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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)
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
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,
Seiten: 296-305
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
German Federal Ministry of Education and Research (BMBF) via the Network University Medicine