Change in Body Fat Mass Is Independently Associated with Executive Functions in Older Women: A Secondary Analysis of a 12-Month Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVES: To investigate the independent contribution of change in sub-total body fat and lean mass to cognitive performance, specifically the executive processes of selective attention and conflict resolution, in community-dwelling older women. METHODS: This secondary analysis included 114 women aged 65 to 75 years old. Participants were randomly allocated to once-weekly resistance training, twice-weekly resistance training, or twice-weekly balance and tone training. The primary outcome measure was the executive processes of selective attention and conflict resolution as assessed by the Stroop Test. Sub-total body fat and lean mass were measured by dual-energy x-ray absorptiometry (DXA) to determine the independent association of change in both sub-total body fat and sub-total body lean mass with Stroop Test performance at trial completion. RESULTS: A multiple linear regression model showed reductions in sub-total body fat mass to be independently associated with better performance on the Stroop Test at trial completion after accounting for baseline Stroop performance, age, baseline global cognitive state, baseline number of comorbidities, baseline depression, and experimental group. The total variance explained was 39.5%; change in sub-total body fat mass explained 3.9% of the variance. Change in sub-total body lean mass was not independently associated with Stroop Test performance (P>0.05). CONCLUSION: Our findings suggest that reductions in sub-total body fat mass - not sub-total lean mass - is associated with better performance of selective attention and conflict resolution.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".