Predictive Accuracy of Gait Speed for Falls: An Individual Participant Data Meta-analysis

Background: Gait speed is included in the World Falls Guidelines (WFG) fall risk algorithm, yet its ability to discriminate fallers from non-fallers remains unclear. This individual participant data meta-analysis examined the discriminative ability of the WFG-recommended cut point (<0.8m/s) and the performance of a higher cut point (<1.0m/s) for predicting falls in community-dwelling older adults and clinical populations.

Methods: Individual data from 28 studies with a quantitative measure of gait speed and at least three months of prospectively reported falls were analysed using modified Poisson regression and negative binomial regression, followed by random-effects meta-analyses.

Results: Twenty-eight studies involving community-dwelling older adults (n = 8) and clinical populations (n = 20) were included (total participants = 7,608). Walking at <0.8m/s was associated with increased fall risk (Relative Risk:1.27 (95%CI 1.17 – 1.38)) and fall rate (Incidence Rate Ratio: 1.54 (95%CI 1.34 – 1.77)). Diagnostic accuracy was modest (58%, specificity 77%, sensitivity 35%), with consistent findings across planned subgroup analyses for population sub-groups, fall history, and sex. Analyses using the <1.0m/s cut point produced similar effect sizes and accuracy metrics but identified a larger proportion of fallers in both community-dwelling (25.7% vs 7.9%) and clinical populations (61.6% vs 44.9%) compared to the <0.8m/s cut point.

Conclusion: Slower gait speed is associated with an increased risk and rate of falling across all population groups, but discriminative accuracy is low. While the <0.8m/s threshold shows consistent associations, a <1.0m/s cut point may be more clinically useful in community-dwelling and clinical settings because it identifies more fallers.