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The future of sleep health: a data-driven revolution in sleep science and medicine.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Perez-Pozuelo, Ignacio  ORCID logo  https://orcid.org/0000-0003-1150-2754
Zhai, Bing 
Palotti, Joao 

Abstract

In recent years, there has been a significant expansion in the development and use of multi-modal sensors and technologies to monitor physical activity, sleep and circadian rhythms. These developments make accurate sleep monitoring at scale a possibility for the first time. Vast amounts of multi-sensor data are being generated with potential applications ranging from large-scale epidemiological research linking sleep patterns to disease, to wellness applications, including the sleep coaching of individuals with chronic conditions. However, in order to realise the full potential of these technologies for individuals, medicine and research, several significant challenges must be overcome. There are important outstanding questions regarding performance evaluation, as well as data storage, curation, processing, integration, modelling and interpretation. Here, we leverage expertise across neuroscience, clinical medicine, bioengineering, electrical engineering, epidemiology, computer science, mHealth and human-computer interaction to discuss the digitisation of sleep from a inter-disciplinary perspective. We introduce the state-of-the-art in sleep-monitoring technologies, and discuss the opportunities and challenges from data acquisition to the eventual application of insights in clinical and consumer settings. Further, we explore the strengths and limitations of current and emerging sensing methods with a particular focus on novel data-driven technologies, such as Artificial Intelligence.

Description

Keywords

Sleep, Biomedical engineering, Preventive Medicine, Diagnostic Markers, Predictive Markers

Journal Title

NPJ digital medicine

Conference Name

Journal ISSN

2398-6352

Volume Title

3

Publisher