title: Finding Significant Stress Episodes in a Discontinuous Time Series of Rapidly Varying Mobile Sensor Data.
abstract: Management of daily stress can be greatly improved by delivering sensor-triggered just-in-time interventions (JITIs) on mobile devices. The success of such JITIs critically depends on being able to mine the time series of noisy sensor data to find the most opportune moments. In this paper, we propose a time series pattern mining method to detect significant stress episodes in a time series of discontinuous and rapidly varying stress data. We apply our model to 4 weeks of physiological, GPS, and activity data collected from 38 users in their natural environment to discover patterns of stress in real-life. We find that the duration of a prior stress episode predicts the duration of the next stress episode and stress in mornings and evenings is lower than during the day. We then analyze the relationship between stress and objectively rated disorder in the surrounding neighborhood and develop a model to predict stressful episodes.
authors.affiliation: University of Memphis.
authors.forename: Hillol
authors.initials: H
authors.lastname: Sarker
authors.affiliation: NIDA Intramural Research Program.
authors.forename: Matthew
authors.initials: M
authors.lastname: Tyburski
authors.affiliation: University of Memphis.
authors.forename: Md Mahbubur
authors.initials: MM
authors.lastname: Rahman
authors.affiliation: Troy University.
authors.forename: Karen
authors.initials: K
authors.lastname: Hovsepian
authors.affiliation: Western Washington University.
authors.forename: Moushumi
authors.initials: M
authors.lastname: Sharmin
authors.affiliation: NIDA Intramural Research Program.
authors.forename: David H
authors.initials: DH
authors.lastname: Epstein
authors.affiliation: NIDA Intramural Research Program.
authors.forename: Kenzie L
authors.initials: KL
authors.lastname: Preston
authors.affiliation: Johns Hopkins Bloomberg School of Public Health.
authors.forename: C Debra
authors.initials: CD
authors.lastname: Furr-Holden
authors.affiliation: Johns Hopkins Bloomberg School of Public Health.
authors.forename: Adam
authors.initials: A
authors.lastname: Milam
authors.affiliation: University of Michigan.
authors.forename: Inbal
authors.initials: I
authors.lastname: Nahum-Shani
authors.affiliation: University of Minnesota Medical School.
authors.forename: Mustafa
authors.initials: M
authors.lastname: al'Absi
authors.affiliation: University of Memphis.
authors.forename: Santosh
authors.initials: S
authors.lastname: Kumar
keywords: Intervention; Mobile Health (mHealth); Stress Management