Relationship of CogScreen-AE to flight simulator performance and pilot age.
OBJECTIVE
We report on the relationship between CogScreen-Aeromedical Edition (AE) factor scores and flight simulator performance in aircraft pilots aged 50-69.


METHODS
Some 100 licensed, civilian aviators (average age 58+/-5.3 yr) performed aviation tasks in a Frasca model 141 flight simulator and the CogScreen-AE battery. The aviation performance indices were: a) staying on course; b) dialing in communication frequencies; c) avoiding conflicting traffic; d) monitoring cockpit instruments; e) executing the approach; and f) a summary score, which was the mean of these scores. The CogScreen predictors were based on a factor structure reported by Kay (11), which comprised 28 CogScreen scores. Through principal components analysis of Kay's nine factors, we reduced the number of predictors to five composite CogScreen scores: Speed/Working Memory (WM), Visual Associative Memory, Motor Coordination, Tracking, and Attribute Identification.


RESULTS
Speed/WM scores had the highest correlation with the flight summary score, Spearman r(rho) = 0.57. A stepwise-forward multiple regression analysis indicated that four CogScreen variables could explain 45% of the variance in flight summary scores. Significant predictors, in order of entry, were: Speed/WM, Visual Associative Memory, Motor Coordination, and Tracking (p<0.05). Pilot age was found to significantly improve prediction beyond that which could be predicted by the four cognitive variables. In addition, there was some evidence for specific ability relationships between certain flight component scores and CogScreen scores, such as approach performance and tracking errors.


CONCLUSIONS
These data support the validity of CogScreen-AE as a cognitive battery that taps skills relevant to piloting.
Taylor
J L
JL
Department of Veterans Affairs Health Care System, Palo Alto, CA 94304-1207, USA. joyt@stanford.edu
O'Hara
R
R
Mumenthaler
M S
MS
Yesavage
J A
JA
D000337:Aerospace Medicine; D000368:Aged; D003071:Cognition; D003198:Computer Simulation; D005163:Factor Analysis, Statistical; D006801:Humans; D008875:Middle Aged; D011597:Psychomotor Performance; D013647:Task Performance and Analysis