NDLI: Assessing the availability of users to engage in just-in-time intervention in the natural environment

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Assessing the availability of users to engage in just-in-time intervention in the natural environment

Content Provider	ACM Digital Library
Author	Sharmin, Moushumi Rahman, Md. Mahbubur Hossain, Syed Monowar Kumar, Santosh Ali, Amin Ahsan Sarker, Hillol Bari, Rummana
Abstract	Wearable wireless sensors for health monitoring are enabling the design and delivery of just-in-time interventions (JITI). Critical to the success of JITI is to time its delivery so that the user is available to be engaged. We take a first step in modeling users' availability by analyzing 2,064 hours of physiological sensor data and 2,717 self-reports collected from 30 participants in a week-long field study. We use delay in responding to a prompt to objectively measure availability. We compute 99 features and identify 30 as most discriminating to train a machine learning model for predicting availability. We find that location, affect, activity type, stress, time, and day of the week, play significant roles in predicting availability. We find that users are least available at work and during driving, and most available when walking outside. Our model finally achieves an accuracy of 74.7% in 10-fold cross-validation and 77.9% with leave-one-subject-out.
Starting Page	909
Ending Page	920
Page Count	12
File Format	PDF QT / MOV
ISBN	9781450329682
DOI	10.1145/2632048.2636082
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-09-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Intervention Interruption Mobile health Ema Mobile application Self-report
Content Type	Video Text
Resource Type	Article

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