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Record W2325742184 · doi:10.1519/jpt.0b013e3182abe793

Does Upper Limb Coordination Predict Walking Speed in Older Adults? A Cross-Sectional Study

2014· article· en· W2325742184 on OpenAlexaboutno aff
John H. Hollman, Katherine C. Beed, Ryan J Buus, K Schleicher, Desiree J. Lanzino

Bibliographic record

VenueJournal of Geriatric Physical Therapy · 2014
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsPreferred walking speedPhysical medicine and rehabilitationRegression analysisPartial correlationMedicineTimed Up and Go testBivariate analysisTrail Making TestIsometric exercisePhysical therapyCorrelationCognitionMathematicsBalance (ability)StatisticsCognitive impairment

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: Walking speed is a measure of physical function in older adults. Older adults are sometimes nonambulatory, however, and proxy measures for walking speed may be indicated. Since limb coordination tests can be conducted in non-weight-bearing positions, they may provide that capability. The purpose of this study was to examine the relationship between timed limb coordination and preferred and maximum walking speed, controlling for other known determinants of walking speed. METHODS: A total of 84 healthy adults (60 women and 24 men) older than 60 years participated. Preferred and maximum walking speed were measured during 10-Meter Walk Tests. Upper limb coordination performance was measured during a timed 5-repetition finger-to-nose test. Other variables measured included isometric knee extension strength, cognition (Montreal Cognitive Assessment), limits of stability (Functional Reach Test), the number of comorbidities (Functional Comorbidity Index), age, height, and sex. Multiple regression and partial correlation analyses (α = .05) were used to identify which variables predicted preferred and maximum walking speed, controlling for all other variables. RESULTS: Participants' mean preferred walking speed was 129 (24) cm·s⁻¹, and mean maximum walking speed was 176 (37) cm·s⁻¹. Finger-to-nose coordination performance, 4.8 (1.3) seconds, correlated negatively with preferred (r = -0.403) and maximum (r = -0.429) walking speed. Those bivariate correlation coefficients, however, were attenuated by other variables in the regression models (partial r = -0.031, P = .786, and partial r = -0.075, P = .513, for preferred and maximum walking speed, respectively). Variance in age, comorbidities, functional reach, knee extension strength, and height accounted for 55.4% of the variance in preferred walking speed. Variance in knee extension strength, cognition, functional reach, age, and comorbidities accounted for 63.5% of the variance in maximum walking speed. After removing knee extension strength and functional reach from the models--those variables that may be difficult or contraindicated to measure in some patient populations--finger-to-nose coordination was not a statistically significant predictor of preferred walking speed. Variance in age, comorbidities, cognition, height, and finger-to-nose coordination accounted for 55.9% of the variance in maximum walking speed. The change in R² attributed to finger-to-nose coordination performance, however, was only 2.9%. DISCUSSION: While knee extension strength, functional reach, comorbidities, and age were most predictive of walking speed, after removing knee extension strength and functional reach from the regression models, finger-to-nose coordination remained a potentially modifiable marker of neuromuscular control that only weakly predicted maximum walking speed in older adults. CONCLUSIONS: The timed finger-to-nose test would not appear to be a valid proxy for walking speed when weight-bearing clinical examination procedures are contraindicated.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.016
GPT teacher head0.359
Teacher spread0.343 · 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

Citations3
Published2014
Admission routes1
Has abstractyes

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