Sleep-disordered breathing (SDB), ranging from habitual snoring to severe obstructive sleep apnea (OSA), is highly prevalent and represents a growing global healthcare burden. Besides disrupting sleep, SDB leads to detrimental outcomes such as excessive daytime sleepiness, neurocognitive impairment, and increased cardiometabolic morbidities.

In the Research Topic Volumes I–III, we observed the technology developments that enable rapid innovations in the field of SDB. Simple diagnostic methods and novel disease management solutions strongly suggest that the SDB diagnostics and management are moving from a one-size-fits-all approach to precision sleep medicine.

Possible topics of interest include, but are not limited to:

1. Novel insights on pathophysiology of OSA from physiological signals collected in standard sleep studies;

2. Novel signal acquisition and sensor technologies;

3. Alternative polysomnography metrics and analyses;

4. Minimally invasive data collection for screening and long-term follow-up of SDB

5. Application of artificial intelligence, machine learning, and big data for SDB diagnostics and management;

6. Biomarkers and phenotyping-based prediction models on treatment outcomes;

7. Big data approaches and telemedicine in sleep medicine;

8. Emerging technologies to provide alternative treatment options for better treatment adherence and clinical outcomes;

9. Disease management approaches encompass phenotyping, and endotyping for better patient characterization including disease severity, daytime symptoms, as well as comorbidity conditions;

10. Patient-reported outcome measures assessment and sleep disparities studies;

We are interested in original works, protocols, literature reviews, meta-analyses, perspectives and expert consensus related to sleep disorders with a specific focus on SDB.

Are you interested in contributing a manuscript to this Research Topic?

Novel Technologies in the Diagnosis and Management of Sleep-disordered Breathing: Volume IV