only a couple of objectively-assessed neighborhood environmental attributes (e.g., transport-related walkability and park/recreation facilities). Nathan and colleagues assessed nine environmental features (walkability, slope, traffic volume, and distance to six types of destinations) surrounding retirement villages in Australia, and found distance to shops to be negatively related to objectively-assessed PA (Nathan et al., 2014). All three studies were conducted in Western, low-to-mid-density urban areas. Hence, their findings are unlikely to apply to high-density Asian countries housing the largest proportion of the global aging population, with China ranked first and predicted to reach over 90 million >80-year-olds in 2050 (United Nations, 2013).
Nearly a quarter of Chinese older adults live in ultra-dense urban areas (World Bank Group, 2015b) with high accessibility to services and public transport, which are deemed to facilitate an active lifestyle (Cerin et al., 2014). These environmental conditions may in part explain why older adults living in Chinese and other high-density Asian cities accumulated substantially more PA (Cerin et al., 2013b, Cerin et al., 2014, Deng et al., 2008, Hanibuchi. et al., 2011, Koh et al., 2015, Zhang et al., 2014) and less sitting time (Barnett et al., 2015b) than their Western counterparts (King et al., 2011, Nathan et al., 2014, Van Dyck et al., 2012, Van Holle et al., 2014). Yet, since PA and SB data from Chinese and other Asian locations were primarily collected via self-reports, these differences may in part be an artifact of cultural and linguistic biases in responding to questionnaire items (e.g., over-reporting), as observed in a recent multi-country study on adults (Cerin et al., 2016). The use of objective measures of PA and SB would help establish the extent to which the observed findings are real or a measurement artifact.
In line with social-ecological models, when studying associations of neighborhood environment characteristics with PA and SB, it is important to consider the interplay of individual-level (e.g. socio-demographics) and environmental factors on behavior, i.e., whether environment-PA associations vary across population segments (Sallis et al., 2008). Such considerations are also important because they allow determination of the impact of environmental interventions on health disparities (Van Cauwenberg et al., 2011). Age (Cerin et al., 2013c, Cerin et al., 2014), sex (Cerin et al., 2013c, Cerin et al., 2014, Hanibuchi. et al., 2011, Inoue et al., 2011), education (Cerin et al., 2013c, Cerin et al., 2014), car ownership (Kamada et al., 2009), and health status (Koh et al., 2015) have been studied as moderators of the associations of neighborhood environmental attributes with self-reported walking in Asian older adults. As environmental interventions need to address the needs of the most vulnerable inactive groups, considering the moderating effects of socio-demographic and health-related characteristics is important. The evidence in this regard is sparse, requiring further investigations.
To address the abovementioned knowledge gaps, this study aimed to examine (1) the associations of a range of objectively-assessed neighborhood environmental attributes with accelerometry-based estimates of total PA and SB in a community sample of older Chinese dwellers of an ultra-dense Chinese metropolis (Hong Kong), and (2) the moderating effects of age, sex, education, car ownership, and health status (number of chronic conditions and physical functionality) on environment-PA associations.
Section snippets
Methods
This study used data from the first wave (collected in 2012–2015) of the Active Lifestyle and the Environment in Chinese Seniors (ALECS) study, an observational study of the associations of aspects of the built and social neighborhood environment, and psychosocial factors with PA, quality of life, and depressive symptoms in Hong Kong community dwellers aged 65+ years (Author et al., 2015; Cerin et al., 2016). The study protocol was approved by the ethics committees of the local institutions.
Results
Overall, participants accumulated ~26 min of moderate-to-vigorous PA per day (Table 2), which would translate to 182 weekly minutes. On average, they accrued 298 (~5 h) and 512 (~8.5 h) minutes of light-to-vigorous PA and sedentary time, respectively, within the 13.5 daily hours of accelerometer wear time. When adjusting for co-variates and environmental variables, no significant differences were observed in average daily minutes of moderate-to-vigorous PA between participants recruited from the
Discussion
In the last decade, the number of studies on neighborhood environmental correlates of older adults’ PA has substantially increased and expanded from Western (Van Cauwenberg et al., 2011) to Asian (Cerin et al., 2013a) and other populations (Parra et al., 2010). In contrast, investigations into aspects of the neighborhood environment related to SB are rare (Barnett et al., 2015b), as are those employing objective measures of environmental exposures and older adults’ PA/SB (King et al., 2011). In
Conflict of interest statement
All authors declare that they have no competing interests.
Funding
This study received a General Research Fund grant from the University Grant Committee (Hong Kong) (HKU 741511H). EC is supported by an Australian Research Council (Australia) Future Fellowship (FT14010085). The information reported in this paper is independent from the funding sources.
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