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Abstract
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OBJECTIVES: This study aimed to produce a patient-centered understanding of mental health symptoms of people with the post-COVID-19 syndrome (PCS).
METHODS: A cross-sectional analysis of 414 participants in a longitudinal study was carried out involving people who self-identified as having symptoms of PCS. People were asked to name their most frequent and most bothersome mental health symptoms affected by PCS using the structure of the Patient Generated Index (PGI). The text threads from the PGI were grouped into topics using BERTopic analysis.
RESULTS: 20 topics were identified from 818 text threads referring to PCS mental health symptoms. 35% of threads were identified as relating to anxiety, discussed in terms of five topics: generalized/social anxiety, fear/worry, post-traumatic stress, panic, and nervous. 29% of threads were identified as relating to low mood, represented by five topics: depression, discouragement, emotional distress, sadness, and loneliness. A cognitive domain (22% of threads) was covered by four topics referring to concentration, memory, brain fog, and mental fatigue. Topics related to frustration, anger, irritability. and mood swings (7%) were considered as one domain and there were separate topics related to motivation, insomnia, and isolation.
CONCLUSIONS: This novel method of digital transformation of unstructured text data uncovered different ways in which people think about classical mental health domains. This information could be used to evaluate whether existing measures cover the content identified by people with PCS, to initiate a clinical conversation, or to justify the development of a new measure of the mental health impact of PCS.
Language: en |