title: Assessing the Availability of Users to Engage in Just-in-Time Intervention in the Natural Environment.
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.
authors: Hillol
authors: H
authors: Sarker
authors: Moushumi
authors: M
authors: Sharmin
authors: Amin Ahsan
authors: AA
authors: Ali
authors: Md Mahbubur
authors: MM
authors: Rahman
authors: Rummana
authors: R
authors: Bari
authors: Syed Monowar
authors: SM
authors: Hossain
authors: Santosh
authors: S
authors: Kumar
keywords: EMA; Interruption; Intervention; Mobile Application; Mobile Health; Self-Report