Validation of Smartphone-Based Cognitive Assessments for Individuals with Major Depressive Disorder
Bibliographic record
Abstract
Cognitive deficits are often present in major depressive disorder (MDD) and negatively impact functional outcomes.However, it remains challenging to assess these impairments in clinical and research settings.Smartphone applications provide the opportunity to measure cognitive impairments in an accessible way.In this study, 24 individuals with MDD and 34 healthy controls (HC) completed the Trail Making Tests (TMT), and the smartphone-based versions, named the Jewels Trail Tests (JTT).Significant positive relationships between the JTT and TMT were observed with a moderate concurrent validity for Parts A and strong concurrent validity for Parts B. The intraclass correlations showed moderate test-retest reliability for Part A of the JTT and good reliability for Part B. This study did not find significant differences between the MDD and HC groups completion time.Lastly, higher sleep quality was associated with a faster completion time on the speed processing task over a period of three months.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".