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. 2017 Jul 14;14(7):783.
doi: 10.3390/ijerph14070783.

Predictors of Daily Mobility of Adults in Peri-Urban South India

Margaux Sanchez  1   2   3 , Albert Ambros  4   5   6 , Maëlle Salmon  7   8   9 , Santhi Bhogadi  10 , Robin T Wilson  11 , Sanjay Kinra  12 , Julian D Marshall  13 , Cathryn Tonne  14   15   16
Affiliations

Predictors of Daily Mobility of Adults in Peri-Urban South India

Margaux Sanchez et al. Int J Environ Res Public Health. .

Abstract

Daily mobility, an important aspect of environmental exposures and health behavior, has mainly been investigated in high-income countries. We aimed to identify the main dimensions of mobility and investigate their individual, contextual, and external predictors among men and women living in a peri-urban area of South India. We used 192 global positioning system (GPS)-recorded mobility tracks from 47 participants (24 women, 23 men) from the Cardiovascular Health effects of Air pollution in Telangana, India (CHAI) project (mean: 4.1 days/person). The mean age was 44 (standard deviation: 14) years. Half of the population was illiterate and 55% was in unskilled manual employment, mostly agriculture-related. Sex was the largest determinant of mobility. During daytime, time spent at home averaged 13.4 (3.7) h for women and 9.4 (4.2) h for men. Women's activity spaces were smaller and more circular than men's. A principal component analysis identified three main mobility dimensions related to the size of the activity space, the mobility in/around the residence, and mobility inside the village, explaining 86% (women) and 61% (men) of the total variability in mobility. Age, socioeconomic status, and urbanicity were associated with all three dimensions. Our results have multiple potential applications for improved assessment of environmental exposures and their effects on health.

Keywords: India; gender; global positioning system (GPS); principal component analysis (PCA); spatial behavior; time-activity patterns.

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Conflict of interest statement

The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

Figures

Figure 1
Figure 1
Percent of daytime spent at home (dark grey), in activity locations (white) and trips (light grey) according to selected characteristics. Home, activity locations, and trips identified by an automated algorithm within GPS tracks. Body mass index is expressed in kg/m2. Night-time light intensity was used as marker of village urbanicity. Low and high categories for night-time light intensity were derived from population median value. All tests comparing men and women values were significant at the 5% level.
Figure 2
Figure 2
Effects of individual, external, village-level and Geographic Information System (GIS)-derived predictors on the three main dimensions of mobility in men and women. Figures are effect estimates (points) and 95% confidence interval (bars) derived from mixed model with random intercept per participant. Each predictor was investigated individually. Adjustment for age did not change the results. Squares indicate reference categories. Stars indicate statistical significance at the 5% level. Distance, industry count, non-residential place count, household count, and road length are considered as continuous variables. For clarity purposes, only the three dimensions common to men and women are presented. We scaled the dimensions scores so that higher values of the estimates indicated more mobility in the corresponding dimension. Body mass index is expressed in kg/m2, distances are expressed in meters. Night-time light intensity was used as marker of village urbanicity. Low and high categories in village-level factors were derived from population median value. Abbreviations: NRP: non-residential place.
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