![]() ![]() Results: Of 364 affective disorder patients who were approached, 242 (66.5%) participated in the study 88.8% of participants (215/242) were diagnosed with major depressive disorder, and 27 (11.2%) had bipolar disorder. Depression severity was additionally assessed by a clinical interviewer at baseline and before discharge. Tablet-handling competency and the speed of data entry were assessed. Methods: We implemented a system for longitudinal digital collection of risk and symptom profiles based on repeated self-reports via tablet computers throughout inpatient treatment of affective disorders at the Department of Psychiatry at the University of Münster. Objective: The objective of this study was to investigate whether patients with severe affective disorders were willing and able to participate in such efforts, whether the feasibility of such systems might vary depending on individual patient characteristics, and if digitally acquired assessments were of sufficient diagnostic validity. Digital collection of self-report measures by patients is a time- and cost-efficient approach to gain such data throughout treatment. Yet, in order to translate personalized predictive modeling from research contexts to psychiatric clinical routine, standardized collection of information of sufficient detail and temporal resolution in day-to-day clinical care is needed. Online Journal of Public Health InformaticsĮmail: Predictive models have revealed promising results for the individual prognosis of treatment response and relapse risk as well as for differential diagnosis in affective disorders. ![]() Asian/Pacific Island Nursing Journal 15 articles.JMIR Bioinformatics and Biotechnology 38 articles.JMIR Biomedical Engineering 76 articles.Journal of Participatory Medicine 84 articles.JMIR Perioperative Medicine 102 articles.JMIR Rehabilitation and Assistive Technologies 234 articles.JMIR Pediatrics and Parenting 323 articles.Interactive Journal of Medical Research 346 articles.JMIR Public Health and Surveillance 1276 articles.Journal of Medical Internet Research 8119 articles. ![]()
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