Unobtrusive inference of diurnal rhythms from smartphone data.
cross_sectional · Level IV
Where this comes from
- Record sourced from PubMed, PMID 41444764.
- Also identified by DOI 10.1038/s41746-025-02254-1 and PMC identifier 12830913.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Diurnal rhythms are an integral feature of psychopathology but difficult to measure at scale. Smartphones are ubiquitous and therefore uniquely positioned to measure such rhythms non-invasively and continuously. Here, we propose a digital phenotyping framework to quantify diurnal rhythms. We use it to predict sleep duration from smartphone typing dynamics and analyse rhythm phase during time zone transitions with a clinical outpatient sample and a year-long longitudinal data set.