FEASIBILITY AND EFFECTS OF HIGH-INTENSITY INTERVAL TRAINING ON COGNITIVE FUNCTION AND BRAIN HEALTH IN PERIMENOPAUSE
Bibliographic record
Abstract
Abstract Neuroendocrine changes during perimenopause negatively impact cognitive function and brain health (e.g., reduced verbal episodic memory and myelin catabolism in the brain). Exercise promotes cognitive performance and mitigates vascular risk factors for cognitive impairment. High-intensity interval training (HIIT) involves short bursts of high-intensity exercise interspersed with active recovery; it improves cardiovascular fitness and vascular risk profile. HIIT addresses the “lack of time” barrier to exercise that perimenopausal females report. This pre-post pilot study assessed the feasibility and potential effects of 12-weeks of HIIT on cardiovascular fitness, verbal episodic memory, hippocampal volume, and myelin content. Feasibility metrics included recruitment rate (i.e., 1/month), withdrawal rate (i.e., < 15%), adherence (i.e., >60%). Training fidelity was assessed by cardiovascular fitness (VO2max.) Verbal episodic memory was assessed by Rey Auditory Verbal Learning Test delayed recall. Hippocampal volume was measured by T1-weighted magnetic resonance imaging. Myelin content was measured by myelin water fraction. Cohen’s d and one-tailed paired t-tests assessed effect sizes and potential changes. Recruitment rate was 0.35 participants/month (six participants in 17 months). Withdrawal rate was 16.67% and adherence was 88.2%. The intervention had a negligible effect on VO2max (Cohen’s d=0.06, p=0.35), large and significant effect on delayed recall (Cohen’s d=0.94, p=0.04), negligible effect on hippocampal volume (Cohen’s d=0.06, p=0.18), and medium to large effect on sagittal stratum myelin content (Cohen’s d=0.68, p=0.06). Future studies should consider home-based exercise and tailored recruitment methods (e.g. social media) to meet recruitment targets. HIIT may be beneficial for cognitive function and myelin content.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".