Screen time is independently associated with serum brain-derived neurotrophic factor (BDNF) in youth with obesity
Bibliographic record
Abstract
Low levels of brain derived-neurotrophic factor (BDNF) and excessive screen exposure are risk factors for neurocognitive deficits and obesity in youth, but the relationship between screen time and BDNF remains unknown. This study examined whether duration and/or type of sedentary screen time behaviour (TV viewing, video games, recreational computer use) are associated with serum BDNF levels in youth with obesity. The sample consisted of 250 inactive, postpubertal adolescents with obesity (172 females/78 males, aged 15.5 ± 1.4 years) at the baseline assessment of the Healthy Eating, Aerobic, Resistance Training in Youth Study. After controlling for self-reported age, sex, race, parental education, puberty stage, physical activity, and diet, higher total screen exposure was significantly associated with lower serum BDNF levels (β = −0.21, p = 0.002). TV viewing was the only type of screen behaviour that was associated with BDNF levels (β = −0.22, p = 0.001). Higher exposure to traditional forms of screen time was independently associated with lower serum BDNF levels, and this association appears to be driven primarily by TV viewing. Future intervention research is needed to determine whether limiting screen time is an effective way to increase BDNF and associated health benefits in a high-risk population of youth with obesity. Trial Registration: ClinicalTrials.Gov NCT00195858. Novelty: This study is the first to show that recreational screen time is inversely associated with serum BDNF levels. The inverse association between screen time and BDNF is driven primarily by TV viewing, indicating the type of screen might matter.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".