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Insomnia is a prevalent sleep disorder characterized by difficulty falling asleep, frequent awakenings during the night, and waking up too early without being able to return to sleep. People with insomnia often experience these disruptions at least three nights a week for at least one month. Chronic insomnia, which lasts for at least three months, can lead to increased anxiety, which in turn can worsen sleep difficulties, creating a cycle of sleeplessness and stress.
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The sleep cycle, an integral part of human health, consists of several stages with distinct characteristics and functions. It begins with a transition from wakefulness to sleep, known as the light sleep phase, followed by the restorative deep sleep phase, essential for physical recovery and growth. The cycle concludes with the Rapid Eye Movement (REM) phase, characterized by high brain activity and vivid dreaming. Insomnia, a prevalent sleep disorder, involves difficulty falling asleep, staying...
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Insomnia-LCA classifier: an open web application for insomnia subtype classification using latent class analysis.

Matteo Carpi1, Daniel Ruivo Marques2

  • 1Department of Human Neuroscience, Sapienza University of Rome, Rome, Italy.

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|February 2, 2026
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Summary

This study introduces the insomnia-LCA classifier, a web tool to assign insomnia subtypes based on Insomnia Severity Index (ISI) responses. It enables practical application and testing of data-driven insomnia phenotypes in research.

Keywords:
Clinical phenotypingInsomniaInsomnia severity indexLatent class analysisReproducibilityWeb application

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Area of Science:

  • Psychiatry and Behavioral Science
  • Computational Psychology

Background:

  • Insomnia heterogeneity complicates diagnosis and treatment.
  • Latent class analysis (LCA) has identified distinct insomnia subtypes.
  • Previous LCA findings were limited to original datasets, hindering broader application.

Purpose of the Study:

  • To develop a practical, open-source tool for classifying insomnia subtypes.
  • To enable the deployment and testing of LCA-derived phenotypes using the Insomnia Severity Index (ISI).

Main Methods:

  • Developed the insomnia-LCA classifier, a web application.
  • Utilized class priors and conditional response probabilities from a prior LCA study.
  • Applied the classifier to assign new ISI profiles to four identified subtypes: no insomnia (NI), subthreshold insomnia (SI), high insomnia risk (HI), and predominant daytime symptoms (DS).
  • Enabled individual and batch processing of ISI responses.

Main Results:

  • The classifier demonstrated high accuracy (accuracy = 0.999) and reliability (Cohen's kappa = 0.999) when reclassifying the original dataset.
  • Synthetic profiles generated by the tool behaved as expected.
  • Outputs include class probabilities, modal assignment, ISI scores, and profile comparisons.

Conclusions:

  • The insomnia-LCA classifier is a practical and reproducible tool.
  • Facilitates the application and validation of insomnia subtypes in clinical research.
  • Aids in understanding and utilizing data-driven insomnia phenotypes.