Iris N. Gomez-Lopez

Objective: Initiatives to reduce neighborhood-based health disparities require access to meaningful, timely, and local information regarding health behavior and its determinants. We examined the validity of Twitter as a source of information for neighborhood-level analysis of dietary choices and attitudes.Materials and Methods: We analyzed the “healthiness” quotient and sentiment in food-related tweets at the census tract level, and associated them with neighborhood characteristics and health outcomes. We analyzed keywords driving the differences in food healthiness between the most and least-affluent tracts, and qualitatively analyzed contents of a random sample of tweets.Results: Significant, albeit weak, correlations existed between healthiness and sentiment in food-related tweets and tract-level measures of affluence, disadvantage, race, age, U.S. density, and mortality from conditions associated with obesity. Analyses of keywords driving the differences in food healthiness revealed foods high in saturated fat (eg, pizza, bacon, fries) were mentioned more frequently in less-affluent tracts. Food-related discussion referred to activities (eating, drinking, cooking), locations where food was consumed, and positive (affection, cravings, enjoyment) and negative attitudes (dislike, personal struggles, complaints).Discussion: Tweet-based healthiness scores largely correlated with offline phenomena in the expected directions. Social media offer less resource-intensive data collection methods than traditional surveys do. Twitter may assist in informing local health programs that focus on drivers of food consumption and could inform interventions focused on attitudes and the food environment.Conclusions: Twitter provided weak but significant signals concerning food-related behavior and attitudes at the neighborhood level, suggesting its potential usefulness for informing local health disparity reduction efforts.

Mobile and tablet apps, ubiquitous and pervasive computing, wearable computing and domotics for health.

BACKGROUND Mobile devices are increasingly used to collect location-based information from individuals about their physical activities, dietary intake, environmental exposures, and mental well-being. Such research, which typically uses wearable devices or smartphones to track location, benefits from the growing availability of fine-grained data regarding human mobility. However, little is known about the comparative geospatial accuracy of such devices. OBJECTIVE In this study, we compared the data quality of location information collected from two mobile devices which determine location in different ways — a GPS watch and a smartphone with Google’s Location History feature enabled. METHODS Twenty-one chronically ill participants carried both devices, which generated digital traces of locations, for 28 days. A smartphone-based brief ecological momentary assessment (EMA) survey asked participants to manually report their location at four random times throughout each day. Participants also took also part in qualitative interviews and completed surveys twice during the study period in which they reviewed recent phone and watch trace data to compare the devices’ trace data to their memory of their activities on those days. Trace data from the devices were compared on the basis of: (1) missing data days; (2) reasons for missing data; (3) distance between the route data collected for matching day and the associated EMA survey locations; and (4) activity space total area and density surfaces. RESULTS The watch resulted in a much higher proportion of missing data days, with missing data explained by technical differences between the devices, as well as participant behaviors. The phone was significantly more accurate in detecting home locations, and marginally significantly more accurate for all types of locations combined. The watch data resulted in a smaller activity space area and more accurately recorded outdoor travel and recreation. CONCLUSIONS The most suitable mobile device for location based health research depends on the particular study objectives. Further, data generated from mobile devices, such as GPS phone and smart watches, requires careful analysis to ensure quality and completeness. Studies that seek precise measurement of outdoor activity and travel, such as measuring outdoor physical activity or exposure to localized environmental hazards, would benefit from use of GPS devices. Conversely, studies that aim to account for time within buildings at home or work, or that document visits to particular places (such as supermarkets, medical facilities, or fast food restaurants), would benefit from the phone’s demonstrated greater precision in recording indoor activities. CLINICALTRIAL N/A