%0 Journal Article
%T Best practices for analyzing large-scale health data from wearables and smartphone apps
%A Abby C. King
%A Bojan Bostjancic
%A Jennifer L. Hicks
%A Jure Leskovec
%A Peter Kuhar
%A Rok Sosic
%A Scott L. Delp
%A Tim Althoff
%J Archive of "NPJ Digital Medicine".
%D 2019
%R 10.1038/s41746-019-0121-1
%X Datasets from apps and wearables are helping researchers identify novel worldwide trends in activity and health. Our team has analyzed data from 717,527 users of the Argus app for tracking physical activity and other health metrics.11 This analysis revealed worldwide inequality in levels of physical activity that varied from country to country. In the map, country area is scaled by the country¡¯s obesity rate, as calculated from the app-reported BMI of users. The countries are shaded according to activity inequality, where warm colors (reds and oranges) indicate high levels of activity inequality (some people are very active and some people are minimally active) and cool colors (blues) indicate low levels of activity inequality (individuals within the country get similar levels of activity). Countries with larger than normal areas (indicative of high obesity) also tend to be shaded with warm colors (indicative of high activity inequality). The map was generated using the Scape Toad software63 and the world borders dataset from the Thematic Mapping API6
%K Data mining
%K Statistical methods
%K Health sciences
%U https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6550237/