An index predictive of cognitive outcome in retired professional American Football players with a history of sports concussion

ABSTRACT

Objective: Various concussion characteristics and personal factors are associated with cognitive recovery in athletes. We developed an index based on concussion frequency, severity, and timeframe, as well as cognitive reserve (CR), and we assessed its predictive power regarding cognitive ability in retired professional football players. Method: Data from 40 retired professional American football players were used in the current study. On average, participants had been retired from football for 20 years. Current neuropsychological performances, indicators of CR, concussion history, and play data were used to create an index for predicting cognitive outcome. Results: The sample displayed a range of concussions, concussion severities, seasons played, CR, and cognitive ability. Many of the participants demonstrated cognitive deficits. The index strongly predicted global cognitive ability (R² = .31). The index also predicted the number of areas of neuropsychological deficit, which varied as a function of the deficit classification system used (Heaton: R² = .15; Wechsler: R² = .28). Conclusions: The current study demonstrated that a unique combination of CR, sports concussion, and game-related data can predict cognitive outcomes in participants who had been retired from professional American football for an average of 20 years. Such indices may prove to be useful for clinical decision making and research.

KEYWORDS:

• Cerebral concussion
• Traumatic brain injury
• Cognition
• Sports
• Cognitive reserve

Disclosure statement

None of the authors has financial interests in the enclosed research.

Notes

¹ Average PTA duration was also considered, but it did not correlate with neuropsychological outcome as strongly as the sum of PTA durations. Also, loss of consciousness (LOC) was considered (LOC average and LOC sum), but it was not significantly correlated with neuropsychological outcome.

² While the Heaton and Wechsler deficit classification systems (deficits scores < 1 SD and < 2 SDs, respectively) are commonly used in clinical neuropsychology, the Wechsler is the more common of the two (Brooks et al., 2011). When choosing a classification system, it is important to consider normal score variability, level of education, and intellectual functioning (Iverson & Brooks, 2011). Specifically, and beyond the impact of age and sex, healthy individuals commonly obtain one or more low test scores when administered a neuropsychological test battery, and the number of low test scores varies as a function of the classification system used (lower thresholds for deficit result in more low scores), the number of tests administered (more tests will result in a greater number of low scores), education (higher levels of education are inversely related to the number of low scores), and intelligence (greater intelligence is related to fewer low scores; see Iverson & Brooks, 2011). We chose to display sample performances using both systems as our participants displayed a broad range of years of education (15–20 years, M = 16.35, SD = 1.21) and premorbid intelligence estimates (91.11–126.46, M = 113.72, SD = 9.21).

³ Centered values are based on the sample reported in the manuscript, and these values may not reflect individuals drawn from other samples.

⁴ These values were restricted to professional play.

⁵ Grober, E., & Sliwinski, M. (1991). Development and validation of a model for estimating premorbid verbal intelligence in the elderly. Journal of Clinical and Experimental Neuropsychology, 13, 933–949.

⁶ Equation based on a simple linear regression, where the COSCI accounted for 31% of the variance in the prediction of global cognitive T-scores.

⁷ Equation based on a simple linear regression, where the COSCI accounted for 15% of the variance in the prediction of the number of cognitive domains with at least one deficit score (T < 40).

⁸ Equation based on a simple linear regression, where the COSCI accounted for 28% of the variance in the prediction of the number of cognitive domains with at least one deficit score (T < 30).

⁹ Adapted from: Brooks, B. L., Sherman, E. M. S., Iverson, G. L., Slick, D. J., & Strauss, E. (2011). Psychometric foundations for the interpretation of neuropsychological test results. The little black book of neuropsychology: A syndrome-based approach (pp. 893–922). New York, NY: Springer Science + Business Media.

Additional information

Funding

This research was supported by National Operating Committee on Standards for Athletic Equipment and the National Center for Research Resources, USA [grant number M01 RR 19975].