The impact of aerobic and resistance training intensity on markers of neuroplasticity in health and disease
Bibliographic record
Abstract
OBJECTIVE: To determine the effects of low- vs. high-intensity aerobic and resistance training on motor and cognitive function, brain activation, brain structure, and neurochemical markers of neuroplasticity and the association thereof in healthy young and older adults and in patients with multiple sclerosis, Parkinson's disease, and stroke. DESIGN: Systematic review and robust variance estimation meta-analysis with meta-regression. DATA SOURCES: Systematic search of MEDLINE, Web of Science, and CINAHL databases. RESULTS: Fifty studies with 60 intervention arms and 2283 in-analyses participants were included. Due to the low number of studies, the three patient groups were combined and analyzed as a single group. Overall, low- (g=0.19, p = 0.024) and high-intensity exercise (g=0.40, p = 0.001) improved neuroplasticity. Exercise intensity scaled with neuroplasticity only in healthy young adults but not in healthy older adults or patient groups. Exercise-induced improvements in neuroplasticity were associated with changes in motor but not cognitive outcomes. CONCLUSION: Exercise intensity is an important variable to dose and individualize the exercise stimulus for healthy young individuals but not necessarily for healthy older adults and neurological patients. This conclusion warrants caution because studies are needed that directly compare the effects of low- vs. high-intensity exercise on neuroplasticity to determine if such changes are mechanistically and incrementally linked to improved cognition and motor function.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".