Unobtrusive inference of diurnal rhythms from smartphone data
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.
Authors
- Anisha Nagpal (ORCID: https://orcid.org/0000-0001-7111-9322)
- Mindy K. Ross (ORCID: https://orcid.org/0000-0001-7974-4309)
- Alex Hwong‐Ruey Leow (ORCID: https://orcid.org/0000-0003-1752-0435)
- Faraz Hussain (ORCID: https://orcid.org/0000-0002-4910-1342)
- André F. Marquand (ORCID: https://orcid.org/0000-0001-5903-203X)
- Andrew P. Burns
- Zachery D. Morrissey (ORCID: https://orcid.org/0000-0002-8346-4876)
- Tory A. Eisenlohr‐Moul (ORCID: https://orcid.org/0000-0002-9231-3105)
- Loran Knol (ORCID: https://orcid.org/0009-0006-8932-4504)
- Christian F. Beckmann
Institutions
- Radboud University Nijmegen (NL)
- Radboud University Medical Center (NL)
- University of Illinois Chicago (US)
- University of Oxford (GB)
- University of Chicago (US)
- Wellcome Centre for Integrative Neuroimaging (GB)
Publication Details
- Journal
- npj Digital Medicine
- Published
- 2025-12-24
- DOI
- https://doi.org/10.1038/s41746-025-02254-1
- Primary Topic
- Digital Mental Health Interventions
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Wellcome Trust
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- National Institute of Mental Health