The relationship between anxiety and diet quality in adolescent populations: a cross-sectional analysis
Bibliographic record
Abstract
Abstract Globally, more than 13 % of adolescents have clinically significant mental health problems, with anxiety and depression comprising over 40 % of cases. Despite the high prevalence of anxiety disorders among youth, dietary research has been focused on youth with depression, resulting in a significant knowledge gap regarding the impact of anxiety on adolescent diet quality. Adolescents with diagnosed anxiety disorders and healthy controls were included in this study. Anxiety symptoms were measured using the Screen for Child Anxiety-Related Disorders. Diagnosis of anxiety disorder was determined using the Kiddie Schedule for Affective Disorders and Schizophrenia interview. Five diet quality indices were scored from FFQ. Diet quality indices associated with anxiety symptoms in the correlation matrix were interrogated using multiple linear regression modelling. All models were adjusted for depression. One hundred and twenty-eight adolescents (mean age 14·8 years ( sd : 2·1); 66·4 % female) were included in this cross-sectional analysis. Although healthy controls and outpatient participants had similar unhealthy dietary index subscale scores, outpatient participants had lower healthy index scores. Higher anxiety symptoms were associated with lower healthy dietary indices in univariate analysis; after adjusting for comorbid depression; however, anxiety symptoms were no longer associated with dietary indices following adjustment for multiple testing ( P = 0·038 to P = 0·077). The association between anxiety symptoms and a poor diet is attenuated by depression. The results of this study support the need for an integrated approach to the assessment of mental and physical well-being and further research aimed at understanding the unique contribution of depression to healthy dietary patterns.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".