{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T08:54:42Z","timestamp":1773392082360,"version":"3.50.1"},"reference-count":57,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T00:00:00Z","timestamp":1773360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:p>Digital biomarker research has generated thousands of publications demonstrating associations between sensor-derived measures and clinical conditions, yet clinical adoption remains negligible. We identify a foundational problem: the field lacks consensus on what constitutes a digital biomarker, applying identical terminology to direct physiological measurement (continuous glucose monitoring), algorithmic prediction of biological substrates (voice analysis for dopaminergic function), and purely behavioral correlates (GPS mobility and depression scores). This terminological ambiguity obscures validation requirements and prevents evidence synthesis. We argue that the \u201cbio-\u201d in \u201cdigital biomarker\u201d refers to a property of the measurement itself\u2014the marker must be derived from biology, not merely predictive of biological or clinical outcomes. Under this restrictive definition, behavioral correlates without demonstrated biological grounding, however statistically robust or clinically useful, should be designated as digital phenotypes or digital health indicators rather than biomarkers. This distinction clarifies validation pathways: biologically derived markers require technical accuracy validation against established biological reference standards; candidate biomarkers inferred from behavior require biological criterion validation before that status is claimed; behavioral indicators require outcome prediction validation. We demonstrate how this framework resolves current validation confusion and accelerates translation by aligning evidence standards with measurement types.<\/jats:p>","DOI":"10.3389\/fdgth.2026.1796008","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T06:47:35Z","timestamp":1773384455000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Toward terminological clarity in digital biomarker research"],"prefix":"10.3389","volume":"8","author":[{"given":"Madhur","family":"Mangalam","sequence":"first","affiliation":[{"name":"Department of Biomechanics, University of Nebraska at Omaha","place":["Omaha, NE, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ken","family":"Kiyono","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering Science, University of Osaka","place":["Toyonaka, Osaka, Japan"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,3,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1038\/s41746-022-00583-z","article-title":"Digital biomarkers: convergence of digital health technologies and biomarkers","volume":"5","author":"Vasudevan","year":"2022","journal-title":"npj Digit Med"},{"key":"B2","doi-asserted-by":"publisher","first-page":"13414","DOI":"10.1038\/s41598-019-50002-9","article-title":"Touchscreen typing pattern analysis for remote detection of the depressive tendency","volume":"9","author":"Mastoras","year":"2019","journal-title":"Sci Rep"},{"key":"B3","doi-asserted-by":"publisher","first-page":"e241","DOI":"10.2196\/jmir.9775","article-title":"Predicting mood disturbance severity with mobile phone keystroke metadata: a biaffect digital phenotyping study","volume":"20","author":"Zulueta","year":"2018","journal-title":"J Med Internet Res"},{"key":"B4","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1038\/s41746-019-0182-1","article-title":"Gps mobility as a digital biomarker of negative symptoms in schizophrenia: a case control study","volume":"2","author":"Depp","year":"2019","journal-title":"npj Digit Med"},{"key":"B5","doi-asserted-by":"publisher","first-page":"e34898","DOI":"10.2196\/34898","article-title":"Longitudinal relationships between depressive symptom severity and phone-measured mobility: dynamic structural equation modeling study","volume":"9","author":"Zhang","year":"2022","journal-title":"JMIR Ment Health"},{"key":"B6","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.jbct.2020.03.018","article-title":"Ecological momentary assessment of the relationships between social activity and mood in bipolar disorder","volume":"30","author":"Kamarsu","year":"2020","journal-title":"J Behav Cogn Ther"},{"key":"B7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3130960","article-title":"Inferring mood instability on social media by leveraging ecological momentary assessments","volume":"1","author":"Saha","year":"2017","journal-title":"Proc ACM Interact Mobile Wearable Ubiquitous Technol"},{"key":"B8","doi-asserted-by":"publisher","first-page":"151","DOI":"10.3390\/s22010151","article-title":"Stress watch: the use of heart rate and heart rate variability to detect stress: a pilot study using smart watch wearables","volume":"22","author":"Chalmers","year":"2021","journal-title":"Sensors"},{"key":"B9","doi-asserted-by":"publisher","first-page":"2873","DOI":"10.3390\/s21082873","article-title":"HRV features as viable physiological markers for stress detection using wearable devices","volume":"21","author":"Dalmeida","year":"2021","journal-title":"Sensors"},{"key":"B10","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1186\/s12889-018-6321-9","article-title":"Is screen time associated with anxiety or depression in young people? 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