{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T02:17:15Z","timestamp":1790389035299,"version":"4.1.0"},"reference-count":57,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Ment Health"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Digital phenotyping refers to the objective measurement of human behavior via devices such as smartphones or watches and constitutes a promising advancement in personalized medicine. Digital phenotypes derived from heart rate, mobility, or sleep schedule data have been used in psychiatry to either diagnose individuals with psychotic disorders or to predict relapse as a binary outcome. Machine learning models so far have achieved predictive accuracies that are significant but not large enough for clinical applications. This could hinge on broad clinical definitions, which encompass heterogeneous symptom and sign ensembles, thus hindering accurate classification. The 5-factor model for the Positive and Negative Syndrome Scale (PANSS), which entails 5 independently varying dimensions, is thought to better capture symptom variability. Using the specific definitions of this refined clinical taxonomy in combination with digital phenotypes could yield more precise results.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aims to investigate potential links between digital phenotypes and each dimension of the 5-factor PANSS model. We also assess whether clinical, demographic, and medication variables confound said reactions.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n In the e-Prevention study, heart rate, accelerometer, gyroscope, and sleep schedule data were continuously collected via smartwatch for a maximum of 26 months in 38 patients with psychotic spectrum disorders. Obtaining the mean and SD for each patient-month resulted in a database consisting of more than 740 monthly data points. A linear mixed model analysis was used to ascertain connections between monthly aggregated heart rate and mobility features and the 5 symptom dimension scores of PANSS, obtained during monthly clinical interviews.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n An increase in positive symptoms was associated with a decrease in heart interpulse variation during sleep (\n t<\/jats:italic>\n 570.7<\/jats:sub>\n =\u22123.3\n \n ,\n P<\/jats:italic>\n <.001,\n f<\/jats:italic>\n 2<\/jats:sup>\n =0.021), while an increase in negative symptoms was associated with a decrease in accelerometer (mean:\n t<\/jats:italic>\n 22.1<\/jats:sub>\n =\u22123.1\n \n ,\n P<\/jats:italic>\n =.005,\n \n f<\/jats:italic>\n 2<\/jats:sup>\n =0.042; SD:\n t<\/jats:italic>\n 20<\/jats:sub>\n =\u22122.4\n \n ,\n P<\/jats:italic>\n =.03,\n \n f<\/jats:italic>\n 2<\/jats:sup>\n =0.019), gyroscope (mean:\n t<\/jats:italic>\n 22.9<\/jats:sub>\n =\u22122.8\n \n ,\n P<\/jats:italic>\n =.01,\n \n f<\/jats:italic>\n 2<\/jats:sup>\n =0.016), and locomotive motor activity (\n t<\/jats:italic>\n 17.2<\/jats:sub>\n =\u22122.4\n \n ,\n P<\/jats:italic>\n =.03,\n \n f<\/jats:italic>\n 2<\/jats:sup>\n =0.016) during wakefulness. An increase in accelerometer (mean:\n t<\/jats:italic>\n 564.4<\/jats:sub>\n =2.8\n \n ,\n P<\/jats:italic>\n =.005,\n \n f<\/jats:italic>\n 2<\/jats:sup>\n =0.017; SD:\n t<\/jats:italic>\n 551.6<\/jats:sub>\n =2.5,\n P<\/jats:italic>\n =.01,\n \n f<\/jats:italic>\n 2<\/jats:sup>\n =0.015) and gyroscope (mean:\n t<\/jats:italic>\n 564.5<\/jats:sub>\n =3.2\n \n ,\n P<\/jats:italic>\n =.001,\n \n f<\/jats:italic>\n 2<\/jats:sup>\n =0.022; SD:\n t<\/jats:italic>\n 569.2<\/jats:sub>\n =2.8\n \n ,\n P<\/jats:italic>\n =.005,\n \n f<\/jats:italic>\n 2<\/jats:sup>\n =0.017) motor activity during sleep was related to an increase in depression\/anxiety symptoms as well as excitement\/hostility symptoms (accelerometer SD:\n t<\/jats:italic>\n 469.7<\/jats:sub>\n =3.2\n \n ,\n P<\/jats:italic>\n =.002,\n \n f<\/jats:italic>\n 2<\/jats:sup>\n =0.031; gyroscope mean:\n t<\/jats:italic>\n 497<\/jats:sub>\n =2.3\n \n ,\n P<\/jats:italic>\n =.03,\n \n f<\/jats:italic>\n 2<\/jats:sup>\n =0.013; SD:\n t<\/jats:italic>\n 507.7<\/jats:sub>\n =3.2\n \n ,\n P<\/jats:italic>\n =.001,\n \n f<\/jats:italic>\n 2<\/jats:sup>\n =0.029). Excitement\/hostility symptoms were further associated with an increase in normalized heart rate during sleep (\n t<\/jats:italic>\n 368.2<\/jats:sub>\n =3.2,\n P<\/jats:italic>\n =.001,\n \n f<\/jats:italic>\n 2<\/jats:sup>\n =0.044) and reduced sleep:wake ratio (\n t<\/jats:italic>\n 562<\/jats:sub>\n =\u22122.7\n \n ,\n P<\/jats:italic>\n =.007,\n \n f<\/jats:italic>\n 2<\/jats:sup>\n =0.013). An increase in cognitive\/disorganization symptoms was related to a decrease in the SD of normalized heart rate during wakefulness (\n t<\/jats:italic>\n 574.5<\/jats:sub>\n =\u22123.5\n \n ,\n P<\/jats:italic>\n <.001,\n \n f<\/jats:italic>\n 2<\/jats:sup>\n =0.013).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n This study provides evidence that biological changes assessed by continuous measurement of digital phenotypes could be characteristic of specific symptom clusters rather than entire diagnostic categories of psychotic disorders. These results support the use of digital phenotypes not only as a means for remote patient monitoring but also as concrete targets for biomarker research in psychotic disorders.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/75774","type":"journal-article","created":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T13:30:06Z","timestamp":1762263006000},"page":"e75774-e75774","source":"Crossref","is-referenced-by-count":8,"title":["Smartwatch-Derived Digital Phenotypes Relate to Psychopathology Dimensions in Patients With Psychotic Spectrum Disorders: Longitudinal Observational 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