S18. THE RELATIONSHIP BETWEEN PHYSICAL ACTIVITY, CLINICAL AND COGNITIVE CHARACTERISTICS AND BDNF MRNA LEVELS IN PATIENTS WITH SEVERE MENTAL DISORDERS
Bibliographic record
Abstract
Here we aimed to clarify the association of physical activity with cognitive function and current mood in severe mental disorders in the most extensive sample to date. Secondly, we aimed to investigate the relationship between physical activity and BDNF mRNA levels. Three hundred and six patients with a DSM-IV Schizophrenia (SZ) or Bipolar Disorder spectrum (BD) diagnosis were included. Diagnostic assessment was undertaken using the Structured Clinical Interview for DSM-IV. Depressive symptomatology was measured using the Inventory of Depressive Symptoms (IDS-C) and the Calgary Depression Scale for Schizophrenia (CDSS). All patients underwent neuropsychological assessment. Physical activity was measured as hours spent on any regular physical activity (≥ or ˂ 90 min) per week, and as a continuous variable. BDNF mRNA was measured in plasma using standardized procedures. Patients with ≥90 min of physical activity per week had fewer depressive symptoms (p˂0.001, Cohen’s d=0.48) and performed significantly better on working memory (p˂0.001, d=0.44) and executive functioning tasks (p˂0.001, d=0.50) compared to the ˂90 min group. BDNF mRNA was positively associated with physical activity (p=0.046) and cognitive functioning (p=0.037). Similar findings were observed when analyzing physical activity as a continuous variable. Our study suggests a positive association between self-reported physical activity, cognitive function and mood in severe mental disorders. Moreover, our study is the first to show a positive association between higher BDNF mRNA levels measured in plasma and physical activity, as well as a positive association between BDNF mRNA levels in plasma and cognitive functioning.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".