TY  - JOUR
AB  - Background: 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. 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. Digital collection of self-report measures by patients is a time- and cost-efficient approach to gain such data throughout treatment. 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. 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. Tablet-handling competency and the speed of data entry were assessed. Depression severity was additionally assessed by a clinical interviewer at baseline and before discharge. 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. During the duration of inpatient treatment, 79% of expected assessments were completed, with an average of 4 completed assessments per participant; 4 participants (4/242, 1.6%) dropped out of the study prematurely. During data entry, 89.3% of participants (216/242) did not require additional support. Needing support with tablet handling and slower data entry pace were predicted by older age, whereas depression severity at baseline did not influence these measures. Patient self-reporting of depression severity showed high agreement with standardized external assessments by a clinical interviewer. Conclusions: Our results indicate that digital collection of self-report measures is a feasible, accessible, and valid method for longitudinal data collection in psychiatric routine, which will eventually facilitate the identification of individual risk and resilience factors for affective disorders and pave the way toward personalized psychiatric care.
AU  - Richter, Maike Frederike
AU  - Storck, Michael
AU  - Storck, Michael Martin
AU  - Blitz, Rogério Marcus
AU  - Goltermann, Janik
AU  - Seipp, Juliana
AU  - Dannlowski, Udo
AU  - Baune, Bernhard Th.
AU  - Baune, Bernhard Theodor
AU  - Dugas, Martin
AU  - Opel, Nils
AU  - Opel, Nils Thorben Leonhardt
DA  - 2020-12-01
DO  - 10.17879/94019506695
KW  - affective disorders
KW  - digital data collection
KW  - psychiatry
KW  - P4 medicine
LA  - eng
N1  - JMIR Mental Health 7 (2020) 12, e24066, 1-12
N1  - Finanziert durch den Open-Access-Publikationsfonds der Westfälischen Wilhelms-Universität Münster (WWU Münster).
PY  - 2020-12-01
TI  - Repeated Digitized Assessment of Risk and Symptom Profiles During Inpatient Treatment of Affective Disorder: Observational Study
UR  - https://nbn-resolving.org/urn:nbn:de:hbz:6-24019714890
Y2  - 2024-11-22T03:58:42
ER  -