Lifestyle psychiatry: a conceptual framework for application in mental healthcare and support
Bibliographic record
Abstract
Lifestyle-related behaviours-such as sedentary behaviour, physical inactivity, poor nutrition, disrupted sleep and substance use-are increasingly recognised as important factors in the onset and persistence of mental illness. Evidence for the efficacy and cost-efficiency of lifestyle interventions in mental health is growing, and such approaches are now embedded in international guidelines and endorsed by major health organisations and associations as 'lifestyle psychiatry'. Nevertheless, despite this progress, these interventions remain underused in mental healthcare and support. One contributing factor is the lack of a shared conceptual understanding of 'lifestyle psychiatry', which risks fragmented practice, inconsistency in research and uncertainty around its role in policy, care and support. This paper presents a conceptual framework for lifestyle psychiatry, developed through an iterative, collaborative process involving 43 contributors across 15 countries, representing clinical, academic, policy and lived experience expertise. The framework defines core domains, outlines key challenges to behaviour change specific to mental health populations and emphasises multilevel and equity-oriented approaches. It aligns with person-centred and recovery-oriented care and serves as a shared reference point for practical application and future development. With this, we aim to support the structured, context-sensitive integration of lifestyle psychiatry into mental healthcare and support.
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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 | 0.024 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.028 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.006 | 0.006 |
| 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".