Early detection of risk for cognitive decline using mobile apps and eye tracking-based biomarkers
Bibliographic record
Abstract
Early detection of Mild Cognitive Impairment (MCI), a precursor to Alzheimer’s disease, is essential for timely interventions. However, traditional cognitive assessments are often inaccessible and unsuitable for continuous monitoring. This study presents a mobile, gaze-based assessment system using eye-tracking as a digital biomarker for cognitive decline. Fourteen older adults with MCI used gamified apps over four months, including an emotionally weighted object-tracking task (PAIRS; Paletta et al., 2020a), an antisaccade task (Mobile Instrumental Recovery of Attention; MIRA; Paletta et al., 2020b), and the psychomotor vigilance task (PVT; Dinges & Powell, 1985). Eye movement features such as blink rate and reaction time significantly correlated with scores of Montreal Cognitive Assessment (MoCA; Nasreddine et al., 2005) scores. A Support Vector Regression model estimated cognitive scores supporting the potential of mobile eye-tracking for home-based cognitive monitoring and early dementia risk detection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".