Michael V. McConnell et al. JACC 2018;71:

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Michael V. McConnell et al. JACC 2018;71:2691-2701 Mobile Health Studies of Physical Activity Patterns and Promotion (A) Machine learning of physical activity patterns from >20,000 participants in the MyHeart Counts study found distinct activity clusters. The scatterplot on the left shows the proportion of time participants’ smartphones indicated they were stationary during weekdays and weekend days. The bar graph on the right compares the 4 main activity clusters, showing the 2 more active groups had lower prevalence of medical conditions (e.g., chest pain, self-reported heart disease). Adapted with permission from McConnell et al. (32). (B) Smartphone physical activity data from >700,000 individuals across the world show that the degree of activity inequality within a country is associated with higher obesity levels (R2 = 0.64). The graph on the right shows how the obesity-activity relationship differs between men (blue) and women (red), with the prevalence of obesity decreasing more rapidly for women as activity level (steps) increases. Adapted with permission from Althoff et al. (33). (C) Analysis of smartphone steps per day in 167 individuals before and after playing Pokémon GO showed a 35% increase in daily physical activity (p < 0.001). Adapted with permission from Xian et al. (50). IQR = interquartile range. Michael V. McConnell et al. JACC 2018;71:2691-2701 2018 American College of Cardiology Foundation