The future of sleep health: a data-driven revolution in sleep science and medicine
<p>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...
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| مؤلفون آخرون: | , , , , , , , |
| منشور في: |
2020
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| _version_ | 1864513566685528064 |
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| author | Ignacio Perez-Pozuelo (14153007) |
| author2 | Bing Zhai (157369) Joao Palotti (8479842) Raghvendra Mall (581171) Michaël Aupetit (14153013) Juan M. Garcia-Gomez (14153016) Shahrad Taheri (57360) Yu Guan (475656) Luis Fernandez-Luque (3572423) |
| author2_role | author author author author author author author author |
| author_facet | Ignacio Perez-Pozuelo (14153007) Bing Zhai (157369) Joao Palotti (8479842) Raghvendra Mall (581171) Michaël Aupetit (14153013) Juan M. Garcia-Gomez (14153016) Shahrad Taheri (57360) Yu Guan (475656) Luis Fernandez-Luque (3572423) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ignacio Perez-Pozuelo (14153007) Bing Zhai (157369) Joao Palotti (8479842) Raghvendra Mall (581171) Michaël Aupetit (14153013) Juan M. Garcia-Gomez (14153016) Shahrad Taheri (57360) Yu Guan (475656) Luis Fernandez-Luque (3572423) |
| dc.date.none.fl_str_mv | 2020-03-23T21:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41746-020-0244-4 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/The_future_of_sleep_health_a_data-driven_revolution_in_sleep_science_and_medicine/21598263 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Health services and systems Information and computing sciences Machine learning Biomedical engineering Diagnostic markers Predictive markers Preventive medicine Sleep |
| dc.title.none.fl_str_mv | The future of sleep health: a data-driven revolution in sleep science and medicine |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>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.</p><h2>Other Information</h2> <p> Published in: npj Digital Medicine<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1038/s41746-020-0244-4" target="_blank">http://dx.doi.org/10.1038/s41746-020-0244-4</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0e2f0c986cfcfe268ca17b3b77552434 |
| identifier_str_mv | 10.1038/s41746-020-0244-4 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/21598263 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The future of sleep health: a data-driven revolution in sleep science and medicineIgnacio Perez-Pozuelo (14153007)Bing Zhai (157369)Joao Palotti (8479842)Raghvendra Mall (581171)Michaël Aupetit (14153013)Juan M. Garcia-Gomez (14153016)Shahrad Taheri (57360)Yu Guan (475656)Luis Fernandez-Luque (3572423)Health sciencesHealth services and systemsInformation and computing sciencesMachine learningBiomedical engineeringDiagnostic markersPredictive markersPreventive medicineSleep<p>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.</p><h2>Other Information</h2> <p> Published in: npj Digital Medicine<br> License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1038/s41746-020-0244-4" target="_blank">http://dx.doi.org/10.1038/s41746-020-0244-4</a></p>2020-03-23T21:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41746-020-0244-4https://figshare.com/articles/journal_contribution/The_future_of_sleep_health_a_data-driven_revolution_in_sleep_science_and_medicine/21598263CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/215982632020-03-23T21:00:00Z |
| spellingShingle | The future of sleep health: a data-driven revolution in sleep science and medicine Ignacio Perez-Pozuelo (14153007) Health sciences Health services and systems Information and computing sciences Machine learning Biomedical engineering Diagnostic markers Predictive markers Preventive medicine Sleep |
| status_str | publishedVersion |
| 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 | The future of sleep health: a data-driven revolution in sleep science and medicine |
| topic | Health sciences Health services and systems Information and computing sciences Machine learning Biomedical engineering Diagnostic markers Predictive markers Preventive medicine Sleep |