Integrating Physical Activity Into Mental Health Services for Persons With Serious Mental Illness
Bibliographic record
Abstract
This article reviews evidence supporting the need for interventions to promote physical activity among persons with serious mental illness. Principles of designing effective physical activity interventions are discussed along with ways to adapt such interventions for this population. Individuals with serious mental illness are at high risk of chronic diseases associated with sedentary behavior, including diabetes and cardiovascular disease. The effects of lifestyle modification on chronic disease outcomes are large and consistent across multiple studies. Evidence for the psychological benefits for clinical populations comes from two meta-analyses of outcomes of depressed patients that showed that effects of exercise were similar to those of psychotherapeutic interventions. Exercise can also alleviate secondary symptoms such as low self-esteem and social withdrawal. Although structured group programs can be effective for persons with serious mental illness, especially walking programs, lifestyle changes that focus on accumulation of moderate-intensity activity throughout the day may be most appropriate. Research suggests that exercise is well accepted by people with serious mental illness and is often considered one of the most valued components of treatment. Adherence to physical activity interventions appears comparable to that in the general population. Mental health service providers can provide effective, evidence-based physical activity interventions for individuals with serious mental illness.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".