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

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...

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Bibliographic Details
Published inProceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference) Vol. 2014; p. 909
Main Authors Sarker, Hillol, Sharmin, Moushumi, Ali, Amin Ahsan, Rahman, Md Mahbubur, Bari, Rummana, Hossain, Syed Monowar, Kumar, Santosh
Format Journal Article Conference Proceeding
LanguageEnglish
Published United States 01.01.2014
Subjects
Intervention
Mobile Application
EMA
Mobile Health
Interruption
Self-Report
Online AccessGet full text
DOI10.1145/2632048.2636082

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Summary: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.
DOI:10.1145/2632048.2636082