Improving Medical Outcomes through At-Home, Longitudinal Health Monitoring

Ubiquitous Computing and Human-Computer Interaction health researchers motivate their work with the promise of improving health outcomes for patients. Although computing devices are producing more health data than ever, turning this data into actionable insights remains a challenge. In this Doctoral Colloquium submission, I argue that to improve health outcomes, medical systems must not only produce health data, but also provide interpretations of that health data personalized to the individual in order to deliver effective, actionable feedback. To illustrate this approach, I introduce two projects: Beacon and ExerciseRx-CP. Beacon is a system that screens for minimal hepatic encephalopathy through a novel critical flicker frequency (CFF) measure. I use Beacon's CFF measure as an illustration of defining ways to interpret new health data by incorporating personal baselines and longitudinal measurements. ExerciseRx-CP is a system that uses real-time feedback of exercise tracking to encourage physical activity among adolescents with cerebral palsy. I use ExerciseRx-CP as a way to show how motion data can be interpreted on multiple levels (e.g., raw sensor signals, subrepetition motion, and aggregated repetition metrics) to deliver different feedback mechanisms. Finally, I describe how an enabling activities framework can help Ubicomp researchers characterize activities necessary for translational research, promote more translational research to be conducted our community, and ultimately create greater impact as a field.