Association between leisure-time physical activity and the built environment in China: Empirical evidence from an accelerometer and GPS-based fitness app

Abstract

To reexamine the relationship between leisure-time physical activity (LTPA) and the built environment (BE), this paper takes advantage of the massive amount of data collected by an accelerometer and GPS-based fitness mobile app. Massive LTPA data from more than 3 million users were recorded by Codoon in 500m by 500m grid cells and aggregated to 742 natural cities in mainland China. Six BE indicators were quantified using GIS at the city scale. Robust regression analysis was used to estimate the correlation between LTPA and BE. Five of six BE indicators—connectivity, road density, land use mix, points of interest density, and density of parks and squares—were significantly, positively, independently, and linearly related to LTPA in the regression analysis. The study obtains findings that are consistent with the previous literature but also provides novel insights into the important role of POI density in encouraging LTPA, as well as how the relationship between LTPA and BE varies by time of day. The study also sheds light on the embrace of new technology and new data in public health and urban studies.

The effect of FTO rs9939609 polymorphism on the association between colorectal cancer and dietary fiber

“…The level of physical activity was assessed using the International Physical Activity Questionnaire, which has already been validated in Iran ( 17 ). The IPAQ collects PA data by asking participants to recall their vigorous and moderate activities during the last 7 days ( 18 ). Using this questionnaire, the activity level of people at home, during exercise, during travel, and in sitting activities was determined.…”

Multiscale Impact of Environmental and Socio-Economic Factors on Low Physical Fitness among Chinese Adolescents and Regionalized Coping Strategies

“…It was found that some of the regional indicators met the normal distribution and could be directly incorporated into the regression model, while the distribution of some factors belonged to the long-tail distribution. We therefore take the natural logarithm for this part of the variable to ensure the data are more normally distributed [ 71 ]. Afterward, OLS, GWR, and MGWR models were constructed and evaluated for random distribution of residuals.…”

“…Around the characteristics and influencing factors of physical fitness mentioned in the introduction, a series of indicators are listed that may have an impact on the situation of low physical fitness of Chinese youth ( Table 1 and Table 2 ). As a composite concept, physical fitness has a wide range of influencing factors and needs to take into account regional indicators that are representative of each province in China, so the indicators for this study were selected from previous studies and existing established evaluation systems [ 63 , 70 , 71 ]. The indicators of regional characteristics such as the environment as well as socio-economic development selected for this study are based on attribute data from the China Statistical Yearbook 2018, China Environment Statistical Yearbook 2018, China Education Statistical Yearbook 2018, China Social Statistical Yearbook 2018, China’s 7th Population Census Bulletin, China’s provincial altitude barometric tables, and provincial statistical yearbooks for the year 2018.…”

The effects of street environment features on road running: An analysis using crowdsourced fitness tracker data and machine learning

“…Urban running trails can be broadly classified into three main types based on their location: streets, parks, and playgrounds. Many researchers have studied the association between physical activity and the environment of streets and parks (Chen et al, 2021; Su et al, 2014; Ying et al, 2015). It has been noticed that city dwellers do not only rely on parks, streets also allow a significant amount of physical activity (Lu, 2019; Lu et al, 2018).…”

“…Studies have found that the duration of physical activity on greenways is related to the distance between settlements, with proximity leading to a significant increase in activity (Humpel et al, 2004; Owen et al, 2004; Zhang et al, 2013). And a high degree function mixture or land-use mix can also encourage physical activity (Chen et al, 2021; Su et al, 2014; Ying et al, 2015). According to the study, trail continuity is a key factor affecting road runners' exercise experience (Majumdar et al, 2021).…”