The effect of reactive balance training on falls in daily life: an updated systematic review and meta-analysis
Bibliographic record
Abstract
ABSTRACT Objective Reactive balance training is an emerging approach to reduce falls risk in people with balance impairments. The purpose of this study was to determine the effect of reactive balance training on falls in daily life among individuals at increased risk of falls, and to document associated adverse events. Data sources Databases searched were Ovid MEDLINE (1946-November 2020), Embase Classic and Embase (1947-November 2020), Cochrane Central Register of Controlled Trials (2014-November 2020), Physiotherapy Evidence Database (PEDro; searched on 9 November 2020). Study selection Randomized controlled trials of reactive balance training were included. The literature search was limited to English language. Records were screened by two investigators separately. Data extraction Outcome measures were number of participants who reported falls after training, number of falls reported after training, and the nature, frequency, and severity of adverse events. Authors of included studies were contacted to obtain additional information. Data synthesis Twenty-five trials were included, of which 14 reported falls and 19 monitored adverse events. Participants assigned to reactive balance training groups were less likely to fall compared to control groups (fall risk ratio: 0.75, 95% confidence interval=[0.60, 0.92]; p=0.006, I 2 =37%) and reported fewer falls than control groups (rate ratio: 0.60, 95% confidence interval=[0.42, 0.86]; p=0.005, I 2 =83%). Prevalence of adverse events was higher in reactive balance training (29%) compared to control groups (19%; p=0.018). Conclusion RBT reduces the likelihood of falls in daily life for older adults and people with balance impairments. More adverse events were reported for reactive balance training than control groups. Impact Balance training that evokes balance reactions can reduce falls among people at increased risk of falls.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".