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Record W4406962662 · doi:10.1161/str.56.suppl_1.22

Abstract 22: Implementation of increased physical therapy intensity for improving walking across inpatient stroke rehabilitation units: Primary results of the Walk ‘n Watch multi-site stepped-wedge cluster randomized controlled trial

2025· article· en· W4406962662 on OpenAlexaffabout
Sue Peters, Stanley Hung, Mark Bayley, Krista L. Best, Louise Connell, Hélène Corriveau, Sarah J. Donkers, Sean P. Dukelow, Victor E. Ezeugwu, Marie-Hélène Milot, Brodie M. Sakakibara, Lisa Sheehy, Hubert Wong, Yuwei Yang, Jennifer Yao, Janice J. Eng

Bibliographic record

VenueStroke · 2025
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsBruyèreUniversity of British Columbia, Okanagan CampusKelowna General HospitalUniversity of CalgaryUniversité de SherbrookeUniversity of AlbertaUniversity of SaskatchewanUniversité LavalUniversity of TorontoUniversity of British ColumbiaWestern University
Fundersnot available
KeywordsMedicineRandomized controlled trialPhysical therapyRehabilitationStroke (engine)Physical medicine and rehabilitationWalk-inCluster randomised controlled trialCluster (spacecraft)Alternative medicineSurgery

Abstract

fetched live from OpenAlex

Introduction: Though clinical practice guidelines support high repetitions of walking after stroke, practice is slow to change with low levels of walking activity in stroke rehabilitation units. We undertook an implementation trial to change practice; we enabled entire stroke units to use the Walk ‘n Watch protocol and determined the effect of implementation on the 6 Minute Walk Test (6MWT) at hospital discharge. Methods: This 12-site clinical trial across 7 Canadian provinces used a stepped-wedge cluster design to randomize when each site switched from Usual Care to the Walk ‘n Watch protocol. At the start of the Walk ‘n Watch phase, we trained all front-line physical therapists on the unit with training workshops, manuals, hands-on practice, and videos. Each site was provided onboarding materials to address staff changes so therapists who did not attend the initial training could adopt the protocol. Each site also identified a ‘protocol champion’ to facilitate initial weekly huddles with therapists to discuss barriers to implementation. Therapists were trained to complete safety screening and to determine eligibility. The Walk ‘n Watch protocol focused on completing a minimum of 30-minutes of daily weight-bearing, walking-related activities that progressively increased in intensity informed by activity trackers measuring heart rate and step number. Blinded assessors completed the outcomes at baseline and 4-weeks later (near discharge). Primary analysis used a linear mixed-effects model adjusted for stratum, date of enrollment, age, sex and baseline 6MWT. Results: The total number of participants was 306 (162 Usual Care, 144 Walk ‘n Watch, 188 males/118 females) with a mean(SD) age of 68(13), 29(17) days since stroke, and a baseline 6MWT of 152(106) m. The improvement on the 6MWT was 43.6m (95%CI 12.7, 76.1) greater in the Walk ‘n Watch group compared to the Usual Care group. Further, the Walk ‘n Watch group improved quality of life (EQ5D), balance and mobility (Short Physical Performance Battery) and gait speed. Conclusions: The implementation trial design enabled the protocol to be tested under real-world conditions, involving all therapists on each unit to deliver the protocol. The trial had a deliberate aim to facilitate changes in practice that resulted in clinically meaningful improvements in walking and quality of life.

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)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0110.001

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 head0.014
GPT teacher head0.321
Teacher spread0.308 · 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 designRandomized trial
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes2
Has abstractyes

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