Body Area Networks: Smart IoT and Big Data for Intelligent Health Management. 14th EAI International Conference, BODYNETS 2019, Florence, Italy, October 2-3, 2019, Proceedings

Research Article

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

  • @INPROCEEDINGS{10.1007/978-3-030-34833-5_24,
        author={Elizabeth Nelson and Rosanne Keijzer and Miriam Vollenbroek-Hutten and Tibert Verhagen and Matthijs Noordzij},
        title={The Relationship Between Diagnosed Burnout and Sleep Measured by Activity Trackers: Four Longitudinal Case Studies},
        proceedings={Body Area Networks:  Smart IoT and Big Data for Intelligent Health Management. 14th EAI International Conference, BODYNETS 2019, Florence, Italy, October 2-3, 2019, Proceedings},
        proceedings_a={BODYNETS},
        year={2019},
        month={11},
        keywords={eHealth Wearable technology Sleep quality Quantified self Digital health Self-tracking},
        doi={10.1007/978-3-030-34833-5_24}
    }
    
  • Elizabeth Nelson
    Rosanne Keijzer
    Miriam Vollenbroek-Hutten
    Tibert Verhagen
    Matthijs Noordzij
    Year: 2019
    The Relationship Between Diagnosed Burnout and Sleep Measured by Activity Trackers: Four Longitudinal Case Studies
    BODYNETS
    Springer
    DOI: 10.1007/978-3-030-34833-5_24
Elizabeth Nelson1,*, Rosanne Keijzer1, Miriam Vollenbroek-Hutten, Tibert Verhagen2, Matthijs Noordzij1
  • 1: University of Twente
  • 2: Amsterdam University of Applied Sciences
*Contact email: elizabeth@learnadaptbuild.com

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.