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      Large-scale physical activity data reveal worldwide activity inequality

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          Abstract

          Understanding the basic principles that govern physical activity is needed to curb the global pandemic of physical inactivity 17 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 dataset 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.

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          Most cited references 32

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          Is Open Access

          Understanding individual human mobility patterns

          Despite their importance for urban planning, traffic forecasting, and the spread of biological and mobile viruses, our understanding of the basic laws governing human motion remains limited thanks to the lack of tools to monitor the time resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six month period. We find that in contrast with the random trajectories predicted by the prevailing Levy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time independent characteristic length scale and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent based modeling.
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            On the measurement of inequality

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              Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures.

               S Golder,  M Macy (2011)
              We identified individual-level diurnal and seasonal mood rhythms in cultures across the globe, using data from millions of public Twitter messages. We found that individuals awaken in a good mood that deteriorates as the day progresses--which is consistent with the effects of sleep and circadian rhythm--and that seasonal change in baseline positive affect varies with change in daylength. People are happier on weekends, but the morning peak in positive affect is delayed by 2 hours, which suggests that people awaken later on weekends.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                Nature
                0028-0836
                1476-4687
                11 January 2018
                10 July 2017
                20 July 2017
                19 January 2018
                : 547
                : 7663
                : 336-339
                Affiliations
                [1 ]Computer Science Department, Stanford University
                [2 ]Department of Bioengineering, Stanford University
                [3 ]Department of Health Research & Policy, Stanford University School of Medicine
                [4 ]Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine
                [5 ]Department of Mechanical Engineering, Stanford University
                [6 ]Chan Zuckerberg Biohub, San Francisco, CA
                Author notes
                [* ]To whom correspondence should be addressed; jure@ 123456cs.stanford.edu
                Article
                NIHMS882836
                10.1038/nature23018
                5774986
                28693034

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