Background
The burden of non-communicable diseases (NCDs) are on the rise in the developing countries and the mortality is reported to be 20–50% higher in Sri Lanka, than in many developed countries [
1]. Among the many NCD risks, physical inactivity is the fourth leading risk factor for death in the world [
2] and being inactive is known to increase the risk of cardiovascular disease, some cancers, type 2 diabetes, stroke, some mental illnesses and premature death [
3‐
5]. In Sri Lanka, the prevalence for physical inactivity was observed to be 22.5% for males and 38.4% for females from the Non-Communicable Disease Risk Factor Survey in 2015 [
6] while in district of Colombo the prevalence of inactivity was 18.0% for males and 20.3% for females [
7].
Out of the many forms of physical activity, walking is one of the most common forms of activity carried out by adults. Many factors are known to influence walking, including personal and environmental factors. In describing the factors in the built environment for PA and walking, measures such as residential density, land use, intersection density, street connectivity and access and distance to facility is considered important [
8]. Commonly used concepts in literature are “proximity”, “connectivity” and “urban design” [
9]. Proximity refers to how close different travel destinations are to one another in space and is operationalized in terms of “density” and “land use mix”. Density measures the concentration of people, dwelling units or households, while land use mix refers to the spatial placement of different types of land uses (industrial, residential, commercial). When the land uses are mixed, there is greater number of destinations that are close to a person’s home or office. “Connectivity” refers to the number and directness of transportation linkages between destinations [
10]. A highly connected neighbourhood has many linkages between destinations and is usually defined in terms of number of road intersections in an area. A highly connected neighbourhood provides more route options for travellers and shortens trip distance, thereby influencing people to use non-motorized forms of transportation. Many environment attributes such as street connectivity, good access to destination such as to recreational facility and public transport have shown relationship with walking in the previous studies [
11‐
17].
To assess this relationship the physical environment must be measured ether by observations of the environment through audits, perception of the environment by individuals or by using secondary data on environment. Recently with the advancement of digital maps and the Geographic Information Systems (GIS) technology, objective measures to assess the environment have been widely used in the area of PA, giving the capability to assess spatial relationships which could be useful to solve public health issues [
18]. An added advantage of GIS based analysis is that that publicly available geospatial data could be a more feasible and cost-effective method for exploring relationships [
19].
When using GIS to assess relationship with walking, density and distance measures should be ideally measured through network analysis. However, in situations where available data are not compatible for network analysis due to incompleteness of the network structure the recommendation is for straight line analysis [
20]. The twin cities walking study environment and physical activity: GIs protocol, gives in details the common attributes that could be operationalised, also giving the concept, formulae, approach, and steps in calculating these attributes using geo referenced data [
20]. Some common environmental attributes that could be assessed using GIS are population per unit area, street lights and street trees within a unit area, distance to facility, sidewalk length, building foot print area, intersections and road length per unit area etc. A GIS Based assessment of physical environment across 12 countries have been carried out assessing residential density, street connectivity, mix of land uses, and access to public transit, parks, and private recreation facilities [
21] and its relationship with PA. As Sri Lanka experiences rapid urbanisation leading to changes in the built environment, it is vital that assessment of built environment and its association with walking be explored as the country progresses to achieve the targets of sustainable development goals for 2030 agenda by reducing one third of premature mortality from NCD [
22]. The aim of this study was to explore the relationship between neighbourhood environment and walking among the adults in Colombo Municipal Council (CMC) area in Sri Lanka.
Discussion
Few studies have examined the relationship of the built environment and walking in developing countries through a GIS approach. This study is one of the first to examine this association in Sri Lanka. A cross sectional study methodology was adopted like many other studies looking at walking and its association with objectively measured physical environmental attributes using GIS [
18]. This study adopted the GIS concepts and formulae derived from the GIS protocol developed for the Twin City Study [
20] and adjusted it according to the availability of secondary data. Although, the environment data were not collected for the purpose of studies related to PA, similar approaches have been used in many other studies where the secondary data were gathered for other purposes [
27,
28] were used for analysis as collecting environment related spatially referenced data is expensive.
It is seen from the current study that most of the walking carried out by the participants were related to transport. Only a very minimal amount of walking was carried out for leisure, and that only less than 25% walked for leisure indicated by the interquartile ranges for leisure time walking. This might be due to the fact that in Sri Lanka people do not engange routinely in physically active leisure be it walking or other forms of activity [
7]. Further it is seen that males and higher income group had a higher median in total minutes of walking carried out in a week. However, when distribution of the transport related walking was considered the mean minutes of walking per week was higher in the low-income category. This might be due to the fact that lower income category commonly uses public transportation and walking to meet their transport requirement, and the higher income group might be walking for leisure which is evident from the mean scores. Similar observations are there in other studies where people in low-income households were twice as likely to walk compared to the higher income households [
27]. Similarly, in China it was seen that level of total PA was higher among the low socio-economic group [
29]. Both male and female respondents were less likely to engage in walking for leisure than walking for transport. However, males engaged more in walking for leisure than females. This may be due to the different roles that the males and females play in the household or due to the fact that the environment being more conducive for the male gender especially in terms of safety or social acceptance for physical activity which was seen in a qualitative inquiry carried out in Colombo, Sri Lanka [
30]. The neighbourhood environment features that were operationalised for this study showed a wide variation when assessed for each as shown by the wide standard deviation, indicating the possibility that the selected neighbourhoods varied much in terms of theses environment variables. Further, the variables that were selected and operationalised were in alignment with perceived environment variables that that was selected for the scale developed and validated to assess the perceived physical and social environment associated with physical activity for Sri Lankan Adults [
31].
In the present study, mild to moderate positive correlations were observed between minutes of walking for transport and total walking with residential density and connectivity at the 200 m buffer. This was similar to that observed in the Twin city study where the correlation coefficients ranged between 0.3–0.5 [
27]. This could be due to people engaging in more walking when there are diverse destinations and the neighbourhood is more connected. Similarly, a study done in Atlanta in 2003 [
9] which assessed the net residential density (number of residential units per residential area), street connectivity (number of intersections per unit area) and land use mix (evenness of distribution of square footage of residential and commercial development) through a GIS database, showed that all of the above indicators were positively associated with the number of minutes of moderate PA per day.
The present study has limitations that should be recognized. As this study was a cross-sectional design, it prevents assessment of causality. The association of environment for leisure time walking was less concusive, possibly due to the fact that there were only few participants who engaged in waliking for leisure. Therefore, the built environment for leisure related walking needs to be further explored adopting a different methodology to compare perception of environment among those walking for leisure and those not walking for leisure. Yet, the current study generated interesting relationships between the built environment and transportation related walking. However, it is important that the findings be viewed in the backdrop of a small sample size which could have driven the possible relationships. The GIS based study had only five environmental factors and was not able to capture some important environmental variables that often are not available in GIS databases such as quality of the sidewalks, safety in the neighbourhood environment, presence of shade, traffic safe etc. which are known to be associated with walking [
13‐
16]. However, it gives the capability to explore the walking and the environment in relation to walking using already available secondary data. The use of secondary data involved certain limitation to the analysis. Network analysis could not be carried out due to incomplete networks in the secondary data that were available. Yet, collecting primary data and carrying out objective measurement through geo-coding was not feasible due to logistic constraints in the present study as would be in many explorative studies. Therefore, this GIS study explored the possibility of using GIS based data for future studies related to PA and the physical environment in developing countries with limited resources.
This study carried many strengths, of which one is the use of a valid measurement of walking. This was assessed through a widely accepted tool, the IPAQ long form, which was also validated for the local setting previously [
23] giving good validity and reliability measures. Another was making use of publicly available data for health-related research. Thirdly, this study used a similar approach is assessing the association between PA and the GIS based physical environment attributes such as residential density, land use, connectivity and used a correlation analysis [
18,
27] enabling comparisons [
28]. Further, the use of three network buffers specific to neighbourhood is a key strength of this analysis. Although the 200 m buffer is presented here all three buffer-specific analysis gave near comparable results. Neighbourhoods were defined around a radius of 200 m, 400 m and 600 m from the participants residents, which was also the method adopted in the Twin City study [
20].
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