A Multidimensional Assessment of Sleep Disorders in Long COVID Using the Alliance Sleep Questionnaire

Area of Science:

• Neurology
• Sleep Medicine
• Public Health

Background:

• Sleep disturbances are prevalent in Long COVID but not well-characterized.
• Limited research exists on the specific nature and prevalence of sleep symptoms in this population.

Purpose of the Study:

• To analyze comprehensive sleep questionnaire data from Long COVID patients.
• To characterize the nature and prevalence of sleep complaints in Long COVID.

Main Methods:

• Cross-sectional analysis of 200 Long COVID patients.
• Utilized intake questionnaires and the Alliance Sleep Questionnaire (ASQ).
• Employed principal component analysis (PCA) and K-means clustering to identify sleep phenotypes.

Main Results:

• High prevalence of sleep-related breathing complaints (57.5%), insomnia (42.5%), and excessive daytime sleepiness (28.5%).
• Identified interpretable domains including insomnia, fatigue, parasomnias, and respiratory symptoms.
• Clustering revealed a higher-burden phenotype with increased BMI, insomnia, daytime sleepiness, GI symptoms, and parasomnias.

Conclusions:

• Sleep disturbances are frequent and diverse in Long COVID.
• Factor analysis and clustering identified distinct sleep symptom domains and a high-burden phenotype.
• Symptom-based screening can help identify individuals at higher risk for severe sleep disturbances.