Health Mashups: Presenting Statistical Patterns between Wellbeing Data and Context in Natural Language to Promote Behavior Change

Authors:
Frank Bentley;Konrad Tollmar;Peter Stephenson;Laura Levy;Brian Jones;Scott Robertson;Ed Price;Richard Catrambone;Jeff Wilson
Affiliations:
Motorola Applied Research Center;Royal Institute of Technology;Humana;Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology;Georgia Institute of Technology
Venue:
ACM Transactions on Computer-Human Interaction (TOCHI)
Year:
2013

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Abstract

People now have access to many sources of data about their health and wellbeing. Yet, most people cannot wade through all of this data to answer basic questions about their long-term wellbeing: Do I gain weight when I have busy days? Do I walk more when I work in the city? Do I sleep better on nights after I work out? We built the Health Mashups system to identify connections that are significant over time between weight, sleep, step count, calendar data, location, weather, pain, food intake, and mood. These significant observations are displayed in a mobile application using natural language, for example, “You are happier on days when you sleep more.” We performed a pilot study, made improvements to the system, and then conducted a 90-day trial with 60 diverse participants, learning that interactions between wellbeing and context are highly individual and that our system supported an increased self-understanding that lead to focused behavior changes.