A personalised nutrition intervention for adolescent depression: a mixed-methods feasibility pilot study
Bibliographic record
Abstract
Abstract Randomised controlled trials have demonstrated the benefit of diet modification to improve diet quality in the treatment of adult major depressive disorder (MDD). However, research examining nutritional interventions for adolescents with MDD is sparse. This pilot study examined the feasibility of a personalised nutrition intervention for adolescents with MDD. Ten adolescents with MDD and their parents recruited from a tertiary care setting participated in an 8-week, single-arm mixed-methods study. Feasibility was assessed using five criteria (demand, acceptability, implementation, adaptation and limited efficacy testing) alongside qualitative interviews. The intervention involved four bi-weekly virtual nutrition counselling sessions with a stepped approach to dietary change, menu planning, grocery delivery and educational eHealth messages. Study participants sought positive changes in diet, health and lifestyle for adolescents and family-wide benefits. Recruitment challenges included concerns about managing mood fluctuations, anticipated dietary restrictions and the potential time and effort required for diet adherence. Feedback based on interviews emphasised moderate to high acceptability, satisfaction with menu planning and counselling and recognition of the benefits of trying new foods and sustaining positive dietary changes beyond the study. Improvements in depression symptoms (Cohen’s d = 0·36, 95 % CI (–0·24, 3·36)), parent food modeling (Cohen’s d = 0·24, 95 % CI (–0·43, 1·16) and the family food environment (Cohen’s d = 0·61, 95 % CI (–0·04, 2·61)) were observed. This nutrition intervention was feasible for adolescents with MDD and was acceptable to both parents and depressed adolescents. These preliminary data suggest that further examination of the intervention and its potential benefits on depression symptoms and family food dynamics are warranted.
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".