The effect of acute exercise on blood concentrations of brain‐derived neurotrophic factor in healthy adults: a meta‐analysis
Bibliographic record
Abstract
It has been hypothesized that one mechanism through which physical activity provides benefits to cognition and mood is via increasing brain-derived neurotrophic factor (BDNF) concentrations. Some studies have reported immediate benefits to mood and various cognitive domains after a single session of exercise. This meta-analysis sought to determine the effect of a single exercise session on concentrations of BDNF in peripheral blood, in order to evaluate the potential role of BDNF in mediating the beneficial effects of exercise on brain health. MEDLINE, Embase, PsycINFO, SPORTDiscus, Rehabilitation & Sports Medicine Source, and CINAHL databases were searched for original, peer-reviewed reports of peripheral blood BDNF concentrations before and after acute exercise interventions. Risk of bias within studies was assessed using standardized criteria. Standardized mean differences (SMDs) were generated from random effects models. Risk of publication bias was assessed using a funnel plot and Egger's test. Potential sources of heterogeneity were explored in subgroup analyses. In 55 studies that met inclusion criteria, concentrations of peripheral blood BDNF were higher after exercise (SMD = 0.59, 95% CI: 0.46-0.72, P < 0.001). In meta-regression analysis, greater duration of exercise was associated with greater increases in BDNF. Subgroup analyses revealed an effect in males but not in females, and a greater BDNF increase in plasma than serum. Acute exercise increased BDNF concentrations in the peripheral blood of healthy adults. This effect was influenced by exercise duration and may be different across genders.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.050 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".