The Relationship Between Diagnosed Burnout and Sleep Measured by Activity Trackers: Four Longitudinal Case Studies

Elizabeth C. Nelson*, Rosanne de Keijzer, Miriam M.R. Vollenbroek-Hutten, Tibert Verhagen, Matthijs L. Noordzij

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Employee burnout is an increasing global problem. Some countries, such as The Netherlands, diagnose and treat burnout as a medical condition. While deficient sleep has been implicated as the primary risk factor for burnout, the longest current sleep measurement of burnout individuals is 4 weeks; and no studies have measured sleep throughout the burnout process (i.e.: pre-burnout, burnout diagnosis, recovery time, and returning to work). During a 7 month longitudinal study on wearable technology use, 4 participants were diagnosed with (pre)burnout by their company doctor using the Maslach’s Burnout Inventory (MBI). Our study captured the participants’ sleep data including: sleep quality, number of awakenings, sleep duration, time awake, and amount of light sleep during the burnout and recovery process. One participant experienced a burnout diagnosis, recovery at home, and returning to work within the 7 months providing the first look at sleep trends during the entire burnout process. Our results show that the burnout participants experienced decreased sleep quality (n = 2), sleep duration (n = 2), and light sleep (n = 3). In contrast, a sample of 3 non-burnout participants sleep remained stable on all measures except for time awake for one participant. The results of this study answer past calls for longer analysis of sleep’s influence on burnout and highlight the vast opportunity to extend burnout research using the millions of active devices currently in use.

Original languageEnglish
Title of host publicationBody Area Networks. Smart IoT and Big Data for Intelligent Health Management
Subtitle of host publication14th EAI International Conference, BODYNETS 2019, Proceedings
EditorsLorenzo Mucchi, Matti Hämäläinen, Sara Jayousi, Simone Morosi
PublisherSpringer US
Pages315-331
Number of pages17
ISBN (Electronic)978-3-030-34833-5
ISBN (Print)978-3-030-34832-8
DOIs
Publication statusPublished - 16 Nov 2019
Event14th EAI International Conference on Body Area Networks, BodyNets 2019 - Florence, Italy
Duration: 2 Oct 20193 Oct 2019
Conference number: 14

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume297 LNICST
ISSN (Print)1867-8211

Conference

Conference14th EAI International Conference on Body Area Networks, BodyNets 2019
Abbreviated titleBODYNETS 2019
CountryItaly
CityFlorence
Period2/10/193/10/19

Keywords

  • Digital health
  • eHealth
  • Quantified self
  • Self-tracking
  • Sleep quality
  • Wearable technology

Fingerprint Dive into the research topics of 'The Relationship Between Diagnosed Burnout and Sleep Measured by Activity Trackers: Four Longitudinal Case Studies'. Together they form a unique fingerprint.

  • Cite this

    Nelson, E. C., de Keijzer, R., Vollenbroek-Hutten, M. M. R., Verhagen, T., & Noordzij, M. L. (2019). The Relationship Between Diagnosed Burnout and Sleep Measured by Activity Trackers: Four Longitudinal Case Studies. In L. Mucchi, M. Hämäläinen, S. Jayousi, & S. Morosi (Eds.), Body Area Networks. Smart IoT and Big Data for Intelligent Health Management: 14th EAI International Conference, BODYNETS 2019, Proceedings (pp. 315-331). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 297 LNICST). Springer US. https://doi.org/10.1007/978-3-030-34833-5_24