Bringing Executive Function Testing Online: Assessment Validation Study
Bibliographic record
Abstract
Background: Executive function encompasses a set of higher-order cognitive processes, including planning, cognitive flexibility, and inhibitory control, that are essential for goal-directed behavior. These abilities are adversely affected by age, with executive dysfunction ultimately impairing the performance of activities of daily living. Objective: This study aimed to assess the validity of a computerized cognitive assessment in predicting executive function performance in healthy older adults. Methods: This retrospective analysis utilized baseline data from the Improving Neurological Health in Aging via Neuroplasticity-Based Computerized Exercise (INHANCE) trial. The study provides normative data for cognitively healthy older adults (aged 65 years and above) and evaluates the usability and validity of Freeze Frame, a cognitive assessment available on the BrainHQ platform. Performance on Freeze Frame was analyzed in relation to self-reported demographic variables and neuropsychological function, using a standardized measure of executive function, the National Institutes of Health Executive Abilities: Measures and Instruments for Neurobehavioral Evaluation and Research (NIH EXAMINER). Results: The intent-to-treat analysis included 92 cognitively healthy older adults (mean age 71.9, SD 4.86, range: 65-83 years), of whom 66% (61/92) were female, with a mean education level of 16.45 (SD 3.40, range: 9-27) years. Performance on Freeze Frame was modestly associated with executive function scores on NIH EXAMINER (P=.02), accounting for 6.8% of the variance. The assessment showed a small but statistically significant relationship to age (ρ=-0.22, P=.046) and gender, with no significant influence of education. Psychometric evaluation supported its usability, with an average completion time of 4 (SD 0.16) minutes. Conclusions: Freeze Frame is a brief, scalable, and accessible computerized cognitive assessment with demonstrated concurrent validity for executive function. Its efficiency and ease of administration across internet-connected devices suggests potential applications for cognitive screening. Future research should explore its utility in detecting executive dysfunction in clinical populations and its potential role in predicting functional performance across activities of daily living.
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".