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Record W2613881360 · doi:10.1093/geronb/gbx047

The Effects of Age and Hearing Loss on Dual-Task Balance and Listening

2017· article· en· W2613881360 on OpenAlexafffund
Halina Bruce, Daniel I Aponte, Nancy St-Onge, Natalie A. Phillips, Jean‐Pierre Gagné, Karen Li

Bibliographic record

VenueThe Journals of Gerontology Series B · 2017
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsInstitut Universitaire de Gériatrie de MontréalMcGill UniversityUniversité de MontréalCentre de réadaptation Lethbridge-Layton-MackayConcordia University
FundersCanadian Institutes of Health Research
KeywordsAudiologyCognitionBalance (ability)Hearing lossActive listeningTask (project management)PresbycusisElementary cognitive taskCognitive resource theoryPsychologyPrioritizationPhysical medicine and rehabilitationMedicineNeuroscienceCommunication

Abstract

fetched live from OpenAlex

Objectives: Among older adults (OA), hearing loss is associated with an increased risk for falls. The aim of the present study was to experimentally investigate the cognitive compensation hypothesis, wherein decreased auditory and motor functioning are compensated by the recruitment of cognitive resources. Method: Twenty-nine younger adults (YA), 26 OA, and 32 OA with age-related hearing loss (ARHL) completed a dual-task paradigm consisting of cognitive and balance recovery tasks performed singly and concurrently. The auditory stimuli were presented with or without background noise. Results: Both older adult groups performed significantly worse than YA on the cognitive task in noisy conditions and ARHL also demonstrated disproportionate negative effects of dual-tasking and noise. The kinematic data indicated that OA and ARHL demonstrated greater plantarflexion when compared with YA. Conversely, YA showed greater hip extension in response to dual-tasking. Discussion: The cognitive and balance results suggest that YA were able to flexibly allocate their attention between tasks, whereas ARHL exhibited prioritization of posture over cognitive performance.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.308
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.043
GPT teacher head0.383
Teacher spread0.340 · 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 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

Citations52
Published2017
Admission routes2
Has abstractyes

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