Behavioral epidemiology studies how lifestyle and behavior relate to the occurrence of a disease and evaluates interventions aimed at changing unhealthy behaviors, such as overeating or smoking. Numerous diseases provide early evidence of their onset from changes in behavior, long before confirmed by clinical studies. Mobile phones include a variety of sensors that can be used to gather data about users' behavior, such as the places they visit, their level of activity, and how frequently and with whom they socialize. The collection and analysis of these data have been the focus of recent attention in pervasive computing, giving rise to the field known as mobile sensing. We present a collaborative mobile sensing toolkit, named InCense, developed to facilitate behavioral data gathering from populations of mobile phone users. InCense aims at providing people with little or no technical background with a tool that assists in the rapid design and implementation of mobile phone sensing campaigns. To illustrate the use of this technology, we describe two sensing campaigns conducted with InCense. The first study aims at detecting behaviors associated with the frailty syndrome among older adults and includes 15 participants monitored during a 3-week period. The second study uses mostly audio data to detect disruptive behaviors among people suffering from dementia. In describing these studies, we highlight some of the technical and social challenges of conducting collaborative mobile sensing campaigns that could be used to develop behavior-aware healthcare systems for applications that range from providing early diagnosis, uncovering lifestyle causes of mobility, inducing behavior change, helping in disease management, to dealing with problematic behaviors.
|Title of host publication||Wireless Computing in Medicine|
|Subtitle of host publication||From Nano to Cloud with Ethical and Legal Implications|
|Number of pages||29|
|State||Published - 1 Jul 2016|
Bibliographical notePublisher Copyright:
© 2016 John Wiley & Sons, Inc. All rights reserved.
- Collaborative sensing
- Detecting problematic behaviors
- Frailty syndrome
- Mobile phone sensing
- Opportunistic sensing