Functional Ability in Everyday Life: Are Associations With an Engaged Lifestyle Mediated by Working Memory?
Bibliographic record
Abstract
OBJECTIVES: An engaged lifestyle has been linked to measures of functional ability in everyday life. However, the underlying mechanism of this link is still understudied. We propose working memory as a potential mediator of this relation. METHODS: Modeling data of 158 older adults with a latent-variables approach, we examined whether working memory mediated the relation between an engaged lifestyle, that is, intellectual, social, and physical activities, and functional ability, that is, self-reported everyday failures and test-based everyday performance. RESULTS: Working memory was found to fully mediate the relation between gaming activities and test-based everyday performance. Furthermore, we found a negative association between sports activities and self-reported everyday failures not mediated through working memory, indicating that individuals who reported high levels of sports activities reported fewer everyday cognitive failures. All other lifestyle activities were, however, neither directly nor indirectly associated with functional ability. DISCUSSION: Working memory is one pathway by which gaming activities are related to test-based measures of functional ability in everyday life. Given the overlapping cognitive demands of working memory, gaming activities, and the test-based measure of functional ability, the findings suggest that while an engaged lifestyle can benefit functional ability, those benefits may be limited to highly similar domains.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".