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<p>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.</p>

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Unobtrusive inference of diurnal rhythms from smartphone data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月24日

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  • Loran Knol,
  • Mindy K. Ross,
  • Anisha Nagpal,
  • Andrew P. Burns,
  • Zachery D. Morrissey,
  • Faraz Hussain,
  • Tory A. Eisenlohr-Moul,
  • Christian F. Beckmann,
  • Alex Leow,
  • Andre F. Marquand

摘要

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.