TY - GEN
T1 - The relationship between diagnosed burnout and sleep measured by activity trackers
T2 - 14th EAI International Conference on Body Area Networks, BodyNets 2019
AU - Nelson, Elizabeth C.
AU - de Keijzer, Rosanne
AU - Vollenbroek-Hutten, Miriam M.R.
AU - Verhagen, Tibert
AU - Noordzij, Matthijs L.
PY - 2019/11/16
Y1 - 2019/11/16
N2 - 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.
AB - 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.
KW - Digital health
KW - eHealth
KW - Quantified self
KW - Self-tracking
KW - Sleep quality
KW - Wearable technology
UR - http://www.scopus.com/inward/record.url?scp=85076558147&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-34833-5_24
DO - 10.1007/978-3-030-34833-5_24
M3 - Conference contribution
AN - SCOPUS:85076558147
SN - 9783030348328
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 315
EP - 331
BT - Body Area Networks
A2 - Mucchi, Lorenzo
A2 - Hämäläinen, Matti
A2 - Jayousi, Sara
A2 - Morosi, Simone
PB - Springer
Y2 - 2 October 2019 through 3 October 2019
ER -