Influence of reactive balance training program characteristics on reactive balance control and fall risk: a systematic review and meta-analysis
Bibliographic record
Abstract
ABSTRACT Introduction : Diverse Reactive Balance Training (RBT) programs have been developed to address age-related deterioration in reactive balance control and increased fall risk. Despite the demonstrated effectiveness of those programs, there is significant variability in intervention characteristics (e.g., type of perturbations, total volume and intensity of training) and in study findings. It is likely that intervention effectiveness depends on features of the intervention; however, little is known about the optimal way to deliver RBT. The purpose of this systematic review and meta-analysis is to determine the optimal intervention characteristics for RBT for improving reactive balance control and preventing falls in daily life. Methods : We searched MEDLINE ALL (July 2023), Embase (July 2023), Physiotherapy Evidence Database (August 2023) and Cochrane (July 2023) for randomized controlled trials of RBT that reported on a measures of reactive balance control and/or falls in daily life. Results were screened by two reviewers independently to determine eligibility. The following details were extracted: study population; intervention characteristics (number of sessions in total, duration, and frequency of sessions; type, intensity and number of perturbations; description of the control intervention; and program duration), number of participants in each group; reactive balance outcomes pre- and post-intervention, and number of falls in daily life post-intervention. Risk of bias (RoB) and certainty of evidence (GRADE) were assessed. Meta-regressions were performed to explore the influence of different study components on reactive balance control and falls in daily life. Results: After screening 7,677 records, 32 studies were included; 25 reported a reactive balance outcome, and 19 reported falls in daily life. RoB of reactive balance control revealed main concerns arising from selection of reported results (20/25). RoB of falls in daily life had high or some concerns in the measurements of the outcome (12/19) and selection of reported results (15/19). RBT programs that included manual perturbations were associated with reduced fall rates compared to the reference (waist pull perturbations; relative risk: 0.45; 95% confidence interval: [0.22, 0.91], p=0.042). There were no other significant relationships between any other training parameters and falls in daily life or reactive balance control. Quality of evidence (GRADE) was low for both reactive balance control and falls in daily life. Discussion: While there was some evidence for superiority of manual perturbations over other perturbation types for fall prevention, we were unable make any definitive conclusions regarding optimal training RBT characteristics. High variability in training protocols between studies and under-reporting of intervention characteristics prevented us from making a meaningful analysis of the existing studies. Future RBT studies should provide more detailed descriptions of training protocols and include head-to-head comparisons of different training parameters (e.g., perturbation types or intensities). RBT studies should also include outcomes for both reactive balance control and falls in daily life.
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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 | 0.019 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.039 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".