EPA guidance on lifestyle interventions for adults with severe mental illness: A meta-review of the evidence
Bibliographic record
Abstract
There is growing interest in lifestyle interventions as stand-alone and add-on therapies in mental health care due to their potential benefits for both physical and mental health outcomes. We evaluated lifestyle interventions focusing on physical activity, diet, and sleep in adults with severe mental illness (SMI) and the evidence for their effectiveness. To this end, we conducted a meta-review and searched major electronic databases for articles published prior to 09/2022 and updated our search in 03/2024. We identified 89 relevant systematic reviews and assessed their quality using the SIGN checklist. Based on the findings of our meta-review and on clinical expertise of the authors, we formulated seven recommendations. In brief, evidence supports the application of lifestyle interventions that combine behavioural change techniques, dietary modification, and physical activity to reduce weight and improve cardiovascular health parameters in adults with SMI. Furthermore, physical activity should be used as an adjunct treatment to improve mental health in adults with SMI, including psychotic symptoms and cognition in adults with schizophrenia or depressive symptoms in adults with major depression. To ameliorate sleep quality, cognitive behavioural informed interventions can be considered. Additionally, we provide an overview of key gaps in the current literature. Future studies should integrate both mental and physical health outcomes to reflect the multi-faceted benefits of lifestyle interventions. Moreover, our meta-review highlighted a relative dearth of evidence relating to interventions in adults with bipolar disorder and to nutritional and sleep interventions. Future research could help establish lifestyle interventions as a core component of mental health care.
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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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.027 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".