Smartphone Google Location History: A Novel Approach to Outdoor Physical Activity Research
Researchers tested whether Google Location History (GLH) from smartphones could be used to measure outdoor physical activity. They analyzed data from 357 people over about 4 years, looking at walking, driving, cycling, and running patterns. When compared to traditional activity monitors, GLH accurately identified different types of movement. Importantly, people who walked more according to their GLH data were less likely to be obese. This suggests that smartphone location data could be a useful new tool for studying physical activity patterns in large populations over long periods.
College of Health researcher(s)
Abstract
Background
Outdoor physical activity (PA) is an important component of overall health; however, it is difficult to measure. Passively collected smartphone location data like Google Location History (GLH) present an opportunity to address this issue.
Objectives
To evaluate the use of GLH data for measuring outdoor PA.
Methods
We collected GLH data for 357 individuals from the Washington State Twin Registry. We first summarized GLH measurements relevant to outdoor PA. Next, we compared accelerometer measurements to GLH classified PA for a subset of 25 participants who completed 2 weeks of global positioning system and accelerometer monitoring. Finally, we examined the association between GLH measured walking and obesity.
Results
Participants provided a mean (SD) average 52 (18.8) months of GLH time-activity data, which included a mean (SD) average of 2421 (1632) trips per participant. GLH measurements were classified as the following: 79,994 unique walking trips (11.6% of all trips), 564,558 (81.8%) trips in a passenger vehicle, 11,974 cycling trips (1.7%), and 890 running trips (0.1%). Sixty-two percent of these trips had location accuracy >80%. In the accelerometry evaluation, GLH walking trips had a corresponding mean vector magnitude of 3150 counts per minute, compared with 489 counts per minute for vehicle trips. In adjusted cross-sectional analyses, we observed an inverse association between both walking minutes and trips per month and the odds of being obese (odds ratio = 0.78; 95% CI, 0.60-0.96, and odds ratio = 0.91; 95% CI, 0.82-0.98, respectively).
Conclusions
GLH data provide a novel method for measuring long-term, retrospective outdoor PA that can provide new opportunities for PA research.