Memory benefits of daily-living-related contextual cueing for individuals with subjective cognitive decline and mild cognitive impairment
Bibliographic record
Abstract
Objective We aimed to assess how daily-living-related contextual cueing (DLCC) affects memory performance in three groups: healthy older adults (HA), those with subjective cognitive decline (SCD), and mild cognitive impairment (MCI), while accounting for age and education. Methods After gathering demographic information, participants underwent neuropsychological assessments including the Montreal Cognitive Assessment (MoCA), Everyday Cognition Scale (ECog-12), Mini Mental State Examination (MMSE), Geriatric Depression Scale (GDS), and Trail Making Test (TMT) to establish baseline characteristics. Participants were categorized into HA ( N = 47), SCD ( N = 54), and MCI ( N = 43) groups based on MoCA and ECog-12 scores. Memory performance was evaluated through two components of the Contextual Memory Test (CMT): one with non-contextual cues and the other with daily-living-related contextual cues. Results Interaction effects between contextual cueing and group for immediate recall ( p < .001), delayed recall ( p < .001), and total recall ( p < .001) were found. All recall scores were lower in the MCI group than in the other two groups in the contextual cueing, not non-contextual. The post-hoc results revealed that scores on immediate recall, delayed recall, and total recall were lower in the MCI group than in the other two groups in the contextual cueing condition but not in the non-contextual cueing one. Conclusions Daily-living-related contextual cueing benefited HA, SCD, and younger-adult MCI groups more than older-adult MCI group, particularly enhancing delayed and total memory performance.
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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 | 0.002 |
| 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) | 0.001 | 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".