The Validity of a Smartphone-Based Application for Assessing Cognitive Function in the Elderly
Bibliographic record
Abstract
Background/Objectives: The early detection of individuals at risk of cognitive impairment is a clinical imperative. With the recent advancement of digital devices, smartphone application-based cognitive assessment is considered a promising tool for cognitive screening and monitoring inside and outside the clinic. This study examined whether a smartphone-based cognitive assessment, Brain OK, was valid for evaluating cognitive performance and identifying people at risk of cognitive impairment. Methods: We recruited 88 study participants aged over 60. They completed two cognitive tests with the Montreal Cognitive Assessment (MoCA), a validated paper-and-pencil cognitive screening tool, and Brain OK, a smartphone-based cognitive testing application. To examine convergent validity, we conducted analyses of Spearman correlations between MoCA and BrainOK, a Bland–Atman plot with regression analysis, and the area under the curve (AUC). Results: There was a significant positive association between Brain OK and the MoCA total score, with a coefficient of 0.9044 (SE = 0.057, t = 15.750, p < 0.001). The Bland–Altman plot represented a reasonable level of agreement between the two tests. We conducted the AUC analysis of Brain OK to compare the cognitively normal and impaired groups. The AUC value for the Brain OK score of 13.5 was the highest at 0.941. The sensitivity and specificity were 0.958 and 0.925, respectively. Conclusions: The smartphone app-based Brain OK test was feasible for assessing cognitive function and acceptable for identifying subjects with cognitive impairment. The results suggest Brain OK complements traditional in-person cognitive assessments and may help enhance cognitive health dialogue between doctors and patients.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".