Playful multimodal activation with assessment of neuropsychological profiles in Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background A key problem in developing interventions in dementia care is the lack of knowledge about the mental processes and individual dependencies between functional impairments evolving over time. Neuropsychological profiles reflect the impact of the disease on distinctive neuroanatomic networks associated with complex cognitive domains. Recently serious games have been successfully validated with high potential as dementia biomarkers but increased estimation accuracy and personalised neuropsychological profiling is still required. Method Tablet‐PC‐based intervention was applied within 10 weeks in Austria, engaging persons with dementia (PwD) with Alzheimer’s disease (AD) living at home in terms of playful multimodal training and activation (n=15, age M=81.7 years, MoCA score M=17.9). PwDs interacted with an integrated version of two serious games: (a) 15 PwD played ‘MIRA’, a playful version of the anti‐saccade task, and (b) 8 PwD played ‘MMA’, a suite of cognitive exercises (puzzle, memory, text gap filling). The games were introduced and assisted by trainers, some PwD learned to play alone. Result The score of gaze‐based MIRA showed significant correlation with MoCA score (Rho= .713**) and enabled individual MoCA score estimates with errors of less than M=2.6 MoCA points. MMA showed correlation with MoCA (Rho=p=.755*) and further MoCA subscores so that the neuropsychological profile could be established including impairments in visuospatial operations, attention, abstraction, language and recall. Conclusion The work outlined within the EU project PLAYTIME indicates successful steps towards daily use of gaze‐based games. MIRA together with the MMA training enables continuous estimates of Alzheimer’s mental state in general but also to estimate individual neuropsychological profiles to identify personal impairments and their course over time. The playful training app was very well accepted by PwD users and offers with its pervasive mental assessment tool a large potential for future long‐term monitoring in numerous AD care services.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".