the correlation between brain performance capacity and covid 19 a cross sectional CORD-Papers-2022-06-02 (Version 1)

Title: The Correlation Between Brain Performance Capacity and COVID-19: A Cross-sectional Survey and Canonical Correlation Analysis
Abstract: Objective : To 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. Methods: A 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. Results: A 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. Conclusion: Multiple 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.
Published: 2022-01-30
DOI: 10.1101/2022.01.29.22270064
DOI_URL: http://doi.org/10.1101/2022.01.29.22270064
Author Name: Liu Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Chen X
Author link: https://covid19-data.nist.gov/pid/rest/local/author/chen_x
Author Name: Xian J S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/xian_j_s
Author Name: Wang R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wang_r
Author Name: Ma K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/ma_k
Author Name: Xu K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/xu_k
Author Name: Yang X
Author link: https://covid19-data.nist.gov/pid/rest/local/author/yang_x
Author Name: Wang F L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wang_f_l
Author Name: Mu N
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mu_n
Author Name: Wang S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wang_s
Author Name: Lai Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/lai_y
Author Name: Li T
Author link: https://covid19-data.nist.gov/pid/rest/local/author/li_t
Author Name: Yang C Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/yang_c_y
Author Name: Quan Y L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/quan_y_l
Author Name: Feng H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/feng_h
Author Name: Chen T
Author link: https://covid19-data.nist.gov/pid/rest/local/author/chen_t
Author Name: Wang L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wang_l
sha: 3b132447f8514cb70df68323535696d1a44db35a
license: medrxiv
source_x: MedRxiv; WHO
source_x_url: https://www.who.int/
url: https://doi.org/10.1101/2022.01.29.22270064 http://medrxiv.org/cgi/content/short/2022.01.29.22270064v1?rss=1
has_full_text: TRUE
Keywords Extracted from Text Content: NASA Task Load Index. BPC brain COVID-15 19 COVID-19 MWL 43.084 CCA people CYS21523 A. between-rater COVID-19 MWL 43.084 ± 17.543 BPCs BPC 60 women medRxiv human medRxiv preprint 8 brain https://doi.org/10 /2022 13 254 255 attention-43 deficit/hyperactivity disorder ADHD Kahn, R L(1 high-tempo 41 medRxiv preprint 7 0.364 L. L. Beason-Held her BPC medRxiv preprint 9 166 ChiCTR2100053133 TLX https://doi.org/10.1101 https://doi.org/10 COVID-19 205 brains participants Brain
Extracted Text Content in Record: First 5000 Characters:Objective: To generate a concept of brain performance capacity (BPC) with sleep, fatigue and mental 14 workload as evaluation indicators and to analyze the correlation between BPC and the impact of COVID-15 19. Methods: A cluster sampling method was adopted to randomly select 259 civil air crew members. The 17 measurements of sleep quality, fatigue and mental workload (MWL) were assessed using the Pittsburgh 18 Sleep Quality Index (PSQI), Multidimensional Fatigue Inventory (MFI-20) and NASA Task Load Index. 19 The impact of COVID-19 included 7 dimensions scored on a Likert scale. Canonical correlation analysis 20 (CCA) was conducted to examine the relationship between BPC and COVID-19. 21 Results: A total of 259 air crew members participated in the survey. Participants' average PSQI score 22 was 7.826 (SD = 3.796), with 49.8% reporting incidents of insomnia, mostly of a minor degree. 23 Participants' MFI was an average 56.112 (SD = 10.040), with 100% reporting some incidence of fatigue, 24 mainly severe. The weighted mental workload (MWL) score was an average of 43.084 (SD = 17.543), 25 with reports of mostly a mid-level degree. There was a significant relationship between BPC and 26 COVID-19, with a canonical correlation coefficient of 0.507 (P=0.000), an eigenvalue of 0.364 and a 27 contribution rate of 69.1%. All components of the BPC variable set: PSQI, MFI and MWL contributed 28 greatly to BPC, with absolute canonical loadings of 0.790, 0.606 and 0.667, respectively; the same was 29 true for the COVID-19 variable set, with absolute canonical loadings ranging from 0.608 to 0.951. 30 Conclusion: Multiple indicators to measure BPC and the interrelationship of BPC and COVID-19 should 31 be used in future research to gain a comprehensive understanding of anti-epidemic measures to ensure 32 victory in the battle against the spread of the disease. Brain performance capacity, a complex concept, is associated with memory, cognitive capacities, 36 attention, and work efficiency. This has been tested by research and resulted in much evidence. As early 37 as 1975, Kahn, R L(1) held the idea that impaired memory and depression were the products of poor 38 brain performance. L. Nyberg also argued that brain maintenance constituted the primary determinant of 39 successful memory aging (2). In modern society, it is vital for optimal performance to enhance or 40 preserve the cognitive performance of personnel working in stressful, demanding and/or high-tempo 41 environments (3). Furthermore, the ability to focus attention on tasks may contribute to peak brain 42 performance and high-level work efficiency. A. Yamashita found that individuals with attention-43 deficit/hyperactivity disorder (ADHD) spent more time and energy maintaining optimal and effective 44 performance behavior during tasks that required sustained attention (4). It is well known that in some professional contexts, such as aviation, where staying in good physical 46 condition is requisite, there are additional traits, such as BPC, that are important factors for success. However, how can this aspect be valuated? We have a remarkably poor understanding of what the direct 48 indicator might be in measuring BPC. However, there are some factors, such as sleep, fatigue-free state, and appropriate level of mental 50 workload, which are thought to be related to performance; vigilant attention and long memory are also 51 traits that may indirectly boost peak brain performance. Much evidence suggests that the right number 52 of hours and quality of sleep improves working memory (5), vigilant attention (6), concentration (7), but 53 fatigue and high-level mental workload impair them (8-13). Taking flight crews as an example, members 54 and particularly pilots, are submitted to high, exigent work demands and must manage multiple tasks at 55 the same time. In addition, they are continuously exposed to stimuli that compete for their attention and . CC-BY 4.0 International license It is made available under a perpetuity. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint The copyright holder for this this version posted January 30, 2022. ; https://doi.org/10.1101 https://doi.org/10. /2022 4 56 ability to manage their resources to make the right decisions. These task-oriented responsibilities are 57 further complicated by complex flying factors, such as long trips and varying shift flights. It was reported that the majority of accidents, 60%~90%, are attributed to "human error" (14-16) 59 and occur when flight crews are subjected to a high and intensive mental workload level (17). Thus, BPC 60 is a prerequisite to ensure flight safety. The COVID-19 pandemic has had a huge impact on people's 61 lives, the economy, physical-mental health, and BPC, since the outbreak began in 2020. Because of the 62 instability and resurgence of the disease, it will continue to have a negative impact for so
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