Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Blind Procedures02:07

Blind Procedures

10.8K
Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...
10.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Performance of AI in Predicting the Progression of Gestational Diabetes to Type 2 Diabetes: Systematic Review and Meta-Analysis.

Journal of medical Internet research·2026
Same author

Patients' mHealth Apps Usage and Data Privacy, Security, and Confidentiality Concerns: Exploratory Study.

JMIR formative research·2026
Same author

Temporal and Behaviour-Aware Multimodal Modelling for Hour-Ahead Hypoglycaemia Prediction During Ramadan Fasting in Type 1 Diabetes.

Sensors (Basel, Switzerland)·2026
Same author

Perspectives Regarding the Privacy, Security, and Confidentiality of Data Collected via mHealth Apps in Saudi Arabia: Qualitative Analysis.

Journal of medical Internet research·2026
Same author

The Performance of Artificial Intelligence in Classifying Molecular Markers in Adult-Type Gliomas Using Histopathological Images: Systematic Review.

Journal of medical Internet research·2026
Same author

Digital mental health needs a purpose-driven approach.

Nature human behaviour·2026

Related Experiment Video

Updated: May 2, 2026

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

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

7.6K

Wearable Artificial Intelligence for Sleep Disorders: Scoping Review.

Sarah Aziz1, Amal A M Ali2,3, Hania Aslam1

  • 1AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.

Journal of Medical Internet Research
|May 6, 2025
PubMed
Summary

Artificial intelligence (AI)-powered wearable devices show promise for screening and diagnosing sleep disorders. However, current research is limited to sleep apnea, highlighting a need for broader applications and treatment-focused studies.

Keywords:
artificial intelligencemachine learningscoping reviewsleep disorderswearable devices

More Related Videos

A Chronic Sleep Fragmentation Model using Vibrating Orbital Rotor to Induce Cognitive Deficit and Anxiety-Like Behavior in Young Wild-Type Mice
06:23

A Chronic Sleep Fragmentation Model using Vibrating Orbital Rotor to Induce Cognitive Deficit and Anxiety-Like Behavior in Young Wild-Type Mice

Published on: September 22, 2020

5.2K
Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

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

Published on: November 8, 2024

403

Related Experiment Videos

Last Updated: May 2, 2026

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

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

7.6K
A Chronic Sleep Fragmentation Model using Vibrating Orbital Rotor to Induce Cognitive Deficit and Anxiety-Like Behavior in Young Wild-Type Mice
06:23

A Chronic Sleep Fragmentation Model using Vibrating Orbital Rotor to Induce Cognitive Deficit and Anxiety-Like Behavior in Young Wild-Type Mice

Published on: September 22, 2020

5.2K
Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

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

Published on: November 8, 2024

403

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Sleep Medicine

Background:

  • Sleep disorders affect 30%-45% of adults globally, increasing risks for diabetes and cardiovascular disease.
  • Traditional in-lab sleep monitoring is costly and impractical for long-term use.
  • Wearable AI solutions offer scalable, continuous monitoring for improved sleep disorder identification and management.

Purpose of the Study:

  • To review artificial intelligence (AI)-powered wearable devices for sleep disorders.
  • To analyze study characteristics, wearable technology features, and AI methodologies for sleep disorder detection and analysis.

Main Methods:

  • Searched seven databases for peer-reviewed literature up to March 2024.
  • Included studies using AI algorithms with wearable device data for sleep disorder detection.
  • Conducted two-step study selection (title/abstract, then full-text) with independent reviewer data extraction and narrative synthesis.

Main Results:

  • 46 studies met eligibility criteria, with most focusing on sleep apnea.
  • Wearable AI was used for diagnosis and screening, but not treatment.
  • Commercial devices, particularly wrist-worn ones, were common; respiratory and heart rate data were frequently used.
  • Convolutional neural networks were the most popular AI algorithm, followed by random forest and support vector machines.

Conclusions:

  • Wearable AI is a promising tool for sleep disorder screening and diagnosis, especially for sleep apnea.
  • Research is limited for sleep disorders beyond sleep apnea, and treatment applications are unexplored.
  • Further research is needed to validate AI techniques with clinical data and develop analytics for comprehensive sleep disorder management.