Effects of high aerobic intensity training in patients with schizophrenia—A controlled trial
Bibliographic record
Abstract
BACKGROUND: Patients with schizophrenia have a high risk of cardiovascular disease (CVD). High aerobic intensity training (HIT) improve peak oxygen uptake (VO(2peak)), net mechanical efficiency of walking and risk factors for CVD but has not been investigated in patients with schizophrenia. AIMS: To investigate effects from HIT on VO(2peak), net mechanical efficiency of walking and risk factors for CVD in patients with schizophrenia. METHODS: 25 inpatients (F20-29, ICD-10) were allocated to either HIT or playing computer games (CG), 3 days per week for 8 weeks. HIT consisted of 4 × 4-min intervals with 3-min break periods, at 85-95% and 70% of peak heart rate, respectively. RESULTS: 12 and seven patients completed HIT and CG, respectively. The baseline VO(2peak) in both groups combined (n = 19) was 36.8 ± 8.2 ml/kg/min and 3.12 ± 0.55 l/min. The HIT group improved VO(2peak) by 12% from 3.17 ± 0.59 to 3.56 ± 0.68 l/min (P < 0.001), more than the CG group (P = 0.014). Net mechanical efficiency of walking improved by 12% in the HIT group from 19.8 ± 3.0% to 22.2 ± 4.5% (P = 0.005), more than the CG group (P = 0.031). The psychiatric symptoms, expressed as the Positive and Negative Syndrome Scale (PANSS) and the Calgary Depression Scale for Schizophrenia (CDSS), did not improve in either group. CONCLUSIONS: VO(2peak) and net mechanical efficiency of walking improved significantly by 8 weeks of HIT. HIT should be included in rehabilitation in order to improve physical capacity and contribute risk reduction of CVD.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".