Abstract
For decades, day–night patterns in behaviour have been investigated by asking people about their sleep–wake timing, their diurnal activity patterns, and their sleep duration. We demonstrate that the increasing digitalization of lifestyle offers new possibilities for research to investigate day–night patterns and related traits with the help of behavioural data. Using smartphone sensing, we collected in vivo data from 597 participants across several weeks and extracted behavioural day–night pattern indicators. Using this data, we explored three popular research topics. First, we focused on individual differences in day–night patterns by investigating whether ‘morning larks’ and ‘night owls’ manifest in smartphone–sensed behavioural indicators. Second, we examined whether personality traits are related to day–night patterns. Finally, exploring social jetlag, we investigated whether traits and work weekly day–night behaviours influence day–night patterns on weekends. Our findings highlight that behavioural data play an essential role in understanding daily routines and their relations to personality traits. We discuss how psychological research can integrate new behavioural approaches to study personality.
Highlights
Are there times of day when you do not use your smartphone at all? Most likely at night
We investigate the associations of day–night behaviour patterns and personality traits
Individual differences in compensatory nightly inactivity on weekends To explore social jetlag, we investigated which intraindividual and interindividual factors predict the duration of nightly inactivity of smartphone usage on weekends
Summary
Are there times of day when you do not use your smartphone at all? Most likely at night. Behavioural manifestations of the underlying circadian system like sleep–wake timing, diurnal activity, or sleep duration have mainly been assessed via self-reports (Adan et al, 2012). Self-reports about behaviour are known to differ from actual records of behaviour (Baumeister et al, 2007; Gosling et al, 1998). Emphasizing this dilemma, Lauderdale et al (2008) correlated behaviourally assessed sleep duration with self-reports and concluded that people systematically misjudge it. An alternative approach is to collect actigraphy-based data to study sleep behaviour: movements and environmental factors like ambient brightness are recorded with wristbands and are jointly converted to indicators for sleep–wake timing by special algorithms
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