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Record W4417297550 · doi:10.1080/08990220.2025.2600492

Textured foot orthotics and proprioception: augmenting cutaneous feedback to improve joint position sense accuracy

2025· article· en· W4417297550 on OpenAlexaff
Kelly A. Robb, Daniel Schmidt, Stephen D. Perry, Andresa M.C. Germano

Bibliographic record

VenueSomatosensory & Motor Research · 2025
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsProprioceptionAnkleDisplacement (psychology)OrthoticsJoint (building)Visual feedbackTask (project management)Position (finance)Hand position

Abstract

fetched live from OpenAlex

The maintenance of proprioception in upright standing posture and joint position sense accuracy (JPSA), is contingent on the coordination of sensory inputs made available to individuals during the task. Adding texture to augment cutaneous feedback on neuromuscular control of the ankle joint has yet to be studied in JPSA, thus motivating the purpose of this research to investigate the changes in ankle joint proprioception when wearing textured (FOTs) and non-textured orthoses (FOs) during a passive ankle joint position reproduction task in healthy young and middle-aged individuals (n = 48; 31 under 30 years, 17 over 30 years). Error accuracy, tibialis anterior and medial gastrocnemius muscle activity, and centre of pressure displacement were recorded from 48 participants while completing four ankle JPSA replication tasks (5°, 10° plantarflexion, 5°, 10° dorsiflexion) in stance. When the middle-aged participants (over 30 years) completed the dorsiflexion 5° JPSA task, a 31% error reduction was observed standing in FOTs compared to FOs. These results provide evidence supporting the effectiveness of adding cutaneous feedback to improve proprioception in middle-aged individuals.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.954
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.057
GPT teacher head0.418
Teacher spread0.361 · 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 teacher head, not a consensus.

Study designBench or experimental
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 routes1
Has abstractyes

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