Running for your life: A review of physical activity and cardiovascular disease risk reduction in individuals with schizophrenia
Bibliographic record
Abstract
Individuals with schizophrenia have a greater risk for cardiometabolic risk factors (e.g. central obesity, insulin resistance, hypertension and dyslipidaemia), cardiovascular diseases and mortality. This risky profile may be explained by the adverse effects of antipsychotic medications and an unhealthy lifestyle (e.g. smoking, poor nutrition and low physical activity). In the general population, physical activity has been shown to be the optimal strategy to improve both cardiometabolic parameters and cardiorespiratory fitness levels. Accordingly, an emerging literature of non-pharmacological interventions (e.g. cognitive behavioural therapy, diet and physical activity) has been studied in individuals with schizophrenia. Therefore, the purpose of this review was 1) to conduct a critical literature review of non-pharmacological interventions that included some kind of physical activity (including supervised and unsupervised exercise training) and target cardiometabolic risk factors in individuals with schizophrenia. 2) To describe the contribution of physical activity alone by reviewing trials of supervised exercise training programmes only. A literature review via systematic keyword search for publications in Medline, PubMed, Embase and PsycINFO was performed. Many non-pharmacological interventions are efficient in reducing cardiovascular disease risk factors when combined with physical activity. Supervised physical activity has been successful in decreasing cardiovascular disease risk, and aerobic interval training appears to provide more benefits by specifically targeting cardiorespiratory fitness levels. In conclusion, physical activity is an effective strategy for addressing cardiovascular disease risk in individuals with schizophrenia. Long-term studies are needed to evaluate the feasibility and impact of exercise training programmes in individuals with schizophrenia.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".