The future of sleep health: a data-driven revolution in sleep science and medicine
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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2020-03-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-020-0244-4 |
| _version_ | 1797421384855453696 |
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| author | Ignacio Perez-Pozuelo Bing Zhai Joao Palotti Raghvendra Mall Michaël Aupetit Juan M. Garcia-Gomez Shahrad Taheri Yu Guan Luis Fernandez-Luque |
| author_facet | Ignacio Perez-Pozuelo Bing Zhai Joao Palotti Raghvendra Mall Michaël Aupetit Juan M. Garcia-Gomez Shahrad Taheri Yu Guan Luis Fernandez-Luque |
| author_sort | Ignacio Perez-Pozuelo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T07:15:09Z |
| format | Article |
| id | doaj.art-48502e85fd554e4883793728caa1dcc7 |
| institution | Directory Open Access Journal |
| issn | 2398-6352 |
| language | English |
| last_indexed | 2024-03-09T07:15:09Z |
| publishDate | 2020-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj.art-48502e85fd554e4883793728caa1dcc72023-12-03T08:33:31ZengNature Portfolionpj Digital Medicine2398-63522020-03-013111510.1038/s41746-020-0244-4The future of sleep health: a data-driven revolution in sleep science and medicineIgnacio Perez-Pozuelo0Bing Zhai1Joao Palotti2Raghvendra Mall3Michaël Aupetit4Juan M. Garcia-Gomez5Shahrad Taheri6Yu Guan7Luis Fernandez-Luque8Department of Medicine, University of CambridgeOpen Lab, University of NewcastleQatar Computing Research Institute, HBKUQatar Computing Research Institute, HBKUQatar Computing Research Institute, HBKUBDSLab, Instituto Universitario de Tecnologias de la Informacion y Comunicaciones-ITACA, Universitat Politecnica de ValenciaDepartment of Medicine and Clinical Research Core, Weill Cornell Medicine - Qatar, Qatar FoundationOpen Lab, University of NewcastleQatar Computing Research Institute, HBKUAbstract 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.https://doi.org/10.1038/s41746-020-0244-4 |
| spellingShingle | Ignacio Perez-Pozuelo Bing Zhai Joao Palotti Raghvendra Mall Michaël Aupetit Juan M. Garcia-Gomez Shahrad Taheri Yu Guan Luis Fernandez-Luque The future of sleep health: a data-driven revolution in sleep science and medicine npj Digital Medicine |
| title | The future of sleep health: a data-driven revolution in sleep science and medicine |
| title_full | The future of sleep health: a data-driven revolution in sleep science and medicine |
| title_fullStr | The future of sleep health: a data-driven revolution in sleep science and medicine |
| title_full_unstemmed | The future of sleep health: a data-driven revolution in sleep science and medicine |
| title_short | The future of sleep health: a data-driven revolution in sleep science and medicine |
| title_sort | future of sleep health a data driven revolution in sleep science and medicine |
| url | https://doi.org/10.1038/s41746-020-0244-4 |
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