Large-scale physical activity data reveal worldwide activity inequality

  • Nature volume 547, pages 336339 (20 July 2017)
  • doi:10.1038/nature23018
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To be able to curb the global pandemic of physical inactivity1,2,3,4,5,6,7 and the associated 5.3 million deaths per year2, we need to understand the basic principles that govern physical activity. However, there is a lack of large-scale measurements of physical activity patterns across free-living populations worldwide1,6. Here we leverage the wide usage of smartphones with built-in accelerometry to measure physical activity at the global 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, are associated with a smaller gender gap in activity and lower 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 found 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 in improving physical activity and health.

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Further information and data are available at http://activityinequality.stanford.edu. We thank Azumio for donating the data for independent research, and T. Uchida and W. Hamilton for comments and discussions. T.A., R.S., J.L.H., A.C.K., S.L.D. and J.L. were supported by a National Institutes of Health (NIH) grant (U54 EB020405, Mobilize Center, NIH Big Data to Knowledge Center of Excellence). T.A. was supported by the SAP Stanford Graduate Fellowship. J.L.H. and S.L.D. were supported by grants R24 HD065690 and P2C HD065690 (NIH National Center for Simulation in Rehabilitation Research). J.L. and R.S. were supported by NSF grant IIS-1149837 and the Stanford Data Science Initiative. J.L. is a Chan Zuckerberg Biohub investigator.

Author information


  1. Computer Science Department, Stanford University, Stanford, California, USA

    • Tim Althoff
    • , Rok Sosič
    •  & Jure Leskovec
  2. Department of Bioengineering, Stanford University, Stanford, California, USA

    • Jennifer L. Hicks
    •  & Scott L. Delp
  3. Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA

    • Abby C. King
  4. Stanford Prevention Research Center, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA

    • Abby C. King
  5. Department of Mechanical Engineering, Stanford University, Stanford, California, USA

    • Scott L. Delp
  6. Chan Zuckerberg Biohub, San Francisco, California, USA

    • Jure Leskovec


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T.A. performed the statistical analysis. T.A., R.S., J.L.H., A.C.K., S.L.D. and J.L. jointly analysed the results and wrote the paper.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Jure Leskovec.

Reviewer Information Nature thanks N. Christakis, P. Hallal, J. Han and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data

Supplementary information

PDF files

  1. 1.

    Supplementary Tables

    This file contains tables 1-3. Table 1 shows a summary of dataset statistics for the 46 countries with more than 1000 subjects. Table 2 shows the United States cities sorted by their walk scores. Table 3 shows three United States cities in close geographic proximity. Table 4 shows number of subjects for each city and group used in the walkability analysis.


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