Impulsivity influences food intake in women with generalized anxiety disorder
Bibliographic record
Abstract
OBJECTIVE: Eating behavior is affected by psychological and neurocognitive factors. However, little is known about this relationship in anxious patients. Our aim was to investigate the associations between impulsivity, inhibitory control, energy-dense food consumption, and body mass index (BMI) in women with generalized anxiety disorder (GAD). METHODS: In this cross-sectional study, 51 adult females with GAD answered the Barratt Impulsiveness Scale (BIS-11) and participated in a go/no-go task using food images. Anthropometric measurements were evaluated. A food frequency questionnaire and a snack test were used to study eating behavior. Pearson correlation and multiple linear regression were performed to analyze the variables of interest, adjusted by age. RESULTS: Impulsivity predicted intake of sugar (p = 0.016, 95%CI 0.67-6.05), total fat (p = 0.007, 95%CI 0.62-3.71), and saturated fat (p = 0.004, 95%CI 0.30-1.48). The snack test showed a positive correlation between presence of impulsivity and intake of biscuits (R = 0.296; p = 0.051). Response inhibition to food images in the go/no-go task paradigm did not predict BMI or food intake. CONCLUSION: Impulsivity was predictive of higher sugar and saturated fat intake in women diagnosed with GAD. Our findings add to the literature regarding the association between neuropsychological factors and food consumption in this specific population.
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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) | 0.001 | 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".