Increasing activity and reducing sedentary behaviour for people with severe mental illness: what are the active ingredients for behaviour change? A systematic review
Bibliographic record
Abstract
Increasing physical activity (PA) and reducing sedentary behaviour (SB) can improve health outcomes and reduce rates of premature mortality for people with severe mental illness (SMI). In this systematic review we aimed to explore the active ingredients of existing PA interventions for people with SMI. We reviewed intervention functions, behaviour change techniques (BCTs), contextual features and underpinning theories. We included 15 PA interventions, of which 4 were classed as effective (effect size >0.273). We identified the frequency of intervention functions and BCTs that were used in each study and compared the number of effective studies that featured a particular BCT or intervention function with the total number that featured those components. We used the TIDieR checklist to document contextual features that might be important within effective interventions including the theories that guided the development of interventions. The most frequently used functions were education and environmental restructuring, both of which were identified in effective interventions. The BCTs that were identified as potentially useful were framing and reframing, feedback on behaviour and self-monitoring. No discernible contextual features were unique to the effective interventions, but combinations of some features seemed to be (PA tracking, educational components and support delivered by community health teams). More high quality and better reported studies are required to strengthen this evidence base.Prospero registration: PROSPERO 2024 CRD42024541859
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".