Gross Motor Skills Training Leads to Increased Brain-Derived Neurotrophic Factor Levels in Healthy Older Adults: A Pilot Study
Bibliographic record
Abstract
peak) have been associated with increases in the peripheral concentration of brain-derived neurotrophic factor (BDNF) and better cognitive performances. However, other training interventions such as resistance training and gross motor skills programs were also linked with improvements in cognitive functions. Thus far, few studies have compared different types of physical exercise training protocols and their impact on BDNF concentrations, especially in participants over 60 years old. The main objective of this study was to compare the effects of three exercise protocols on plasma BDNF concentrations at rest in healthy older adults. Thirty-four older adults were randomized into three interventions: (1) lower body strength and aerobic training (LBS-A), (2) upper body strength and aerobic training (UBS-A), or (3) gross motor activities (GMA). All interventions were composed of 3 weekly sessions over a period of 8 weeks. Physical, biochemical, and cognitive assessments were performed pre and post-intervention. All interventions resulted in improved cognitive functions but the GMA intervention induced a larger increase in plasma BDNF concentrations than LBS-A. No correlation was observed between changes in BDNF concentrations and cognitive performances. These findings suggest that a program of GMA could lead to enhancements in plasma BDNF concentrations. Moreover, cognition improvement could occur without concomitant detectable changes in BDNF, which highlights the multifactorial nature of the exercise-cognition relationship in older adults.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".