Larger Lateral Prefrontal Cortex Volume Predicts Better Exercise Adherence Among Older Women: Evidence From Two Exercise Training Studies
Bibliographic record
Abstract
BACKGROUND: Recent research has suggested an important role of lateral prefrontal cortex (lPFC) in consistent implementation of positive health behaviors and avoidance of negative health behaviors. METHODS: We examined whether gray matter volume in the lPFC prospectively predicts exercise class attendance among older women (n = 122) who underwent either a 52-week or 26-week exercise training intervention. Structural magnetic resonance imaging determined gray matter volume at baseline. RESULTS: Independent of intracranial volume, age, education, body composition, mobility, depressive symptoms, and general cognitive functioning, larger lPFC volume predicted greater exercise class attendance (all p values < .05). Follow-up whole-brain analyses further confirmed that regions in the lPFC-especially the left middle frontal gyrus (p < .005)-predicted future exercise adherence as well as identified other regions, especially in the insula and temporal cortex, that predicted exercise adherence. CONCLUSIONS: These findings suggest that sustained engagement in exercise training might rely in part on functions of the lPFC and that lPFC volume might be a reasonable proxy for such functions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".