Association between Street Greenery and Physical Activity among Chinese Older Adults in Beijing, China

Association between Street Greenery and Physical Activity among Chinese Older Adults in Beijing, China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Association between Street Greenery and Physical Activity among Chinese Older Adults in Beijing, China Yiling Song, Mingzhong Zhou, Jiale Tan, Jiali Cheng, Yangyang Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5323147/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Jun, 2025 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Background The association between urban green spaces, especially street greenery, and physical activity (PA) in older adults is understudied. This study utilized Baidu Street View images and deep learning techniques to objectively assess street greenery exposure and its relationship with different types of PA among older adults in China. Methods This study investigated 1326 older adults (aged 60 or above) living in Beijing, China. Physical Activity Scale for the Elderly (PASE) was used to assess the PA level of older adults. Baidu Street View images and deep learning were used to assess the level of street greenery in the 500-meter buffer zone around the community. The study employed ANOVA, Chi-square tests, and multilevel linear regression to analyze the data. Results After controlling for individual factors, household economic income, and other confounders, the multilevel linear regression model showed that street greenery was significantly and positively correlated with transportation PA (β = 0.08, P 0.05). Conclusions The level of street greenery around the community is significantly associated with transportation PA among Chinese older adults. It is recommended that the planning of urban green spaces should focus on street greenery, add bicycle lanes and sidewalks, and provide safe and comfortable environments to motivate older adults to actively participate in PA. Health sciences/Health care Health sciences/Risk factors Street greenery Street view images Physical activity Older adults China 1 Introduction With the gradual increase of the older population, population aging has become a seriously global issue. It has been reported that the global population of people aged 65 and over is expected to reach 16.7% by 2050 ( 1 ). China has the largest older population, with 18.7% aged 60 and above by 2020, which poses a serious challenge to social development( 2 ). In the face of the challenges of population ageing, the urgent need to build age-friendly cities, which need to provide environments and services that are suitable for older adults, is becoming increasingly evident. Good urban planning and infrastructure can provide older adults with safe and convenient places to exercise and space to do so, stimulating their motivation to participate in physical activity (PA)( 3 , 4 ). In addition, community services and amenities in friendly cities provide diverse options for health promotion and PA for older adults( 5 ), who will be more willing to engage in PA, improve their physical fitness and slow down the ageing process, thereby promoting healthy ageing and harmonious social development. The World Health Organization(WHO) defines PA as any physical movement produced by skeletal muscles that requires energy expenditure, and that both moderate-intensity and vigorous-intensity PA can improve health( 6 ). Previous studies have reported that engaging in PA plays a key role in healthy aging by slowing the decline in health and functioning of older adults and that promoting PA should be a focus of healthy aging policies( 7 ). In studies promoting PA among older persons, it has been found that the built environment of a community affects the level of PA of older adults, with green space, a key element, being associated with PA levels and health among older adults( 8 , 9 ). Some researchers have argued that building greenways should be considered an effective public health intervention( 10 ). Green space is defined as space created by green plants, including green areas or parks composed of lawns, shrubs, and trees( 11 ). The health benefits of green space are realized by using the site space, which is dominated by natural elements, as an environmental context to achieve intervention or promotion through a series of participatory processes that positively affect the health status of the population( 12 ). Among them, research studies have shown that the relationship between green space and health is stronger in lower socio-economic status and older age groups( 13 , 14 ). For example, a prospective cohort study showed that green space in the home community prevents older adults from decreasing their level of PA as they age( 15 ). Moreover, several scholars have measured the level of green space around homes using cloud-free and satellite-derived Normalized Difference Vegetation Index (NDVI) and have shown that the level of green space around homes is associated with healthy weight status in older adults( 16 ). A research study from the United States showed a significant correlation between the percentage of neighborhood green space coverage and the number of minutes of community walking per day for older adults( 17 ). Another study by Xie et al. showed that park accessibility was related to the health of older adults( 18 ). However, a study from Belgium showed that older participants with more parks around their homes experienced a greater decline in PA levels than older participants with fewer parks around their homes. The researchers thus concluded that increasing park availability is a viable strategy to support PA levels in younger people but not in older adults and that more research is needed in the future to explore the potential beneficial effects of park quantity on older adults( 19 ). Furthermore, some researchers have concluded that there is no significant correlation between distance from home to the nearest park, park size, and PA( 20 , 21 ). By reviewing the results of previous studies, there are still three gaps in the research on the relationship between green space and the PA of older adults to date. First, the results of current studies on the impact of green space on PA are inconsistent, and in the context of increasing aging, detailed research is needed for the older adult population in order to promote their health. Secondly, most studies now commonly use indicators such as the area of parks around the community, or the NDVI to assess the level of green space, and very few studies have explored the impact of street greenery on the PA and health of older people. Streetscape greenery is often assessed through streetscape imagery and the Green View Index (GVI) is the percentage of vegetation pixels in a streetscape photograph based on eye level, which more closely approximates the level of exposure to green space as perceived by the human eye's perspective( 22 ). In this study, we will focus on the interaction between individuals and their environment in the measurement of green space exposure, extend the traditional two-dimensional green environment measurement to the three-dimensional level, and utilize static street images and deep learning to compensate for the lack of individual visual perception. Third, different types of physical activities play different roles in the lives of older adults and are directly related to their health and quality of life. The presence of street green space can provide opportunities and places for older adults to engage in a variety of physical activities, but few previous studies have explored the association between street greenery and different types of PA among older adults. Therefore, this study aims to explore the association between street greenery and different types of PA among Chinese older adults using Baidu Street View images and deep learning, to provide a theoretical basis for urban green space planning and related policymakers. 2 Methods 2.1 Participants The study was conducted in October 2023 using a cross-sectional design. Data for this study were obtained from the Tsinghua University Retiree Health Survey( 23 ). Participants were recruited from an all-university party organized by Tsinghua University for retired faculty and staff, where participants were recruited to participate in a health survey. Before the study began, the researcher introduced the purpose and procedures of the study to the participants. Those who agreed to participate signed an informed consent form and completed the questionnaire. The questionnaire collected information on the demographics and socioeconomic status of the retirees, their level of education, health behaviors, health conditions, and level of PA. As soon as participants submitted the questionnaire, trained graduate students checked the submitted questionnaire for completeness. Participation in the survey is voluntary and a gift of $ 1 will be given upon receipt of a completed questionnaire. In China, people aged 60 years and above are referred to as the older adults, so we included only retired faculty and staff aged 60 years and above in this study. All participants were able to engage in PA independently and had lived in their residence for more than one year, and data from a total of 1326 retired older adults were included in this study. The study protocol was approved by the local research ethics committee. 2.2 Measurement of street greenery The eye-level street greenery was derived from Baidu street view images using the PSPNet technique( 24 ). Baidu Street View Map is one of the Chinese biggest street view image providers( 22 ). The street greenery measurement process is as follows. Firstly, the coordinates corresponding to the home address information of the older people in the questionnaire were obtained. Secondly, a buffer zone with a 500-meter radius is delineated, with this coordinate as the center. The 500-meter buffer zone was chosen mainly because, based on the review of prior literature and the characteristics of the study population( 25 – 27 ), the 500-meter buffer zone may more closely reflect the real activity environment and behavioral habits of older adults. Thirdly, sampling points are set within the buffer zone, using ArcGIS software. Fourthly, the sampling points are inputted into a Python script that utilized Baidu Maps' API interface to download four Street View images with a 90-degree field of view for each point. Finally, these street view images are merged to construct a panoramic visual representation of each sampling point( 28 ). The quantity of street greenery was measured by the Green View Index (GVI). Previous studies have confirmed the accuracy of deep learning models in image segmentation( 29 , 30 ). In this study, a deep learning model named PSPNet was used to obtain the GVI from street view images. After the training, PSPNet can accurately and stably segment the greenery in the street view images and calculate the GVI. As shown in the following equation, GVI calculates the proportion of street greenery pixels in the total pixels of the photo, which is the ratio of greenery pixels to total pixels in the four images( 22 , 31 ). The “ i ” in the equation is the streetscape image corresponding to each point. The GVI ranges from 0 to 1, with higher values indicating more street greenery. 2.3 Measurement of physical activity The Physical Activity Scale for the Elderly (PASE) was used to assess physical activity (PA). The PASE is an extensively validated self-administered assessment tool for measuring PA in Chinese older adults( 32 – 34 ). The PASE examines the types of activities typically chosen by older adults such as walking, recreational activities, exercise, housework, and caring for others. Total hours of walking in the last week were constructed based on the answers to the two questions from the PASE assessment tool( 33 )—“How many days over the past seven days did you take a walk outside your home or yard for any reason? For example, for fun or exercise, walking to work, walking the dog, etc.?”, and “On average, how many hours per day did you spend walking?” Total hours of walking in the last week were calculated by multiplying the daily average number of hours spent walking by the corresponding number of days. Total hours of cycling in the last week were constructed based on the answers to the two questions adapted from the U.S. Centers for Disease Control and Prevention’s Physical Activity Questionnaire( 35 )—“How many days over the past seven days did you bike for at least 10 minutes continuously to get to and from places?”, and “On average, how many hours per day did you bike?” Total hours of cycling in the last week were calculated by multiplying the daily average number of hours spent biking by the corresponding number of days. The total number of hours of light physical activity (LPA) in the past week is based on responses to two questions in the PASE assessment tool( 33 ) —"How many days in the past seven days have you engaged in the following light physical activities? For example, yoga, watering flowers, hiking, etc.?" and "On average, how many hours per day do you spend engaged in LPA? Multiply the average number of hours of LPA per day by the corresponding number of days to calculate the total number of hours of LPA for the last week. The total number of hours of moderate physical activity (MPA) in the past week is based on responses to two questions in the PASE assessment tool( 33 ) —"How many days in the past seven days have you engaged in the following moderate physical activities? For example, aerobics, Tai Chi, table tennis, etc.?" and "On average, how many hours per day do you spend engaged in MPA? Multiply the average number of hours of MPA per day by the corresponding number of days to calculate the total number of hours of MPA for the last week. The total number of hours of vigorous physical activity (VPA) in the past week is based on responses to two questions in the PASE assessment tool( 33 ) —"How many days in the past seven days have you engaged in the following vigorous physical activities? For example, tennis, soccer, rope skipping, etc.?" and "On average, how many hours per day do you spend engaged in VPA? Multiply the average number of hours of VPA per day by the corresponding number of days to calculate the total number of hours of VPA for the last week. The total number of hours of household PA in the past week is based on responses to two questions in the PASE assessment tool( 33 ) —"How many days in the past seven days have you engaged in the following household physical activities? For example, cooking, dishes, laundry, cleaning, etc.?" and "On average, how many hours per day do you spend engaged in household PA? Multiply the average number of hours of household PA per day by the corresponding number of days to calculate the total number of hours of household PA for the last week. The PASE uses frequency, duration, and intensity level of activity over the last seven days to assign a score, ranging from 0 to 400, with higher scores indicating greater PA( 33 ). We calculated last week’s transportation PA score, leisure PA score, household PA score, and total PASE score for each survey participant based on their answers to the assessment questions. Among them, transportation PA refers to physical activities done for the purpose of transportation and contains two types of physical activities: walking and cycling. Leisure PA is physical activities done by participants during leisure time and contains LPA, MPA, and VPA. Household PA is physical activities done for the purpose of doing household chores. In this study, total PA was the sum of transportation PA, leisure PA, and household PA. 2.4 Statistical analyses Descriptive statistics for the quantity of greenery, participant demographic information, and PA are listed. In this study, the quantity of greenery was referred to as the streetscape GVI within 500-meter of the participant's home address. Participants' demographic information contained age, gender, height, weight, BMI, education level, and household economic income, smoking, drinking. Physical activity (PA) contained transportation PA, leisure PA, household PA, and total PA. For quantitative variables, mean and standard deviation (SD) values were reported. For categorical variables, percentages were reported. Anova and Chi-square tests were performed to test whether the differences between variables are significant. The association between the street greenery and participants' PA was analyzed by Spearman correlation. When the p-value was less than 0.05, it was considered to be correlational and significant. Multilevel linear regression models were used to explore independent associations between street greenery around residences and PA among older adults. Two models were successively used for the analysis. Model 1 included only all individual covariates: age, gender, height, weight, smoking, drinking, household income, and education level, and Model 2 further added the quantity of street greenery. Standardized β, 95% confidence intervals (CI), and adjusted R 2 were reported for the models. All statistical procedures were performed in SPSS 27.0 and significance was set at P < 0.05. 3 Results Table 1 presents descriptive statistics for all participants. A majority of the participants were composed by females (60.94%). The mean age of the sample was 71.99 (SD = 7.06). The mean height and mean weight of all participants were 162.38 cm (SD = 7.78) and 63.52 kg (SD = 9.86), respectively. The mean body mass index was 24.07kg/m 2 (SD = 3.20). In addition, a large percentage of participants (32.7%) had an annual household income of less than or equal to 10,000 yuan. Only 5.7% of the participants had annual household incomes greater than or equal to 160,000 yuan. More than one-half (n = 669; 50.5%) of the participants' education level was college. A rather small proportions of these participants were current smokers (7.4%) and drinkers (8.0%). Table 1 Descriptive statistics of all participants Characteristics Total Male Female P N 1326 518 808 Age (yr), mean (SD) 71.99 (7.06) 74.30 (6.84) 70.51 (6.80) < 0.001 Height (cm), mean (SD) 162.38 (7.78) 168.67 (6.25) 158.35 (5.71) < 0.001 Weight (kg), mean (SD) 63.52 (9.86) 68.48 (9.84) 60.35 (8.47) < 0.001 Body mass index (kg/m 2 ), mean (SD) 24.07 (3.20) 24.06 (3.13) 24.08 (3.25) 0.896 Annual household income, N (%) < 0.001 ≤ 10000 yuan 434 (32.7) 141 (27.2) 293 (36.3) 20000 ~ 40000 yuan 229 (17.3) 80(15.4) 149 (18.4) 50000 ~ 80000 yuan 319 (24.1) 120 (23.2) 199 (24.6) 90000 ~ 150000 yuan 269 (20.3) 136 (26.3) 133 (16.5) ≥ 160000 yuan 75 (5.7) 41 (7.9) 34 (4.2) Education level, N (%) < 0.001 Below elementary 2 (0.2) 0 (0) 2 (0.2) Elementary school 20 (1.5) 9 (1.7) 11 (1.4) Middle school 241 (18.2) 84 (16.2) 157 (19.4) High school 328 (24.7) 78 (15.1) 250 (30.9) College 669 (50.5) 295 (56.9) 374 (46.3) Graduate 66 (5.0) 52 (10.0) 14 (1.7) Smoking, N (%) < 0.001 Current smoker 98 (7.4) 77 (14.9) 21 (2.6) Current nonsmoker 1228 (92.6) 441 (85.1) 787 (97.4) Drinking, N (%) < 0.001 Current drinker 106 (8.0) 91 (17.6) 15 (1.9) Current nondrinker 1220 (92.0) 427 (82.4) 793 (98.1) Table 2 shows the descriptive information for the quantity of street greenery and physical activity (PA). As shown in Table 2 , the average quality of street greenery was 0.25 (SD = 0.07). The transportation PA scores for all survey participants was 26.28 (SD = 23.80), with a walking PA scores of 20.00 (SD = 17.54) and a cycling PA scores of 6.28 (SD = 12.48). The leisure PA scores of the participants were 33.04 (SD = 32.17), of which the LPA, MPA, and VPA scores were 17.20 (SD = 19.81), 9.60 (SD = 15.47) and 6.23 (SD = 11.63), respectively. In addition, the participants' housework PA scores were 43.67 (SD = 34.50), and the total PA scores were 102.99 (SD = 61.91). There were significant differences between male and female participants in total PA scores, MPA scores, and household PA scores (P<0.001). Table 2 Descriptive information for quantity of street greenery and physical activity scores Variables Total Male Female P Quantity of greenery, mean (SD) Street greenery 0.25 (0.07) 0.25 (0.07) 0.25 (0.07) 0.555 Physical Activity Scores, mean (SD) Transportation physical activity 26.28 (23.80) 27.21 (22.99) 25.68 (24.30) 0.252 Walking 20.00 (17.54) 20.61 (17.49) 19.62(17.56) 0.315 Cycling 6.28 (12.48) 6.61 (12.19) 6.06 (12.66) 0.440 Leisure physical activity 33.04 (32.17) 31.82 (30.43) 33.82 (33.23) 0.268 Light physical activity 17.20 (19.81) 17.45 (18.96) 17.05 (20.35) 0.720 Moderate physical activity 9.60 (15.47) 7.56 (13.06) 10.91 (16.71) < 0.001 Vigorous physical activity 6.23 (11.63) 6.81 (12.97) 5.86 (10.68) 0.150 Household physical activity 43.67 (34.50) 31.05 (27.45) 51.76 (36.30) < 0.001 Total physical activity 102.99 (61.91) 90.08 (54.33) 111.27 (65.01) < 0.001 Table 3 presents the results of the correlation analysis between participants' PA scores and street greenery. The results showed that all participants' transportation PA scores were significantly positively correlated with street greenery (P < 0.05), with cycling PA scores being significantly positively correlated with street greenery(P 0.05). In addition, female participants' cycling PA scores were significantly positively correlated with street greenery (P < 0.01). There was no significant correlation between street greenery and leisure PA, household PA and total PA of the older adults. Table 3 Results of correlation analysis between physical activity and street greenery Variables Total Male Female Transportation physical activity 0.065* 0.058 0.068 Walking 0.019 0.022 0.016 Cycling 0.098** 0.078 0.109** Leisure physical activity -0.039 0.004 -0.063 Light physical activity -0.040 -0.008 -0.058 Moderate physical activity -0.015 -0.004 -0.019 Vigorous physical activity -0.021 0.025 -0.057 Household physical activity -0.053 -0.015 -0.068 Total physical activity -0.025 0.020 -0.045 Notes : *P < 0.05; **P < 0.01 Table 4 shows the results of the multilevel linear regression models. In the model predicting older adults' transportation PA scores, Model 1 showed a significant correlation between age, education level and transportation PA scores. Model 2 showed a significant positive correlation between street greenery and transportation PA scores (β = 0.08, P < 0.01) after controlling for confounders, suggesting that street greenery is an important facilitator for enhancing transportation PA among older adults. In the models for predicting leisure PA scores for older adults, both Models 1 and 2 showed significant correlations between age, education level, and leisure PA scores among the individual factors. There was no significant correlation between street greenery and leisure PA(β = -0.02, P>0.05). In the models for predicting household PA scores of older adults, both Model 1 and Model 2 showed that among the individual factors, there were significant correlations between age, gender, annual household income, and household PA scores. However, there was no significant correlation between street greenery and household PA(β = -0.04, P>0.05). Table 4 Multilevel linear regression models for predicting different types of physical activity Model predictors Transportation physical activity Leisure physical activity Household physical activity Model 1 Model 2 Model 1 Model 2 Model 1 Model 2 B (95% CI) β B (95% CI) β B (95% CI) β B (95% CI) β B (95% CI) β B (95% CI) β Individual factors Age -0.30(-0.50, -0.10) -0.09** -0.33(-0.53, -0.12) -0.10** -0.45(-0.72, -0.17) -0.10** -0.44(-0.71, -0.16) -0.10** -0.60(-0.89, -0.32) -0.12*** -0.58(-0.87, -0.30) -0.12*** Gender -3.14(-6.91,0.63) -0.06 -3.36(-7.12, 0.40) -0.07 2.56(-2.50,7.62) 0.04 2.63(-2.44, 7.69) 0.04 18.07(12.83,23.31) 0.26*** 18.23(12.99, 23.57) 0.26*** Height 0.83(-0.32,1.97) 0.27 0.88(-0.27, 2.02) 0.29 -0.27(-1.81,1.27) -0.07 -0.29(-1.83, 1.25) -0.07 0.23(-1.36,1.83) 0.05 0.20(-1.40, 1.79) 0.04 Weight -0.96(-2.40,0.47) -0.40 -1.03(-2.46, 0.40) -0.43 0.89(-1.03,2.81) 0.27 0.91(-1.01, 2.84) 0.28 -0.41(-2.40,1.58) -0.12 -0.36(-2.35, 1.63) -0.10 BMI 2.42(-1.35,6.18) 0.33 2.63(-1.12, 6.39) 0.35 -1.65(-6.70,3.40) -0.16 -1.71(-6.77, 3.34) -0.17 1.06(-4.18,6.28) 0.10 0.90(-4.33, 6.13) 0.08 Smoking 0.10(-5.53,5.73) 0.00 -0.14(-5.75, 5.47) 0.00 -1.45(-9.00,6.10) -0.01 -1.38(-8.93, 6.17) -0.01 1.06(-6,76,8.88) 0.01 1.23(-6.59, 9.05) 0.01 Drinking 1.71(-3.78,7.21) 0.02 2.42(-3.08, 7.92) 0.03 6.86(-0.51,14.23) 0.06 6.65(-0.76, 14.05) 0.06 -1.07(-8.70,6.57) -0.01 -1.57(-9.23, 6.09) -0.01 Education level -1.86(-2.91, -0.81) -0.11*** -1.91(-2.96, -0.85) -0.11*** -2.39(-3.81, -0.97) -0.10*** -2.38(-3.80, -0.96) -0.10** -0.31(-1.78, 1.17) -0.01 -0.27(-1.74, -1.20) -0.01 Annual household income 0.15(-0.89,1.19) 0.01 0.11(-0.92, 1.15) 0.01 -0.45(-1.84,0.94) -0.02 -0.44(-1.83, 0.95) -0.02 1.84(0.40,3.27) 0.07* 1.86(0.42, 3.30) 0.07* Urban greenness Street greenery 27.15(9.29,45.01) 0.08** -8.25(-32.28, 15.79) -0.02 -19.35(-44.23, 5.53) -0.04 Adjusted R 2 0.018 0.024 0.033 0.032 0.097 0.098 F 3.663*** 4.206*** 5.989*** 5.434*** 16.890*** 15.449*** AIC 12154.918 12147.982 12933.753 12935.296 13027.535 13027.191 Notes : *P < 0.05; **P < 0.01; ***P < 0.001; AIC: Akaike information criterion 4 Discussion This study objectively measured street greenery exposure based on eye level using Baidu Street View images and deep learning and explored the association between street greenery levels and different types of physical activity (PA) among older adults. Our study found significant correlations between street greenery in the 500-meter buffer zone around residences and transportation PA among Chinese older adults. However, there was no significant correlation between street greenery and leisure PA and household PA of older adults. Our findings support the idea that urban green spaces have a positive impact on PA in older adults( 9 , 36 , 37 ). A previous cross-sectional study investigating the relationship between PA and street greenery exposure among older adults in Shanghai, China, showed that higher levels of street greenery increased total PA levels and active transportation PA among older adults( 38 ). Moreover, a study from Hong Kong, China, assessed the quantity and quality of street greenery using Google Street View images and showed that the quantity and quality of street greenery was positively correlated with the amount of time residents spent in recreational physical activities, which included physical activities such as walking, jogging, and bicycling( 39 ). Beijing, like Shanghai and Hong Kong, is a high-density metropolis, and the results of this study further emphasize the positive impact of street greenery levels on the PA of urban residents in a high-density metropolitan environment. Under the context of China's rapid urbanization and increasing population aging, it is recommended that urban planners, policymakers, and relevant authorities rationally plan urban green spaces to promote healthy cities. In many countries, there is a growing trend towards an ageing population, making it particularly important to focus on the health and quality of life of older adults. The impact of green spaces on the PA of older adults is one of the research areas of international interest. Research has generally shown that good green spaces can provide a comfortable and safe environment that encourages older adults to participate in outdoor activities( 40 ). One of the international studies comparing the relationship between outdoor recreation and green space for older adults in cities such as Sydney, Singapore and Dhaka found that high-quality green space was associated with walking activity among older adults( 41 ). These findings emphasize the importance of actively promoting green space in urban planning to promote the health of older adults. However, it is important to note that culture, climate and urban planning in different countries and regions may affect the level of utilization of green spaces by older adults. It is therefore important to take into account the needs and habits of older adults in the local context when designing and planning green spaces to ensure that green spaces can maximize their role in promoting PA among older adults. For different types of PA, the main results of this study showed a significant correlation between street greenery exposure and transportation PA among older adults. This was similar to the findings of Schoner et al. that higher tree cover encourages people to engage in active transportation PA and increase the amount of time spent cycling and walking( 42 ). In high-density metropolises such as Beijing, cycling and walking are the main transportation physical activities and the main source of PA for older people. Studies have shown that tree-lined streets are more likely to increase active transportation behaviors such as biking and walking than parks( 43 ). Therefore, higher street greenery exposure is more likely to increase transportation PA among older adults. However, in a correlation analysis, the results of this study showed that street greenery was significantly and positively correlated only with bicycling among older adults, with no significant correlation with walking. Furthermore, a systematic review and meta-analysis showed that greenery and aesthetically pleasing landscaping were not associated with transitory walking( 44 ). The explanation for this may be that people may choose different types of transportation-based PA behaviors, influenced by different urban and cultural contexts. Green space and PA assessment methods may also make a difference in the results. In addition, it has also been suggested that urban greenery may be a peripheral facilitator of walking rather than an important determinant( 44 ). The analyses in this study showed no significant correlation between street greenery and leisure PA among older adults. A previous cross-sectional survey from the United Kingdom also showed no correlation between the level of urban green space and the leisure PA of middle-aged and older adults( 45 ). However, the study by Lu et al. showed a positive correlation between the level of street greenery and residents' leisure PA( 39 ). The inconsistency of the results across studies may be due to factors such as individual differences in the study population, differences in geographic location, and different levels of moderation of potential confounders across studies. In addition, leisure physical activities in this study mainly consisted of physical exercise, such as running, swimming, and dancing. These leisure physical activities are influenced by a variety of environmental factors, such as the safety and attractiveness of the environment, as well as fitness facilities, all of which can affect an individual's level of PA( 46 , 47 ). In this study, only one important environmental factor, street greenery, was considered, and in future studies, other environmental factors can be combined to comprehensively investigate the environmental factors affecting leisure PA. Not only can green spaces influence the PA behaviors of residents, but there is strong recent evidence that green spaces can also have a range of effects on people's health( 48 – 50 ). For example, a systematic review has shown that there is a positive correlation between green space and PA and that green space can also have a positive impact on individual health by preventing poor mental health outcomes, cardiovascular disease, and mortality( 51 ). It is clear that green space may have a positive impact on both mental health and physical health. In terms of mental health, Wang et al. showed that the higher the level of greenery in a residential area, the lower the chances of depressive symptoms among older adults, and suggested that increasing urban and community green spaces may help prevent and intervene in depressive symptoms among older adults in the community( 52 ). With regard to physical health, a study by Yang et al. showed a beneficial association between living in areas with higher levels of greenery and blood lipid levels, especially among women and older adults( 53 ). At present, the specific mechanisms by which green spaces influence the mental health and physical health of individuals are inconclusive. Current research focuses on the idea that green spaces may further affect individual health by influencing air quality, PA, and social cohesion, and helping to reduce stress and that PA, in particular, may be an important mediator of the effects of green spaces on health( 54 ). The results of the correlation between street greenery and PA in this study may also help to further explain the mechanism of green space on health. Future research could further analyze the mechanisms by which green space affects the health of older adults based on the PA perspective. This study focuses on analyzing the association between street greenery and PA among older adults. The strength of this study lies in the fact that the street greenery exposures around the residences are calculated from Baidu Street View images and deep learning, and the green space identification of Baidu Street View images is closer to the pedestrian and human eye's perception of street greenery exposures, and the data are more realistic and reliable compared to other measurement methods. In addition, this study fully explored the relationship between street greenery and different types of PA of the older adults. This is important for the effective planning of street green space to enhance the PA level and improve the quality of life of the older adults. However, this study also has some limitations. First, this study used a cross-sectional research design, which can only make educated guesses and assumptions about the connections between variables, and cannot determine the causal relationships between variables. Second, in terms of the age distribution of the participants, we focused only on older adults aged 60 years and older and did not examine other age groups. Self-reported PA is another limitation. Future studies could expand the sample size and objectively PA measurement to explore the relationship between street greenery and PA in different age groups. And cohort studies and experimental studies should be conducted to further analyze the mechanism of the impact of street greenery on PA. This study did not explore the relationship between street greenery and different intensities of PA in older adults, nor did it analyze gender differences; these limitations could be further analyzed with a larger sample size. In addition, the coronavirus disease 2019 (COVID-19) outbreak may have had an impact on the health behaviors of older adults( 55 ), and whether the relationship between street greenery and PA in older adults changed after the outbreak warrants further exploration in the future in the context of COVID-19. 5 Conclusion This study showed a significant association between street greenery around residences and transportation PA among Chinese older adults. It is recommended that urban planners, public health promoters, and relevant authorities further scientifically plan urban green spaces to enhance the PA levels of older adults and promote healthy aging. Declarations Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions Yiling Song: Conceptualization, Writing − original draft, Formal analysis; Mingzhong Zhou: Methodology, Data curation; Jiale Tan: Methodology; Jiali Cheng: Investigation; Yangyang Wang: Data curation; Xiaolu Feng: Writing −review & editing; Hongjun Yu: Writing −review & editing, Funding acquisition. Funding This study was supported by the National Social Science Foundation of China (17CTY020,20BTY004), Beijing Social Science Foundation of China (21YTA009), and the Tsinghua University “Shuang Gao” Scientific Research Program (2021TSG08208), the Tsinghua Education Reform Project (2021ZY01_01), the Tsinghua Graduated Education Reform Project (202303J039). Ethics approval and consent to participate The experimental protocol for involving humans was following the national/ international/institutional boards and the Declaration of Helsinki. This study was approved by the Tsinghua University Institutional Review Board (No.20110170). All the participants gave written informed consent before completing the survey. Confidentiality of participants' information was guaranteed. Consent for publication Not applicable Availability of data and materials The data that support the findings of this study are available upon request to the corresponding author. Acknowledgements We would like to acknowledge the contributions of all the individuals who have contributed to this study. We thank the study staff and participants. References He W, Goodkind D, Kowal P. An Aging World: 20152016. Hanmo Y. Dynamic Trend of China's Population Ageing and New Characteristics of the Elderly. Population Research. 2022;46(5):104-16. Mooney SJ, Joshi S, Cerdá M, Kennedy GJ, Beard JR, Rundle AG. Neighborhood Disorder and Physical Activity among Older Adults: A Longitudinal Study. J Urban Health. 2017;94(1):30-42. Boakye KA, Amram O, Schuna JM, Jr., Duncan GE, Hystad P. GPS-based built environment measures associated with adult physical activity. Health Place. 2021;70:102602. Laddu D, Paluch AE, LaMonte MJ. The role of the built environment in promoting movement and physical activity across the lifespan: Implications for public health. Prog Cardiovasc Dis. 2021;64:33-40. Organization WH. Physical activity 2020 [cited 2023 July 12]. Available from: https://www.who.int/news-room/fact-sheets/detail/physical-activity. Moreno-Agostino D, Daskalopoulou C, Wu YT, Koukounari A, Haro JM, Tyrovolas S, et al. The impact of physical activity on healthy ageing trajectories: evidence from eight cohort studies. Int J Behav Nutr Phys Act. 2020;17(1):92. Xiao Y, Miao S, Zhang Y, Chen H, Wu W. Exploring the health effects of neighborhood greenness on Lilong residents in Shanghai. Urban Forestry & Urban Greening. 2021;66:127383. He H, Lin X, Yang Y, Lu Y. Association of street greenery and physical activity in older adults: A novel study using pedestrian-centered photographs. Urban Forestry & Urban Greening. 2020;55:126789. Deng Y, Liang J, Chen Q. Greenway interventions effectively enhance physical activity levels—A systematic review with meta-analysis. Frontiers in Public Health. 2023;11:1268502. Beyer KM, Kaltenbach A, Szabo A, Bogar S, Nieto FJ, Malecki KM. Exposure to neighborhood green space and mental health: evidence from the survey of the health of Wisconsin. Int J Environ Res Public Health. 2014;11(3):3453-72. Carmen R, Jacobs S, Leone M, Palliwoda J, Pinto L, Misiune I, et al. Keep it real: Selecting realistic sets of urban green space indicators. Environmental Research Letters. 2020;15. de Vries S, Verheij RA, Groenewegen PP, Spreeuwenberg P. Natural Environments—Healthy Environments? An Exploratory Analysis of the Relationship between Greenspace and Health. Environment and Planning A. 2003;35:1717 - 31. Maas J, Verheij RA, Groenewegen PP, de Vries S, Spreeuwenberg P. Green space, urbanity, and health: how strong is the relation? J Epidemiol Community Health. 2006;60(7):587-92. Rolland Y, Lauwers-Cances V, Cristini C, Grandjean H, Banks WA, Morley JE, Vellas B. Disability in obese elderly women: Lower limb strength and recreational physical activity. Obes Res Clin Pract. 2007;1(1):1-78. Zhou W, Wang Q, Kadier A, Wang W, Zhou F, Li R, Ling L. The role of residential greenness levels, green land cover types and diversity in overweight/obesity among older adults: A cohort study. Environmental Research. 2023;217:114854. Besser LM, Mitsova DP. Neighborhood Green Land Cover and Neighborhood-Based Walking in U.S. Older Adults. Am J Prev Med. 2021;61(1):e13-e20. Xie B, An Z, Zheng Y, Li Z. Healthy aging with parks: Association between park accessibility and the health status of older adults in urban China. Sustainable Cities and Society. 2018;43:476-86. Poppe L, Deforche B, Van Cauwenberg J, Brondeel R, Mertens L, Van de Weghe N, et al. The association between the number of parks near home and levels of physical activity among community-dwelling older adults: A longitudinal study. Cities. 2022;130:103931. Klompmaker JO, Hoek G, Bloemsma LD, Gehring U, Strak M, Wijga AH, et al. Green space definition affects associations of green space with overweight and physical activity. Environ Res. 2018;160:531-40. Kaczynski AT, Potwarka LR, Saelens BE. Association of park size, distance, and features with physical activity in neighborhood parks. Am J Public Health. 2008;98(8):1451-6. Chen X, Meng Q, Hu D, Zhang L, Yang J. Evaluating Greenery around Streets Using Baidu Panoramic Street View Images and the Panoramic Green View Index. Forests. 2019;10(12):1109. Yu H, An R, Andrade F. Ambient Fine Particulate Matter Air Pollution and Physical Activity: A Longitudinal Study of University Retirees in Beijing, China. Am J Health Behav. 2017;41(4):401-10. Zhao H, Shi J, Qi X, Wang X, Jia J, editors. Pyramid Scene Parsing Network. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017 21-26 July 2017. Hino AA, Reis RS, Sarmiento OL, Parra DC, Brownson RC. The built environment and recreational physical activity among adults in Curitiba, Brazil. Preventive medicine. 2011;52(6):419-22. Florindo AA, Turrell G, Garcia LMT, dos Anjos Souza Barbosa JP, Cruz MS, Failla MA, et al. Mix of destinations and sedentary behavior among Brazilian adults: a cross-sectional study. BMC Public Health. 2021;21:1-7. Sarkar C, Zhang B, Ni M, Kumari S, Bauermeister S, Gallacher J, Webster C. Environmental correlates of chronic obstructive pulmonary disease in 96 779 participants from the UK Biobank: a cross-sectional, observational study. The Lancet Planetary Health. 2019;3(11):e478-e90. Li X, Zhang C, Li W, Ricard R, Meng Q, Zhang W. Assessing street-level urban greenery using Google Street View and a modified green view index. Urban Forestry & Urban Greening. 2015;14(3):675-85. Lu Y. The Association of Urban Greenness and Walking Behavior: Using Google Street View and Deep Learning Techniques to Estimate Residents' Exposure to Urban Greenness. Int J Environ Res Public Health. 2018;15(8). Helbich M, Yao Y, Liu Y, Zhang J, Liu P, Wang R. Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China. Environment International. 2019;126:107-17. Dong R, Zhang Y, Zhao J. How Green Are the Streets Within the Sixth Ring Road of Beijing? An Analysis Based on Tencent Street View Pictures and the Green View Index. Int J Environ Res Public Health. 2018;15(7). Yu HJ, Zhu WM, Qiu J, Zhang CG. Physical Activity Scale for Elderly (PASE): A Cross-validation study for Chinese Older Adults. Medicine and Science in Sports and Exercise. 2012;44:647-. Vaughan K, Miller WC. Validity and reliability of the Chinese translation of the Physical Activity Scale for the Elderly (PASE). Disabil Rehabil. 2013;35(3):191-7. Ngai SP, Cheung RT, Lam PL, Chiu JK, Fung EY. Validation and reliability of the Physical Activity Scale for the Elderly in Chinese population. J Rehabil Med. 2012;44(5):462-5. Prevention UCfDCa. Physical and activity and physical fitness-PAQAugust 23, 2023. Available from: https://www.cdc.gov/nchs/data/nhanes/nhanes_11_12/paq_capi.pdf. Song Y, Li H, Yu H. Effects of green space on physical activity and body weight status among Chinese adults: a systematic review. Front Public Health. 2023;11:1198439. Feng X, Toms R, Astell-Burt T. Association between green space, outdoor leisure time and physical activity. Urban Forestry & Urban Greening. 2021;66:127349. Xiao Y, Miao S, Zhang Y, Xie B, Wu W. Exploring the associations between neighborhood greenness and level of physical activity of older adults in shanghai. Journal of Transport & Health. 2022;24:101312. Lu Y. Using Google Street View to investigate the association between street greenery and physical activity. Landscape and Urban Planning. 2019;191:103435. Juul V, Nordbo ECA. Examining activity-friendly neighborhoods in the Norwegian context: green space and walkability in relation to physical activity and the moderating role of perceived safety. BMC Public Health. 2023;23(1):259. Shuvo FK, Feng X, Astell-Burt T. Urban green space quality and older adult recreation: an international comparison. Cities & Health. 2021;5(3):329-49. Schoner J, Chapman J, Brookes A, MacLeod KE, Fox EH, Iroz-Elardo N, Frank LD. Bringing health into transportation and land use scenario planning: Creating a National Public Health Assessment Model (N-PHAM). Journal of Transport & Health. 2018;10:401-18. Vich G, Marquet O, Miralles-Guasch C. Green streetscape and walking: Exploring active mobility patterns in dense and compact cities. Journal of Transport & Health. 2019;12:50-9. Cerin E, Nathan A, van Cauwenberg J, Barnett DW, Barnett A. The neighbourhood physical environment and active travel in older adults: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. 2017;14(1):15. Hillsdon M, Panter J, Foster C, Jones A. The relationship between access and quality of urban green space with population physical activity. Public Health. 2006;120(12):1127-32. Brownson RC, Hoehner CM, Day K, Forsyth A, Sallis JF. Measuring the built environment for physical activity: state of the science. Am J Prev Med. 2009;36(4 Suppl):S99-123.e12. Zhang Y, Koene M, Reijneveld SA, Tuinstra J, Broekhuis M, van der Spek S, Wagenaar C. The impact of interventions in the built environment on physical activity levels: a systematic umbrella review. Int J Behav Nutr Phys Act. 2022;19(1):156. Hunter RF, Nieuwenhuijsen M, Fabian C, Murphy N, O'Hara K, Rappe E, et al. Advancing urban green and blue space contributions to public health. Lancet Public Health. 2023;8(9):e735-e42. Barboza EP, Cirach M, Khomenko S, Iungman T, Mueller N, Barrera-Gomez J, et al. Green space and mortality in European cities: a health impact assessment study. Lancet Planet Health. 2021;5(10):e718-e30. Ali MJ, Rahaman M, Hossain SI. Urban green spaces for elderly human health: A planning model for healthy city living. Land Use Policy. 2022;114:105970. James P, Banay RF, Hart JE, Laden F. A Review of the Health Benefits of Greenness. Curr Epidemiol Rep. 2015;2(2):131-42. Wang P, Wang M, Shan J, Liu X, Jing Y, Zhu H, et al. Association between residential greenness and depression symptoms in Chinese community-dwelling older adults. Environ Res. 2024;243:117869. Yang BY, Markevych I, Heinrich J, Bloom MS, Qian Z, Geiger SD, et al. Residential greenness and blood lipids in urban-dwelling adults: The 33 Communities Chinese Health Study. Environ Pollut. 2019;250:14-22. Hartig T, Mitchell R, de Vries S, Frumkin H. Nature and Health. Annual Review of Public Health. 2014;35(1):207-28. Dwijayani R, Soraya N, Alimuddin S. Quo Vadis Vaccine Research Trends of Coronavirus Disease-19 in Indonesia and Malaysia: A Bibliometric Analysis. Journal of Contemporary Governance and Public Policy. 2023;4:175-94. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Jun, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 14 Apr, 2025 Reviews received at journal 11 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers agreed at journal 09 Apr, 2025 Reviewers agreed at journal 08 Apr, 2025 Reviews received at journal 16 Feb, 2025 Reviewers agreed at journal 05 Feb, 2025 Reviewers agreed at journal 04 Feb, 2025 Reviewers invited by journal 16 Nov, 2024 Editor assigned by journal 16 Nov, 2024 Editor invited by journal 11 Nov, 2024 Submission checks completed at journal 08 Nov, 2024 First submitted to journal 24 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5323147","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":379617329,"identity":"dc2e11c0-2377-4300-a0a6-9502fa06f870","order_by":0,"name":"Yiling Song","email":"","orcid":"","institution":"Department of physical education, Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Yiling","middleName":"","lastName":"Song","suffix":""},{"id":379617330,"identity":"728e1341-efe9-4433-bfee-270ef29c9604","order_by":1,"name":"Mingzhong Zhou","email":"","orcid":"","institution":"Department of physical education, Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Mingzhong","middleName":"","lastName":"Zhou","suffix":""},{"id":379617331,"identity":"29c7f7f6-4ace-4691-b56f-d7bdf465ad81","order_by":2,"name":"Jiale Tan","email":"","orcid":"","institution":"Department of physical education, Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Jiale","middleName":"","lastName":"Tan","suffix":""},{"id":379617332,"identity":"f1be9a9a-8619-4fc3-abf6-6e4458a55cde","order_by":3,"name":"Jiali Cheng","email":"","orcid":"","institution":"Faculty of Public Physical Education, Hebei Normal University","correspondingAuthor":false,"prefix":"","firstName":"Jiali","middleName":"","lastName":"Cheng","suffix":""},{"id":379617333,"identity":"56709dd2-859d-4cb6-9d04-091763cf279d","order_by":4,"name":"Yangyang Wang","email":"","orcid":"","institution":"Department of physical education, Tsinghua University","correspondingAuthor":false,"prefix":"","firstName":"Yangyang","middleName":"","lastName":"Wang","suffix":""},{"id":379617334,"identity":"abc8e727-aea1-49b8-94c5-932e6f897b7b","order_by":5,"name":"Xiaolu Feng","email":"","orcid":"","institution":"Department of Sports Science, College of Education, Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Xiaolu","middleName":"","lastName":"Feng","suffix":""},{"id":379617335,"identity":"933e3cb8-fe79-4825-949d-7ea0b75645db","order_by":6,"name":"Hongjun Yu","email":"data:image/png;base64,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","orcid":"","institution":"Department of physical education, Tsinghua University","correspondingAuthor":true,"prefix":"","firstName":"Hongjun","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2024-10-24 06:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5323147/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5323147/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-03050-3","type":"published","date":"2025-06-04T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84242629,"identity":"a0ff70c1-d718-426c-b398-828e3e006efa","added_by":"auto","created_at":"2025-06-09 16:10:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":926489,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5323147/v1/3c4f3c2f-c8d2-4a31-98f5-6a82b14a3620.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between Street Greenery and Physical Activity among Chinese Older Adults in Beijing, China","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWith the gradual increase of the older population, population aging has become a seriously global issue. It has been reported that the global population of people aged 65 and over is expected to reach 16.7% by 2050 (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). China has the largest older population, with 18.7% aged 60 and above by 2020, which poses a serious challenge to social development(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In the face of the challenges of population ageing, the urgent need to build age-friendly cities, which need to provide environments and services that are suitable for older adults, is becoming increasingly evident. Good urban planning and infrastructure can provide older adults with safe and convenient places to exercise and space to do so, stimulating their motivation to participate in physical activity (PA)(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In addition, community services and amenities in friendly cities provide diverse options for health promotion and PA for older adults(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), who will be more willing to engage in PA, improve their physical fitness and slow down the ageing process, thereby promoting healthy ageing and harmonious social development.\u003c/p\u003e \u003cp\u003eThe World Health Organization(WHO) defines PA as any physical movement produced by skeletal muscles that requires energy expenditure, and that both moderate-intensity and vigorous-intensity PA can improve health(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Previous studies have reported that engaging in PA plays a key role in healthy aging by slowing the decline in health and functioning of older adults and that promoting PA should be a focus of healthy aging policies(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In studies promoting PA among older persons, it has been found that the built environment of a community affects the level of PA of older adults, with green space, a key element, being associated with PA levels and health among older adults(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Some researchers have argued that building greenways should be considered an effective public health intervention(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGreen space is defined as space created by green plants, including green areas or parks composed of lawns, shrubs, and trees(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The health benefits of green space are realized by using the site space, which is dominated by natural elements, as an environmental context to achieve intervention or promotion through a series of participatory processes that positively affect the health status of the population(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Among them, research studies have shown that the relationship between green space and health is stronger in lower socio-economic status and older age groups(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). For example, a prospective cohort study showed that green space in the home community prevents older adults from decreasing their level of PA as they age(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Moreover, several scholars have measured the level of green space around homes using cloud-free and satellite-derived Normalized Difference Vegetation Index (NDVI) and have shown that the level of green space around homes is associated with healthy weight status in older adults(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA research study from the United States showed a significant correlation between the percentage of neighborhood green space coverage and the number of minutes of community walking per day for older adults(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Another study by Xie et al. showed that park accessibility was related to the health of older adults(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). However, a study from Belgium showed that older participants with more parks around their homes experienced a greater decline in PA levels than older participants with fewer parks around their homes. The researchers thus concluded that increasing park availability is a viable strategy to support PA levels in younger people but not in older adults and that more research is needed in the future to explore the potential beneficial effects of park quantity on older adults(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Furthermore, some researchers have concluded that there is no significant correlation between distance from home to the nearest park, park size, and PA(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy reviewing the results of previous studies, there are still three gaps in the research on the relationship between green space and the PA of older adults to date. First, the results of current studies on the impact of green space on PA are inconsistent, and in the context of increasing aging, detailed research is needed for the older adult population in order to promote their health. Secondly, most studies now commonly use indicators such as the area of parks around the community, or the NDVI to assess the level of green space, and very few studies have explored the impact of street greenery on the PA and health of older people. Streetscape greenery is often assessed through streetscape imagery and the Green View Index (GVI) is the percentage of vegetation pixels in a streetscape photograph based on eye level, which more closely approximates the level of exposure to green space as perceived by the human eye's perspective(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). In this study, we will focus on the interaction between individuals and their environment in the measurement of green space exposure, extend the traditional two-dimensional green environment measurement to the three-dimensional level, and utilize static street images and deep learning to compensate for the lack of individual visual perception. Third, different types of physical activities play different roles in the lives of older adults and are directly related to their health and quality of life. The presence of street green space can provide opportunities and places for older adults to engage in a variety of physical activities, but few previous studies have explored the association between street greenery and different types of PA among older adults.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to explore the association between street greenery and different types of PA among Chinese older adults using Baidu Street View images and deep learning, to provide a theoretical basis for urban green space planning and related policymakers.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Participants\u003c/h2\u003e\n \u003cp\u003eThe study was conducted in October 2023 using a cross-sectional design. Data for this study were obtained from the Tsinghua University Retiree Health Survey(\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e). Participants were recruited from an all-university party organized by Tsinghua University for retired faculty and staff, where participants were recruited to participate in a health survey. Before the study began, the researcher introduced the purpose and procedures of the study to the participants. Those who agreed to participate signed an informed consent form and completed the questionnaire. The questionnaire collected information on the demographics and socioeconomic status of the retirees, their level of education, health behaviors, health conditions, and level of PA. As soon as participants submitted the questionnaire, trained graduate students checked the submitted questionnaire for completeness. Participation in the survey is voluntary and a gift of \u003cspan\u003e$\u003c/span\u003e1 will be given upon receipt of a completed questionnaire. In China, people aged 60 years and above are referred to as the older adults, so we included only retired faculty and staff aged 60 years and above in this study. All participants were able to engage in PA independently and had lived in their residence for more than one year, and data from a total of 1326 retired older adults were included in this study. The study protocol was approved by the local research ethics committee.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Measurement of street greenery\u003c/h2\u003e\n \u003cp\u003eThe eye-level street greenery was derived from Baidu street view images using the PSPNet technique(\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e). Baidu Street View Map is one of the Chinese biggest street view image providers(\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e). The street greenery measurement process is as follows. Firstly, the coordinates corresponding to the home address information of the older people in the questionnaire were obtained. Secondly, a buffer zone with a 500-meter radius is delineated, with this coordinate as the center. The 500-meter buffer zone was chosen mainly because, based on the review of prior literature and the characteristics of the study population(\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e), the 500-meter buffer zone may more closely reflect the real activity environment and behavioral habits of older adults. Thirdly, sampling points are set within the buffer zone, using ArcGIS software. Fourthly, the sampling points are inputted into a Python script that utilized Baidu Maps\u0026apos; API interface to download four Street View images with a 90-degree field of view for each point. Finally, these street view images are merged to construct a panoramic visual representation of each sampling point(\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe quantity of street greenery was measured by the Green View Index (GVI). Previous studies have confirmed the accuracy of deep learning models in image segmentation(\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e). In this study, a deep learning model named PSPNet was used to obtain the GVI from street view images. After the training, PSPNet can accurately and stably segment the greenery in the street view images and calculate the GVI. As shown in the following equation, GVI calculates the proportion of street greenery pixels in the total pixels of the photo, which is the ratio of greenery pixels to total pixels in the four images(\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e). The \u0026ldquo; i \u0026rdquo; in the equation is the streetscape image corresponding to each point. The GVI ranges from 0 to 1, with higher values indicating more street greenery.\u003c/p\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" height=\"78\" width=\"336\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Measurement of physical activity\u003c/h2\u003e\n \u003cp\u003eThe Physical Activity Scale for the Elderly (PASE) was used to assess physical activity (PA). The PASE is an extensively validated self-administered assessment tool for measuring PA in Chinese older adults(\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e). The PASE examines the types of activities typically chosen by older adults such as walking, recreational activities, exercise, housework, and caring for others.\u003c/p\u003e\n \u003cp\u003eTotal hours of walking in the last week were constructed based on the answers to the two questions from the PASE assessment tool(\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e)\u0026mdash;\u0026ldquo;How many days over the past seven days did you take a walk outside your home or yard for any reason? For example, for fun or exercise, walking to work, walking the dog, etc.?\u0026rdquo;, and \u0026ldquo;On average, how many hours per day did you spend walking?\u0026rdquo; Total hours of walking in the last week were calculated by multiplying the daily average number of hours spent walking by the corresponding number of days.\u003c/p\u003e\n \u003cp\u003eTotal hours of cycling in the last week were constructed based on the answers to the two questions adapted from the U.S. Centers for Disease Control and Prevention\u0026rsquo;s Physical Activity Questionnaire(\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e)\u0026mdash;\u0026ldquo;How many days over the past seven days did you bike for at least 10 minutes continuously to get to and from places?\u0026rdquo;, and \u0026ldquo;On average, how many hours per day did you bike?\u0026rdquo; Total hours of cycling in the last week were calculated by multiplying the daily average number of hours spent biking by the corresponding number of days.\u003c/p\u003e\n \u003cp\u003eThe total number of hours of light physical activity (LPA) in the past week is based on responses to two questions in the PASE assessment tool(\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e) \u0026mdash;\u0026quot;How many days in the past seven days have you engaged in the following light physical activities? For example, yoga, watering flowers, hiking, etc.?\u0026quot; and \u0026quot;On average, how many hours per day do you spend engaged in LPA? Multiply the average number of hours of LPA per day by the corresponding number of days to calculate the total number of hours of LPA for the last week.\u003c/p\u003e\n \u003cp\u003eThe total number of hours of moderate physical activity (MPA) in the past week is based on responses to two questions in the PASE assessment tool(\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e) \u0026mdash;\u0026quot;How many days in the past seven days have you engaged in the following moderate physical activities? For example, aerobics, Tai Chi, table tennis, etc.?\u0026quot; and \u0026quot;On average, how many hours per day do you spend engaged in MPA? Multiply the average number of hours of MPA per day by the corresponding number of days to calculate the total number of hours of MPA for the last week.\u003c/p\u003e\n \u003cp\u003eThe total number of hours of vigorous physical activity (VPA) in the past week is based on responses to two questions in the PASE assessment tool(\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e) \u0026mdash;\u0026quot;How many days in the past seven days have you engaged in the following vigorous physical activities? For example, tennis, soccer, rope skipping, etc.?\u0026quot; and \u0026quot;On average, how many hours per day do you spend engaged in VPA? Multiply the average number of hours of VPA per day by the corresponding number of days to calculate the total number of hours of VPA for the last week.\u003c/p\u003e\n \u003cp\u003eThe total number of hours of household PA in the past week is based on responses to two questions in the PASE assessment tool(\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e) \u0026mdash;\u0026quot;How many days in the past seven days have you engaged in the following household physical activities? For example, cooking, dishes, laundry, cleaning, etc.?\u0026quot; and \u0026quot;On average, how many hours per day do you spend engaged in household PA? Multiply the average number of hours of household PA per day by the corresponding number of days to calculate the total number of hours of household PA for the last week.\u003c/p\u003e\n \u003cp\u003eThe PASE uses frequency, duration, and intensity level of activity over the last seven days to assign a score, ranging from 0 to 400, with higher scores indicating greater PA(\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e). We calculated last week\u0026rsquo;s transportation PA score, leisure PA score, household PA score, and total PASE score for each survey participant based on their answers to the assessment questions. Among them, transportation PA refers to physical activities done for the purpose of transportation and contains two types of physical activities: walking and cycling. Leisure PA is physical activities done by participants during leisure time and contains LPA, MPA, and VPA. Household PA is physical activities done for the purpose of doing household chores. In this study, total PA was the sum of transportation PA, leisure PA, and household PA.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Statistical analyses\u003c/h2\u003e\n \u003cp\u003eDescriptive statistics for the quantity of greenery, participant demographic information, and PA are listed. In this study, the quantity of greenery was referred to as the streetscape GVI within 500-meter of the participant\u0026apos;s home address. Participants\u0026apos; demographic information contained age, gender, height, weight, BMI, education level, and household economic income, smoking, drinking. Physical activity (PA) contained transportation PA, leisure PA, household PA, and total PA. For quantitative variables, mean and standard deviation (SD) values were reported. For categorical variables, percentages were reported. Anova and Chi-square tests were performed to test whether the differences between variables are significant. The association between the street greenery and participants\u0026apos; PA was analyzed by Spearman correlation. When the p-value was less than 0.05, it was considered to be correlational and significant.\u003c/p\u003e\n \u003cp\u003eMultilevel linear regression models were used to explore independent associations between street greenery around residences and PA among older adults. Two models were successively used for the analysis. Model 1 included only all individual covariates: age, gender, height, weight, smoking, drinking, household income, and education level, and Model 2 further added the quantity of street greenery. Standardized \u0026beta;, 95% confidence intervals (CI), and adjusted R\u003csup\u003e2\u003c/sup\u003e were reported for the models. All statistical procedures were performed in SPSS 27.0 and significance was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents descriptive statistics for all participants. A majority of the participants were composed by females (60.94%). The mean age of the sample was 71.99 (SD\u0026thinsp;=\u0026thinsp;7.06). The mean height and mean weight of all participants were 162.38 cm (SD\u0026thinsp;=\u0026thinsp;7.78) and 63.52 kg (SD\u0026thinsp;=\u0026thinsp;9.86), respectively. The mean body mass index was 24.07kg/m\u003csup\u003e2\u003c/sup\u003e (SD\u0026thinsp;=\u0026thinsp;3.20). In addition, a large percentage of participants (32.7%) had an annual household income of less than or equal to 10,000 yuan. Only 5.7% of the participants had annual household incomes greater than or equal to 160,000 yuan. More than one-half (n\u0026thinsp;=\u0026thinsp;669; 50.5%) of the participants\u0026apos; education level was college. A rather small proportions of these participants were current smokers (7.4%) and drinkers (8.0%).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics of all participants\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eN\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAge (yr), mean (SD)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.99 (7.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74.30 (6.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.51 (6.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eHeight (cm), mean (SD)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162.38 (7.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168.67 (6.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e158.35 (5.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eWeight (kg), mean (SD)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.52 (9.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.48 (9.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.35 (8.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBody mass index (kg/m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e), mean (SD)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.07 (3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.06 (3.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.08 (3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAnnual household income, N (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026le;\u0026thinsp;10000 yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e434 (32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141 (27.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e293 (36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20000\u0026thinsp;~\u0026thinsp;40000 yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e229 (17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80(15.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50000\u0026thinsp;~\u0026thinsp;80000 yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e319 (24.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e120 (23.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e199 (24.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90000\u0026thinsp;~\u0026thinsp;150000 yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e269 (20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136 (26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e133 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;160000 yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75 (5.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eEducation level, N (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBelow elementary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElementary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e241 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e157 (19.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e328 (24.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 (15.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250 (30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollege\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e669 (50.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e295 (56.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e374 (46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGraduate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSmoking, N (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e98 (7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77 (14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent nonsmoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1228 (92.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e441 (85.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e787 (97.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eDrinking, N (%)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e106 (8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91 (17.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent nondrinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1220 (92.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e427 (82.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e793 (98.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e shows the descriptive information for the quantity of street greenery and physical activity (PA). As shown in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, the average quality of street greenery was 0.25 (SD\u0026thinsp;=\u0026thinsp;0.07). The transportation PA scores for all survey participants was 26.28 (SD\u0026thinsp;=\u0026thinsp;23.80), with a walking PA scores of 20.00 (SD\u0026thinsp;=\u0026thinsp;17.54) and a cycling PA scores of 6.28 (SD\u0026thinsp;=\u0026thinsp;12.48). The leisure PA scores of the participants were 33.04 (SD\u0026thinsp;=\u0026thinsp;32.17), of which the LPA, MPA, and VPA scores were 17.20 (SD\u0026thinsp;=\u0026thinsp;19.81), 9.60 (SD\u0026thinsp;=\u0026thinsp;15.47) and 6.23 (SD\u0026thinsp;=\u0026thinsp;11.63), respectively. In addition, the participants\u0026apos; housework PA scores were 43.67 (SD\u0026thinsp;=\u0026thinsp;34.50), and the total PA scores were 102.99 (SD\u0026thinsp;=\u0026thinsp;61.91). There were significant differences between male and female participants in total PA scores, MPA scores, and household PA scores (P\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive information for quantity of street greenery and physical activity scores\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eQuantity of greenery, mean (SD)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStreet greenery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePhysical Activity Scores, mean (SD)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTransportation physical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.28 (23.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.21 (22.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.68 (24.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWalking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.00 (17.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.61 (17.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.62(17.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.315\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCycling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.28 (12.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.61 (12.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.06 (12.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.440\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeisure physical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.04 (32.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.82 (30.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.82 (33.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.268\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLight physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.20 (19.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.45 (18.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.05 (20.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.60 (15.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.56 (13.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.91 (16.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVigorous physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.23 (11.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.81 (12.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.86 (10.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold physical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.67 (34.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.05 (27.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.76 (36.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal physical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102.99 (61.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.08 (54.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111.27 (65.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of the correlation analysis between participants\u0026apos; PA scores and street greenery. The results showed that all participants\u0026apos; transportation PA scores were significantly positively correlated with street greenery (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with cycling PA scores being significantly positively correlated with street greenery(P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while there was no significant correlation between walking scores and street greenery(P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In addition, female participants\u0026apos; cycling PA scores were significantly positively correlated with street greenery (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). There was no significant correlation between street greenery and leisure PA, household PA and total PA of the older adults.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eResults of correlation analysis between physical activity and street greenery\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTransportation physical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWalking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCycling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.098**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.109**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLeisure physical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLight physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerate physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVigorous physical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold physical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal physical activity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cem\u003eNotes\u003c/em\u003e: \u003cem\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the results of the multilevel linear regression models. In the model predicting older adults\u0026apos; transportation PA scores, Model 1 showed a significant correlation between age, education level and transportation PA scores. Model 2 showed a significant positive correlation between street greenery and transportation PA scores (\u0026beta;\u0026thinsp;=\u0026thinsp;0.08, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) after controlling for confounders, suggesting that street greenery is an important facilitator for enhancing transportation PA among older adults. In the models for predicting leisure PA scores for older adults, both Models 1 and 2 showed significant correlations between age, education level, and leisure PA scores among the individual factors. There was no significant correlation between street greenery and leisure PA(\u0026beta; = -0.02, P\u0026gt;0.05). In the models for predicting household PA scores of older adults, both Model 1 and Model 2 showed that among the individual factors, there were significant correlations between age, gender, annual household income, and household PA scores. However, there was no significant correlation between street greenery and household PA(\u0026beta; = -0.04, P\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultilevel linear regression models for predicting different types of physical activity\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\" style=\"width: 3.4206%;\"\u003e\n \u003cp\u003eModel predictors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\" style=\"width: 16.3531%;\"\u003e\n \u003cp\u003eTransportation physical activity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\" style=\"width: 14.7599%;\"\u003e\n \u003cp\u003eLeisure physical activity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\" style=\"width: 15.9782%;\"\u003e\n \u003cp\u003eHousehold physical activity\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 15.9407%;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 16.4041%;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 8.2%;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 6.5131%;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 8.7154%;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\" style=\"width: 7.2628%;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 5.248%;\"\u003e\n \u003cp\u003eB (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 5.5607%;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 10.9361%;\"\u003e\n \u003cp\u003eB (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 5.3885%;\"\u003e\n \u003cp\u003eB (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 4.0765%;\"\u003e\n \u003cp\u003eB (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 2.4366%;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 5.904%;\"\u003e\n \u003cp\u003eB (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 4.4514%;\"\u003e\n \u003cp\u003eB (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 3.4206%;\"\u003e\n \u003cp\u003e\u003cem\u003eIndividual factors\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.248%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.5607%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9361%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.3885%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.0765%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.4366%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.904%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.4514%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 3.4206%;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.248%;\"\u003e\n \u003cp\u003e-0.30(-0.50, -0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.5607%;\"\u003e\n \u003cp\u003e-0.09**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9361%;\"\u003e\n \u003cp\u003e-0.33(-0.53, -0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.10**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.3885%;\"\u003e\n \u003cp\u003e-0.45(-0.72, -0.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.10**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.0765%;\"\u003e\n \u003cp\u003e-0.44(-0.71, -0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.4366%;\"\u003e\n \u003cp\u003e-0.10**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.904%;\"\u003e\n \u003cp\u003e-0.60(-0.89, -0.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.12***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.4514%;\"\u003e\n \u003cp\u003e-0.58(-0.87, -0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.12***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 3.4206%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.248%;\"\u003e\n \u003cp\u003e-3.14(-6.91,0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.5607%;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9361%;\"\u003e\n \u003cp\u003e-3.36(-7.12, 0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.3885%;\"\u003e\n \u003cp\u003e2.56(-2.50,7.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.0765%;\"\u003e\n \u003cp\u003e2.63(-2.44, 7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.4366%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.904%;\"\u003e\n \u003cp\u003e18.07(12.83,23.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.26***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.4514%;\"\u003e\n \u003cp\u003e18.23(12.99, 23.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.26***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 3.4206%;\"\u003e\n \u003cp\u003eHeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.248%;\"\u003e\n \u003cp\u003e0.83(-0.32,1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.5607%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9361%;\"\u003e\n \u003cp\u003e0.88(-0.27, 2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.3885%;\"\u003e\n \u003cp\u003e-0.27(-1.81,1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.0765%;\"\u003e\n \u003cp\u003e-0.29(-1.83, 1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.4366%;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.904%;\"\u003e\n \u003cp\u003e0.23(-1.36,1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.4514%;\"\u003e\n \u003cp\u003e0.20(-1.40, 1.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 3.4206%;\"\u003e\n \u003cp\u003eWeight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.248%;\"\u003e\n \u003cp\u003e-0.96(-2.40,0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.5607%;\"\u003e\n \u003cp\u003e-0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9361%;\"\u003e\n \u003cp\u003e-1.03(-2.46, 0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.3885%;\"\u003e\n \u003cp\u003e0.89(-1.03,2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.0765%;\"\u003e\n \u003cp\u003e0.91(-1.01, 2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.4366%;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.904%;\"\u003e\n \u003cp\u003e-0.41(-2.40,1.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.4514%;\"\u003e\n \u003cp\u003e-0.36(-2.35, 1.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 3.4206%;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.248%;\"\u003e\n \u003cp\u003e2.42(-1.35,6.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.5607%;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9361%;\"\u003e\n \u003cp\u003e2.63(-1.12, 6.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.3885%;\"\u003e\n \u003cp\u003e-1.65(-6.70,3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.0765%;\"\u003e\n \u003cp\u003e-1.71(-6.77, 3.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.4366%;\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.904%;\"\u003e\n \u003cp\u003e1.06(-4.18,6.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.4514%;\"\u003e\n \u003cp\u003e0.90(-4.33, 6.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 3.4206%;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.248%;\"\u003e\n \u003cp\u003e0.10(-5.53,5.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.5607%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9361%;\"\u003e\n \u003cp\u003e-0.14(-5.75, 5.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.3885%;\"\u003e\n \u003cp\u003e-1.45(-9.00,6.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.0765%;\"\u003e\n \u003cp\u003e-1.38(-8.93, 6.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.4366%;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.904%;\"\u003e\n \u003cp\u003e1.06(-6,76,8.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.4514%;\"\u003e\n \u003cp\u003e1.23(-6.59, 9.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 3.4206%;\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.248%;\"\u003e\n \u003cp\u003e1.71(-3.78,7.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.5607%;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9361%;\"\u003e\n \u003cp\u003e2.42(-3.08, 7.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.3885%;\"\u003e\n \u003cp\u003e6.86(-0.51,14.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.0765%;\"\u003e\n \u003cp\u003e6.65(-0.76, 14.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.4366%;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.904%;\"\u003e\n \u003cp\u003e-1.07(-8.70,6.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.4514%;\"\u003e\n \u003cp\u003e-1.57(-9.23, 6.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 3.4206%;\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.248%;\"\u003e\n \u003cp\u003e-1.86(-2.91, -0.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.5607%;\"\u003e\n \u003cp\u003e-0.11***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9361%;\"\u003e\n \u003cp\u003e-1.91(-2.96, -0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.11***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.3885%;\"\u003e\n \u003cp\u003e-2.39(-3.81, -0.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.10***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.0765%;\"\u003e\n \u003cp\u003e-2.38(-3.80, -0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.4366%;\"\u003e\n \u003cp\u003e-0.10**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.904%;\"\u003e\n \u003cp\u003e-0.31(-1.78, 1.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.4514%;\"\u003e\n \u003cp\u003e-0.27(-1.74, -1.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 3.4206%;\"\u003e\n \u003cp\u003eAnnual household income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.248%;\"\u003e\n \u003cp\u003e0.15(-0.89,1.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.5607%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9361%;\"\u003e\n \u003cp\u003e0.11(-0.92, 1.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.3885%;\"\u003e\n \u003cp\u003e-0.45(-1.84,0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.0765%;\"\u003e\n \u003cp\u003e-0.44(-1.83, 0.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.4366%;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.904%;\"\u003e\n \u003cp\u003e1.84(0.40,3.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.07*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.4514%;\"\u003e\n \u003cp\u003e1.86(0.42, 3.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.07*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 3.4206%;\"\u003e\n \u003cp\u003e\u003cem\u003eUrban greenness\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.248%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.5607%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9361%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.3885%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.0765%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.4366%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.904%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.4514%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 3.4206%;\"\u003e\n \u003cp\u003eStreet greenery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.248%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.5607%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 10.9361%;\"\u003e\n \u003cp\u003e27.15(9.29,45.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e0.08**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.3885%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.0765%;\"\u003e\n \u003cp\u003e-8.25(-32.28, 15.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.4366%;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 5.904%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 4.4514%;\"\u003e\n \u003cp\u003e-19.35(-44.23, 5.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 2.8114%;\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 3.4206%;\"\u003e\n \u003cp\u003e\u003cem\u003eAdjusted R\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 15.9407%;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.4041%;\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.2%;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 6.5131%;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.7154%;\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 7.2628%;\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 3.4206%;\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 15.9407%;\"\u003e\n \u003cp\u003e3.663***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.4041%;\"\u003e\n \u003cp\u003e4.206***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.2%;\"\u003e\n \u003cp\u003e5.989***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 6.5131%;\"\u003e\n \u003cp\u003e5.434***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.7154%;\"\u003e\n \u003cp\u003e16.890***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 7.2628%;\"\u003e\n \u003cp\u003e15.449***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 3.4206%;\"\u003e\n \u003cp\u003e\u003cem\u003eAIC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 15.9407%;\"\u003e\n \u003cp\u003e12154.918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 16.4041%;\"\u003e\n \u003cp\u003e12147.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.2%;\"\u003e\n \u003cp\u003e12933.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 6.5131%;\"\u003e\n \u003cp\u003e12935.296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 8.7154%;\"\u003e\n \u003cp\u003e13027.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\" style=\"width: 7.2628%;\"\u003e\n \u003cp\u003e13027.191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\" style=\"width: 50.8866%;\"\u003e\u003cem\u003eNotes\u003c/em\u003e: \u003cem\u003e*P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; AIC: Akaike information criterion\u003c/em\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study objectively measured street greenery exposure based on eye level using Baidu Street View images and deep learning and explored the association between street greenery levels and different types of physical activity (PA) among older adults. Our study found significant correlations between street greenery in the 500-meter buffer zone around residences and transportation PA among Chinese older adults. However, there was no significant correlation between street greenery and leisure PA and household PA of older adults.\u003c/p\u003e \u003cp\u003eOur findings support the idea that urban green spaces have a positive impact on PA in older adults(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). A previous cross-sectional study investigating the relationship between PA and street greenery exposure among older adults in Shanghai, China, showed that higher levels of street greenery increased total PA levels and active transportation PA among older adults(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Moreover, a study from Hong Kong, China, assessed the quantity and quality of street greenery using Google Street View images and showed that the quantity and quality of street greenery was positively correlated with the amount of time residents spent in recreational physical activities, which included physical activities such as walking, jogging, and bicycling(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Beijing, like Shanghai and Hong Kong, is a high-density metropolis, and the results of this study further emphasize the positive impact of street greenery levels on the PA of urban residents in a high-density metropolitan environment. Under the context of China's rapid urbanization and increasing population aging, it is recommended that urban planners, policymakers, and relevant authorities rationally plan urban green spaces to promote healthy cities.\u003c/p\u003e \u003cp\u003eIn many countries, there is a growing trend towards an ageing population, making it particularly important to focus on the health and quality of life of older adults. The impact of green spaces on the PA of older adults is one of the research areas of international interest. Research has generally shown that good green spaces can provide a comfortable and safe environment that encourages older adults to participate in outdoor activities(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). One of the international studies comparing the relationship between outdoor recreation and green space for older adults in cities such as Sydney, Singapore and Dhaka found that high-quality green space was associated with walking activity among older adults(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). These findings emphasize the importance of actively promoting green space in urban planning to promote the health of older adults. However, it is important to note that culture, climate and urban planning in different countries and regions may affect the level of utilization of green spaces by older adults. It is therefore important to take into account the needs and habits of older adults in the local context when designing and planning green spaces to ensure that green spaces can maximize their role in promoting PA among older adults.\u003c/p\u003e \u003cp\u003eFor different types of PA, the main results of this study showed a significant correlation between street greenery exposure and transportation PA among older adults. This was similar to the findings of Schoner et al. that higher tree cover encourages people to engage in active transportation PA and increase the amount of time spent cycling and walking(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). In high-density metropolises such as Beijing, cycling and walking are the main transportation physical activities and the main source of PA for older people. Studies have shown that tree-lined streets are more likely to increase active transportation behaviors such as biking and walking than parks(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Therefore, higher street greenery exposure is more likely to increase transportation PA among older adults. However, in a correlation analysis, the results of this study showed that street greenery was significantly and positively correlated only with bicycling among older adults, with no significant correlation with walking. Furthermore, a systematic review and meta-analysis showed that greenery and aesthetically pleasing landscaping were not associated with transitory walking(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). The explanation for this may be that people may choose different types of transportation-based PA behaviors, influenced by different urban and cultural contexts. Green space and PA assessment methods may also make a difference in the results. In addition, it has also been suggested that urban greenery may be a peripheral facilitator of walking rather than an important determinant(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe analyses in this study showed no significant correlation between street greenery and leisure PA among older adults. A previous cross-sectional survey from the United Kingdom also showed no correlation between the level of urban green space and the leisure PA of middle-aged and older adults(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). However, the study by Lu et al. showed a positive correlation between the level of street greenery and residents' leisure PA(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). The inconsistency of the results across studies may be due to factors such as individual differences in the study population, differences in geographic location, and different levels of moderation of potential confounders across studies. In addition, leisure physical activities in this study mainly consisted of physical exercise, such as running, swimming, and dancing. These leisure physical activities are influenced by a variety of environmental factors, such as the safety and attractiveness of the environment, as well as fitness facilities, all of which can affect an individual's level of PA(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). In this study, only one important environmental factor, street greenery, was considered, and in future studies, other environmental factors can be combined to comprehensively investigate the environmental factors affecting leisure PA.\u003c/p\u003e \u003cp\u003eNot only can green spaces influence the PA behaviors of residents, but there is strong recent evidence that green spaces can also have a range of effects on people's health(\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). For example, a systematic review has shown that there is a positive correlation between green space and PA and that green space can also have a positive impact on individual health by preventing poor mental health outcomes, cardiovascular disease, and mortality(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). It is clear that green space may have a positive impact on both mental health and physical health. In terms of mental health, Wang et al. showed that the higher the level of greenery in a residential area, the lower the chances of depressive symptoms among older adults, and suggested that increasing urban and community green spaces may help prevent and intervene in depressive symptoms among older adults in the community(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). With regard to physical health, a study by Yang et al. showed a beneficial association between living in areas with higher levels of greenery and blood lipid levels, especially among women and older adults(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). At present, the specific mechanisms by which green spaces influence the mental health and physical health of individuals are inconclusive. Current research focuses on the idea that green spaces may further affect individual health by influencing air quality, PA, and social cohesion, and helping to reduce stress and that PA, in particular, may be an important mediator of the effects of green spaces on health(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). The results of the correlation between street greenery and PA in this study may also help to further explain the mechanism of green space on health. Future research could further analyze the mechanisms by which green space affects the health of older adults based on the PA perspective.\u003c/p\u003e \u003cp\u003eThis study focuses on analyzing the association between street greenery and PA among older adults. The strength of this study lies in the fact that the street greenery exposures around the residences are calculated from Baidu Street View images and deep learning, and the green space identification of Baidu Street View images is closer to the pedestrian and human eye's perception of street greenery exposures, and the data are more realistic and reliable compared to other measurement methods. In addition, this study fully explored the relationship between street greenery and different types of PA of the older adults. This is important for the effective planning of street green space to enhance the PA level and improve the quality of life of the older adults.\u003c/p\u003e \u003cp\u003eHowever, this study also has some limitations. First, this study used a cross-sectional research design, which can only make educated guesses and assumptions about the connections between variables, and cannot determine the causal relationships between variables. Second, in terms of the age distribution of the participants, we focused only on older adults aged 60 years and older and did not examine other age groups. Self-reported PA is another limitation. Future studies could expand the sample size and objectively PA measurement to explore the relationship between street greenery and PA in different age groups. And cohort studies and experimental studies should be conducted to further analyze the mechanism of the impact of street greenery on PA. This study did not explore the relationship between street greenery and different intensities of PA in older adults, nor did it analyze gender differences; these limitations could be further analyzed with a larger sample size. In addition, the coronavirus disease 2019 (COVID-19) outbreak may have had an impact on the health behaviors of older adults(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e), and whether the relationship between street greenery and PA in older adults changed after the outbreak warrants further exploration in the future in the context of COVID-19.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study showed a significant association between street greenery around residences and transportation PA among Chinese older adults. It is recommended that urban planners, public health promoters, and relevant authorities further scientifically plan urban green spaces to enhance the PA levels of older adults and promote healthy aging.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYiling Song: Conceptualization, Writing\u0026nbsp;\u0026minus;\u0026nbsp;original draft, Formal analysis; Mingzhong Zhou: Methodology, Data curation; Jiale Tan: Methodology; Jiali Cheng: Investigation; Yangyang Wang: Data curation; Xiaolu Feng: Writing\u0026nbsp;\u0026minus;review \u0026amp; editing; Hongjun Yu: Writing\u0026nbsp;\u0026minus;review \u0026amp; editing, Funding acquisition.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Social Science Foundation of China (17CTY020,20BTY004), Beijing Social Science Foundation of China (21YTA009), and the Tsinghua University \u0026ldquo;Shuang Gao\u0026rdquo; Scientific Research Program (2021TSG08208), the Tsinghua Education Reform Project (2021ZY01_01), the Tsinghua Graduated Education Reform Project (202303J039). \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental protocol for involving humans was following the national/ international/institutional boards and the Declaration of Helsinki. This study was approved by the Tsinghua University Institutional Review Board (No.20110170). All the participants gave written informed consent before completing the survey. Confidentiality of participants\u0026apos; information was guaranteed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available upon request to the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge the contributions of all the individuals who have contributed to this study. We thank the study staff and participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHe W, Goodkind D, Kowal P. An Aging World: 20152016.\u003c/li\u003e\n\u003cli\u003eHanmo Y. Dynamic Trend of China\u0026apos;s Population Ageing and New Characteristics of the Elderly. Population Research. 2022;46(5):104-16.\u003c/li\u003e\n\u003cli\u003eMooney SJ, Joshi S, Cerd\u0026aacute; M, Kennedy GJ, Beard JR, Rundle AG. Neighborhood Disorder and Physical Activity among Older Adults: A Longitudinal Study. J Urban Health. 2017;94(1):30-42.\u003c/li\u003e\n\u003cli\u003eBoakye KA, Amram O, Schuna JM, Jr., Duncan GE, Hystad P. GPS-based built environment measures associated with adult physical activity. Health Place. 2021;70:102602.\u003c/li\u003e\n\u003cli\u003eLaddu D, Paluch AE, LaMonte MJ. The role of the built environment in promoting movement and physical activity across the lifespan: Implications for public health. Prog Cardiovasc Dis. 2021;64:33-40.\u003c/li\u003e\n\u003cli\u003eOrganization WH. Physical activity 2020 [cited 2023 July 12]. Available from: https://www.who.int/news-room/fact-sheets/detail/physical-activity.\u003c/li\u003e\n\u003cli\u003eMoreno-Agostino D, Daskalopoulou C, Wu YT, Koukounari A, Haro JM, Tyrovolas S, et al. The impact of physical activity on healthy ageing trajectories: evidence from eight cohort studies. Int J Behav Nutr Phys Act. 2020;17(1):92.\u003c/li\u003e\n\u003cli\u003eXiao Y, Miao S, Zhang Y, Chen H, Wu W. Exploring the health effects of neighborhood greenness on Lilong residents in Shanghai. Urban Forestry \u0026amp; Urban Greening. 2021;66:127383.\u003c/li\u003e\n\u003cli\u003eHe H, Lin X, Yang Y, Lu Y. Association of street greenery and physical activity in older adults: A novel study using pedestrian-centered photographs. Urban Forestry \u0026amp; Urban Greening. 2020;55:126789.\u003c/li\u003e\n\u003cli\u003eDeng Y, Liang J, Chen Q. Greenway interventions effectively enhance physical activity levels\u0026mdash;A systematic review with meta-analysis. Frontiers in Public Health. 2023;11:1268502.\u003c/li\u003e\n\u003cli\u003eBeyer KM, Kaltenbach A, Szabo A, Bogar S, Nieto FJ, Malecki KM. Exposure to neighborhood green space and mental health: evidence from the survey of the health of Wisconsin. Int J Environ Res Public Health. 2014;11(3):3453-72.\u003c/li\u003e\n\u003cli\u003eCarmen R, Jacobs S, Leone M, Palliwoda J, Pinto L, Misiune I, et al. Keep it real: Selecting realistic sets of urban green space indicators. Environmental Research Letters. 2020;15.\u003c/li\u003e\n\u003cli\u003ede Vries S, Verheij RA, Groenewegen PP, Spreeuwenberg P. Natural Environments\u0026mdash;Healthy Environments? An Exploratory Analysis of the Relationship between Greenspace and Health. Environment and Planning A. 2003;35:1717 - 31.\u003c/li\u003e\n\u003cli\u003eMaas J, Verheij RA, Groenewegen PP, de Vries S, Spreeuwenberg P. Green space, urbanity, and health: how strong is the relation? J Epidemiol Community Health. 2006;60(7):587-92.\u003c/li\u003e\n\u003cli\u003eRolland Y, Lauwers-Cances V, Cristini C, Grandjean H, Banks WA, Morley JE, Vellas B. Disability in obese elderly women: Lower limb strength and recreational physical activity. Obes Res Clin Pract. 2007;1(1):1-78.\u003c/li\u003e\n\u003cli\u003eZhou W, Wang Q, Kadier A, Wang W, Zhou F, Li R, Ling L. The role of residential greenness levels, green land cover types and diversity in overweight/obesity among older adults: A cohort study. Environmental Research. 2023;217:114854.\u003c/li\u003e\n\u003cli\u003eBesser LM, Mitsova DP. Neighborhood Green Land Cover and Neighborhood-Based Walking in U.S. Older Adults. Am J Prev Med. 2021;61(1):e13-e20.\u003c/li\u003e\n\u003cli\u003eXie B, An Z, Zheng Y, Li Z. Healthy aging with parks: Association between park accessibility and the health status of older adults in urban China. Sustainable Cities and Society. 2018;43:476-86.\u003c/li\u003e\n\u003cli\u003ePoppe L, Deforche B, Van Cauwenberg J, Brondeel R, Mertens L, Van de Weghe N, et al. The association between the number of parks near home and levels of physical activity among community-dwelling older adults: A longitudinal study. Cities. 2022;130:103931.\u003c/li\u003e\n\u003cli\u003eKlompmaker JO, Hoek G, Bloemsma LD, Gehring U, Strak M, Wijga AH, et al. Green space definition affects associations of green space with overweight and physical activity. Environ Res. 2018;160:531-40.\u003c/li\u003e\n\u003cli\u003eKaczynski AT, Potwarka LR, Saelens BE. Association of park size, distance, and features with physical activity in neighborhood parks. Am J Public Health. 2008;98(8):1451-6.\u003c/li\u003e\n\u003cli\u003eChen X, Meng Q, Hu D, Zhang L, Yang J. Evaluating Greenery around Streets Using Baidu Panoramic Street View Images and the Panoramic Green View Index. Forests. 2019;10(12):1109.\u003c/li\u003e\n\u003cli\u003eYu H, An R, Andrade F. Ambient Fine Particulate Matter Air Pollution and Physical Activity: A Longitudinal Study of University Retirees in Beijing, China. Am J Health Behav. 2017;41(4):401-10.\u003c/li\u003e\n\u003cli\u003eZhao H, Shi J, Qi X, Wang X, Jia J, editors. Pyramid Scene Parsing Network. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2017 21-26 July 2017.\u003c/li\u003e\n\u003cli\u003eHino AA, Reis RS, Sarmiento OL, Parra DC, Brownson RC. The built environment and recreational physical activity among adults in Curitiba, Brazil. Preventive medicine. 2011;52(6):419-22.\u003c/li\u003e\n\u003cli\u003eFlorindo AA, Turrell G, Garcia LMT, dos Anjos Souza Barbosa JP, Cruz MS, Failla MA, et al. Mix of destinations and sedentary behavior among Brazilian adults: a cross-sectional study. BMC Public Health. 2021;21:1-7.\u003c/li\u003e\n\u003cli\u003eSarkar C, Zhang B, Ni M, Kumari S, Bauermeister S, Gallacher J, Webster C. Environmental correlates of chronic obstructive pulmonary disease in 96 779 participants from the UK Biobank: a cross-sectional, observational study. The Lancet Planetary Health. 2019;3(11):e478-e90.\u003c/li\u003e\n\u003cli\u003eLi X, Zhang C, Li W, Ricard R, Meng Q, Zhang W. Assessing street-level urban greenery using Google Street View and a modified green view index. Urban Forestry \u0026amp; Urban Greening. 2015;14(3):675-85.\u003c/li\u003e\n\u003cli\u003eLu Y. The Association of Urban Greenness and Walking Behavior: Using Google Street View and Deep Learning Techniques to Estimate Residents\u0026apos; Exposure to Urban Greenness. Int J Environ Res Public Health. 2018;15(8).\u003c/li\u003e\n\u003cli\u003eHelbich M, Yao Y, Liu Y, Zhang J, Liu P, Wang R. Using deep learning to examine street view green and blue spaces and their associations with geriatric depression in Beijing, China. Environment International. 2019;126:107-17.\u003c/li\u003e\n\u003cli\u003eDong R, Zhang Y, Zhao J. How Green Are the Streets Within the Sixth Ring Road of Beijing? An Analysis Based on Tencent Street View Pictures and the Green View Index. Int J Environ Res Public Health. 2018;15(7).\u003c/li\u003e\n\u003cli\u003eYu HJ, Zhu WM, Qiu J, Zhang CG. Physical Activity Scale for Elderly (PASE): A Cross-validation study for Chinese Older Adults. Medicine and Science in Sports and Exercise. 2012;44:647-.\u003c/li\u003e\n\u003cli\u003eVaughan K, Miller WC. Validity and reliability of the Chinese translation of the Physical Activity Scale for the Elderly (PASE). Disabil Rehabil. 2013;35(3):191-7.\u003c/li\u003e\n\u003cli\u003eNgai SP, Cheung RT, Lam PL, Chiu JK, Fung EY. Validation and reliability of the Physical Activity Scale for the Elderly in Chinese population. J Rehabil Med. 2012;44(5):462-5.\u003c/li\u003e\n\u003cli\u003ePrevention UCfDCa. Physical and activity and physical fitness-PAQAugust 23, 2023. Available from: https://www.cdc.gov/nchs/data/nhanes/nhanes_11_12/paq_capi.pdf.\u003c/li\u003e\n\u003cli\u003eSong Y, Li H, Yu H. Effects of green space on physical activity and body weight status among Chinese adults: a systematic review. Front Public Health. 2023;11:1198439.\u003c/li\u003e\n\u003cli\u003eFeng X, Toms R, Astell-Burt T. Association between green space, outdoor leisure time and physical activity. Urban Forestry \u0026amp; Urban Greening. 2021;66:127349.\u003c/li\u003e\n\u003cli\u003eXiao Y, Miao S, Zhang Y, Xie B, Wu W. Exploring the associations between neighborhood greenness and level of physical activity of older adults in shanghai. Journal of Transport \u0026amp; Health. 2022;24:101312.\u003c/li\u003e\n\u003cli\u003eLu Y. Using Google Street View to investigate the association between street greenery and physical activity. Landscape and Urban Planning. 2019;191:103435.\u003c/li\u003e\n\u003cli\u003eJuul V, Nordbo ECA. Examining activity-friendly neighborhoods in the Norwegian context: green space and walkability in relation to physical activity and the moderating role of perceived safety. BMC Public Health. 2023;23(1):259.\u003c/li\u003e\n\u003cli\u003eShuvo FK, Feng X, Astell-Burt T. Urban green space quality and older adult recreation: an international comparison. Cities \u0026amp; Health. 2021;5(3):329-49.\u003c/li\u003e\n\u003cli\u003eSchoner J, Chapman J, Brookes A, MacLeod KE, Fox EH, Iroz-Elardo N, Frank LD. Bringing health into transportation and land use scenario planning: Creating a National Public Health Assessment Model (N-PHAM). Journal of Transport \u0026amp; Health. 2018;10:401-18.\u003c/li\u003e\n\u003cli\u003eVich G, Marquet O, Miralles-Guasch C. Green streetscape and walking: Exploring active mobility patterns in dense and compact cities. Journal of Transport \u0026amp; Health. 2019;12:50-9.\u003c/li\u003e\n\u003cli\u003eCerin E, Nathan A, van Cauwenberg J, Barnett DW, Barnett A. The neighbourhood physical environment and active travel in older adults: a systematic review and meta-analysis. Int J Behav Nutr Phys Act. 2017;14(1):15.\u003c/li\u003e\n\u003cli\u003eHillsdon M, Panter J, Foster C, Jones A. The relationship between access and quality of urban green space with population physical activity. Public Health. 2006;120(12):1127-32.\u003c/li\u003e\n\u003cli\u003eBrownson RC, Hoehner CM, Day K, Forsyth A, Sallis JF. Measuring the built environment for physical activity: state of the science. Am J Prev Med. 2009;36(4 Suppl):S99-123.e12.\u003c/li\u003e\n\u003cli\u003eZhang Y, Koene M, Reijneveld SA, Tuinstra J, Broekhuis M, van der Spek S, Wagenaar C. The impact of interventions in the built environment on physical activity levels: a systematic umbrella review. Int J Behav Nutr Phys Act. 2022;19(1):156.\u003c/li\u003e\n\u003cli\u003eHunter RF, Nieuwenhuijsen M, Fabian C, Murphy N, O\u0026apos;Hara K, Rappe E, et al. Advancing urban green and blue space contributions to public health. Lancet Public Health. 2023;8(9):e735-e42.\u003c/li\u003e\n\u003cli\u003eBarboza EP, Cirach M, Khomenko S, Iungman T, Mueller N, Barrera-Gomez J, et al. Green space and mortality in European cities: a health impact assessment study. Lancet Planet Health. 2021;5(10):e718-e30.\u003c/li\u003e\n\u003cli\u003eAli MJ, Rahaman M, Hossain SI. Urban green spaces for elderly human health: A planning model for healthy city living. Land Use Policy. 2022;114:105970.\u003c/li\u003e\n\u003cli\u003eJames P, Banay RF, Hart JE, Laden F. A Review of the Health Benefits of Greenness. Curr Epidemiol Rep. 2015;2(2):131-42.\u003c/li\u003e\n\u003cli\u003eWang P, Wang M, Shan J, Liu X, Jing Y, Zhu H, et al. Association between residential greenness and depression symptoms in Chinese community-dwelling older adults. Environ Res. 2024;243:117869.\u003c/li\u003e\n\u003cli\u003eYang BY, Markevych I, Heinrich J, Bloom MS, Qian Z, Geiger SD, et al. Residential greenness and blood lipids in urban-dwelling adults: The 33 Communities Chinese Health Study. Environ Pollut. 2019;250:14-22.\u003c/li\u003e\n\u003cli\u003eHartig T, Mitchell R, de Vries S, Frumkin H. Nature and Health. Annual Review of Public Health. 2014;35(1):207-28.\u003c/li\u003e\n\u003cli\u003eDwijayani R, Soraya N, Alimuddin S. Quo Vadis Vaccine Research Trends of Coronavirus Disease-19 in Indonesia and Malaysia: A Bibliometric Analysis. Journal of Contemporary Governance and Public Policy. 2023;4:175-94.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Street greenery, Street view images, Physical activity, Older adults, China","lastPublishedDoi":"10.21203/rs.3.rs-5323147/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5323147/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe association between urban green spaces, especially street greenery, and physical activity (PA) in older adults is understudied. This study utilized Baidu Street View images and deep learning techniques to objectively assess street greenery exposure and its relationship with different types of PA among older adults in China.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study investigated 1326 older adults (aged 60 or above) living in Beijing, China. Physical Activity Scale for the Elderly (PASE) was used to assess the PA level of older adults. Baidu Street View images and deep learning were used to assess the level of street greenery in the 500-meter buffer zone around the community. The study employed ANOVA, Chi-square tests, and multilevel linear regression to analyze the data.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAfter controlling for individual factors, household economic income, and other confounders, the multilevel linear regression model showed that street greenery was significantly and positively correlated with transportation PA (β\u0026thinsp;=\u0026thinsp;0.08, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). There was no significant correlation between street greenery and leisure PA, household PA (P\u0026gt;0.05).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe level of street greenery around the community is significantly associated with transportation PA among Chinese older adults. It is recommended that the planning of urban green spaces should focus on street greenery, add bicycle lanes and sidewalks, and provide safe and comfortable environments to motivate older adults to actively participate in PA.\u003c/p\u003e","manuscriptTitle":"Association between Street Greenery and Physical Activity among Chinese Older Adults in Beijing, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-27 10:44:28","doi":"10.21203/rs.3.rs-5323147/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-14T10:08:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-12T02:24:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166349553398822892634492093175597565176","date":"2025-04-10T01:54:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68772978547621889001566858915714264206","date":"2025-04-09T06:43:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98649425461966092908358778771691846039","date":"2025-04-08T12:27:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-16T11:44:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223905990894717596735819524256789369256","date":"2025-02-05T09:46:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"328149444795290147511239950863059362661","date":"2025-02-05T00:12:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-16T15:29:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-16T15:27:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-11-11T11:44:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-08T13:10:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-10-24T06:16:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"98cefc6e-5059-4410-8396-767ba0dfdff6","owner":[],"postedDate":"November 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":40425111,"name":"Health sciences/Health care"},{"id":40425112,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-06-09T16:04:07+00:00","versionOfRecord":{"articleIdentity":"rs-5323147","link":"https://doi.org/10.1038/s41598-025-03050-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-06-04 15:57:47","publishedOnDateReadable":"June 4th, 2025"},"versionCreatedAt":"2024-11-27 10:44:28","video":"","vorDoi":"10.1038/s41598-025-03050-3","vorDoiUrl":"https://doi.org/10.1038/s41598-025-03050-3","workflowStages":[]},"version":"v1","identity":"rs-5323147","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5323147","identity":"rs-5323147","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}