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Monitoring Kleine-Levin Syndrome Recovery Using Human-Smartphone Interactions: A Digital Phenotyping Approach.

Yi-Guang Wang1,2, Hsiang-Chih Chang3,4, Chen Lin4

  • 1Department of Psychiatry, Tri-Service General Hospital, School of Medicine, National Defense Medical Center, Taipei, Taiwan.

Journal of Sleep Research
|March 20, 2026
PubMed
Summary
This summary is machine-generated.

Kleine-Levin syndrome (KL S) is a rare neurological disorder. Digital phenotyping using a smartphone app successfully monitored sleep-wake patterns and functional recovery in a KL S patient.

Keywords:
Kleine–Levin syndromecircadian rhythmdigital phenotypinghuman–smartphone interactionshypersomniainterdaily stability

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Area of Science:

  • Neurology
  • Sleep Medicine
  • Digital Health

Background:

  • Kleine-Levin syndrome (KL S) is a rare neurological disorder characterized by recurrent hypersomnia and behavioral disturbances.
  • Symptoms include excessive sleep (hypersomnia), increased appetite (hyperphagia), and altered sexual behavior (hypersexuality).

Purpose of the Study:

  • To report a case of Kleine-Levin syndrome in a 25-year-old Taiwanese male.
  • To demonstrate the utility of digital phenotyping via a smartphone application for monitoring sleep-wake patterns and functional changes in KL S.

Main Methods:

  • A validated smartphone app (Rhythm) was used to track human-smartphone interactions.
  • Sleep-wake patterns and functional changes were monitored longitudinally.
  • Circadian rhythm metrics, specifically interdaily stability, were analyzed.

Main Results:

  • The patient experienced episodes of hypersomnia exceeding 15 hours per day.
  • Smartphone usage patterns reflected clinical states: inactivity during episodes and structured usage during recovery.
  • Interdaily stability improved from low values during episodes to >0.37 upon recovery, indicating circadian rhythm normalization.

Conclusions:

  • Digital phenotyping offers a scalable and objective method for assessing functional and circadian dynamics in Kleine-Levin syndrome.
  • This approach complements traditional assessment methods, providing continuous longitudinal data.
  • Smartphone-based monitoring can aid in understanding the fluctuating nature of KL S.