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Updated: May 2, 2026

Quantitative Static and Dynamic Assessment of Balance Control in Stroke Patients
Published on: May 17, 2020
Prospective evaluation of balance and mobility tests as part of the World Falls Guidelines algorithm
Stephanie Saunders1, Mark Speechley2, Lauren E Griffith3,4
1School of Rehabilitation Science, Faculty of Health Sciences, McMaster University, Hamilton ON, Canada.
Background:
Falls are the leading cause of unintentional injury in older adults. While guidelines recommend balance and mobility tests to identify individuals at risk, their ability to predict future falls and improve the World Falls Guidelines (WFG) risk stratification remains uncertain.
Methods:
A 12-month prospective cohort study of community-dwelling older adults in Canada (≥65 years) who could walk 10 m without physical assistance. Balance and mobility assessments included the timed up and go (TUG) at usual and fast pace and with a cognitive dual task (cog), the Brief BESTest, five times sit-to-stand, single-leg stance (SLS) and gait speed. Falls were recorded prospectively using monthly fall calendars and quarterly telephone interviews.
Results:
Of 514 participants who completed baseline assessments, 486 (95%) completed the 12-month follow-up. Mean age was 76.4 years, 64% were female, and 266 (52%) experienced ≥1 fall during follow-up. For predicting ≥1 fall, area under the curve (AUC) values ranged from 0.561 [95% confidence interval (CI) = 0.51-0.61] for SLS to 0.593 (95% CI = 0.50-0.69) for gait speed. For ≥2 falls, AUCs ranged from 0.581 (95% CI = 0.52-0.64) for TUGcog to 0.639 (95% CI = 0.54-0.74) for gait speed and for injurious falls from 0.620 (95% CI = 0.54-0.70) for TUGcog to 0.70 (95% CI = 0.54-0.86) for gait speed. Using the WFG algorithm without mobility tests deemed 282 participants as low risk, 41% of whom fell; adding mobility tests to the algorithm increased this proportion to 56%.
Conclusion And Relevance:
Common balance and mobility tests showed limited predictive validity for future falls. Incorporating these tests into the WFG algorithm did not improve fall risk stratification in community-dwelling older adults.
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