Currently submitted to: JMIR mHealth and uHealth
Date Submitted: Aug 30, 2019
Open Peer Review Period: Aug 30, 2019 - Oct 1, 2019
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Wear-IT: Low-burden Mobile Monitoring and Intervention through Real-time Analysis
Mobile health methods often rely on active input from participants, for example in the form of self-report questionnaires delivered via web or smartphone, to measure health and behavioral indicators and deliver interventions in everyday life settings. For short-term studies or interventions, these techniques are deployed intensively, causing nontrivial participant burden. For cases of where the goal is long-term maintenance, limited infrastructure exists to balance information needs with participant constraints. Yet the increasing precision of passive sensors such as wearable physiology monitors, smartphone-based location history, and Internet-of-Things (IoT) devices, in combination with statistical feature selection and adaptive interventions have begun to make such things possible. In this paper, we introduce Wear-IT, a smartphone app and cloud framework intended to begin to fill this gap by allowing researchers to leverage commodity electronics and real-time decision-making to optimize the amount of useful data collected while minimizing participant burden.
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