Background
Physical activity (PA) has been reported to have many health benefits including reduced risk of mortality [
1,
2] and the prevention of chronic diseases, such as cardiovascular disease, diabetes, cancer, hypertension, obesity, or depression [
3]. However, a large part of the population does not regularly engage in exercise. For example, less than one third of the Japanese population (32.2% of men and 27.0% of women) regularly engage in exercise; thirty minutes or more of exercise two or more times per week, for more than a year [
4]. In this regard, exploring factors that are associated with increased levels of physical activity is important for public health research.
In studies of the health impacts of neighborhood environments, the association between the built environment (BE) and physical activity is of central importance [
5]. PA has been reported to be related to residential density, street connectivity, and land use mix [
6‐
8]. Although many empirical studies have analyzed the associations using perceived measures [
9‐
11], objective measures [
12‐
15], or both [
16‐
19], the findings continue to be heterogeneous (e.g., no association, or associations in the opposite direction) [
20,
21]. This may be explained by variations in the environmental measures, study populations, or geographical settings, in which the respondents reside.
Age (adolescent, middle aged, or older adults) is an important source of the between-study heterogeneity. Compared to the studies of adolescents and adults, few studies have explored the association between the BE and the PA of older adults [
14,
22]. Only recently, especially in the late 2000s, have researchers begun to analyze the association with a variety of objective and/or perceived measures of BE (e.g., residential density, land use mix, street connectivity, access to local destinations, walking/cycling facilities, etc.) and different types of PA (e.g., total PA, recreational PA, recreational walking, transportation walking, etc.). In a systematic review of 31 articles concerning the relationship between BE and PA in older adults, Van Cauwenberg et al. [
23] concluded that the results were inconsistent, though most of the studied environmental characteristics were reported to be unrelated to PA. The authors pointed out that this might reflect some methodological issues within this developing field, such as the measurement of PA and environment. For example, Nagel et al. [
14] found no association between any of the variables of objectively measured BEs and the likelihood of engaging in walking, based on the samples of community-dwelling older adults in Portland, Oregon. However, amongst those reporting some degree of walking activity, the average time spent walking was associated with some variables of BE; amount of automobile traffic and number of commercial establishments.
Furthermore, the broader social and cultural context may be important in studies of the environment and PA. Although many studies have been conducted, especially in the US, Europe, and Australia [
23], the association between BE and PA may not be clearly generalizable to other societies. For example, a study of an elderly population from Latin America (Bogotá) recently showed the negative associations between street connectivity and walking for at least 60 minutes, which, according to the authors, differs from most of the evidence gathered from studies in Europe and the US [
24]. Generally speaking, the spatial forms of residence, transportation infrastructure, and retail or business locations may all vary according to the given country, region and cultural context. Thus, research is needed to explore the association between the BE and PA outside the US, Europe, and Australia [
7,
23,
25], especially in Asian countries, where a dearth of studies have been conducted.
Based on the above mentioned challenges, the aim of this paper is to fill the gaps in the literature by examining the association between neighborhood BEs and PA of older adults in Japan.
Results
Basic characteristics of the respondents are shown in Table
1 and characteristics of the BEs in neighborhoods are shown in Table
2. More than half respondents did not engage in sports activity. However, among those who did engage in sports activity, many people were more likely to engage in it frequently. As for walking time, although approximately one third of the respondents answered they walk less than 30 minutes a day, the responses were more evenly distributed than the frequency of sports activity. Table
3 represents the correlation coefficient between BE measures. Some combinations showed a high correlation, especially at the radial distance of 1,000 m. For example, population density was positively correlated with the number of intersections (r = 0.66) and the number of destinations (r = 0.74).
Table 1
Characteristics of the respondents
Overall | 9414 | 100.0 | Equivalized income | | |
Sports activity | | | <1 million yen | 937 | 10.0 |
No sports activities | 5227 | 55.5 | 1-2 million yen | 2123 | 22.6 |
Several times a year | 70 | .7 | 2-3 million yen | 2161 | 23.0 |
Once or twice a month | 230 | 2.4 | 3-4 million yen | 1451 | 15.4 |
Once a week | 676 | 7.2 | ≥4 million yen | 929 | 9.9 |
Twice or three times a week | 1176 | 12.5 | Missing | 1813 | 19.3 |
Almost everyday | 1495 | 15.9 | Having paid work | | |
Missing | 540 | 5.7 | Yes | 2272 | 24.1 |
Walking time/day | | | No | 6970 | 74.0 |
Less than 30 minutes | 2936 | 31.2 | Missing | 172 | 1.8 |
30 to 60 minutes | 3074 | 32.7 | SRH | | |
60 to 90 minutes | 1183 | 12.6 | Good | 6591 | 70.0 |
More than 90 minutes | 1104 | 11.7 | Poor | 2585 | 27.5 |
Missing | 1117 | 11.9 | Missing | 238 | 2.5 |
Age | | | GDS | | |
65-69 | 3386 | 36.0 | Low depressive symptoms | 7401 | 78.6 |
70-74 | 2765 | 29.4 | High depressive symptoms | 565 | 6.0 |
75-79 | 1886 | 20.0 | Missing | 1448 | 15.4 |
80-84 | 938 | 10.0 | IADL | | |
≥85 | 439 | 4.7 | High IADL | 7178 | 76.2 |
Gender | | | Low IADL | 1837 | 19.5 |
Male | 4519 | 48.0 | Missing | 399 | 4.2 |
Female | 4895 | 52.0 | Location | | |
Marital status | | | North | 3856 | 41.0 |
Married | 6759 | 71.8 | South | 5558 | 59.0 |
Divorced/widowed | 2321 | 24.7 | Years of residence | | |
Never married | 127 | 1.3 | <50 years | 4819 | 51.2 |
Missing | 207 | 2.2 | ≥50 years | 4138 | 44.0 |
Educational attainment | | | Missing | 457 | 4.9 |
<6 | 430 | 4.6 | | | |
6-9 | 5242 | 55.7 | | | |
10-12 | 2711 | 28.8 | | | |
≥13 | 903 | 9.6 | | | |
Missing | 128 | 1.4 | | | |
Table 2
Characteristics of neighborhoods of respondents
(r = 250 m) | | | | | |
Population density (per hectare) | 9414 | 35.3 | 18.3 | 0.0 | 140.8 |
No. of intersections | 9414 | 22.4 | 10.3 | 0.0 | 69.0 |
No. of dead-ends | 9414 | 2.8 | 2.5 | 0.0 | 18.0 |
No. of destinations | 9414 | 2.6 | 3.2 | 0.0 | 33.0 |
Parks or green spaces | 9414 | 0.1 | 0.3 | 0.0 | 1.0 |
Schools | 9414 | 0.1 | 0.3 | 0.0 | 1.0 |
Land slope | 9414 | 2.9 | 2.2 | 0.0 | 14.5 |
(r = 500 m) | | | | | |
Population density (per hectare) | 9414 | 34.0 | 16.5 | 0.0 | 108.5 |
No. of intersections | 9414 | 70.6 | 27.3 | 2.0 | 176.0 |
No. of dead-ends | 9414 | 7.8 | 6.0 | 0.0 | 39.0 |
No. of destinations | 9414 | 8.2 | 7.7 | 0.0 | 57.0 |
Parks or green spaces | 9414 | 0.1 | 0.3 | 0.0 | 1.0 |
Schools | 9414 | 0.3 | 0.5 | 0.0 | 1.0 |
Land slope | 9414 | 2.7 | 1.6 | 0.1 | 12.0 |
(r = 1000 m) | | | | | |
Population density (per hectare) | 9414 | 25.2 | 13.4 | 1.3 | 91.7 |
No. of intersections | 9414 | 252.1 | 91.4 | 16.0 | 552.0 |
No. of dead-ends | 9414 | 39.7 | 23.0 | 1.0 | 108.0 |
No. of destinations | 9414 | 29.6 | 21.6 | 0.0 | 117.0 |
Parks or green spaces | 9414 | 0.2 | 0.4 | 0.0 | 1.0 |
Schools | 9414 | 0.6 | 0.5 | 0.0 | 1.0 |
Land slope | 9414 | 3.1 | 2.0 | 0.2 | 12.7 |
Table 3
Correlation coefficient between BE measures
(r = 250 m) | | | | | | | |
a) | Population density | 1.00 | | | | | | |
b) | No. of intersections | 0.31 | 1.00 | | | | | |
c) | No. of dead-ends | -0.07 | -0.01 | 1.00 | | | | |
d) | No. of destinations | 0.23 | 0.32 | 0.02 | 1.00 | | | |
e) | Parks or green spaces | 0.39 | 0.16 | -0.13 | 0.08 | 1.00 | | |
f) | Schools | 0.04 | 0.10 | 0.03 | 0.10 | 0.02 | 1.00 | |
g) | Land slope | -0.29 | -0.24 | 0.01 | -0.18 | -0.08 | 0.04 | 1.00 |
(r = 500 m) | | | | | | | |
a) | Population density | 1.00 | | | | | | |
b) | No. of intersections | 0.42 | 1.00 | | | | | |
c) | No. of dead-ends | -0.01 | 0.17 | 1.00 | | | | |
d) | No. of destinations | 0.44 | 0.44 | 0.07 | 1.00 | | | |
e) | Parks or green spaces | 0.43 | 0.29 | -0.22 | 0.18 | 1.00 | | |
f) | Schools | 0.07 | 0.19 | -0.01 | 0.17 | 0.09 | 1.00 | |
g) | Land slope | -0.27 | -0.38 | -0.07 | -0.32 | -0.08 | -0.01 | 1.00 |
(r = 1000 m) | | | | | | | |
a) | Population density | 1.00 | | | | | | |
b) | No. of intersections | 0.66 | 1.00 | | | | | |
c) | No. of dead-ends | 0.15 | 0.35 | 1.00 | | | | |
d) | No. of destinations | 0.74 | 0.58 | 0.23 | 1.00 | | | |
e) | Parks or green spaces | 0.43 | 0.42 | -0.20 | 0.25 | 1.00 | | |
f) | Schools | 0.04 | 0.12 | -0.01 | 0.03 | 0.25 | 1.00 | |
g) | Land slope | -0.45 | -0.59 | -0.26 | -0.43 | -0.19 | 0.08 | 1.00 |
Table
4 shows the results of the ordinal logistic regression analysis for leisure time sports activity. Population density was related to increased sports activity at radial distances of 250 m (OR = 1.004, 95%CI = 1.001-1.006), 500 m (OR = 1.004, 95%CI = 1.002-1.007), and 1,000 m (OR = 1.005, 95%CI = 1.002-1.008) for the neighborhood. The presence of parks or green spaces also showed a consistent association with sports activity at 250 m (OR = 1.258, 95%CI = 1.082-1.462), 500 m (OR = 1.152, 95%CI = 1.021-1.300), and 1,000 m radius (OR = 1.162, 95%CI = 1.056-1.280). The number of dead-ends was inversely related to sports activity, at radial distances of 500 m (OR = 0.992, 95%CI = 0.985-0.999), while the number of intersections had a positive association only at a radial distance of 1,000 m (OR = 1.001, 95%CI = 1.000-1.001). The presence of schools was not associated with sports activity measures. Land slope were negatively related to sports activity at 250 m (OR = 0.961, 95%CI = 0.941-0.981), 500 m (OR = 0.957, 95%CI = 0.931-0.983), and 1000 m radius (OR = 0.944, 95%CI = 0.923-0.967).
Table 4
Associations between frequency of sports activity and each of the BEs by ordinal logistic regression analysis
Population density | | 1.004 | (1.001, 1.006) | 1.004 | (1.002, 1.007) | 1.005 | (1.002, 1.008) |
No. of intersections | | 1.001 | (0.997, 1.006) | 1.001 | (0.999, 1.002) | 1.001 | (1.000, 1.001) |
No. of dead-ends | | 0.991 | (0.975, 1.008) | 0.992 | (0.985, 0.999) | 1.000 | (0.998, 1.002) |
No. of destinations | | 1.002 | (0.989, 1.015) | 1.000 | (0.994, 1.005) | 1.001 | (0.999, 1.003) |
Parks or green spaces | | 1.258 | (1.082, 1.462) | 1.152 | (1.021, 1.300) | 1.162 | (1.056, 1.280) |
Schools | | 0.967 | (0.852, 1.097) | 1.008 | (0.919, 1.106) | 0.992 | (0.909, 1.082) |
Land slope | | 0.961 | (0.941, 0.981) | 0.957 | (0.931, 0.983) | 0.944 | (0.923, 0.967) |
(Quartiles) | | | | | | | |
Population density (Ref. Lowest) | Low | 1.017 | (0.900, 1.150) | 0.899 | (0.796, 1.015) | 1.074 | (0.950, 1.215) |
| High | 1.067 | (0.945, 1.205) | 0.971 | (0.861, 1.096) | 1.160 | (1.027, 1.310) |
| Highest | 1.186 | (1.054, 1.335) | 1.147 | (1.021, 1.29) | 1.200 | (1.066, 1.351) |
No. of intersections (Ref. Lowest) | Low | 1.052 | (0.934, 1.184) | 1.075 | (0.952, 1.212) | 1.233 | (1.088, 1.397) |
| High | 1.063 | (0.943, 1.198) | 0.982 | (0.869, 1.108) | 1.138 | (1.004, 1.290) |
| Highest | 1.034 | (0.919, 1.163) | 1.089 | (0.966, 1.228) | 1.191 | (1.051, 1.350) |
No. of dead-ends (Ref. Lowest) | Low | 0.880 | (0.778, 0.994) | 0.896 | (0.800, 1.002) | 0.929 | (0.824, 1.047) |
| High | 0.852 | (0.762, 0.952) | 0.926 | (0.823, 1.041) | 0.990 | (0.879, 1.116) |
| Highest | 0.954 | (0.852, 1.069) | 0.884 | (0.787, 0.992) | 0.999 | (0.888, 1.123) |
No. of destinations (Ref. Lowest) | Low | 1.095 | (0.984, 1.219) | 1.096 | (0.972, 1.236) | 1.185 | (1.049, 1.338) |
| High | 1.097 | (0.969, 1.243) | 1.101 | (0.982, 1.233) | 1.069 | (0.948, 1.206) |
| Highest | 1.042 | (0.919, 1.181) | 0.980 | (0.872, 1.100) | 1.161 | (1.033, 1.306) |
Land slope (Ref. Lowest) | Low | 1.129 | (1.001, 1.273) | 1.026 | (0.910, 1.156) | 1.059 | (0.941, 1.193) |
| High | 1.115 | (0.988, 1.258) | 1.088 | (0.965, 1.227) | 1.053 | (0.934, 1.187) |
| Highest | 0.948 | (0.837, 1.073) | 0.933 | (0.825, 1.057) | 0.840 | (0.740, 0.953) |
When looking at the results using variables that were categorized into quartiles, some non-linear associations were observed. For the number of dead-ends at 250 m radius, Low (OR = 0.880, 95%CI = 0.778-0.994) and High (OR = 0.852, 95%CI = 0.762-0.952) showed differences compared to the reference category (Lowest), but Highest (OR = 0.954, 95%CI = 0.852-1.069) did not. Although no linear relation was observed, when using the categorized variable, the number of destinations was associated with sports activity at a radial distance of 1,000 m (Low: OR = 1.185, 95%CI = 1.049-1.338, Highest: OR = 1.161, 95%CI = 1.033-1.306). Mixed results were observed for land slope; a negative association was observed at a radial distance of 1,000 m (Highest: OR = 0.840, 95%CI = 0.740-0.953), while a positive association was observed at a radial distance of 250 m (Low: OR = 1.129, 95%CI = 1.001-1.273).
Table
5 represents the results of mutually adjusted models including the variables that were shown to be associated with sports activity in the separated models at each buffer radius. Associations remained for two out of three BE variables at 250 m radius, three out of four at 500 m radius, and two out of four at 1,000 m radius.
Table 5
Associations between frequency of sports activity and BE (mutually adjusted)
Population density | 1.002 | (0.999, 1.004) | 1.003 | (1.000, 1.006) | 1.002 | (0.997, 1.006) |
No. of intersections | | | | | 1.000 | (0.999, 1.000) |
No. of dead-ends | | | 0.992 | (0.985, 0.999) | | |
Parks or green spaces | 1.186 | (1.007, 1.397) | 1.036 | (0.902, 1.190) | 1.118 | (1.003, 1.247) |
Land slope | 0.966 | (0.945, 0.987) | 0.964 | (0.938, 0.992) | 0.947 | (0.921, 0.975) |
Table
6 shows the results of regression analysis for total walking time. Only a few associations were observed. The land slope showed a consistent positive association with walking time at 250 m (OR = 1.037, 95%CI = 1.018-1.056), 500 m (OR = 1.048, 95%CI = 1.023-1.074), and 1,000 m radius (OR = 1.036, 95%CI = 1.015-1.058), suggesting that the respondents living in areas with steeper slopes tended to report longer times for walking per day. When using variables that were categorized into quartiles, in addition to the land slope, the number of intersections at a radial distance of 500 m (Low: OR = 0.876, 95%CI = 0.782-0.981, Highest: OR = 0.891, 95%CI = 0.796-0.998) and the number of destinations at a radial distance of 1,000 m (Low: OR = 0.879, 95%CI = 0.784-0.985) were negatively associated with walking time.
Table 6
Associations between total walking time and each of the BEs by ordinal logistic regression analysis
Population density | | 1.000 | (0.998, 1.002) | 0.999 | (0.997, 1.002) | 0.999 | (0.996, 1.002) |
No. of intersections | | 0.997 | (0.993, 1.001) | 0.999 | (0.998, 1.001) | 1.000 | (0.999, 1.000) |
No. of dead-ends | | 0.997 | (0.981, 1.012) | 0.997 | (0.991, 1.004) | 0.999 | (0.997, 1.001) |
No. of destinations | | 0.998 | (0.986, 1.010) | 0.996 | (0.990, 1.001) | 0.999 | (0.997, 1.001) |
Parks or green spaces | | 1.045 | (0.902, 1.211) | 1.057 | (0.940, 1.188) | 1.019 | (0.929, 1.117) |
Schools | | 1.078 | (0.958, 1.214) | 1.063 | (0.974, 1.160) | 1.085 | (0.999, 1.178) |
Land slope | | 1.037 | (1.018, 1.056) | 1.048 | (1.023, 1.074) | 1.036 | (1.015, 1.058) |
(Quartiles) | | | | | | | |
Population density (Ref. Lowest) | Low | 1.088 | (0.973, 1.218) | 0.971 | (0.868, 1.086) | 0.998 | (0.892, 1.117) |
| High | 0.919 | (0.820, 1.030) | 0.900 | (0.804, 1.008) | 0.911 | (0.813, 1.020) |
| Highest | 1.042 | (0.933, 1.165) | 0.970 | (0.868, 1.084) | 0.937 | (0.838, 1.048) |
No. of intersections (Ref. Lowest) | Low | 0.941 | (0.843, 1.051) | 0.876 | (0.782, 0.981) | 0.992 | (0.884, 1.113) |
| High | 0.926 | (0.827, 1.037) | 0.959 | (0.856, 1.074) | 0.914 | (0.814, 1.027) |
| Highest | 0.937 | (0.839, 1.046) | 0.891 | (0.796, 0.998) | 0.998 | (0.888, 1.121) |
No. of dead-ends (Ref. Lowest) | Low | 0.977 | (0.871, 1.095) | 1.031 | (0.928, 1.145) | 1.073 | (0.960, 1.200) |
| High | 0.962 | (0.867, 1.069) | 0.930 | (0.832, 1.041) | 0.996 | (0.890, 1.114) |
| Highest | 0.926 | (0.832, 1.032) | 0.937 | (0.840, 1.046) | 0.980 | (0.877, 1.096) |
No. of destinations (Ref. Lowest) | Low | 0.930 | (0.841, 1.029) | 0.926 | (0.827, 1.038) | 0.879 | (0.784, 0.985) |
| High | 1.011 | (0.899, 1.136) | 1.007 | (0.905, 1.121) | 1.035 | (0.926, 1.156) |
| Highest | 0.977 | (0.869, 1.098) | 0.935 | (0.838, 1.042) | 0.942 | (0.843, 1.052) |
Land slope (Ref. Lowest) | Low | 1.039 | (0.927, 1.165) | 1.025 | (0.915, 1.149) | 1.061 | (0.947, 1.189) |
| High | 1.025 | (0.914, 1.149) | 1.053 | (0.939, 1.180) | 1.153 | (1.029, 1.292) |
| Highest | 1.178 | (1.049, 1.322) | 1.189 | (1.059, 1.334) | 1.154 | (1.027, 1.297) |
The regression analysis was performed using subgroups stratified by gender, location (North vs. South Chita Peninsula), and years of residence (<50 years vs. ≥50 years). The results were shown in Table
7 (sports activity) and Table
8 (walking time). Regarding sports activity, the association between the BE and PA was clearly apparent among male respondents. Associations were observed in 10 out of the 21 models (seven BE measures and three buffer radii) for the male group, while only one was observed among the female respondents. In seven out of the 10 models, interactions between gender and BE were observed (results not shown). When the analysis was stratified by location, even though associations were seen in both North and South, only a few were observed in North. For example, parks or green spaces were not associated with sports activity in the North subgroup. Among respondents who had resided longer in the municipality (≥50 years), associations were observed in eight models, while only one was seen among respondents who had resided for a shorter time in the municipality (<50 years). For example, associations between population density and sports activity were only seen among the residents residing for 50 years or more (at three radii). Interactions between years of residence and BE were observed in three of the models.
Table 7
Associations between frequency of sports activity and the BE stratified by gender, location, and years of residence
Male | | | | | | |
Population density | 1.005 | (1.002, 1.009) | 1.007 | (1.003, 1.010) | 1.008 | (1.003, 1.012) |
No. of intersections | 1.003 | (0.997, 1.008) | 1.001 | (0.999, 1.003) | 1.001 | (1.000, 1.002) |
No. of dead-ends | 0.999 | (0.976, 1.022) | 0.993 | (0.984, 1.003) | 1.000 | (0.998, 1.003) |
No. of destinations | 1.009 | (0.991, 1.028) | 0.997 | (0.989, 1.005) | 1.002 | (0.999, 1.004) |
Parks or green spaces | 1.336 | (1.089, 1.640) | 1.253 | (1.064, 1.476) | 1.233 | (1.081, 1.407) |
Schools | 0.854 | (0.711, 1.024) | 0.964 | (0.846, 1.098) | 0.972 | (0.862, 1.096) |
Land slope | 0.943 | (0.915, 0.972) | 0.945 | (0.909, 0.982) | 0.924 | (0.893, 0.956) |
Female | | | | | | |
Population density | 1.001 | (0.998, 1.005) | 1.002 | (0.998, 1.006) | 1.003 | (0.998, 1.007) |
No. of intersections | 1.000 | (0.994, 1.006) | 1.000 | (0.998, 1.003) | 1.000 | (1.000, 1.001) |
No. of dead-ends | 0.982 | (0.959, 1.006) | 0.990 | (0.980, 1.000) | 0.999 | (0.997, 1.002) |
No. of destinations | 0.993 | (0.974, 1.012) | 1.002 | (0.995, 1.010) | 1.001 | (0.998, 1.003) |
Parks or green spaces | 1.172 | (0.937, 1.464) | 1.033 | (0.862, 1.238) | 1.083 | (0.939, 1.249) |
Schools | 1.092 | (0.916, 1.301) | 1.060 | (0.928, 1.210) | 1.019 | (0.898, 1.157) |
Land slope | 0.979 | (0.952, 1.008) | 0.971 | (0.935, 1.009) | 0.964 | (0.934, 0.995) |
North | | | | | | |
Population density | 1.001 | (0.998, 1.004) | 1.003 | (0.999, 1.006) | 1.001 | (0.997, 1.006) |
No. of intersections | 1.007 | (1.001, 1.013) | 1.001 | (0.998, 1.003) | 1.000 | (0.999, 1.001) |
No. of dead-ends | 0.987 | (0.957, 1.019) | 0.994 | (0.979, 1.009) | 0.998 | (0.995, 1.002) |
No. of destinations | 1.017 | (0.998, 1.035) | 1.004 | (0.996, 1.011) | 1.000 | (0.998, 1.003) |
Parks or green spaces | 1.138 | (0.970, 1.336) | 1.024 | (0.893, 1.174) | 1.007 | (0.884, 1.147) |
Schools | 0.975 | (0.809, 1.175) | 0.999 | (0.874, 1.143) | 0.920 | (0.783, 1.081) |
Land slope | 0.954 | (0.907, 1.003) | 0.968 | (0.911, 1.029) | 1.041 | (0.965, 1.123) |
South | | | | | | |
Population density | 1.004 | (1.000, 1.008) | 1.005 | (1.000, 1.009) | 1.008 | (1.003, 1.012) |
No. of intersections | 0.995 | (0.989, 1.001) | 1.000 | (0.998, 1.002) | 1.001 | (1.000, 1.001) |
No. of dead-ends | 1.004 | (0.984, 1.025) | 0.998 | (0.989, 1.007) | 1.002 | (1.000, 1.004) |
No. of destinations | 0.989 | (0.970, 1.008) | 0.996 | (0.988, 1.004) | 1.001 | (0.999, 1.004) |
Parks or green spaces | | | | | | |
Schools | 0.951 | (0.799, 1.132) | 0.977 | (0.857, 1.115) | 0.941 | (0.840, 1.055) |
Land slope | 0.963 | (0.941, 0.986) | 0.953 | (0.924, 0.983) | 0.943 | (0.919, 0.967) |
< 50 years | | | | | | |
Population density | 1.001 | (0.998, 1.003) | 1.002 | (0.998, 1.005) | 1.001 | (0.997, 1.005) |
No. of intersections | 1.002 | (0.996, 1.007) | 1.000 | (0.998, 1.002) | 1.000 | (1.000, 1.001) |
No. of dead-ends | 0.990 | (0.968, 1.012) | 0.995 | (0.985, 1.004) | 0.999 | (0.996, 1.001) |
No. of destinations | 1.007 | (0.988, 1.026) | 0.999 | (0.992, 1.006) | 0.998 | (0.996, 1.001) |
Parks or green spaces | 1.198 | (1.011, 1.420) | 1.086 | (0.942, 1.251) | 1.051 | (0.931, 1.185) |
Schools | 0.990 | (0.829, 1.181) | 1.029 | (0.905, 1.170) | 0.975 | (0.869, 1.094) |
Land slope | 0.975 | (0.942, 1.009) | 0.979 | (0.938, 1.022) | 0.970 | (0.932, 1.010) |
≥ 50 years | | | | | | |
Population density | 1.005 | (1.001, 1.009) | 1.005 | (1.000, 1.010) | 1.007 | (1.001, 1.012) |
No. of intersections | 0.999 | (0.992, 1.005) | 1.000 | (0.998, 1.003) | 1.001 | (1.000, 1.001) |
No. of dead-ends | 1.001 | (0.975, 1.028) | 0.993 | (0.982, 1.004) | 1.002 | (0.999, 1.004) |
No. of destinations | 1.003 | (0.984, 1.023) | 1.002 | (0.994, 1.010) | 1.005 | (1.002, 1.008) |
Parks or green spaces | 1.034 | (0.708, 1.509) | 1.065 | (0.821, 1.381) | 1.217 | (1.022, 1.450) |
Schools | 0.976 | (0.808, 1.179) | 1.029 | (0.892, 1.185) | 1.041 | (0.905, 1.198) |
Land slope | 0.968 | (0.942, 0.995) | 0.961 | (0.926, 0.997) | 0.954 | (0.926, 0.984) |
Table 8
Associations between total walking time and the BE stratified by gender, location, and years of residence
Male | | | | | | |
Population density | 1.002 | (0.999, 1.005) | 1.001 | (0.998, 1.005) | 1.002 | (0.998, 1.006) |
No. of intersections | 0.999 | (0.993, 1.004) | 1.000 | (0.998, 1.002) | 1.000 | (1.000, 1.001) |
No. of dead-ends | 1.000 | (0.978, 1.022) | 0.998 | (0.988, 1.007) | 0.999 | (0.997, 1.002) |
No. of destinations | 0.993 | (0.976, 1.012) | 0.997 | (0.989, 1.004) | 1.000 | (0.997, 1.002) |
Parks or green spaces | 1.133 | (0.925, 1.386) | 1.157 | (0.985, 1.360) | 1.085 | (0.954, 1.233) |
Schools | 1.122 | (0.945, 1.331) | 1.003 | (0.886, 1.136) | 1.053 | (0.938, 1.181) |
Land slope | 1.028 | (1.000, 1.056) | 1.038 | (1.002, 1.074) | 1.021 | (0.991, 1.053) |
Female | | | | | | |
Population density | 0.998 | (0.995, 1.001) | 0.997 | (0.994, 1.001) | 0.996 | (0.991, 1.000) |
No. of intersections | 0.995 | (0.990, 1.001) | 0.998 | (0.996, 1.000) | 1.000 | (0.999, 1.000) |
No. of dead-ends | 0.994 | (0.972, 1.016) | 0.997 | (0.988, 1.007) | 0.999 | (0.997, 1.002) |
No. of destinations | 1.003 | (0.986, 1.020) | 0.995 | (0.988, 1.002) | 0.998 | (0.996, 1.001) |
Parks or green spaces | 0.953 | (0.770, 1.181) | 0.951 | (0.802, 1.128) | 0.948 | (0.829, 1.083) |
Schools | 1.043 | (0.885, 1.229) | 1.130 | (0.999, 1.278) | 1.124 | (0.998, 1.265) |
Land slope | 1.045 | (1.019, 1.072) | 1.058 | (1.022, 1.094) | 1.048 | (1.019, 1.078) |
North | | | | | | |
Population density | 1.000 | (0.997, 1.003) | 1.000 | (0.997, 1.004) | 1.002 | (0.998, 1.006) |
No. of intersections | 0.999 | (0.993, 1.005) | 0.999 | (0.997, 1.001) | 1.000 | (0.999, 1.001) |
No. of dead-ends | 0.989 | (0.960, 1.019) | 0.999 | (0.985, 1.014) | 1.002 | (0.998, 1.005) |
No. of destinations | 1.006 | (0.988, 1.024) | 0.999 | (0.992, 1.006) | 1.001 | (0.998, 1.004) |
Parks or green spaces | 0.990 | (0.846, 1.159) | 1.003 | (0.878, 1.145) | 0.931 | (0.821, 1.057) |
Schools | 1.078 | (0.899, 1.293) | 0.994 | (0.872, 1.132) | 1.028 | (0.878, 1.204) |
Land slope | 0.997 | (0.950, 1.047) | 1.004 | (0.947, 1.065) | 1.019 | (0.948, 1.096) |
South | | | | | | |
Population density | 0.998 | (0.994, 1.001) | 0.996 | (0.992, 1.000) | 0.994 | (0.989, 0.998) |
No. of intersections | 0.995 | (0.989, 1.000) | 0.999 | (0.997, 1.001) | 1.000 | (0.999, 1.000) |
No. of dead-ends | 1.005 | (0.986, 1.025) | 1.000 | (0.992, 1.008) | 0.999 | (0.997, 1.001) |
No. of destinations | 0.992 | (0.975, 1.009) | 0.992 | (0.985, 1.000) | 0.997 | (0.995, 1.000) |
Parks or green spaces | | | | | | |
Schools | 1.084 | (0.926, 1.267) | 1.112 | (0.987, 1.253) | 1.081 | (0.974, 1.201) |
Land slope | 1.043 | (1.022, 1.065) | 1.056 | (1.028, 1.085) | 1.042 | (1.019, 1.066) |
< 50 years | | | | | | |
Population density | 1.002 | (0.999, 1.004) | 1.001 | (0.998, 1.005) | 1.001 | (0.997, 1.005) |
No. of intersections | 1.001 | (0.996, 1.007) | 1.000 | (0.998, 1.002) | 1.000 | (1.000, 1.001) |
No. of dead-ends | 0.987 | (0.966, 1.008) | 0.993 | (0.984, 1.002) | 0.999 | (0.996, 1.001) |
No. of destinations | 1.006 | (0.988, 1.025) | 0.996 | (0.989, 1.004) | 0.999 | (0.997, 1.002) |
Parks or green spaces | 1.049 | (0.886, 1.243) | 1.130 | (0.982, 1.300) | 1.091 | (0.969, 1.229) |
Schools | 1.098 | (0.922, 1.307) | 1.029 | (0.907, 1.168) | 1.084 | (0.968, 1.214) |
Land slope | 1.019 | (0.986, 1.054) | 1.034 | (0.992, 1.077) | 1.026 | (0.988, 1.065) |
≥ 50 years | | | | | | |
Population density | 0.996 | (0.993, 1.000) | 0.996 | (0.992, 1.001) | 0.997 | (0.992, 1.002) |
No. of intersections | 0.992 | (0.986, 0.997) | 0.998 | (0.996, 1.000) | 1.000 | (0.999, 1.001) |
No. of dead-ends | 1.006 | (0.982, 1.030) | 1.001 | (0.991, 1.011) | 1.000 | (0.997, 1.002) |
No. of destinations | 0.992 | (0.975, 1.009) | 0.995 | (0.987, 1.002) | 0.999 | (0.996, 1.002) |
Parks or green spaces | 1.082 | (0.770, 1.520) | 0.925 | (0.728, 1.175) | 0.982 | (0.835, 1.155) |
Schools | 1.095 | (0.926, 1.295) | 1.092 | (0.961, 1.240) | 1.075 | (0.947, 1.219) |
Land slope | 1.044 | (1.020, 1.069) | 1.056 | (1.023, 1.090) | 1.035 | (1.008, 1.062) |
In terms of walking time, a clear difference was seen between North and South. Among the respondents who resided in the South, associations were detected in eight models, and the direction of the associations were negative for population density, number of intersections, and number of destinations, and positive for land slope. Out of the eight models, three models showed interactions between location and BE measures.
Discussion
The present study revealed that, in Japan, some neighborhood BEs were associated with the PA levels of older residents. For example, population density and the presence of parks or green spaces were associated with increased sports activity, regardless of the buffer zone selected. Land slope was also consistently associated with sports activity in the expected direction (i.e., negative association), but only when it was used as continuous variable. The number of destinations was not linearly related to the frequency of sports activity, while using the variable, categorized into quartiles, yielded some positive associations. In the mutually adjusted models, some BE variables were not associated with sports activity, suggesting that not all the BE variables have independent effects on sports activity, or, that it can be difficult to disentangle the effects of BEs due to their similar spatial distribution. On the other hand, the results of the analysis for total walking time showed only a few associations, and most of them were in an unexpected direction. Therefore, our findings provide mixed support for the association between PA and the characteristics of BEs, previously used in Western settings.
Our study also showed mixed results based on the stratified samples. In particular, the unexpected direction of the associations between the BE and walking time (i.e., negative associations for population density, the number of intersections, and number of destinations, and positive associations for land slope) in Southern Chita Peninsula deserves comment. As a possible explanation for this finding, the respondents who were engaged in farming or forestry may have considered their daily work routines to be walking time. Southern Chita is more rural than northern Chita. Additionally, respondents who do not drive a car (most likely females) may have had to walk longer in areas with less public transportation.
As for gender differences, associations with sports activity were mostly seen among the male respondents. In particular, population density, parks or green spaces, and land slope showed associations with sports activity at any of the buffer radii. This may reflect gender differences in the context of sports activity. For older men, games or sports events (e.g., ground golf or gateball [Japanese croquet]) are more likely to be accessible and preferred, while daily physical activities (e.g., walking or jogging), that can be performed alone or with a few people, may be more popular among older women. Neighborhood BEs, such as population density or availability of parks, may play a role especially for such games or events. Regarding years of residence, many associations were observed in the subgroup of residents residing for 50 years or more. This may suggest that the results are not necessarily attributable to reverse causation brought about by differences in residential preference.
The present study has some advantages. Our analysis was based on the respondents of Japanese older adults. Although PA levels have been reported to be related to neighborhood BEs, less is known about these associations for older adults or in countries besides the US, Europe, and Australia [
14,
22,
23]. Some studies have recently reported associations between BEs and PA in Japan; however, they were mainly based on perceived measures [
9,
10,
32]. A few studies have analyzed a limited set of BE characteristics using GIS techniques [
33], or have focused on small samples from a narrow study area [
34]. In contrast, we measured various BEs using GIS, based on network distances with multiple radii. Particularly, a method for calculating population density, based on a combination of topographic maps and census data, may have contributed to a more accurate measurement and the detection of its association with sports activity.
In addition, using samples from a variety of regions, covering eight municipalities with urban, suburban, and rural areas, was another advantage in the current study. Van Cauwenberg et al. [
23] pointed out that the low number of positive relationships in previous studies of older adults could be due to the limited range of environmental variation in the study areas. Since broader regional variations in the BE may occur between urban and rural areas, analysing respondents from a variety of regions might have allowed us to detect some associations between BE and PA. In our stratified analysis by location (North vs. South), results similar to the entire sample were obtained for the South subgroup, while only a few associations were seen for the North strata. For example, parks or green spaces were not associated with sports activity when the analysis was limited within the North strata. Including samples from the South, the region which has less access to parks or green spaces (no parks or green spaces were located within 1,000 m from the respondents' home in the South region), may have contributed to the increasing regional variation and the detection of the association with sports activity.
Using multiple radii for buffer zones was also considered to be an advantage of this study, though the main results were basically consistent between all radii. However, as for street connectivity, the number of dead-ends was associated with sports activity at 250 m and 500 m, while the number of intersections showed an association at 1,000 m radius. Many dead-ends in immediate neighborhoods may indicate that the residents lived in a much less connected area. In the stratified analysis, there were also some different results for the selected buffer radii. For example, associations were only seen at 250 m radius in the North subgroup. Although this is not very clear from our results, an appropriate radius could vary by population, region, or types of BE. Therefore, using multiple radii would be useful, at least until an appropriate radius is established, and further study is needed to explore a better definition for neighborhood in many localities.
The present study also has some limitations. First, self-reported measured PA was a limitation, as they might have been misreported, and/or recognition of "leisure time" and "sports activity" might differ between each respondent. Our outcome variables were not based on well-established measures, such as the International Physical Activity Questionnaire (IPAQ). For example, we could not distinguish between walking for transport vs. walking for PA. Since specific environments may affect specific physical activities, the variety of physical activities should be considered, such as the purpose of walking (e.g., for daily errands, for leisure, or for commuting to work) [
10]. In our study, leisure time sports activity was more specific in its purpose than total walking time, which could explain the different associations with the BE.
Another limitation of our study was its cross-sectional design, which prevents inferences being made about causality from the observed associations. Residents are likely to reside in different neighborhoods based partly on their preferences for PA. For example, those inclined to be regularly active may choose to live in areas that offer a variety of features (access to parks, sidewalks) that are conducive to PA. Recent studies have begun to tease out the effects of residential preferences, which may confound the associations between BE characteristics and PA. For example, Frank et al. [
35] found that the low preference for living in a walkable neighborhood was indeed associated with less walking (for both transport and leisure). Importantly, within the strata of residential preferences, the objective BE independently predicted walking behavior. In other words, even among residents expressing a high preference for living in a highly walkable neighborhood, low walkability (objectively assessed by GIS) was associated with less walking.
Finally, some have suggested that the social environment influence PA [
36], and that walkable BEs may increase social capital [
37,
38]. Thus, the neighborhood social capital is often considered as the causal pathway from the BE to PA [
8,
39]. Nevertheless, since the social environment may interact with the BE, it could be both a mediator and a confounder in the association between BE and PA. These complex causal relationships among the environmental features need to be further elucidated in future studies.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
TH conceived of the study, performed the statistical analysis, and drafted the manuscript. IK participated in the design of the study and helped to draft the manuscript. TN assisted in the statistical analysis and contributed to the interpretation of the results. HH and KK contributed to the data acquisition, interpretation of the results, and revision of the manuscript. All authors read and approved the final manuscript.