DOI 10.1097/psy.0000000000001380
Unmasking the Psychological Landscape of Long COVID: A Cluster-analytical Approach.
Аннотация
OBJECTIVE
Since the emergence of SARS-CoV-2, an increasing number of people report long-term physical and psychological impairments. Research on the immunological sequalae of long COVID (LC) is growing, though its relationship with mental health remains underexplored. We investigated the psychological impairments associated with LC, identified related psychological symptom clusters, and their relationship with physical symptoms and pandemic-related variables.
METHODS
This is a cross-sectional descriptive study using an online questionnaire (September 2020 to December 2022) in German and French to assess depression, anxiety, fatigue, stress, and somatic symptoms. Clusters were identified using hierarchical and machine learning techniques (Kmeans and AffinityPropagation) and compared based on LC symptoms, past physical and mental health, substance use, COVID-19 variants, and family dynamics.
RESULTS
Among 1218 LC participants (78.7% female), 4 clusters were identified using AffinityPropagation: (1) low anxiety, depression, and somatoform symptoms, (2) low anxiety and depression but moderate somatoform symptoms, (3) high anxiety and depression with high somatoform symptoms, and (4) high anxiety and depression with moderate somatoform symptoms. Cluster 3 reported the most severe physical and neurological symptoms, the largest life impact (including relationship deterioration and professional difficulties), and the highest prevalence of past mental disorders (depression and post-traumatic stress disorder). Cluster 1 reported the least symptoms.
CONCLUSIONS
LC significantly impacts mental health, particularly through increased anxiety, depression, and somatoform symptoms, especially in patients with severe LC physical symptoms. Psychological interventions targeting distinct symptom clusters may improve both mental and physical outcomes. Early mental health screening and tailored interventions are recommended for LC assessment and treatment.
Предметные рубрики MeSH
Подготовка и перевод публикации
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
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