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Record W2040686964 · doi:10.1159/000346075

Using Aerobic Exercise to Improve Health Outcomes and Quality of Life in Stroke: Evidence-Based Exercise Prescription Recommendations

2013· review· en· W2040686964 on OpenAlexafffund
Marco Y.C. Pang, Sarah Charlesworth, Ricky W.K. Lau, Raymond C. K. Chung

Bibliographic record

VenueCerebrovascular Diseases · 2013
Typereview
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsUniversity of British Columbia
FundersPublic Health Agency of Canada
KeywordsMedicineAerobic exercisePhysical therapyStroke (engine)Quality of life (healthcare)Exercise prescriptionPsychosocialSample size determinationRandomized controlled trialModalitiesPhysical medicine and rehabilitationInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Stroke patients often suffer from poor cardiovascular health and deficits in physical, psychosocial and cognitive functioning. Aerobic exercise training may be a viable treatment approach to address these health issues. The objective of this systematic review was to determine the effects of aerobic exercise on various indicators of health, functioning and quality of life in stroke patients. It was hypothesized that the systematic review would reveal compelling support for the effectiveness of aerobic exercise in stroke patients, such that detailed evidence-based exercise prescription recommendations could be derived. METHODS: Major electronic databases were searched systematically to identify randomized controlled studies that examined the effects of aerobic exercise in stroke patients (last search performed in January 2012). The methodological quality of each study was evaluated using the PEDro scale (9-10 = excellent; 6-8 = good; 4-5 = fair; <4 = poor). Based on the methodological quality and sample size used, the level of evidence was determined for each study (level 1: PEDro ≥6 and sample size >50; level 2: PEDro ≤5 or sample size ≤50). Meta-analysis was performed on a given outcome when appropriate. RESULTS: Twenty-five trials fulfilled the selection criteria, of which 8 were level 1 studies. Treadmill and cycle ergometer were the two most popular modalities used to provide aerobic training. The most commonly adopted exercise session duration and frequency was 21-40 min and 3-5 days per week, respectively. The duration of the training programme varied, ranging from 3 weeks to 6 months. Over 60% of the trials used a high training intensity [60-80% heart rate reserve (HRR)]. Meta-analysis showed a significant effect on peak oxygen consumption (p < 0.001), peak workload (p < 0.001), maximal gait speed (p = 0.003) and walking endurance (p < 0.001) in favour of aerobic exercise. Meta-analysis revealed no significant effect on self-selected gait speed, Berg balance score and Functional Independence Measure score. The efficacy of aerobic exercise in improving other health outcomes in physical, psychosocial and cognitive domains as well as quality of life was inconclusive. The health risk associated with engaging in such exercise is small. CONCLUSIONS: There is strong evidence that aerobic exercise (40-50% HRR progressing to 60-80%) conducted 20-40 min and 3-5 days per week is beneficial for enhancing aerobic fitness, walking speed and walking endurance in people who have had mild to moderate stroke and are deemed to have low cardiovascular risk with exercise after proper screening assessments (grade A recommendation). The effects of aerobic exercise on other health outcomes require further study.

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)0.019
metaresearch head score (Gemma)0.053
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: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.007
Bibliometrics0.0060.004
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0030.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.147
GPT teacher head0.407
Teacher spread0.260 · 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

Citations181
Published2013
Admission routes2
Has abstractyes

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