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Effects of Augmented Exercise Therapy Time After Stroke

2004· review· en· W2141635852 on OpenAlexaff
Gert Kwakkel, Roland van Peppen, Robert Wagenaar, Sharon Wood Dauphinée, Carol L. Richards, Ann Ashburn, K. J. Miller, Nadina B. Lincoln, Cecily Partridge, Ian Wellwood, Peter Langhorne

Bibliographic record

VenueStroke · 2004
Typereview
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsMcGill UniversityUniversité LavalCentre for Interdisciplinary Research in Rehabilitation
FundersKoninklijk Nederlands Genootschap voor FysiotherapieZonMw
KeywordsMedicineStroke (engine)Physical therapyPhysical medicine and rehabilitationExercise therapyIschemic strokeCardiologyInternal medicineRandomized controlled trialIschemia

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: To present a systematic review of studies that addresses the effects of intensity of augmented exercise therapy time (AETT) on activities of daily living (ADL), walking, and dexterity in patients with stroke. SUMMARY OF REVIEW: A database of articles published from 1966 to November 2003 was compiled from MEDLINE, CINAHL, Cochrane Central Register of Controlled Trials, PEDro, DARE, and PiCarta using combinations of the following key words: stroke, cerebrovascular disorders, physical therapy, physiotherapy, occupational therapy, exercise therapy, rehabilitation, intensity, dose-response relationship, effectiveness, and randomized controlled trial. References presented in relevant publications were examined as well as abstracts in proceedings. Studies that satisfied the following selection criteria were included: (1) patients had a diagnosis of stroke; (2) effects of intensity of exercise training were investigated; and (3) design of the study was a randomized controlled trial (RCT). For each outcome measure, the estimated effect size (ES) and the summary effect size (SES) expressed in standard deviation units (SDU) were calculated for ADL, walking speed, and dexterity using fixed and random effect models. Correlation coefficients were calculated between observed individual effect sizes on ADL of each study, additional time spent on exercise training, and methodological quality. Cumulative meta-analyses (random effects model) adjusted for the difference in treatment intensity in each study was used for the trials evaluating the effects of AETT provided. Twenty of the 31 candidate studies, involving 2686 stroke patients, were included in the synthesis. The methodological quality ranged from 2 to 10 out of the maximum score of 14 points. The meta-analysis resulted in a small but statistically significant SES with regard to ADL measured at the end of the intervention phase. Further analysis showed a significant homogeneous SES for 17 studies that investigated effects of increased exercise intensity within the first 6 months after stroke. No significant SES was observed for the 3 studies conducted in the chronic phase. Cumulative meta-analysis strongly suggests that at least a 16-hour difference in treatment time between experimental and control groups provided in the first 6 months after stroke is needed to obtain significant differences in ADL. A significant SES supporting a higher intensity was also observed for instrumental ADL and walking speed, whereas no significant SES was found for dexterity. CONCLUSIONS: The results of the present research synthesis support the hypothesis that augmented exercise therapy has a small but favorable effect on ADL, particularly if therapy input is augmented at least 16 hours within the first 6 months after stroke. This meta-analysis also suggests that clinically relevant treatment effects may be achieved on instrumental ADL and gait speed.

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)0.011
metaresearch head score (Gemma)0.045
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 score0.011
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.045
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.009
Bibliometrics0.0060.005
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.012
GPT teacher head0.299
Teacher spread0.287 · 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

Citations1,073
Published2004
Admission routes1
Has abstractyes

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