Using a larger dataset than for any previous human movement study, National Institutes of Health-funded researchers at Stanford University (Palo Alto, California), have tracked physical activity by population for more than 100 countries. Their research follows on a recent estimate that more than 5 million people die each year from causes associated with inactivity.
The large-scale study of daily step data from anonymous smartphone users dials in on how countries, genders, and community types fare in terms of physical activity and what results may mean for intervention efforts around physical activity and obesity. The study exemplifies how smartphones can deliver new insights about key health behaviors, including what the authors categorize as the global pandemic of physical inactivity.
“Data science and modeling can be immensely powerful tools. They can aid in harnessing and analyzing all the personalized data that we get from our phones and wearable devices.”
Some three quarters of adults in developed countries and half of adults in developing economies carry a smartphone. The devices are equipped with tiny accelerometers – a computer chip that maintains the orientation of the screen, but can also automatically record stepping motions. The users whose data contributed to this study subscribed to the Azumio Argus app, a free application for tracking physical activity and other health behaviors.
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Smartphone data from more than 68 million days of activity by 717,527 individuals across 111 countries reveal variability in physical activity around the world. Credit: T Althoff et al, Stanford University.
According to Delp, the study is 1,000 times larger than any previous study on human movement. “There have been wonderful health surveys done, but our new study provides data from more countries, many more subjects, and tracks people’s activity on an ongoing basis in their free-living environments versus a survey in which you rely on people to self-report their activity. This opens the door to new ways of doing science at a much larger scale than we have been able to do before.”
“These results reveal how much of a population is activity-rich, and how much of a population is activity-poor,” Delp said. “In regions with high activity inequality there are many people who are activity poor, and activity inequality is a strong predictor of health outcomes.”
Globally, the average user recorded about 5,000 steps per day. The smartphone data reflected the degree of difference, or inequality, for activity among people within a given country. By comparing countries with more uniform activity patterns and those with unequal activity, certain patterns and health dynamics emerged.
For instance, countries with the greatest activity inequality are also the countries with the greatest obesity problem. Individuals in the five countries with the greatest activity inequality are nearly 200 percent more likely to be obese than individuals from the five countries with the lowest activity inequality.
The average activity—measured in steps counted by smartphone accelerometers—is equivalent for people in the United States and Mexico, for example. But individuals in the US reflect a wider range of activity levels than in Mexico. This wider gap between active and sedentary people corresponds with higher prevalence of obesity in the United States overall.
The researchers found out that countries with greater activity variation have a larger proportion of inactive women. In countries where activity is more uniform among members of the population, such as Japan, males and females are similarly active. But in countries with greater activity disparity, such as Saudi Arabia and the US, there is disproportionately reduced activity for females. This gender gap accounts for 43 percent of activity inequality in those countries.
The researchers investigated the idea that making improvements in a city’s walkability—creating an environment that is safe and enjoyable to walk—could reduce activity inequality and the activity gender gap. “If you must cross major highways to get from point A to point B in a city, the walkability is low; people rely on cars,” Delp said. “In cities like New York and San Francisco, where you can get across town on foot safely, the city has high walkability.”
On a related note, the U.S. Department of Health and Human Services last month announced their Healthy Behavior Data Challenge to tap into the potential of innovative data sources and alternative methodologies for public health surveillance. Click here for more information on the Healthy Behavior Data Challenge.
The large-scale study of daily step data from anonymous smartphone users dials in on how countries, genders, and community types fare in terms of physical activity and what results may mean for intervention efforts around physical activity and obesity. The study exemplifies how smartphones can deliver new insights about key health behaviors, including what the authors categorize as the global pandemic of physical inactivity.
Big data is about detecting patterns
“Big data is not just about big numbers, but also the patterns that can explain important health trends,” believes Grace Peng, Ph.D., director of the National Institute of Biomedical Imaging and Bioengineering (NIBIB) program in Computational Modeling, Simulation and Analysis.“Data science and modeling can be immensely powerful tools. They can aid in harnessing and analyzing all the personalized data that we get from our phones and wearable devices.”
Some three quarters of adults in developed countries and half of adults in developing economies carry a smartphone. The devices are equipped with tiny accelerometers – a computer chip that maintains the orientation of the screen, but can also automatically record stepping motions. The users whose data contributed to this study subscribed to the Azumio Argus app, a free application for tracking physical activity and other health behaviors.
Biggest study ever on human movement
Scott L. Delp, Ph.D., James H. Clark Professor of Bioengineering and director of the Mobilize Center at Stanford University, analyzed 68 million days of minute-by-minute step recordings from 717,527 anonymous users of the smartphone app together with his team. The researchers focused on 46 countries out of 111, each with at least 1,000 users. Of those, 90 percent of users were from 32 high income countries and 10 percent were from 14 middle income countries.Smartphone data from more than 68 million days of activity by 717,527 individuals across 111 countries reveal variability in physical activity around the world. Credit: T Althoff et al, Stanford University.
According to Delp, the study is 1,000 times larger than any previous study on human movement. “There have been wonderful health surveys done, but our new study provides data from more countries, many more subjects, and tracks people’s activity on an ongoing basis in their free-living environments versus a survey in which you rely on people to self-report their activity. This opens the door to new ways of doing science at a much larger scale than we have been able to do before.”
Activity-rich versus activity-poor
In addition to the step records, the researchers accessed age, gender, and height and weight status of users who registered the smartphone app. They used the same calculation that economists use for income inequality—called the Gini index—to calculate activity inequality by country.“These results reveal how much of a population is activity-rich, and how much of a population is activity-poor,” Delp said. “In regions with high activity inequality there are many people who are activity poor, and activity inequality is a strong predictor of health outcomes.”
Globally, the average user recorded about 5,000 steps per day. The smartphone data reflected the degree of difference, or inequality, for activity among people within a given country. By comparing countries with more uniform activity patterns and those with unequal activity, certain patterns and health dynamics emerged.
For instance, countries with the greatest activity inequality are also the countries with the greatest obesity problem. Individuals in the five countries with the greatest activity inequality are nearly 200 percent more likely to be obese than individuals from the five countries with the lowest activity inequality.
The average activity—measured in steps counted by smartphone accelerometers—is equivalent for people in the United States and Mexico, for example. But individuals in the US reflect a wider range of activity levels than in Mexico. This wider gap between active and sedentary people corresponds with higher prevalence of obesity in the United States overall.
Target for obesity intervention
The authors suggest that this inequality is an important target for obesity intervention. Using a computer simulation, they showed that targeted interventions could result in up to a four times greater reduction in obesity than non-targeted approaches.The researchers found out that countries with greater activity variation have a larger proportion of inactive women. In countries where activity is more uniform among members of the population, such as Japan, males and females are similarly active. But in countries with greater activity disparity, such as Saudi Arabia and the US, there is disproportionately reduced activity for females. This gender gap accounts for 43 percent of activity inequality in those countries.
The researchers investigated the idea that making improvements in a city’s walkability—creating an environment that is safe and enjoyable to walk—could reduce activity inequality and the activity gender gap. “If you must cross major highways to get from point A to point B in a city, the walkability is low; people rely on cars,” Delp said. “In cities like New York and San Francisco, where you can get across town on foot safely, the city has high walkability.”
On a related note, the U.S. Department of Health and Human Services last month announced their Healthy Behavior Data Challenge to tap into the potential of innovative data sources and alternative methodologies for public health surveillance. Click here for more information on the Healthy Behavior Data Challenge.
