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Record W3164088784 · doi:10.1097/tgr.0000000000000312

The Effect of Different Exercise Training Types on Functionality in Older Fallers

2021· article· en· W3164088784 on OpenAlexaboutno aff
Ayşe Abit Kocaman, Nuray Kırdı, Songül Aksoy, Özgün Elmas, Burcu Balam Doğu

Bibliographic record

VenueTopics in Geriatric Rehabilitation · 2021
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsnot available
Fundersnot available
KeywordsPosturographyMedicineBalance (ability)Physical therapyPhysical medicine and rehabilitationBerg Balance ScaleMontreal Cognitive AssessmentPopulationCognitionCognitive impairment

Abstract

fetched live from OpenAlex

Background: Fall is one of the most common geriatric syndromes in the elderly population. It is important to determine the most effective exercise training in elderly individuals who are at risk of falling. Aim: To investigate the effects of different exercise trainings on functionality in older fallers. Method: A total of 30 older adults, 16 females and 14 males, were enrolled in this randomized controlled trial. The older adults were divided into 3 groups: vestibular exercise (VE), posturography balance exercise (PBE), and square step exercise (SSE) groups. All groups received VE training. Sensory Organization Test (SOT), Adaptation Test (ADT), Fall Efficacy Scale (FES), Montreal Cognitive Assessment (MoCA), Vestibular Disorders Activities of Daily Life Scale (VADL), and the World Health Organization Quality of Life Scale–Older Adults Module (WHOQOL-Old) were administered before and at the end of the 24 training sessions. Results: The MoCA and the composite balance score of the SOT were improved in the PBE and SSE groups and the FES in all groups. According to multiple comparison analyses, toes up in the VE and PBE groups, toes down of the ADT in the PBE group, VADL in the PBE and SSE groups, the WHOQOL-Old in all groups significantly improved ( P < .010). Conclusion: VE training alone is not sufficient for older fallers. A combination of PBE and SSE training, which was applied 3 times a week for 8 weeks, was more effective in improving functionality in older fallers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.148
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.019
GPT teacher head0.340
Teacher spread0.321 · 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 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

Citations5
Published2021
Admission routes1
Has abstractyes

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