Assessing the Availability of Users to Engage in Just-in-time Intervention in the Natural Environment. Sarker, H., Sharmin, M., Ali, A., A., Md, Bari, R., Hossain, S., M., & Kumar, S. In Proceedings of the ACM International Joint Conference on Pervasive and Ubiquitous Computing, of UbiComp, pages 909-920, 2014. ACM. ![Assessing the Availability of Users to Engage in Just-in-time Intervention in the Natural Environment [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website abstract bibtex 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.
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title = {Assessing the Availability of Users to Engage in Just-in-time Intervention in the Natural Environment},
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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.},
bibtype = {inProceedings},
author = {Sarker, Hillol and Sharmin, Moushumi and Ali, Amin A and Md, undefined and Bari, Rummana and Hossain, Syed M and Kumar, Santosh},
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