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Record W1968929298 · doi:10.1093/schbul/sbq049

Exercise Therapy for Schizophrenia

2010· review· en· W1968929298 on OpenAlexaff
Paul Gorczynski, Guy Faulkner

Bibliographic record

VenueSchizophrenia Bulletin · 2010
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsycINFOMedicineConfidence intervalRandomized controlled trialMeta-analysisSchizophrenia (object-oriented programming)CINAHLPhysical therapyPsychiatryMEDLINEMental healthRelative riskNumber needed to treatPsychological interventionInternal medicine

Abstract

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The health benefits of physical activity and exercise are well documented, and these effects could help people with schizophrenia. To determine the mental health effects of exercise/physical activity programs for people with schizophrenia or schizophrenia-like illnesses. We searched the Cochrane Schizophrenia Group Trials Register (December 2008), which is based on regular searches of CINAHL, EMBASE, MEDLINE, and PsycINFO. We also inspected references within relevant papers. We included all randomized controlled trials comparing any intervention where physical activity or exercise was considered to be the main or active ingredient with standard care or other treatments for people with schizophrenia or schizophrenia-like illnesses. We independently inspected citations and abstracts, ordered papers, quality assessed, and data extracted. For binary outcomes, we calculated a fixed-effect risk ratio and its 95% CI. Where possible, the weighted number needed to treat/harm statistic (NNT/H) and its 95% CI was also calculated. For continuous outcomes, endpoint data were preferred to change data. We synthesized nonskewed data from valid scales using a weighted mean difference. Three randomized controlled trials met the inclusion criteria. Trials assessed the effects of exercise on physical and mental health. Overall numbers leaving the trials were similar. Two trials compared exercise with standard care and both found exercise to significantly improve negative symptoms of mental state (Mental Health Inventory Depression: 1 RCT, n = 10, Mean Difference [MD] 17.50 CI 6.70–28.30, Positive and Negative Syndrome Scale [PANSS] negative: 1 RCT, n = 10, MD −8.50 CI −11.11 to −5.89; figure 1). No absolute effects were found for positive symptoms of mental state. Physical health improved significantly in the exercise group compared with those in standard care (1 RCT, n = 13, MD 79.50 CI 33.82–125.18; figure 2), but no effect on peoples’ weight/BMI was apparent. One study compared exercise with yoga and found that yoga had a better outcome for mental state (PANSS total: 1 RCT, n = 41, MD 14.95 CI 2.60–27.30). The same trial also found that those in the yoga group had significantly better quality of life scores (World Health Organization Quality of Life physical: 1 RCT, n = 41, MD −9.22 CI −18.86 to 0.42). Adverse effects (Abnormal Voluntary Movements Scale total scores) were, however, similar. Comparison 1: Exercise vs Standard Care; Outcome: Mental state PANSS Negative endpoint score (low score = good). Comparison 1: Exercise vs Standard Care; Outcome: Physical fitness: 6-min walking test—average endpoint score (high score = good). Although studies included in this review are small and used various measures of physical and mental health, results indicated that regular exercise programs are possible in this population and that they can have healthful effects on both the physical and mental health and well being of individuals with schizophrenia. Larger randomized studies are required before any definitive conclusions can be drawn. Internal Sources of Support: Faculty of Physical Education and Health, University of Toronto, Canada. External Sources of Support: Ontario Mental Health Foundation, Canada; Centre for Urban Health Initiatives, Canada.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.002

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.

Opus teacher head0.045
GPT teacher head0.345
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations273
Published2010
Admission routes1
Has abstractyes

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