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Optimization of personalized treatment strategies requires accurate patient phenotyping. Data to describe the broad variety of phenotypes can come from electronic health records, health insurance claims, socio\u2010economic administrative databases, environmental monitoring, social media, etc. Connected devices in and outside homes collect vast amount of data amassed in databases. All this contributes to \u2018Big Data\u2019 that, if used appropriately, has great potential for the benefit of health, well\u2010being and therapeutics. Sleep apnoea is particularly well placed with regards to Big Data because the primary treatment is positive airway pressure (PAP). PAP devices, used every night over long periods by millions of patients across the world, generate an enormous amount of data. In this review, we discuss how different types of Big Data have, and could be, used to improve our understanding of sleep\u2010disordered breathing, to identify undiagnosed sleep apnoea, to personalize treatment and to adapt health policies and better allocate resources. We discuss some of the challenges of Big Data including the need for appropriate data management, compilation and analysis techniques employing innovative statistical approaches alongside machine learning\/artificial intelligence; closer collaboration between data scientists and physicians; and respect of the ethical and regulatory constraints of collecting and using Big Data. Lastly, we consider how Big Data can be used to overcome the limitations of randomized clinical trials and advance real\u2010life evidence\u2010based medicine for sleep apnoea.<\/jats:p>","DOI":"10.1111\/resp.13669","type":"journal-article","created":{"date-parts":[[2019,8,14]],"date-time":"2019-08-14T12:57:45Z","timestamp":1565787465000},"page":"486-494","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["Big Data in sleep apnoea: Opportunities and challenges"],"prefix":"10.1111","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3832-2358","authenticated-orcid":false,"given":"Jean\u2010Louis","family":"P\u00e9pin","sequence":"first","affiliation":[{"name":"HP2 Laboratory, INSERM U1042 University Grenoble Alpes  Grenoble France"},{"name":"EFCR Laboratory CHU de Grenoble Alpes  Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S\u00e9bastien","family":"Bailly","sequence":"additional","affiliation":[{"name":"HP2 Laboratory, INSERM U1042 University Grenoble Alpes  Grenoble France"},{"name":"EFCR Laboratory CHU de Grenoble Alpes  Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renaud","family":"Tamisier","sequence":"additional","affiliation":[{"name":"HP2 Laboratory, INSERM U1042 University Grenoble Alpes  Grenoble France"},{"name":"EFCR Laboratory CHU de Grenoble Alpes  Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,8,14]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/nrdp.2015.15"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2016.11.069"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.117.029400"},{"key":"e_1_2_9_5_1","doi-asserted-by":"crossref","unstructured":"BaillyS DestorsM GrilletY RichardP StachB VivodtzevI TimsitJF LevyP TamisierR PepinJL;Scientific Council and Investigators of the French National Sleep Apnea Registry (OSFP). 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