A new behavioural intervention to enhance memory in older people–evening autobiographical recall
Bibliographic record
Abstract
Episodic memory deteriorates with age and in Alzheimer's Disease (AD). Interventions to enhance memory in these cohorts are limited and associated with disadvantages inherent in pharmaceuticals and in the cost/availability of formal cognitive enhancement programmes. Here we tested whether an autobiographical retrieval task could enhance performance in a separate word-list recognition task. The RESTED-AD Study (Remote Evaluation of Sleep To enhance understanding in Early Dementia) was a cohort study comprising individuals with AD MCI/early dementia and age-matched healthy controls (HC). Participants completed a word recognition task twice, with learning and test phases separated by sleep. On one occasion, participants wrote down 5 autobiographical events occurring in their day before bedtime (Autobiographical Condition). Episodic memory accuracy was compared in the Autobiographical vs Standard Condition. Sleep was recorded utilising a home-based EEG headband. Twenty-six participants (AD = 8, HC = 18, 5 Female) had mean (SD) age of 70.0 (6.6), and Montreal Cognitive Assessment Score of 26.1 (2.4). Full cohort accuracy score increased in the Autobiographical Condition [% Mean (SD) Standard = 82.0 (11.0), Autobiographical = 86.4 (8.1), Cohen's D = 0.452, p = 0.024]. This relationship maintained after correction for confounding variables and task order. After False Discovery correction, no evidence was found to support sleep-mediated mechanisms. Autobiographical memory evocation was positively associated with recognition memory performance in older adults and individuals with AD. As an intervention with no foreseeable risks, replication of this finding and further work to establish underlying mechanisms is warranted.
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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 | 0.001 |
| 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) | 0.002 | 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".