Association between sedentary behavior and Brain-Derived Neurotrophic Factor (BDNF) in children and adolescents: A protocol for systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: There is evidence in the literature suggesting that high time spent in sedentary behavior (SB) can reduce the Brain-Derived Neurotrophic Factor (BDNF) levels, an important neurotrophin that plays a role in modulating cognition, learning and memory. Children and adolescents usually spend many hours a day on SB, either sitting for a long time and/or using screen equipment. OBJECTIVE: The aim of this study is to describe a systematic review and meta-analysis protocol on the associations between SB and BDNF levels in children and adolescents. METHODS: This protocol is guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocols and was published in the International Prospective Register of Systematic Reviews database (PROSPERO: CRD42023392246). The databases that will be searched are EMBASE, Scopus, ScienceDirect, Web of Science, SPORTDiscus, CINAHL, and PubMed. Cross-sectional and cohort studies conducted with children and adolescents (5 to 19 yr) that analyzed the association between SB and BDNF will be included in the systematic review. The characteristics of the studies, methodological aspects, and main results will be described. Then, the risk of bias (assessed by STROBE and Newcastle-Ottawa scales) and the level of evidence (assessed by the GRADE tool) from included studies will be evaluated. Sub-group analysis will also be performed. Two experienced reviewers will perform the studies selection, data extraction, and methodological quality assessment. CONCLUSION: This systematic review and meta-analysis will analyze the association between SB and BDNF in children and adolescents. The results will provide subsidies to better understand this relationship and will strengthen evidence-based practice for both health professionals and future researches.
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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 | 0.104 | 0.163 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.022 | 0.031 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.046 | 0.005 |
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".