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Related Concept Videos

Insufficient Sleep and Sleep Deprivation01:13

Insufficient Sleep and Sleep Deprivation

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...
Substance Use Disorders Affecting Sleep01:24

Substance Use Disorders Affecting Sleep

Substance use disorders involve a pattern of using drugs more extensively than intended and continuing use despite harmful consequences. This includes legal substances like alcohol and nicotine, as well as illegal drugs. These disorders often involve both physical and psychological dependence, reflecting compulsive use of substances that significantly alter thoughts, feelings, and behaviors, contributing to a major public health issue.
Understanding the concepts of physical dependence,...
Understanding Sleep01:11

Understanding Sleep

Sleep, an essential biological state, involves significant reductions in physical activity, sensory awareness, and interaction with the environment. This complex physiological process is primarily regulated by specific brain regions, notably the hypothalamus and pons, which govern the sleep-wake cycle or circadian rhythm.
The circadian rhythm, a nearly 24-hour cycle, is deeply influenced by environmental light cues. Light exposure directly affects the hypothalamus, which in turn regulates...
Management of Insomnia01:19

Management of Insomnia

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...
REM Sleep Behavior Disorder01:15

REM Sleep Behavior Disorder

REM Sleep Behavior Disorder (RBD) is a sleep disorder characterized by the absence of muscle paralysis that normally occurs during the REM phase of sleep. This absence allows individuals to physically act out their dreams, which are often vivid and disturbing. Common behaviors exhibited during episodes include kicking, punching, and yelling. These actions can be dangerous, potentially leading to injuries for the person with RBD or their bed partner.
RBD is significantly associated with...
Insomnia01:27

Insomnia

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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Related Experiment Video

Updated: Jun 18, 2026

Multi-Modal Home Sleep Monitoring in Older Adults
07:40

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

Identifying Older Adults at Risk for Future Poor Sleep Quality: A Multidimensional Predictive Framework.

Jiale Xu1, Yuanxiao Ma1

  • 1School of Psychology, Nanjing Normal University, Nanjing, China.

Clinical Gerontologist
|June 16, 2026
PubMed
Summary
This summary is machine-generated.

Poor sleep quality in older adults is linked to multiple factors. A machine learning model was developed to predict this risk, aiding early intervention for better health outcomes.

Keywords:
Machine learningolder adultspredictive modelsleep quality

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Area of Science:

  • Gerontology
  • Sleep Medicine
  • Artificial Intelligence in Healthcare

Background:

  • Poor sleep quality is a prevalent issue among community-dwelling older adults.
  • Identifying multidimensional risk factors is crucial for effective management and prevention strategies.

Purpose of the Study:

  • To identify multidimensional risk factors for poor sleep quality in older adults.
  • To develop and validate a machine learning model for predicting 2-year risk of poor sleep quality.

Main Methods:

  • Utilized data from the China Health and Retirement Longitudinal Study (2011-2018).
  • Employed random forest imputation and LASSO regression for predictor selection.
  • Trained and evaluated multiple machine learning algorithms, including LightGBM, using cross-validation and SHAP for interpretation.

Main Results:

  • 23.2% of 3,471 participants developed poor sleep quality.
  • Fifteen predictors were identified across demographic, biological, psychological, and social-behavioral domains.
  • The LightGBM model achieved an AUC of 0.641, indicating acceptable predictive performance.

Conclusions:

  • The developed model shows potential for early identification of individuals at risk of poor sleep quality.
  • A multidimensional assessment framework is essential for comprehensive evaluation and targeted interventions in clinical and community settings.