Normative Data for a Tablet-Based Dual-Task Assessment in Healthy Older Adults
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to provide normative data for a tablet-based dual-task assessment in older adults without cognitive deficits. METHOD: In total, 264 participants aged between 60 and 90 years, French and English-speaking, were asked to perform two discrimination tasks, alone and concurrently. The participants had to answer as fast as possible to one or two images appearing in the center of the tablet by pressing to the corresponding buttons. Normative data are provided for reaction time (RT), coefficient of variation, and accuracy. Analyses of variance were performed by trial types (single-pure, single-mixed, dual-mixed), and linear regressions assessed the relationship between performance and sociodemographic characteristics. RESULTS: The participants were highly educated and a large proportion of them were women (73.9%). The accuracy on the task was very high across all blocks. RT data revealed both a task-set cost and a dual-task cost between the blocks. Age was associated with slower RT and with higher coefficient of variability. Men were significantly slower on dual-mixed trials, but their coefficient of variability was lower on single-pure trials. Education was not associated with performance. CONCLUSIONS: This study provides normative data for a tablet-based dual-task assessment in older adults without cognitive impairment, which was lacking. All participants completed the task with good accuracy in less than 15 minutes and thus, the task is transferable to clinical and research settings.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".