Interventions for addressing low balance confidence in older adults: a systematic review and meta-analysis
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.020 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".