Fruit and Vegetable Intake and Mental Health in Adults: A Systematic Review
Bibliographic record
Abstract
The role of a properly balanced diet in the prevention and treatment of mental disorders has been suggested, while vegetables and fruits have a high content of nutrients that may be of importance in the case of depressive disorders. The aim of the study was to conduct a systematic review of the observational studies analyzing association between fruit and vegetable intake and mental health in adults. The search adhered to the guidelines of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), and the review was registered in the International Prospective Register of Systematic Reviews (PROSPERO) database (CRD42019138148). A search for peer-reviewed observational studies published until June 2019 was performed in PubMed and Web of Science databases, followed by an additional manual search for publications conducted via analyzing the references of the found studies. With respect to the intake of fruit and/or vegetable, studies that assessed the intake of fruits and/or vegetables, or their processed products (e.g., juices), as a measure expressed in grams or as the number of portions were included. Those studies that assessed the general dietary patterns were not included in the present analysis. With respect to mental health, studies that assessed all the aspects of mental health in both healthy participants and subjects with physical health problems were included, but those conducted in groups of patients with intellectual disabilities, dementia, and eating disorders were excluded. To assess bias, the Newcastle-Ottawa Scale (NOS) was applied. A total of 5911 studies were independently extracted by 2 researchers and verified if they met the inclusion criteria using a 2-stage procedure (based on the title, based on the abstract). After reviewing the full text, a total of 61 studies were selected. A narrative synthesis of the findings from the included studies was performed, which was structured around the type of outcome. The studies included mainly focused on depression and depressive symptoms, but also other characteristics ranging from general and mental well-being, quality of life, sleep quality, life satisfaction, flourishing, mood, self-efficacy, curiosity, creativity, optimism, self-esteem, stress, nervousness, or happiness, to anxiety, minor psychiatric disorders, distress, or attempted suicide, were analyzed. The most prominent results indicated that high total intake of fruits and vegetables, and some of their specific subgroups including berries, citrus, and green leafy vegetables, may promote higher levels of optimism and self-efficacy, as well as reduce the level of psychological distress, ambiguity, and cancer fatalism, and protect against depressive symptoms. However, it must be indicated that the studies included were conducted using various methodologies and in different populations, so their results were not always sufficiently comparable, which is a limitation. Taken together, it can be concluded that fruits and/or vegetables, and some of their specific subgroups, as well as processed fruits and vegetables, seems to have a positive influence on mental health, as stated in the vast majority of the included studies. Therefore, the general recommendation to consume at least 5 portions of fruit and vegetables a day may be beneficial also for 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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".