41 Concussion History, Physical Activity, and Athletic Status Predict Subjective but not Objective Executive Functioning
Bibliographic record
Abstract
Objective: Factors such as physical activity and sports participation may have a positive effect on executive functioning. However, people involved in sports are at a higher risk of experiencing a concussion, which may have a detrimental effect. Previous research has yet to investigate those combined negative and positive effects while also utilizing a comprehensive assessment of executive function. This study aims precisely to examine the effects of physical activity, athletic status and concussion history on subjective (e.g., questionnaire) and objective measures (e.g., latent variables) of three well-established components of executive function (i.e., inhibiting, shifting, and updating) in young adults. Participants and Methods: 247 Canadian university students (ages 18 - 25; 83% female) completed a remote assessment of executive function involving nine computerized tasks and a behavioural self-report, in addition to demographic questionnaires and items assessing weekly physical activity, athletic status, and concussion history. A linear regression analysis was used to assess the effects of the predictor variables (age, sex, concussion history, physical activity and athletic status) on subjective reporting of executive functioning using the Executive Function Index. Furthermore, structural equation modelling (SEM) was used to predict objective executive function using a three-factor model (shifting, updating, inhibition). Results: The three-factor measurement model of executive function fit the data adequately: x2 = 26.10, df = 17, p = 0.07, CFI = 0.97, TLI = 0.95, RMSEA = 0.05 [90% CI: 0.00-0.09], SRMR = 0.04. Then, the three-factor SEM of executive function also fit the data adequately: X2 = 66.38, df = 51, p = 0.07, CFI = 0.95, TLI = 0.93, RMSEA = 0.04 [90% CI: 0.00-0.06], SRMR = 0.05. Using SEM, no direct relationship was found between the factors of executive function and the predictor variables (i.e., age, physical activity, concussion history, and athletic status). Sex was significantly related to inhibition (b = 0.52, p = 0.02), such that males had greater inhibition. For the regression, physical activity (b = 0.09, p < .01), concussion history (b = 3.29, p < .05) and athletic status (b = -4.01, p < .05) were found to be significant predictors for the Executive Function Index. Conclusions: Concussion history, physical activity, and athletic status were all predictive of subjective but not objective measures of executive function. Interestingly, these findings align with previous research that demonstrated performance-based executive function measures often do not align with self-report measures, which may suggest they are complementary but measure slightly different aspects of the underlying executive function construct. Mixed findings in the extant literature regarding sex differences and executive function require continued research to understand better the relationship and mechanisms behind the sex differences in inhibition. In summary, these findings offer support for the differentiation between subjective and objective measures of executive function when investigating their relationship with physical activity, sport participation, concussion history, age and sex.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".