Stess@Work: from measuring stress to its understanding, prediction and handling with personalized coaching

Authors:
Jorn Bakker;Leszek Holenderski;Rafal Kocielnik;Mykola Pechenizkiy;Natalia Sidorova
Affiliations:
Eindhoven University of Technology, Eindhoven, Netherlands;Philips Research, Eindhoven, Netherlands;Eindhoven University of Technology, Eindhoven, Netherlands;Eindhoven University of Technology, Eindhoven, Netherlands;Eindhoven University of Technology, Eindhoven, Netherlands
Venue:
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Year:
2012

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Abstract

The problem of job stress is generally recognized as one of the major factors leading to a spectrum of health problems. People with certain professions, like intensive care specialists or call-center operators, and people in certain phases of their lives, like working parents with young children, are at increased risk of getting overstressed. For instance, one third of the intensive care specialists in the Netherlands are reported to have (had) a burn-out. Stress management should start far before the stress starts causing illnesses. The current state of sensor technology allows to develop systems measuring physical symptoms reflecting the stress level. We propose to use data mining and predictive modeling for gaining insight in the stress effects of the events at work and for enabling better stress management by providing timely and personalized coaching. In this paper we present a general framework allowing to achieve this goal and discuss the lessons learnt from the conducted case study.