Morningness-eveningness assessment from mobile phone communication analysis.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
16 07 2021
Historique:
received: 06 04 2021
accepted: 24 06 2021
entrez: 17 7 2021
pubmed: 18 7 2021
medline: 18 7 2021
Statut: epublish

Résumé

Human behaviour follows a 24-h rhythm and is known to be governed by the individual chronotypes. Due to the widespread use of technology in our daily lives, it is possible to record the activities of individuals through their different digital traces. In the present study we utilise a large mobile phone communication dataset containing time stamps of calls and text messages to study the circadian rhythms of anonymous users in a European country. After removing the effect of the synchronization of East-West sun progression with the calling activity, we used two closely related approaches to heuristically compute the chronotypes of the individuals in the dataset, to identify them as morning persons or "larks" and evening persons or "owls". Using the computed chronotypes we showed how the chronotype is largely dependent on age with younger cohorts being more likely to be owls than older cohorts. Moreover, our analysis showed how on average females have distinctly different chronotypes from males. Younger females are more larkish than males while older females are more owlish. Finally, we also studied the period of low calling activity for each of the users which is considered as a marker of their sleep period during the night. We found that while "extreme larks" tend to sleep more than "extreme owls" on the weekends, we do not observe much variation between them on weekdays. In addition, we have observed that women tend to sleep even less than males on weekdays while there is not much difference between them on the weekends.

Identifiants

pubmed: 34272421
doi: 10.1038/s41598-021-93799-0
pii: 10.1038/s41598-021-93799-0
pmc: PMC8285513
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

14606

Informations de copyright

© 2021. The Author(s).

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Auteurs

Chandreyee Roy (C)

Department of Computer Science, Aalto University School of Science, Espoo, Finland. chandreyee.roy@aalto.fi.

Daniel Monsivais (D)

Department of Computer Science, Aalto University School of Science, Espoo, Finland.

Kunal Bhattacharya (K)

Department of Computer Science, Aalto University School of Science, Espoo, Finland.
Department of Industrial Engineering and Management, Aalto University School of Science, Espoo, Finland.

Robin I M Dunbar (RIM)

Department of Experimental Psychology, University of Oxford, Oxford, UK.

Kimmo Kaski (K)

Department of Computer Science, Aalto University School of Science, Espoo, Finland.
The Alan Turing Institute, London, UK.

Classifications MeSH