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Record W3024264387 · doi:10.1101/2020.05.10.068957

Test-retest reliability of force plate-derived measures of reactive stepping

2020· preprint· en· W3024264387 on OpenAlexafffund
Tyler M. Saumur, Sunita Mathur, Jacqueline Nestico, Stephen D. Perry, George Mochizuki, Avril Mansfield

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsYork UniversitySunnybrook Health Science CentreToronto Rehabilitation InstituteUniversity Health NetworkUniversity of TorontoWilfrid Laurier University
FundersCanadian Institutes of Health ResearchPeterborough K. M. Hunter Charitable FoundationToronto Rehabilitation Institute
KeywordsIntraclass correlationReliability (semiconductor)Session (web analytics)Standard errorBalance (ability)PsychologyTest (biology)Physical medicine and rehabilitationPhysical therapyStatisticsMathematicsReproducibilityMedicineComputer science

Abstract

fetched live from OpenAlex

Abstract Background Characterizing reactive stepping is important to describe the response’s effectiveness. Measures of reactive stepping related to step initiation, execution, and termination phases have been frequently reported to characterize reactive balance control. However, the test-retest reliability of these measures are unknown. Research questions What is the between- and within-session test-retest reliability of various force plate-derived measures of reactive stepping? Methods Nineteen young, healthy adults responded to 6 small (~8-10% of body weight) and 6 large perturbations (~13-15% of body weight) using an anterior lean-and-release system. Tests were conducted on two visits separated by at least two days. Participants were instructed to recover balance in as few steps as possible. Step onset, foot-off, swing, and restabilization times were extracted from force plates. Relative test-retest reliability was determined through intraclass correlation coefficients (ICCs) and 95% confidence intervals (CIs). Absolute test-retest reliability was assessed using the standard error of the measurement (SEM). Results Foot-off and swing times had the highest between- and within-session test-retest reliabilities regardless of perturbation size (between-session ICC=0.898–0.942; within-session ICC=0.455–0.753). Conversely, step onset and restabilization time had lower ICCs and wider CIs (between-session ICC=0.495–0.825; within-session ICC=−0.040–0.174). Between-session test-retest reliability was higher (ICC=0.495-0.942) for all measures than within-session test-retest reliability (ICC=−0.040–0.753). SEMs were low (3–10% of mean) for all measures, except time to restabilization (SEM=15-20% of mean), indicating good absolute reliability. Significance These findings suggest multiple baseline sessions are needed for measuring restabilization and step onset times. The SEMs provide an index for measuring meaningful change due to an intervention.

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)0.008
metaresearch head score (Gemma)0.022
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 score0.008
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.041
GPT teacher head0.297
Teacher spread0.257 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

Explore more

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