{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:03:33Z","timestamp":1755219813888,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643686080"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,7]]},"abstract":"<jats:p>This study examined clinical trial trends to guide digital biomarker (dBM) guideline development. Analysis of 2005\u20132023 data was conducted to assess the frequency and types of dBM used as endpoints (dEP) in these trials and the associated target diseases. Clinical trials using dEP increased from 0\u20137 per year (2005\u20132019) to 15\u201320 annually from 2020. Endocrine and metabolic conditions were the most common targets, showing a distinct disease distribution compared to overall trials. Most measurements used actigraphy devices or blood glucose sensors, with glucose sensors focusing on metabolic conditions while actigraphy covered broader applications. Additionally, 42.4% of trials used dEP as primary endpoints. While dEP use is growing, it remains limited in disease scope and device variety. Expanding both would enhance their utility in clinical research.<\/jats:p>","DOI":"10.3233\/shti250868","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:33:33Z","timestamp":1754566413000},"source":"Crossref","is-referenced-by-count":0,"title":["Trend of Digital Biomarkers (dBM) as Endpoints in Clinical Trials: Secondary Analysis of Open Data"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8592-5499","authenticated-orcid":false,"given":"Mizuki","family":"Morita","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, Faculty of Interdisciplinary Science and Engineering in Health Systems, Okayama University"},{"name":"Faculty of Health Sciences, Okayama University Medical School"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mina","family":"Honjoh","sequence":"additional","affiliation":[{"name":"Faculty of Health Sciences, Okayama University Medical School"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3251-384X","authenticated-orcid":false,"given":"Takahiro","family":"Yamane","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, Faculty of Interdisciplinary Science and Engineering in Health Systems, Okayama University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250868","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:33:33Z","timestamp":1754566413000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250868"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250868","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}