Lifetime Physical Activity and White Matter Hyperintensities in Cognitively Intact Adults
Bibliographic record
Abstract
BACKGROUND: White matter hyperintensities (WMHs) observed on magnetic resonance images are associated with depression and increase the risk of stroke, dementia, and death. The association between physical activity and WMHs has been inconsistently reported in the literature, perhaps because studies did not account for a lifetime of physical activity or depression. OBJECTIVES: The aim of this study was to determine the extent to which a lifetime of leisure-time physical activity is associated with less WMHs while accounting for depression. METHODS: Face-to-face interviews were conducted with the Lifetime Total Physical Activity Questionnaire, where the metabolic equivalent of task hours per week per year was calculated. Cognitively intact participants also underwent magnetic resonance imaging, where WMHs as a percentage of intracranial volume was obtained. Hierarchical multiple linear regression was performed to compare WMHs in a more active group with a group with no psychiatric history (n = 20, mean age = 62.2 years), with a less active group with no psychiatric history (n = 13, mean age = 64.0 years), and a less active group with history of late-onset depression (n = 14, mean age = 62.8 years). RESULTS: There was not a statistically significant difference in WMHlg10 between the more and less active groups without a psychiatric history (b = .09, p > .05) or between the more active group without a psychiatric history and the less active group with a history of depression (b = .01, p > .05). The model was predictive of WMHlg10, explaining an adjusted 15% of the variance in WMHs (p = .041). DISCUSSION: A lifetime of leisure-time physical activity was not associated with WMHs when accounting for depression.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".