Effects of Moderate and Regular Exercise on Improving Neurocognition and Preventing Neurodegenerative Diseases in the Elderly
Bibliographic record
Abstract
This study evaluated the effects of regular moderate-intensity exercise on neurocognitive function in older adults. 120 eligible elderly participants were randomly assigned to either an exercise intervention group or a control group. The intervention group underwent a 12-week structured, multicomponent moderate-intensity exercise program, incorporating aerobic, resistance, flexibility, and balance training. Six composite indices were developed: the Brain-Derived Neurotrophic Factor (BDNF)-mediated Hippocampal Plasticity Index (BHPI), the Exercise-Induced Neuroinflammation-Oxidative Stress Index (ENOSI), the Cognitive Executive Network Connectivity Index (CENCI), the Exercise-Metabolism-Cognition Coupling Index (EMCCI), the Neurovascular-Cognitive Function Coupling Index (NVCFI), and the Cognitive Resilience Variability Index (CRVI). The intervention group exhibited a mean hippocampal volume increase of 0.018 cm3 greater than controls (p < .001) and a serum BDNF increase of 7.7 ng/mL greater than controls (p < .001), corresponding to relative increases of 1.8% and 38.5%, respectively, linked to Montreal Cognitive Assessment (MoCA) gains (r = 0.64). Tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) decreased by ~ 25%, superoxide dismutase (SOD) rose by 36%, and the ENOSI dropped by 32.5%. Executive network connectivity and Stroop performance improved. Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) declined (Δ = –0.56), correlating with better memory (r = 0.49). Middle cerebral artery (MCA) velocity increased by 15.6%, enhancing attention. Dopamine D2/D3 binding rose by 11.2%, associated with balance gains. Cognitive variability dropped by 40%. Moderate exercise improves multidimensional cognition in the elderly through neuroplastic, anti-inflammatory, metabolic, and vascular mechanisms, enhancing both performance and system stability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".