{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T18:03:19Z","timestamp":1773511399511,"version":"3.50.1"},"reference-count":77,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T00:00:00Z","timestamp":1726099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Epidemiological and Social Psychiatric Research Institute (ESPRi)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The rising popularity of wearable devices allows for extensive and unobtrusive collection of personal health data for extended periods of time. Recent studies have used machine learning to create predictive algorithms to assess symptoms of major depressive disorder (MDD) based on these data. This review evaluates the clinical relevance of these models. Studies were selected to represent the range of methodologies and applications of wearables for MDD algorithms, with a focus on wrist-worn devices. The reviewed studies demonstrated that wearable-based algorithms were able to predict symptoms of MDD with considerable accuracy. These models may be used in the clinic to complement the monitoring of treatments or to facilitate early intervention in high-risk populations. In a preventative context, they could prompt users to seek help for earlier intervention and better clinical outcomes. However, the lack of standardized methodologies and variation in which performance metrics are reported complicates direct comparisons between studies. Issues with reproducibility, overfitting, small sample sizes, and limited population demographics also limit the generalizability of findings. As such, wearable-based algorithms show considerable promise for predicting and monitoring MDD, but there is significant room for improvement before this promise can be fulfilled.<\/jats:p>","DOI":"10.3390\/a17090408","type":"journal-article","created":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T02:30:23Z","timestamp":1726108223000},"page":"408","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Using Wearable Technology to Detect, Monitor, and Predict Major Depressive Disorder\u2014A Scoping Review and Introductory Text for Clinical Professionals"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7798-4713","authenticated-orcid":false,"given":"Quinty","family":"Walschots","sequence":"first","affiliation":[{"name":"Faculty of Medicine, Leiden University, P.O. Box 9500, 2300 RA Leiden, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2043-8566","authenticated-orcid":false,"given":"Milan","family":"Zarchev","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Epidemiological and Social Psychiatric Research Institute (ESPRi), Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands"},{"name":"Department of Psychiatry, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1920-6001","authenticated-orcid":false,"given":"Maurits","family":"Unkel","sequence":"additional","affiliation":[{"name":"Department of Neuroscience, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4455-6492","authenticated-orcid":false,"given":"Astrid","family":"Kamperman","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Epidemiological and Social Psychiatric Research Institute (ESPRi), Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands"},{"name":"Department of Psychiatry, Erasmus MC, University Medical Center Rotterdam, P.O. Box 2040, 3000 CA Rotterdam, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.pmedr.2016.10.014","article-title":"National physical activity surveillance: Users of wearable activity monitors as a potential data source","volume":"5","author":"Omura","year":"2017","journal-title":"Prev. Med. 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