Associations between fruit and vegetable consumption and depressive symptoms: evidence from a national Canadian longitudinal survey
Bibliographic record
Abstract
BACKGROUND: Several cross-sectional studies have demonstrated associations between diet quality, including fruit and vegetable consumption, and mental health. However, research examining these associations longitudinally, while accounting for related lifestyle factors (eg, smoking, physical activity) is scarce. METHODS: This study used data from the National Population Health Survey (NPHS), a large, national longitudinal survey of Canadians. The sample included 8353 participants aged 18 and older. Every 2 years from 2002/2003 to 2010/2011, participants completed self-reports of daily fruit and vegetable consumption, physical activity, smoking and symptoms of depression and psychological distress. Using generalised estimating equations, we modelled the associations between fruit and vegetable consumption at each timepoint and depression at the next timepoint, adjusting for relevant covariates. RESULTS: Fruit and vegetable consumption at each cycle was inversely associated with next-cycle depression (β=-0.03, 95% CI -0.05 to -0.01, p<0.01) and psychological distress (β=-0.03, 95% CI -0.05 to -0.02, p<0.0001). However, once models were adjusted for other health-related factors, these associations were attenuated (β=-0.01, 95% CI -0.04 to 0.02, p=0.55; β=-0.00, 95% CI -0.03 to 0.02, p=0.78 for models predicting depression and distress, respectively). CONCLUSIONS: These findings suggest that relations between fruit and vegetable intake, other health-related behaviours and depression are complex. Behaviours such as smoking and physical activity may have a more important impact on depression than fruit and vegetable intake. Randomised control trials of diet are necessary to disentangle the effects of multiple health behaviours on mental health.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".