Nutrition interventions in populations with mental health conditions: a scoping review
Bibliographic record
Abstract
Nutrition is a modifiable factor for intervention in mental disorders. This scoping review characterized nutrition intervention research in mental disorders. A 3-category framework characterized nutrition interventions: Guide (e.g., counselling), Provide (e.g., food provisions), and Add (e.g., supplementation). Nutrition interventions were classified as single-component (e.g., Guide) or complex (e.g., Guide-Provide). Sixty-nine trials met inclusion criteria, 96% were randomized controlled trials. Most commonly diagnosed mental disorders were depressive disorder (i.e., persistent) or major depressive disorder (n = 39), schizophrenia (n = 17), and other psychotic disorders (n = 13). Few trials included patients with anxiety disorders (n = 2) or bipolar disorders (n = 3). Several trials (n = 15, 22%) assessed and implemented nutrition interventions to improve dietary patterns, of which 11 (73%) reported statistically significant and clinically important positive effects of nutrition interventions on mental disorders. The majority of the trials (n = 61, 90%) investigated supplementation, most commonly adding essential fatty acids, vitamins, or minerals. The majority (n = 48, 70%) reported either statistically significant or clinically important effect and 31 (51%) reported both. Though most interventions led to statistically significant improvements, trials were heterogeneous for targeted mental disorders, nutrition interventions, and outcomes assessed. Given considerable heterogeneity, further research from robust and clinically relevant trials is required to support high-quality health care with effective nutrition interventions. Novelty Future research on whole-diet interventions powered to detect changes in mental health outcomes as primary objectives is needed. Dietitians may be an opportunity to improve feasibility and efficacy of nutrition interventions for mental disorder patients. Dietitians may be of value to educate mental health practitioners on the importance of nutrition.
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".