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.
|Original language||English (US)|
|Title of host publication||CHI 2016 - Proceedings, 34th Annual CHI Conference on Human Factors in Computing Systems|
|Publisher||Association for Computing Machinery|
|Number of pages||13|
|State||Published - May 7 2016|
|Event||34th Annual Conference on Human Factors in Computing Systems, CHI 2016 - San Jose, United States|
Duration: May 7 2016 → May 12 2016
|Name||Conference on Human Factors in Computing Systems - Proceedings|
|Other||34th Annual Conference on Human Factors in Computing Systems, CHI 2016|
|Period||5/7/16 → 5/12/16|
Bibliographical noteFunding Information:
We thank Rummana Bari, Soujanya Chatterjee, Syed Monowar Hossain, and Barbara Burch Kuhn from University of Memphis, Emre Ertin from Ohio State University, Susan Murphy from University of Michigan, Ida Sim from University of California San Francisco, and Bonnie Spring from Northwestern University. The authors acknowledge support by the National Science Foundation under award numbers CNS-1212901 and IIS-1231754 and by the National Institutes of Health under grants R01DA035502 (by NIDA) through funds provided by the trans-NIH OppNet initiative and U54EB020404 (by NIBIB) through funds provided by the trans-NIH Big Data-to-Knowledge (BD2K) initiative.
- Mobile health (mHealth)
- Stress management