Boosting Cognitive Training through Social Engagement: Impacts on Older Adults With Subjective Cognitive Decline
Bibliographic record
Abstract
This study examined the combined effects of StrongerMemory program (brain exercises involving reading, writing, and math) and weekly social engagement on cognitive, behavioral, and emotional outcomes in older adults with subjective cognitive decline (SCD). A 12-week randomized controlled trial was conducted with 50 participants, who were randomly assigned to either a control group (StrongerMemory only) or an intervention group (StrongerMemory plus weekly social engagement). Cognitive function (MoCA), perceived cognitive decline (SCD-Q), health behaviors (GHPS), and emotional well-being (SWEMWBS) were assessed at baseline and post-intervention. Both groups showed significant cognitive improvements (increased MoCA, decreased SCD-Q) post-intervention. ANCOVA revealed significantly better cognitive function in the intervention group, demonstrating the synergistic benefits of social engagement. The intervention group also experienced enhanced emotional well-being. These findings suggest that incorporating social engagement into cognitive training programs enhances their effectiveness in improving cognitive function and emotional well-being in older adults with SCD, potentially mitigating further decline. While the findings are promising, this exploratory study's small sample size resulted in modest achieved power (0.64), which may limit the generalizability of the results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".