{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T20:59:48Z","timestamp":1781125188469,"version":"3.54.1"},"reference-count":62,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Big Data &amp; Society"],"published-print":{"date-parts":[[2021,7]]},"abstract":"<jats:p>Precision medicine and digital phenotyping are two prominent data-based approaches within digital medicine. While precision medicine historically used primarily genetic data to find targeted treatment options, digital phenotyping relies on the usage of big data deriving from digital devices such as smartphones, wearables and other connected devices. This paper first focusses on the aspect of data type to explore differences and similarities between precision medicine and digital phenotyping. It outlines different ways of data collection and production and the consequences thereof. Second, it shows how these sorts of data influence dominant beliefs in the field: The field of precision medicine relying on the dominant understanding of \u2018genetic determinism\u2019 imported from genetics, digital phenotyping building on the logic of \u2018data fundamentalism\u2019. In the end, the analysis shows how digital data informs potentials as well as challenges of precision medicine and digital phenotyping: a better health care for (some) individuals connected with individualisation and responsibilisation for all, with a prognosed shift from reactive to preventive medicine. Additionally, data-based approaches might facilitate epistemological and ontological redirections for the whole field of medicine that will also affect knowledge production and a reassessment of the value of different types of knowledge (quantifiable vs. non-quantifiable) with all its consequences. Institutionally, it might lead to shifts in distribution of power to experts in big data related technologies, i.e. private companies.<\/jats:p>","DOI":"10.1177\/20539517211066452","type":"journal-article","created":{"date-parts":[[2021,12,22]],"date-time":"2021-12-22T12:49:21Z","timestamp":1640177361000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":37,"title":["Precision medicine and digital phenotyping: Digital medicine's way from more data to better health"],"prefix":"10.1177","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3401-1870","authenticated-orcid":false,"given":"Renate","family":"Baumgartner","sequence":"first","affiliation":[{"name":"University of T\u00fcbingen, Center for Gender and Diversity Research (ZGD), T\u00fcbingen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2021,12,22]]},"reference":[{"key":"bibr1-20539517211066452","doi-asserted-by":"publisher","DOI":"10.28968\/cftt.v5i1.29612"},{"key":"bibr2-20539517211066452","doi-asserted-by":"publisher","DOI":"10.1038\/s41386-018-0030-z"},{"key":"bibr3-20539517211066452","doi-asserted-by":"publisher","DOI":"10.1111\/1467-9566.13175"},{"key":"bibr4-20539517211066452","doi-asserted-by":"publisher","DOI":"10.1080\/1369118X.2012.678878"},{"key":"bibr5-20539517211066452","doi-asserted-by":"publisher","DOI":"10.1016\/j.neubiorev.2019.07.009"},{"key":"bibr6-20539517211066452","unstructured":"Burnett F (2015) Four questions for NIMH Director Thomas R. 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