The increasingly demanding work environments today make job strain a timely and relevant subject of investigation.
Early detection and tailored treatment of job strain is important because it negatively affects the health condition of employees, the performance of organizations, and the overall costs of the health care system likewise. Although there exist several self-report instruments for measuring job strain, one major limitation is the low number of measurements and, related to it, high-effort and high-costs associated with each wave of data collection. That is, measurements are usually conducted only two times per health intervention with several weeks or even months in between. As a result and significant shortcoming, short-term episodes of high job strain cannot be identified reliably.
The current research aims therefore to design, implement and evaluate a scalable, low-cost and minimal-invasive Job Strain Information System Service (JSISS) that continuously senses the degree of job strain in knowledge workers. Based on recent findings, which relate variations in mouse interactions to the degree of arousal and because arousal and job strain are hypothesized to be associated, two research questions are addressed in this project:
One lab experiment and one longitudinal field study are conducted to identify the relevant features of mouse interactions and to develop and validate machine learning models that infer job strain, and thus, answer the first research question. By contrast, a literature review, workshops, focus groups and two cross-sectional online surveys in the educational and engineering sector are conducted to answer the second research question.
The result of the current investigation will be a validated JSISS that paves the way for a novel class of individually tailored interventions that are expected to positively influence health and well being of employees, the performance of organizations, and the serious development of today’s increasing health care costs.
Kowatsch, T., Wahle, F., Filler, A. (2017) Design and Lab Experiment of a Stress Detection Service based on Mouse Movements, The 11th Mediterranean Conference on Information Systems (MCIS), Genoa, Italy ***Best Paper Award*** (PDF)
Kowatsch, T., Wahle, F., Filler, A. (2017) stressOUT: Design, Implementation and Evaluation of a Mouse-based Stress Management Service, In: Designing the Digital Transformation: DESRIST 2017 Research in Progress Proceedings, Maedche, A., vom Brocke, J., Hevner, A. (eds), KIT Scientific Working Papers; 64, Karlsruhe, Germany, 37-45. ***Nominated for the Best Prototype Award*** (PDF)
Kowatsch, T., Wahle, F., Filler, A., Kehr, F., Volland, D., Haug, S., Jenny, G., Bauer, G., Fleisch, E., Towards Short-Term Detection of Job Strain in Knowledge Workers with a Minimal-Invasive Information System Service: Theoretical Foundation and Experimental Design, 23rd European Conference on Information Systems (ECIS), Münster, Germany. [Paper-PDF][Poster-PDF]
Filler, A., Kowatsch, T., Haug, S., Wahle, F., Staake, T. & Fleisch, E. (2015) MobileCoach: A Novel Open Source Platform for the Design of Evidence-based, Scalable and Low-Cost Behavioral Health Interventions – Overview and Preliminary Evaluation in the Public Health Context. Wireless Telecommunications Symposium 2015 (WTS 2015), New York, USA. *** Outstanding Paper Award & Best Graduate Student Paper Award***[PDF]