74 * ASSESSMENT OF COGNITION USING COGNITIVE TRAINING APPLICATIONS
Bibliographic record
Abstract
Introduction: Cognitive training (CT) has been suggested as a treatment to improve cognition in patients with dementia. Given the increased availability and use of smartphone and tablets applications, we investigated the ability of older adults, particularly those with dementia, to engage with these technologies, and whether CT has an alternative role in the assessment of cognition. Methods: Patients with cognitive impairment attending a university hospital memory clinic and day hospital, completed a questionnaire (n = 40) detailing the frequency and breadth of technology use. Participants were then instructed to use a tablet computer and complete three CT apps. CT scores were correlated with demographics, questionnaire results and total Montreal Cognitive Assessment (MoCA) scores. Results: All three CT app tasks were fully completed by 85% (n = 34) of participants; 79.4% (n = 27) would use them again, and 23.5% (n = 8) found using the CT apps ‘easy’. There was a moderate, significant correlation between the number of technology based devices used in the home, and total CT scores (r = 0.41, p = 0.02). Total CT scores were found to be significantly correlated with total MoCA scores (r = 0.78, p < .01). MoCA subtests, apart from delayed recall, were also significantly related to CT scores. After correcting for frequency of technology use, CT scores were found to be significantly predictive of MoCA scores. Conclusions: Total CT scores for patients with mild to moderate dementia reflect MoCA scores, thus providing a possible marker of cognitive function. CT applications may represent a combined diagnostic and treatment modality, which can track cognition over time. It also may be more acceptable to older adults than traditional confrontational cognitive testing.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.011 | 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".