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After quantifying 46 behavioral markers (eg, % time at home), we applied a machine learning approach to (1) determine which markers are relevant for depression\/anxiety recognition and (2) develop and evaluate diagnostic prediction models for doing so.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Our analysis identifies the total number of GPS-based trajectories as a potential marker of depression\/anxiety, where individuals with fewer trajectories are more likely to be symptomatic. Models using this feature in combination with demographics or in isolation outperformed demographics-only models (area under the receiver operating characteristic curve\n \n Mdn<\/jats:sub>\n <\/jats:italic>\n =0.60 vs 0.60 vs 0.51).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Collectively, these findings indicate that smartphone-tracked behavioral markers have limited discriminant ability in our study but potential to support future depression\/anxiety diagnostics.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/80765","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T14:10:09Z","timestamp":1761919809000},"page":"e80765-e80765","source":"Crossref","is-referenced-by-count":2,"title":["Using Smartphone-Tracked Behavioral Markers to Recognize Depression and Anxiety Symptoms: Cross-Sectional Digital Phenotyping Study"],"prefix":"10.2196","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7140-1536","authenticated-orcid":false,"given":"George","family":"Aalbers","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7158-634X","authenticated-orcid":false,"given":"Andrea","family":"Costanzo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5320-3341","authenticated-orcid":false,"given":"Raj","family":"Jagesar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4344-5766","authenticated-orcid":false,"given":"Femke","family":"Lamers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4471-8618","authenticated-orcid":false,"given":"Martien J H","family":"Kas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7779-9672","authenticated-orcid":false,"given":"Brenda W J H","family":"Penninx","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,1,26]]},"reference":[{"issue":"9690","key":"R1","doi-asserted-by":"publisher","first-page":"609","DOI":"10.1016\/S0140-6736(09)60879-5","article-title":"Clinical diagnosis of depression in primary care: a meta-analysis","volume":"374","author":"Mitchell","journal-title":"Lancet"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jad.2021.09.046","article-title":"What is the short-term remission rate for people with untreated depression? 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