Subjective brain fog: a four-dimensional characterization in 25,796 participants, 2024, Alim-Marvasti et al

Subjective brain fog: a four-dimensional characterization in 25,796 participants
Ali Alim-Marvasti, Matteo Ciocca, Narayan Kuleindiren, Aaron Lin, Hamzah Selim, Mohammad Mahmud

Abstract
Importance: Brain fog is associated with significant morbidity and reduced productivity and gained increasing attention after COVID-19. However, this subjective state has not been systematically characterised.

Objective: To characterise self-reported brain fog.

Design: We systematically studied the cross-sectional associations between 29 a priori variables with the presence of "brain fog." The variables were grouped into four categories: demographics, symptoms and functional impairments, comorbidities and potential risk factors (including lifestyle factors), and cognitive score. Univariate methods determined the correlates of brain fog, with long-COVID and non-long-COVID subgroups. XGBoost machine learning model retrospectively characterised subjective brain fog. Bonferroni-corrected statistical significance was set at 5%.

Setting: Digital application for remote data collection.

Participants: 25,796 individuals over the age of 18 who downloaded and completed the application.

Results: 7,280 of 25,796 individuals (28.2%) reported experiencing brain fog, who were generally older (mean brain fog 35.7 ± 11.9 years vs. 32.8 ± 11.6 years, p < 0.0001) and more likely to be female (OR = 1.2, p < 0.001). Associated symptoms and functional impairments included difficulty focusing or concentrating (OR = 3.3), feeling irritable (OR = 1.6), difficulty relaxing (OR = 1.2, all p < 0.0001), difficulty following conversations (OR = 2.2), remembering appointments (OR = 1.9), completing paperwork and performing mental arithmetic (ORs = 1.8, all p < 0.0001). Comorbidities included long-COVID-19 (OR = 3.8, p < 0.0001), concussions (OR = 2.4, p < 0.0001), and higher migraine disability assessment scores (MIDAS) (+34.1%, all p < 0.0001). Cognitive scores were marginally lower with brain fog (-0.1 std., p < 0.001). XGBoost achieved a training accuracy of 85% with cross-validated accuracy of 74%, and the features most predictive of brain fog in the model were difficulty focusing and following conversations, long-COVID, and severity of migraines.

Conclusions and relevance: This is the largest study characterising subjective brain fog as an impairment of concentration associated with functional impairments in activities of daily living. Brain fog was particularly associated with a history of long-COVID-19, migraines, concussion, and with 0.1 standard deviations lower cognitive scores, especially on modified Stroop testing, suggesting impairments in the ability to inhibit cognitive interference. Further prospective studies in unselected brain fog sufferers should explore the full spectrum of brain fog symptoms to differentiate it from its associated conditions.

Conflict of interest statement
All authors are or have been employees of Mindset Technologies, Ltd., London, UK.

Link | Full Text (Frontiers in Human Neuroscience, open access)

 

Is it just me, or does this seem to be a circular definition: that their results (from questionnaires) matches their definition of brainfog, which is defined by the results? Seems pretty useless.