{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T03:19:28Z","timestamp":1790306368684,"version":"4.1.0"},"reference-count":33,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T00:00:00Z","timestamp":1716854400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Acta Psychiatr Scand"],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Clinical assessment of mood and anxiety change often relies on clinical assessment or self\u2010reported scales. Using smartphone digital phenotyping data and resulting markers of behavior (e.g., sleep) to augment clinical symptom scores offers a scalable and potentially more valid method to understand changes in patients' state. This paper explores the potential of using a combination of active and passive sensors in the context of smartphone\u2010based digital phenotyping to assess mood and anxiety changes in two distinct cohorts of patients to assess the preliminary reliability and validity of this digital phenotyping method.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Participants from two different cohorts, each\n                      <jats:italic>n<\/jats:italic>\n                      \u2009=\u200976, one with diagnoses of depression\/anxiety and the other schizophrenia, utilized mindLAMP to collect active data (e.g., surveys on mood\/anxiety), along with passive data consisting of smartphone digital phenotyping data (geolocation, accelerometer, and screen state) for at least 1\u2009month. Using anomaly detection algorithms, we assessed if statistical anomalies in the combination of active and passive data could predict changes in mood\/anxiety scores as measured via smartphone surveys.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The anomaly detection model was reliably able to predict symptom change of 4 points or greater for depression as measured by the PHQ\u20109 and anxiety as measured for the GAD\u20108 for both patient populations, with an area under the ROC curve of 0.65 and 0.80 for each respectively. For both PHQ\u20109 and GAD\u20107, these AUCs were maintained when predicting significant symptom change at least 7\u2009days in advance. Active data alone predicted around 52% and 75% of the symptom variability for the depression\/anxiety and schizophrenia populations respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>These results indicate the feasibility of anomaly detection for predicting symptom change in transdiagnostic cohorts. These results across different patient groups, different countries, and different sites (India and the US) suggest anomaly detection of smartphone digital phenotyping data may offer a reliable and valid approach to predicting symptom change. Future work should emphasize prospective application of these statistical methods.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/acps.13712","type":"journal-article","created":{"date-parts":[[2024,5,29]],"date-time":"2024-05-29T02:29:01Z","timestamp":1716949741000},"page":"388-400","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Digital phenotyping data and anomaly detection methods to assess changes in mood and anxiety symptoms across a transdiagnostic clinical sample"],"prefix":"10.1111","volume":"151","author":[{"given":"Asher","family":"Cohen","sequence":"first","affiliation":[{"name":"Beth Israel Deaconess Medical Center Harvard Medical School  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Naslund","sequence":"additional","affiliation":[{"name":"Department of Global Health and Social Medicine Harvard Medical School  Boston Massachusetts 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