Back

The Correlation Between Brain Performance Capacity and COVID-19: A Cross-sectional Survey and Canonical Correlation Analysis

Liu, Y.; Chen, X.; Xian, J. S.; Wang, R.; Ma, K.; Xu, K.; Yang, X.; Wang, F. L.; Mu, N.; Wang, S.; Lai, Y.; Li, T.; Yang, C. Y.; Quan, Y. L.; Feng, H.; Chen, T.; Wang, L.

2022-01-30 occupational and environmental health
10.1101/2022.01.29.22270064 medRxiv
Show abstract

ObjectiveTo generate a concept of brain performance capacity (BPC) with sleep, fatigue and mental workload as evaluation indicators and to analyze the correlation between BPC and the impact of COVID-19. MethodsA cluster sampling method was adopted to randomly select 259 civil air crew members. The measurements of sleep quality, fatigue and mental workload (MWL) were assessed using the Pittsburgh Sleep Quality Index (PSQI), Multidimensional Fatigue Inventory (MFI-20) and NASA Task Load Index. The impact of COVID-19 included 7 dimensions scored on a Likert scale. Canonical correlation analysis (CCA) was conducted to examine the relationship between BPC and COVID-19. ResultsA total of 259 air crew members participated in the survey. Participants average PSQI score was 7.826 (SD = 3.796), with 49.8% reporting incidents of insomnia, mostly of a minor degree. Participants MFI was an average 56.112 (SD = 10.040), with 100% reporting some incidence of fatigue, mainly severe. The weighted mental workload (MWL) score was an average of 43.084 (SD = 17.543), with reports of mostly a mid-level degree. There was a significant relationship between BPC and COVID-19, with a canonical correlation coefficient of 0.507 (P=0.000), an eigenvalue of 0.364 and a contribution rate of 69.1%. All components of the BPC variable set: PSQI, MFI and MWL contributed greatly to BPC, with absolute canonical loadings of 0.790, 0.606 and 0.667, respectively; the same was true for the COVID-19 variable set, with absolute canonical loadings ranging from 0.608 to 0.951. ConclusionMultiple indicators to measure BPC and the interrelationship of BPC and COVID-19 should be used in future research to gain a comprehensive understanding of anti-epidemic measures to ensure victory in the battle against the spread of the disease.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.