White Matter Volume Mediates the Relationship Between Self-Efficacy and Mobility in Older Women
Bibliographic record
Abstract
Background/Study Context: With our aging population, understanding determinants of healthy aging is a priority. One essential component of healthy aging is mobility. Although self-efficacy can directly impact mobility in older adults, it is unknown what role brain health may play in this relationship. METHODS: The authors conducted a cross-sectional pilot analysis of community-dwelling women (N = 80, mean age = 69 years) to examine whether brain volume mediates the relationship between falls-related self-efficacy, as measured by the Activities-specific Balance Confidence (ABC) scale, and mobility, as measured by the Timed Up and Go (TUG) test. Age, depression, education, functional comorbidities, and Montreal Cognitive Assessment (MoCA) were included in the model as covariates. RESULTS: The authors report that total white matter volume, specifically, significantly mediates the relationship between self-efficacy and mobility, where higher self-efficacy was associated with greater white matter volume (r = .28), which, in turn, was associated with better mobility (r = -.30). CONCLUSION: This pilot study extends our understanding of the psychosocial and neurological factors that contribute to mobility and provides insight into effective strategies that may be used to improve functional independence among older adults. Future prospective and intervention studies are required to further elucidate the nature of the relationship between self-efficacy, mobility, and brain health.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".