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Kinematic Analysis of Land and Aquatic Treadmill Walking in Individuals with Stroke

2006· article· en· W2007093432 on OpenAlexaboutno aff
Kiran Rambhatla, Taeyou Jung, Konstant D. Vrongistinos

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2006
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
Fundersnot available
KeywordsTreadmillGaitPhysical medicine and rehabilitationCadenceKinematicsStroke (engine)Preferred walking speedSTRIDEGait analysisPhysical therapyRehabilitationPower walkingMedicineEngineering

Abstract

fetched live from OpenAlex

Partial weight bearing (PWB) treadmill-based gait training has shown to be effective in gait rehabilitation of people with stroke. The PWB effect can be achieved by using aquatic treadmill. PURPOSE: The purpose of this study was to analyze biomechanical gait variables of conventional treadmill and aquatic treadmill walking in individuals with stroke. The findings will help clinicians have better understanding of aquatic treadmill-based gait training for people with stroke. METHODS: A multivariate repeated measure study was used to compare biomechanical differences between land and aquatic treadmill walking. Nine individuals with stroke (M=7, F=2; age range 50–76 yrs, mean 55) participated in the study. A two dimensional video-based motion analysis system (E-Human software, HMA Tech., Ontario, Canada) was used to capture and analyze all walking trials. Underwater digital motion capturing system (Equinox, Galesburg, MI) and aquatic treadmill (AquaGaiter, FERNO, Willington, OH) were used. A total of 12 water-proof markers were attached on both legs for image digitization. Initially all participants were given five-minute familiarization trials on both treadmills. They were asked to select their comfortable walking speeds during the familiarization trial on land treadmill. The selected speed was applied to both walking conditions. Three two-minute testing trials were captured for each treadmill walking. RESULTS: The results showed that there were significant differences in spatiotemporal and kinematic variables. The stride time and stride length of both paretic and non-paretic limbs were significantly greater during aquatic treadmill walking (all Ps < .05). However, the cadence of both limbs was significantly lower during aquatic treadmill walking at a matched speed as compared to land treadmill walking (all Ps < .05). No significant differences were noted in the hip joint of both limbs. While the paretic limb did not show any significant difference in the knee joint, non-paretic limb showed less peak knee flexion angle and greater peak knee extension angle during aquatic treadmill walking (all Ps< .05). The peak ankle dorsiflexion was less and the peak ankle plantarflexion was greater on both limbs during aquatic treadmill walking (all Ps< .05). CONCLUSIONS: The study results indicate that aquatic treadmill-based gait training can be used to improve stride length of people with stroke. However, the increased plantarflexion during aquatic treadmill walking should be considered when providing aquatic gait training for individuals with ankle deformities.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.272
Teacher spread0.262 · 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 designObservational
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
Published2006
Admission routes1
Has abstractyes

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