The Health Benefits and Challenges of Exercise Training in Persons Living with Schizophrenia: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: In addition to the hallmark cognitive and functional impairments mounting evidence indicates that schizophrenia is also associated with an increased risk for the development of secondary complications, in particular cardio-metabolic disease. This is thought to be the result of various factors including physical inactivity and the metabolic side effects of psychotropic medications. Therefore, non-pharmacological approaches to improving brain health, physical health, and overall well-being have been promoted increasingly. METHODS: We report on the health-related physical fitness (body composition, blood pressure, heart rate, and aerobic fitness) and lipid profile of persons living with schizophrenia and effective means to address the challenges of exercise training in this population. RESULTS: There was a markedly increased risk for cardio-metabolic disease in 13 persons living with schizophrenia (Age = 31 ± 7 years) including low aerobic fitness (76% ± 34% of predicted), reduced HDL (60% of cohort), elevated resting heart rate (80% of cohort), hypertension (40% of cohort), overweight and obesity (69% of cohort), and abdominal obesity (54% of cohort). Individualized exercise prescription (3 times/week) was well tolerated, with no incidence of adverse exercise-related events. The exercise adherence rate was 81% ± 21% (Range 48%-100%), and 69% of the participants were able to complete the entire exercise training program. Exercise training resulted in clinically important changes in physical activity, aerobic fitness, exercise tolerance, blood pressure, and body composition. CONCLUSION: Persons living with schizophrenia appear to be at an increased risk for cardio-metabolic disease. An individualized exercise program has shown early promise for the treatment of schizophrenia and the various cognitive, functional, and physiological impairments that ultimately affect health and well-being.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".