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

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Body Area Networks: Smart IoT and Big Data for Intelligent Health Management (BODYNETS 2019)

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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.

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Author information

Authors and Affiliations

  1. University of Twente, 7522 NB, Enschede, The Netherlands

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

  2. Ziekenhuis Groep Twente, 7555 DL, Hengelo, The Netherlands

    Miriam M. R. Vollenbroek-Hutten

  3. Amsterdam University of Applied Sciences, 1097 DZ, Amsterdam, The Netherlands

    Tibert Verhagen

Authors
  1. Elizabeth C. Nelson
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  2. Rosanne de Keijzer
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  3. Miriam M. R. Vollenbroek-Hutten
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  4. Tibert Verhagen
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  5. Matthijs L. Noordzij
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Corresponding author

Correspondence to Elizabeth C. Nelson .

Editor information

Editors and Affiliations

  1. Department of Information Engineering, University of Florence, Florence, Italy

    Lorenzo Mucchi

  2. Centre for Wireless Communications, University of Oulu, Oulu, Finland

    Matti Hämäläinen

  3. University of Florence, Florence, Italy

    Sara Jayousi

  4. University of Florence, Florence, Italy

    Simone Morosi

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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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: Mucchi, L., Hämäläinen, M., Jayousi, S., Morosi, S. (eds) Body Area Networks: Smart IoT and Big Data for Intelligent Health Management. BODYNETS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 297. Springer, Cham. https://doi.org/10.1007/978-3-030-34833-5_24

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  • DOI: https://doi.org/10.1007/978-3-030-34833-5_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34832-8

  • Online ISBN: 978-3-030-34833-5

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