{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T10:26:15Z","timestamp":1791887175812,"version":"4.3.4"},"reference-count":53,"publisher":"JMIR Publications Inc.","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J Med Internet Res"],"abstract":"\n Background<\/jats:title>\n The lack of access to mental health care could be addressed, in part, through the development of automated screening technologies for detecting the most common mental health disorders without the direct involvement of clinicians. Objective smartphone-collected data may contain sufficient information about individuals\u2019 behaviors to infer their mental states and therefore screen for anxiety disorders and depression.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n The objective of this study is to compare how a single set of recognized and novel features, extracted from smartphone-collected data, can be used for predicting generalized anxiety disorder (GAD), social anxiety disorder (SAD), and depression.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n An Android app was designed, together with a centralized server system, to collect periodic measurements of objective smartphone data. The types of data included samples of ambient audio, GPS location, screen state, and light sensor data. Subjects were recruited into a 2-week observational study in which the app was run on their personal smartphones. The subjects also completed self-report severity measures of SAD, GAD, and depression. The participants were 112 Canadian adults from a nonclinical population. High-level features were extracted from the data of 84 participants, and predictive models of SAD, GAD, and depression were built and evaluated.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Models of SAD and depression achieved a significantly greater screening accuracy than uninformative models (area under the receiver operating characteristic means of 0.64, SD 0.13 and 0.72, SD 0.12, respectively), whereas models of GAD failed to be predictive. Investigation of the model coefficients revealed key features that were predictive of SAD and depression.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n We demonstrate the ability of a common set of features to act as predictors in the models of both SAD and depression. This suggests that the types of behaviors that can be inferred from smartphone-collected data are broad indicators of mental health, which can be used to study, assess, and track psychopathology simultaneously across multiple disorders and diagnostic boundaries.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/28918","type":"journal-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T09:31:47Z","timestamp":1628847107000},"page":"e28918","source":"Crossref","is-referenced-by-count":50,"title":["Automated Screening for Social Anxiety, Generalized Anxiety, and Depression From Objective Smartphone-Collected Data: Cross-sectional Study"],"prefix":"10.2196","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6082-267X","authenticated-orcid":false,"given":"Daniel","family":"Di 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