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Record W4295507617 · doi:10.1159/000525951

The Effect of Auditory Cues on Static Postural Control: A Systematic Review and Meta-Analysis

2022· review· en· W4295507617 on OpenAlexaboutno aff
Hamed Zarei, Ali Asghar Norasteh, Laurie A. King

Bibliographic record

VenueAudiology and Neurotology · 2022
Typereview
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsProprioceptionBalance (ability)Physical medicine and rehabilitationVestibular systemMeta-analysisSensory cueCINAHLAudiologyPsychologyMEDLINEMedicineCognitive psychologyPsychological intervention

Abstract

fetched live from OpenAlex

INTRODUCTION: The purpose of this systematic review and meta-analysis was to summarize the results related to the effects of auditory cues on static postural control. METHODS: MEDLINE/PubMed, EMBASE, SCOPUS, LILACS, CINAHL, CENTRAL, Web of Science, PEDro, and Google Scholar were searched from inception until September 2020. Risk of bias was evaluated by both reviewers using Newcastle-Ottawa Quality Assessment Scale (NOS). RESULTS: Twelve studies with 403 participants were included in the review and 9 studies with 305 participants in the meta-analysis. Results show that auditory cues have significant effects on postural sway in the anterior-posterior direction (p = 0.001), postural sway in the medial-lateral direction (p = 0.001), and static balance (p = 0.001). A low to high heterogeneity was observed across all comparisons. CONCLUSIONS: Results of this meta-analysis revealed that auditory cues decrease postural sway in the anterior-posterior and medial-lateral direction; it also improves static balance. Thus, it can be concluded that auditory cues improve static postural control. Our results suggest that the auditory system can be a determinant of static postural control along with other sensory systems including visual, vestibular, and proprioception systems. Also, this study implies that auditory cues can be a significant therapeutic approach to improve static postural control.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.916
Threshold uncertainty score0.825

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0090.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.067
GPT teacher head0.425
Teacher spread0.358 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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

Citations13
Published2022
Admission routes1
Has abstractyes

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