Health, demographic Change and Wellbeing - Sleep Revolution
Health, demographic Change and Wellbeing - Sleep Revolution
Health, demographic Change and Wellbeing - Sleep Revolution
Revolution of sleep diagnostics and personalized health care based on digital diagnostics and therapeutics with health data integration
Project description
Obstructive sleep apnea (OSA) is associated with various negative health consequences including increased risk of heart disease, hypertension and daytime sleepiness causing road accidents. The economic burden of OSA is rising as almost 1 billion people worldwide are estimated to have OSA. The apnea-hypopnea-index, being the current diagnostic metric, relates poorly to these symptoms and comorbidities. It merely measures the frequency of breathing cessations without assessing OSA severity in any other physiologically relevant way. Furthermore, the clinical methods for analyzing signals from clinical sleep recording (polysomnography – PSG) are outdated, expensive and laborious. Due to this, the majority of OSA patients remain without diagnosis or have an inaccurate diagnosis leading to sub-optimal treatment. Thus, it is evident that more personalized diagnostics are required including predictive and preventive health care and patient participation.
The Sleep Revolution aims to develop machine learning techniques to better estimate OSA severity and treatment needs to improve health outcomes and quality of life. These techniques are implemented to high-end wearables developed in this project to alleviate the costs and increase the availability of PSGs. Finally, we aim to design a digital platform that functions as a bridge between researchers, patients and healthcare professionals.
These ambitious goals will be achieved throughout extensive collaboration between sleep specialists, computer scientists and industry partners. The collaboration network consists of over 30 sleep centers working together to provide the needed retrospective data (over 10.000 sleep studies). The multi-center prospective trials involve experts and end-users to assess and validate the new Sleep Revolution diagnostic algorithms, wearables and platforms. With the commitment of the European Sleep Research Society and Assembly of National Sleep Societies (over 8000 members), we have the unique possibility to create new standardized guidelines for sleep medicine in the EU.
The Somnology group of the Dpt. of Respiratory Diseases at the Ghent University Hospital will commit to taking a leading role in the development of new innovative approaches to characterising sleep disorders in general and obstructive sleep apnea (OSA) in particular. Such new approach is mandatory now that it has become clear that OSA and many other sleep disorders are heterogeneous conditions that present as distinct phenotypes and frequently overlap. To come to a structures approach, the development of a new comprehensive questionnaire covering all aspects of sleep and sleep disorders seems important. The setting of academic research units in the Sleep Revolution consortium offers a unique opportunity for the development and validation of this new tool.
Objectives
These are the objectives of Sleep revolution:
To develop machine learning techniques to better estimate severity of obstructive sleep apnea (OSA) and treatment needs to improve health outcomes and quality of life
To implement these techniques to high-end wearables developed in this project
To alleviate the costs and increase the availability of sleep recording.
To design a digital platform that functions as a bridge between researchers, patients and healthcare professionals.
To achieve these ambitious goals throughout extensive collaboration between sleep specialists, computer scientists and industry partners.
Role of Ghent University
To create a new patient-reported outcome measure (PROM) for assessing sleep disorders in clinical patients
The new questionnaire will be comprehensive and cover all aspects of sleep and sleep disorders as well as indices of daytime functioning
To validate the instrument regarding internal consistency, test-retest reliability and other measurement properties
Prof. Dr. Dirk Pevernagie Faculty of Medicine and health Sciences – Dpt. Internal Medicine and Pediatrics C. Heymanslaan 10, ingang 12 - verdieping 6 9000 Gent Phone number: +32 9 332 15 32 E-mail
