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Related Concept Videos

Management of Insomnia01:19

Management of Insomnia

244
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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Insomnia01:27

Insomnia

84
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.
Multiple factors contribute...
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Insufficient Sleep and Sleep Deprivation01:13

Insufficient Sleep and Sleep Deprivation

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Insufficient sleep refers to not getting the recommended amount of sleep for optimal functioning, even if it's just slightly less than needed. Sleep insufficiency may occur due to lifestyle choices, such as staying up late for social events or work, resulting in routinely getting less sleep than required. For example, consistently sleeping 6 hours when the body needs 7-9 hours can lead to cumulative effects on health and well-being.
Sleep deprivation is a more severe form of sleep loss...
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Sedatives and Hypnotics Drugs: Miscellaneous Agents01:17

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Sedatives and hypnotics encompass a wide range of substances, each with its unique mechanism of action, uses, and potential adverse effects.
Melatonin congeners like ramelteon (Rozerem) and tasimelteon (Hetlioz) selectively bind to melatonin receptors (MT1 and MT2) and thus mimic the actions of melatonin, a hormone that regulates sleep-wake cycles. Tasimelteon is primarily used for non-24-hour sleep-wake disorder, common in blind patients. They are also used to treat conditions like insomnia...
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Related Experiment Video

Updated: Jun 27, 2025

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
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Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

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Data-driven shortened Insomnia Severity Index (ISI): a machine learning approach.

Hyeontae Jo1,2, Myna Lim3, Hong Jun Jeon4

  • 1Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea.

Sleep & Breathing = Schlaf & Atmung
|April 29, 2024
PubMed
Summary

A new data-driven shortened Insomnia Severity Index (ISI), called ISI-3m, accurately predicts insomnia severity. This machine learning tool aids clinicians in efficient insomnia screening and monitoring during treatment.

Keywords:
Exploratory factor analysisInsomniaInsomnia Severity IndexMachine-LearningShortened questionnairesXGBoost

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Area of Science:

  • Psychiatry and Sleep Medicine
  • Machine Learning in Healthcare
  • Psychometrics

Background:

  • The Insomnia Severity Index (ISI) is a standard 7-item questionnaire for assessing insomnia disorder risk.
  • While short, there's a need for even briefer versions for routine clinical monitoring.
  • This study aimed to create a data-driven, shortened ISI to predict insomnia severity accurately.

Purpose of the Study:

  • To develop a data-driven, shortened version of the Insomnia Severity Index (ISI).
  • To ensure the shortened version accurately predicts insomnia severity.
  • To validate the machine learning framework for developing shortened questionnaires.

Main Methods:

  • Utilized 800 responses from the EMBRAIN survey system.
  • Applied exploratory factor analysis (EFA) to group similar items.
  • Selected the most representative item per group using eXtreme Gradient Boosting (XGBoost).

Main Results:

  • Developed ISI-3m, a 3-item questionnaire based on sleep maintenance, daily function interference, and sleep concerns.
  • ISI-3m achieved a high coefficient of determination (R² = 0.910) for ISI score prediction.
  • Demonstrated high accuracy (0.965), precision (0.841), and recall (0.838) in multiclass classification, outperforming prior shortened versions.

Conclusions:

  • ISI-3m offers a highly accurate and efficient method for clinical insomnia screening and monitoring.
  • The EFA and XGBoost framework can be applied to shorten other clinical questionnaires.
  • This approach facilitates efficient patient assessment and treatment tracking in routine care.