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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Hallal, P. C. et al. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet 380, 247–257 (2012)
Lee, I.-M. et al. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet 380, 219–229 (2012)
UN Secretary General. Prevention and control of non-communicable diseases. http://www.who.int/nmh/publications/2011-report-of-SG-to-UNGA.pdf (Regional Office for South-East Asia, World Health Organisation, 2011); accessed 21 April 2016
World Health Organization (WHO). Global Recommendations On Physical Activity For Health. http://www.who.int/dietphysicalactivity/publications/9789241599979/en/ (WHO, 2010); accessed 21 April 2016
Kohl, H. W. et al. The pandemic of physical inactivity: global action for public health. Lancet 380, 294–305 (2012)
Tudor-Locke, C., Hatano, Y., Pangrazi, R. P. & Kang, M. Revisiting “how many steps are enough?”. Med. Sci. Sports Exerc. 40, S537–S543 (2008)
Sallis, J. F. et al. Progress in physical activity over the Olympic quadrennium. Lancet 388, 1325–1336 (2016)
Bauman, A. E. et al. Correlates of physical activity: why are some people physically active and others not? Lancet 380, 258–271 (2012)
Physical Activity Guidelines Advisory Committee. Physical Activity Guidelines Report. https://health.gov/paguidelines/report/pdf/CommitteeReport.pdf (US Department of Health and Human Services, 2008); accessed 21 April 2016
Chokshi, D. A. & Farley, T. A. Changing behaviors to prevent noncommunicable diseases. Science 345, 1243–1244 (2014)
Sallis, J. F. et al. Physical activity in relation to urban environments in 14 cities worldwide: a cross-sectional study. Lancet 387, 2207–2217 (2016)
Servick, K. Mind the phone. Science 350, 1306–1309 (2015)
Reis, R. S. et al. Scaling up physical activity interventions worldwide: stepping up to larger and smarter approaches to get people moving. Lancet 388, 1337–1348 (2016)
Prince, S. A. et al. A comparison of direct versus self-report measures for assessing physical activity in adults: a systematic review. Int. J. Behav. Nutr. Phys. Act. 5, 56 (2008)
Van Dyck, D. et al. International study of objectively measured physical activity and sedentary time with body mass index and obesity: IPEN adult study. Int. J. Obes. 39, 199–207 (2015)
Walch, O. J., Cochran, A. & Forger, D. B. A global quantification of “normal” sleep schedules using smartphone data. Sci. Adv. 2, e1501705 (2016)
González, M. C., Hidalgo, C. A. & Barabási, A.-L. Understanding individual human mobility patterns. Nature 453, 779–782 (2008); addendum 458, 238 (2009)
Golder, S. A. & Macy, M. W. Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333, 1878–1881 (2011)
Wesolowski, A. et al. Quantifying the impact of human mobility on malaria. Science 338, 267–270 (2012)
Blumenstock, J., Cadamuro, G. & On, R. Predicting poverty and wealth from mobile phone metadata. Science 350, 1073–1076 (2015)
Anthes, E. Mental health: there’s an app for that. Nature 532, 20–23 (2016)
Case, M. A., Burwick, H. A., Volpp, K. G. & Patel, M. S. Accuracy of smartphone applications and wearable devices for tracking physical activity data. J. Am. Med. Assoc. 313, 625–626 (2015)
Hekler, E. B. et al. Validation of physical activity tracking via android smartphones compared to ActiGraph accelerometer: laboratory-based and free-living validation studies. JMIR mHealth uHealth 3, e36 (2015)
Atkinson, A. B. On the measurement of inequality. J. Econ. Theory 2, 244–263 (1970)
Allison, P. D. Measures of inequality. Am. Sociol. Rev. 43, 865–880 (1978)
Brown, W. J., Mielke, G. I. & Kolbe-Alexander, T. L. Gender equality in sport for improved public health. Lancet 388, 1257–1258 (2016)
Wagstaff, A. & Van Doorslaer, E. Income inequality and health: what does the literature tell us? Annu. Rev. Public Health 21, 543–567 (2000)
Lynch, J. W., Smith, G. D., Kaplan, G. A. & House, J. S. Income inequality and mortality: importance to health of individual income, psychosocial environment, or material conditions. Br. Med. J. 320, 1200 (2000)
Centers for Disease Control and Prevention (CDC) Vital Signs: More People Walk to Better Health. http://www.cdc.gov/vitalsigns/walking/ (2012); accessed 3 November 2016
Stuart, E. A. Matching methods for causal inference: a review and a look forward. Stat. Sci. 25, 1–21 (2010)
Bassett, D. R., Wyatt, H. R., Thompson, H., Peters, J. C. & Hill, J. O. Pedometer-measured physical activity and health behaviors in U.S. adults. Med. Sci. Sports Exerc. 42, 1819–1825 (2010)
Troiano, R. P. et al. Physical activity in the United States measured by accelerometer. Med. Sci. Sports Exerc. 40, 181–188 (2008)
Tudor-Locke, C., Johnson, W. D. & Katzmarzyk, P. T. Accelerometer-determined steps per day in US adults. Med. Sci. Sports Exerc. 41, 1384–1391 (2009)
World Health Organization. Prevalence of Insufficient Physical Activity among Adults: Data by Country. http://apps.who.int/gho/data/node.main.A893?lang=en (Global Health Observatory data repository, WHO, accessed 19 May 2016)
World Health Organization. Obesity (Body Mass Index ≥ 30) (Age-Standardized Estimate): Estimates by Country. http://apps.who.int/gho/data/node.main.A900A?lang=en. (Global Health Observatory data repository, WHO, accessed 19 May 2016)
Basner, M. et al. American time use survey: sleep time and its relationship to waking activities. Sleep 30, 1085–1095 (2007)
De Maio, F. G. Income inequality measures. J. Epidemiol. Community Health 61, 849–852 (2007)
Kawachi, I. & Kennedy, B. P. The relationship of income inequality to mortality: does the choice of indicator matter? Soc. Sci. Med. 45, 1121–1127 (1997)
Steiger, J. H. Tests for comparing elements of a correlation matrix. Psychol. Bull. 87, 245–251 (1980)
CIA World Factbook. Field Listing: Median Age https://www.cia.gov/library/publications/the-world-factbook/fields/2177.html (CIA, accessed 22 June 2017)
World Bank. World Bank Country and Lending Groups. https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups (World Bank, accessed 5 October 2016)
World Bank. Population, Female (% of Total). http://data.worldbank.org/indicator/SP.POP.TOTL.FE.ZS (World Bank, accessed 10 May 2016)
Efron, B. & Tibshirani, R. J. An Introduction to the Bootstrap (CRC Press, 1994)
Walk Score . https://www.walkscore.com/cities-and-neighborhoods/ (Walk Score, accessed 17 May 2016)
Duncan, D. T., Aldstadt, J., Whalen, J., Melly, S. J. & Gortmaker, S. L. Validation of Walk Score for estimating neighborhood walkability: an analysis of four US metropolitan areas. Int. J. Environ. Res. Public Health 8, 4160–4179 (2011)
United States Census Bureau (USCB). American Community Survey. http://www.census.gov/programs-surveys/acs/ (USCB, accessed 5 October 2016)
United States Census Bureau (USCB). Bay Area Census. 2010 Census and American Community Survey 2006-2010. http://www.bayareacensus.ca.gov/cities/cities.htm (accessed 5 July 2016)
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.
The authors declare no competing financial interests.
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.
Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Figure 1 Activity and obesity data gathered with smartphones exhibit well established trends.
Daily step counts are shown across age (a) and BMI groups (b) 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 age1,8,31,32 and BMI8,15,31, and is lower in females than in males1,8,31,32,33.
Extended Data Figure 2 Activity and obesity data gathered with smartphones are significantly correlated with previously reported estimates based on self-report.
a, WHO physical activity measure34 versus smartphone activity measure (LOESS fit). 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 differ14, and the WHO confidence intervals are very large for many countries (Methods). b, WHO obesity estimates35, based on self-reports to survey conductors, versus obesity estimates in our dataset, based on height and weight reported to the activity-tracking app (LOESS fit). 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 (LOESS fit). We find that the difference in average steps per day between females and males is strongly correlated to the difference in the fraction of each gender who report being sufficiently active according to the WHO (r = 0.52, P < 10−3).
Extended Data Figure 3 Activity inequality remains a strong predictor of obesity levels across countries when reweighting the sample based on officially reported gender distributions and when stratifying by gender or age.
a, Obesity versus activity inequality on a country level where subjects are reweighted to accurately reflect the official gender distribution in each country (LOESS fit; Methods). The gender-unbiased estimates are very similar to estimates using all data (r = 0.953 for activity inequality and r = 0.986 for obesity). b, Obesity versus activity inequality on a country level for males and females. Activity inequality predicts obesity for both genders (LOESS fit). c, Obesity versus activity inequality on a country level across different age groups. We find that associations between activity inequality and obesity persist within every age group (LOESS fits). These results indicate that our main result—activity inequality predicts obesity—is independent of any potential gender and age bias in our sample.
Extended Data Figure 4 Relationship between activity inequality and obesity holds within countries of similar income.
Of the 46 countries included in Fig. 2a, we have 32 high-income (green) and 14 middle-income (orange) countries according to the current World Bank classification40. We find that activity inequality is a strong predictor of obesity levels in both high-income countries and middle-income countries (LOESS fit). Although in middle-income countries, iPhone users might belong to the wealthiest in the population, in high-income countries iPhones are used by a larger proportion of the population. That we find a strong relationship between activity inequality and obesity in both groups of countries suggests that our findings are robust to differences in wealth in our sample.
Extended Data Figure 5 Graphical definition of activity inequality measure using the Gini coefficient.
The Lorenz curve plots the share of total physical activity of the population on the y axis that is cumulatively performed by the bottom X% of the population, ordered by physical activity level. The diagonal line at 45 degrees represents perfect equality of physical activity (that is, everyone in the population is equally active). The Gini coefficient is defined as the ratio of the area that lies between the line of equality and the Lorenz curve (marked A in the diagram) to the total area under the line of equality (marked A and B in the diagram). The Gini coefficient for physical activity can range from 0 (complete equality) to 1 (complete inequality).
Extended Data Figure 6 Activity inequality is a better predictor of obesity than the average activity level.
a, Obesity is significantly correlated with the average number of daily steps in each country (LOESS fit; R2 = 0.47). b, However, activity inequality is the better predictor of obesity (LOESS fit; R2 = 0.64). The difference is significant according to Steiger’s Z-test (P < 0.01; Methods). This shows that there is value to measuring and modelling physical activity across countries beyond average activity levels. Activity inequality captures the variance of the distribution; that is, how many activity-rich and activity-poor people there are, allowing for better prediction of obesity levels. Panel b is repeated from Fig. 2a for comparison.
Extended Data Figure 7 Female activity is reduced disproportionately in countries with high activity inequality.
a, Distribution of daily steps for females, males, and all users in representative countries of increasing activity inequality (Japan, the UK, the USA and Saudi Arabia). While in countries with low activity inequality females and males have very similar levels of activity (for example, Japan), the distributions of female and male activity differ greatly for countries with high activity inequality (for example, Saudi Arabia and the USA). Activity distributions in these countries demonstrate that larger variances in activity (Fig. 1c) are due to a disproportionate reduction in the activity of females and not just an increase in variance overall. b, Activity inequality increases with the relative activity gender gap on a country level (linear fit; Methods). We find that the relative gender gap ranges between 0.041 (Sweden) and 0.380 (Qatar). The average of daily steps for females is lower than for males in all 46 countries. The gender gap explains 43% of the observed variance in activity inequality (linear fit; R2 = 0.43). This suggests that activity inequality could be greatly reduced through increases in female activity alone.
Extended Data Figure 8 Interventions focused on reducing activity inequality could result in reductions in obesity prevalence up to four times greater than for population-wide approaches.
Given a fixed activity budget (100 daily steps per individual) to distribute across the population, we compare an inequality-centric strategy that distributes this budget equally to minimize activity inequality (100/X% daily steps increase for the activity-poorest X% where X minimizes the country’s resulting activity inequality; Methods) and a population-wide strategy which distributes the budget equally across the entire population (100 daily steps per individual; Methods). From our simulations, we find that the inequality-centric strategy would lead to predicted reductions in obesity prevalence of up to 8.3% (median 4.0%), whereas the population-wide approach would lead to predicted reductions of up to 2.3% (median 1.0%). Lines correspond to LOESS fits.
Extended Data Figure 9 Relationship between walkability and activity inequality holds within cities in the USA of similar income.
Walkable environments are associated with lower levels of activity inequality within socioeconomically similar groups of cities. We group the 69 cities into quartiles based on median household income (data from the 2015 American Community Survey46). We find that walkable environments are associated with lower levels of activity inequality for all four groups (LOESS fit). The effect appears to be attenuated for cities in the lowest median household income quartile. These results suggest that our main result—activity inequality predicts obesity and is mediated by factors of the physical environment—is independent of any potential socioeconomic bias in our sample.
Extended Data Figure 10 Differences in country level daily steps are not explained by differences in estimated wear time.
Users have an average span of 14.0 h between the first and last recorded step, our proxy for daily wear time (Methods). While on an individual level, longer estimated wear time is associated with more daily steps (r = 0.427, P < 10−10), on a country level, there is no significant association between wear time and daily steps (r = −0.086, P = 0.57). The line shows the linear fit using the 46 countries with at least 1,000 users. This suggests that differences in recorded steps between countries are due to actual differences in physical activity behaviour and are not explained by differences in wear time.
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. (PDF 474 kb)
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Althoff, T., Sosič, R., Hicks, J. et al. Large-scale physical activity data reveal worldwide activity inequality. Nature 547, 336–339 (2017). https://doi.org/10.1038/nature23018
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