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Record W2100785741 · doi:10.1093/ageing/afr037

Interventions for addressing low balance confidence in older adults: a systematic review and meta-analysis

2011· review· en· W2100785741 on OpenAlexafffund
Debbie Rand, William C. Miller, Jeanne Yiu, Janice J. Eng

Bibliographic record

VenueAge and Ageing · 2011
Typereview
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsVancouver General HospitalUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsMedicineMeta-analysisPsychological interventionGerontologyBalance (ability)Confidence intervalPhysical therapyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: low balance confidence is a major health problem among older adults restricting their participation in daily life. OBJECTIVES: to determine what interventions are most effective in increasing balance confidence in older adults. DESIGN: systematic review with meta-analysis of randomised controlled trials including at least one continuous end point of balance confidence. Studies, including adults 60 years or older without a neurological condition, were included in our study. METHODS: the standardised mean difference (SMD) of continuous end points of balance confidence was calculated to estimate the pooled effect size with random-effect models. Methodological quality of trials was assessed using the Physical Therapy Evidence Database (PEDro) Scale. RESULTS: thirty studies were included in this review and a meta-analysis was conducted for 24 studies. Interventions were pooled into exercise (n = 9 trials, 453 subjects), Tai Chi (n = 5 trials, 468 subjects), multifactorial intervention (n = 10 trials, 1,233 subjects). Low significant effects were found for exercise and multifactorial interventions (SMD 0.22-0.31) and medium (SMD 0.48) significant effects were found for Tai Chi. CONCLUSION: Tai chi interventions are the most beneficial in increasing the balance confidence of older adults.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.343
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.180
GPT teacher head0.452
Teacher spread0.272 · 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 designSystematic review
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

Citations91
Published2011
Admission routes2
Has abstractyes

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