Journal of physical activity & health · 2024
Smartphone Google Location History: A Novel Approach to Outdoor Physical Activity Research.
СтатьяTwin StudyНазвание и аннотация: Перевод в очереди
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Amram Ofer, Oje Olufunso, Larkin Andrew, Boakye Kwadwo, Avery Ally, Gebremedhin Assefaw, Williams Bethany, Duncan Glen E, Hystad Perry
Аннотация
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.
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.