Balance impairment in individuals with COPD: a systematic review with meta-analysis
Bibliographic record
Abstract
Background People with chronic obstructive pulmonary disease (COPD) are four times more likely to fall than healthy peers, leading to increased morbidity and mortality. Poor balance is a major risk factor for falls. This review aims to quantify the extent of balance impairment in COPD, and establish contributing clinical factors, which at present are sparse. Methods Five electronic databases were searched, in July 2017 and updated searches were performed in March 2019, for studies comparing balance in COPD with healthy controls. Meta-analyses were conducted on sample mean differences (MD) and reported correlations between balance and clinical factors. Meta-regression was used to quantify the association between mean difference in percentage predicted forced expiratory volume in 1 s (FEV1) and mean balance impairment. Narrative summaries were provided where data were insufficient for meta-analysis. Results Twenty-three studies were included (n=2751). Meta-analysis indicated COPD patients performed worse than healthy controls on timed up and go (MD=2.77 s, 95% CI 1.46 s to 4.089 s, p=<0.005), single leg stance (MD=−11.75 s, 95% CI −15.12 s to −8.38 s, p=<0.005) and berg balance scale (MD=−6.66, 95% CI −8.95 to −4.37, p=<0.005). The pooled correlation coefficient between balance and reduced quadriceps strength was weak-moderate (r=0.37, 95% CI 0.23 to 0.45, p=<0.005). The relationship between differences in percentage predicted FEV1and balance were negligible (r2=<0.04). Conclusions Compared with healthy controls, people with COPD have a clinically meaningful balance reduction, which may be related to reduced muscle strength, physical activity and exercise capacity. Our findings support a need to expand the focus of pulmonary rehabilitation to include balance assessment and training, and further exploration of balance impairment in COPD. PROSPERO registration number CRD4201769041
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".