Assessing the Availability of Users to Engage in Just-in-Time Intervention in the Natural Environment

Proceedings of the ... ACM International Conference on Ubiquitous Computing
Hillol SarkerSantosh Kumar

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.

Citations

Sep 14, 2015·Current Obesity Reports·D Spruijt-MetzS Narayanan
Mar 10, 2015·Internet Interventions : the Application of Information Technology in Mental and Behavioural Health·Brian G DanaherTim Woolley
Sep 25, 2016·Annals of Behavioral Medicine : a Publication of the Society of Behavioral Medicine·Inbal Nahum-ShaniSusan A Murphy
Feb 15, 2017·Journal of Medical Internet Research·Helga ÞórarinsdóttirMaria Faurholt-Jepsen
Feb 25, 2021·JMIR Research Protocols·Laura M HernandezChristine Vinci
Mar 9, 2020·Psychiatry Research·Aki RintalaWolfgang Viechtbauer
Dec 1, 2019·Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies·Mashfiqui RabbiSusan Murphy

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