Large-scale physical activity data reveal worldwide activity inequality

Tim Althoff, Rok Sosič, Jennifer L. Hicks, Abby C. King, Scott L. Delp, Jure Leskovec
2017 Nature  
Understanding the basic principles that govern physical activity is needed to curb the global pandemic of physical inactivity 1-7 and the 5.3 million deaths per year associated with in-activity 2 . Our knowledge, however, remains limited owing to the lack of large-scale measurements of physical activity patterns across free-living populations worldwide 1, 6 . Here, we leverage the wide usage of smartphones with built-in accelerometry to measure physical activity at planetary scale. We study a
more » ... taset consisting of 68 million days of physical activity for 717,527 people, giving us a window into activity in 111 countries across the globe. We find inequality in how activity is distributed within countries and that this inequality is a better predictor of obesity prevalence in the population than average activity volume. Reduced activity in females contributes to a large portion of the observed activity inequality. Aspects of the built environment, such as the walkability of a city, were associated with less gender gap in activity and activity inequality. In more walkable cities, activity is greater throughout the day and throughout the week, across age, gender, and body mass index (BMI) groups, with the greatest increases in activity for females. Our findings have implications for global public health policy and urban planning and highlight the role of activity inequality and the built environment for improving physical activity and health. Physical activity improves musculoskeletal health and function, prevents cognitive decline, reduces symptoms of depression and anxiety, and helps maintain a healthy weight 4, 7 . While Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature. Extended Data Figure 1. Activity and obesity data gathered with smartphones exhibit well established trends (a) Daily step counts across age and (b) BMI groups for all users. Error bars correspond to bootstrapped 95% confidence intervals. Observed trends in the dataset are consistent with previous findings; that is, activity decreases with increasing age 1, 8, 31, 32 and BMI 8, 15, 31 , and is lower in females than in males 1, 8, 31-33 . Althoff et al. Figure 2 . Activity and obesity data gathered with smartphones are significantly correlated with previously reported estimates based on self-report (a) WHO physical activity measure 34 versus smartphone activity measure. The WHO measure corresponds to the percentage of the population meeting the WHO guidelines for moderate to vigorous physical activity based on self-report. The smartphone activity measure is based on accelerometer-defined average daily steps. We find a correlation of r=0.3194 between the two measures (p < 0.05). Note that this comparison is limited because there is no direct correspondence between the two measures-values of self-report and accelerometer-defined activity can differ 14 , and the WHO confidence intervals are very large for many countries (Methods). (b) WHO obesity estimates 35 , based on self-reports to survey conductors, versus obesity estimates in our dataset, based on height and weight reported to the activity-tracking app. We find a significant correlation of r=0.691 between the two estimates (p < 10 −6 ). (c) Gender gap in activity estimated from smartphones is strongly correlated with previously reported estimates based on self-report. We find that the difference in average steps per day between females and males is strongly correlated to the Althoff et al. Extended Data
doi:10.1038/nature23018 pmid:28693034 pmcid:PMC5774986 fatcat:ig34vixlkzei5nkplijluedrde