The Efficacy of Non-Pharmacological Interventions on Brain-Derived Neurotrophic Factor in Schizophrenia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Several studies have investigated the relationship between non-pharmacological interventions (NPIs) and peripheral brain-derived neurotrophic factor (BDNF) in schizophrenia patients. We conducted a systematic review and meta-analysis to review the efficacy of NPIs on peripheral serum and plasma BDNF in subjects with schizophrenia (including schizoaffective disorder). Meta-analyses were conducted to examine the effects of NPIs on blood BDNF levels by using the standardized mean differences (SMDs) between the intervention groups and controls. In total, six randomized controlled trials with 289 participants were included. Of them, five studies used exercise, physical training or diet products. One study used cognitive training. Overall, the BDNF levels in the NPI group increased significantly compared with the control groups (SMD = 0.95, 95% confidence interval (CI) = 0.07 to 1.83, p = 0.03). Subgroup analyses indicated beneficial effects of a non-exercise intervention on peripheral BDNF levels (SMD = 0.41, 95% CI = 0.08 to 0.74, p = 0.01). Meta-regression analyses showed that the completion rate influenced the variation in SMD (p = 0.01). Despite insufficient evidence to draw a conclusion, our results suggest that use of NPIs as adjunctive treatments, specifically non-exercise interventions, may affect positively serum or plasma BDNF in patients with schizophrenia.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.028 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".