Feasibility, reliability and predictive validity of digital cognitive assessments in a diverse cohort of community‐residing older adults
Bibliographic record
Abstract
Abstract Background Digital cognitive assessments repeated over multiple days improve reliability compared to “single‐shot” assessments due to within person variability in function. Further, digital assessments are possible when clinic visits are not. We show the utility of digital approaches using data from the Einstein Aging Study (EAS) diverse cohort of community‐dwelling older adults. Method Participants were administered conventional and ambulatory digital assessments annually. The later consisted of a 2‐week burst of digital cognitive and behavioral/exposure measures obtained 6 times/day. Digital cognitive assessments included three tests (processing speed (Symbol Match), visual short‐term memory binding (Color Shape), and spatial working memory (Grid Memory)). Exposure assessment included daily stress, pain, and subjective sleep quality. Feasibility was measured as compliance defined as proportion of completed assessments. Frequency of annual assessments completed was evaluated throughout the COVID‐19 pandemic. For digital measures, reliability was calculated using intraclass correlations (ICCs) across days within a burst and across annual visits. ICCs for conventional measures across annual visits were also computed. To assess predictive validity, we used Cox proportional hazard models to examine whether mean and variability of performance on the 3 digital cognitive tasks predicted incident MCI. Result Compliance rates overall were 88% and were ≥ 85% in strata based on MCI status, age, race/ethnicity, education or prior experience with smartphones (Table 1). Data collection was maintained throughout the pandemic (Figure 1). ICCs for the digital cognitive measures was consistently high (>0.93). The same was true for exposure measures of stress, subjective sleep and pain (>.88). ICCs across annual follow‐up for digital measures ranged from 0.87 to 0.96 compared to 0.51 to 0.87 for conventional cognitive measures. Regarding predictive validity, worse mean performance in all three digital cognitive measures and higher within‐person variability (SD) in symbol search and color shape were associated with higher risk of incident MCI. Conclusion Digital assessments are feasible to administer, have high reliability and show predictive validity for MCI. They may provide useful tools in observational studies and clinical trials due to improved classification of cognitive status and feasibility in situations where in person assessments are not possible.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".