Behavioural biometrics: Using smartphone keyboard activity as a proxy for rest-activity patterns.

Autor: Druijff-van de Woestijne GB; Neurocast B.V., Zeist, the Netherlands.; Radboud University Nijmegen, Nijmegen, the Netherlands., McConchie H; Neurocast B.V., Zeist, the Netherlands., de Kort YAW; Eindhoven University of Technology, Eindhoven, the Netherlands., Licitra G; Neurocast B.V., Zeist, the Netherlands., Zhang C; Utrecht University, Utrecht, the Netherlands., Overeem S; Eindhoven University of Technology, Eindhoven, the Netherlands.; Sleep Medicine Center Kempenhaeghe, Heeze, the Netherlands., Smolders KCHJ; Eindhoven University of Technology, Eindhoven, the Netherlands.
Jazyk: angličtina
Zdroj: Journal of sleep research [J Sleep Res] 2021 Oct; Vol. 30 (5), pp. e13285. Date of Electronic Publication: 2021 Mar 05.
DOI: 10.1111/jsr.13285
Abstrakt: Rest-activity patterns are important aspects of healthy sleep and may be disturbed in conditions like circadian rhythm disorders, insomnia, insufficient sleep syndrome, and neurological disorders. Long-term monitoring of rest-activity patterns is typically performed with diaries or actigraphy. Here, we propose an unobtrusive method to obtain rest-activity patterns using smartphone keyboard activity. The present study investigated whether this proposed method reliably estimates rest and activity timing compared to daily self-reports within healthy participants. First-year students (n = 51) used a custom smartphone keyboard to passively and objectively measure smartphone use behaviours and completed the Consensus Sleep Diary for 1 week. The time of the last keyboard activity before a nightly absence of keystrokes, and the time of the first keyboard activity following this period were used as markers. Results revealed high correlations between these markers and user-reported onset and offset of resting period (r ranged from 0.74 to 0.80). Linear mixed models could estimate onset and offset of resting periods with reasonable accuracy (R 2 ranged from 0.60 to 0.66). This indicates that smartphone keyboard activity can be used to estimate rest-activity patterns. In addition, effects of chronotype and type of day were investigated. Implementing this method in longitudinal studies would allow for long-term monitoring of (disturbances to) rest-activity patterns, without user burden or additional costly devices. It could be particularly interesting to replicate these findings in studies amongst clinical populations with sleep-related problems, or in populations for whom disturbances in rest-activity patterns are secondary complaints, such as neurological disorders.
(© 2021 Neurocast BV. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.)
Databáze: MEDLINE
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