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Weiß, M.J.* ; Gutzeit, J.* ; Appel, K.S.* ; Bahmer, T.* ; Beutel, M.* ; Deckert, J.* ; Fricke, J.* ; Hans, S.* ; Hettich-Damm, N.* ; Heuschmann, P.U.* ; Horn, A.* ; Jauch-Chara, K.* ; Kohls, M.* ; Krist, L.* ; Lorenz-Depiereux, B. ; Otte, C.* ; Pape, D.* ; Reese, J.P.* ; Schreiber, S.* ; Störk, S.* ; Vehreschild, J.J.* ; Hein, G.*

Depression and fatigue six months post-COVID-19 disease are associated with overlapping symptom constellations: A prospective, multi-center, population-based cohort study.

J. Affect. Disord. 352, 296-305 (2024)
Verlagsversion DOI PMC
Open Access Gold (Paid Option)
Creative Commons Lizenzvertrag
BACKGROUND: Depression and fatigue are commonly observed sequelae following viral diseases such as COVID-19. Identifying symptom constellations that differentially classify post-COVID depression and fatigue may be helpful to individualize treatment strategies. Here, we investigated whether self-reported post-COVID depression and post-COVID fatigue are associated with the same or different symptom constellations. METHODS: To address this question, we used data from COVIDOM, a population-based cohort study conducted as part of the NAPKON-POP platform. Data was collected in three different German regions (Kiel, Berlin, Würzburg). We analyzed data from >2000 individuals at least six months past a PCR-confirmed COVID-19 disease, using elastic net regression and cluster analysis. The regression model was developed in the Kiel data set, and externally validated using data sets from Berlin and Würzburg. RESULTS: Our results revealed that post-COVID depression and fatigue are associated with overlapping symptom constellations consisting of difficulties with daily activities, perceived health-related quality of life, chronic exhaustion, unrestful sleep, and impaired concentration. Confirming the overlap in symptom constellations, a follow-up cluster analysis could categorize individuals as scoring high or low on depression and fatigue but could not differentiate between both dimensions. LIMITATIONS: The data presented are cross-sectional, consisting primarily of self-reported questionnaire or medical records rather than biometrically collected data. CONCLUSIONS: In summary, our results suggest a strong link between post-COVID depression and fatigue and thus highlighting the need for integrative treatment approaches.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Elastic Net Regression ; Machine Learning ; Post-covid Depression ; Post-covid Fatigue; Cognitive-behavioral Therapy; Graded-exercise; Cancer
ISSN (print) / ISBN 0165-0327
e-ISSN 1573-2517
Zeitschrift Journal of Affective Disorders
Quellenangaben Band: 352, Heft: , Seiten: 296-305 Artikelnummer: , Supplement: ,
Verlag Elsevier
Verlagsort Radarweg 29, 1043 Nx Amsterdam, Netherlands
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epidemiology II (EPI2)
Förderungen Federal states of Schles-wig-Holstein and Bavaria
German Federal Ministry of Education and Research (BMBF) via the Network University Medicine