Not logged in.

Contribution Details

Type Journal Article
Scope Discipline-based scholarship
Title Observing and predicting knowledge worker stress, focus and awakeness in the wild
Organization Unit
  • Mauricio Soto
  • Chris Satterfield
  • Thomas Fritz
  • Gail C Murphy
  • David C Shepherd
  • Nicholas Kraft
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Journal Title International Journal of Human-Computer Studies
Publisher Elsevier
Geographical Reach international
ISSN 1071-5819
Volume 146
Page Range 102560
Date 2021
Abstract Text Knowledge workers face many challenges in the workplace: work is fragmented, disruptions are constant, tasks are complex, and work hours can be long. These challenges can affect knowledge workers’ stress, focus and awakeness, and in turn their interaction with the digital environment, the quality of work performed and their productivity in general. We report on a field study with 14 knowledge workers over an eight-week period in which we investigated, using experience sampling, how the workers experience stress and awakeness over time. During this field study, we also collected biometric data including heart- and skin-related measures, which we then used to investigate if it is possible to predict stress, focus and awakeness, in the moment. We observed and report on various trends in knowledge worker stress and awakeness levels over several weeks, finding that people tend to have certain “baseline” levels for these aspects. Moreover, we found that days with high levels of stress tend to cluster, similarly as the days with low awakeness. We further show that machine learning models can be built from the data of a single minimally invasive device to predict stress, focus, and awakeness. Overall, we found that our models were capable of large improvements in precision and recall in comparison to a random classifier for stress (25.9% increase over random for precision, 4.2% for recall) and awakeness (52.4% increase in precision, 40.8% in recall). The abstract concept of focus proved to be the hardest to predict (26.0% increase in precision, 27.8% decrease in recall).
Official URL
Digital Object Identifier 10.1016/j.ijhcs.2020.102560
Other Identification Number merlin-id:22138
PDF File Download from ZORA
Export BibTeX