AIMC Topic: Sleep Initiation and Maintenance Disorders
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Sleep disturbances and PTSD: identifying baseline predictors of insomnia response in an intensive treatment programme.
European journal of psychotraumatology
Jun 23, 2025
This study examined whether baseline demographic and clinical variables could predict clinically significant reductions in insomnia symptoms among veterans receiving a 2-week Cognitive Processing Therapy (CPT)-based intensive PTSD treatment programm...
Exploring predictors of insomnia severity in shift workers using machine learning model.
Frontiers in public health
Mar 14, 2025
INTRODUCTION: Insomnia in shift workers has distinctive features due to circadian rhythm disruption caused by reversed or unstable sleep-wake cycle work schedules. While previous studies have primarily focused on a limited number of predictors for in...
Applying Natural Language Processing Techniques to Map Trends in Insomnia Treatment Terms on the r/Insomnia Subreddit: Infodemiology Study.
Journal of medical Internet research
Jan 9, 2025
BACKGROUND: People share health-related experiences and treatments, such as for insomnia, in digital communities. Natural language processing tools can be leveraged to understand the terms used in digital spaces to discuss insomnia and insomnia treat...
Comparison of model feature importance statistics to identify covariates that contribute most to model accuracy in prediction of insomnia.
PloS one
Jul 2, 2024
IMPORTANCE: Sleep is critical to a person's physical and mental health and there is a need to create high performing machine learning models and critically understand how models rank covariates.
Data-driven shortened Insomnia Severity Index (ISI): a machine learning approach.
Sleep & breathing = Schlaf & Atmung
Apr 29, 2024
BACKGROUND: The Insomnia Severity Index (ISI) is a widely used questionnaire with seven items for identifying the risk of insomnia disorder. Although the ISI is still short, more shortened versions are emerging for repeated monitoring in routine clin...
Dysfunctional Beliefs and Attitudes about Sleep-6 (DBAS-6): Data-driven shortened version from a machine learning approach.
Sleep medicine
Apr 27, 2024
BACKGROUND: The Dysfunctional Beliefs and Attitudes about Sleep Scale (DBAS-16) is a widely used self-report instrument for identifying sleep-related cognition. However, its length can be cumbersome in clinical practice. This study aims to develop a ...
A robot intervention for adults with ADHD and insomnia-A mixed-method proof-of-concept study.
PloS one
Sep 1, 2023
OBJECTIVE: To investigate individual effects of a three-week sleep robot intervention in adults with ADHD and insomnia, and to explore participants' experiences with the intervention.
Uncovering psychiatric phenotypes using unsupervised machine learning: A data-driven symptoms approach.
European psychiatry : the journal of the Association of European Psychiatrists
Feb 21, 2023
BACKGROUND: Current categorical classification systems of psychiatric diagnoses lead to heterogeneity of symptoms within disorders and common co-occurrence of disorders. We investigated the heterogeneous and overlapping nature of symptom endorsement ...
Insomnia disorder diagnosed by resting-state fMRI-based SVM classifier.
Sleep medicine
Apr 30, 2022
BACKGROUND: The main classification systems of sleep disorders are based on the subjective self-reported criteria. Objective measures are essential to characterize the nocturnal sleep disturbance, identify daytime impairment, and determine the course...
The effects of a sleep robot intervention on sleep, depression and anxiety in adults with insomnia - Study protocol of a randomized waitlist-controlled trial.
Contemporary clinical trials
Oct 2, 2021
Insomnia is a common sleep disorder characterized by difficulties initiating sleep, maintaining sleep and/or early-morning awakenings. Hyperarousal is a common causal and maintaining factor in insomnia models. Different techniques to decrease arousal...
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