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The Instrumented Stand and Walk (ISAW) test to predict falls in older men
Fay B Horak1,2, Amy Laird3, Patricia Carlson-Kuhta4
1Department of Neurology, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, OP-32, Portland, OR, 97239, USA. horakf@ohsu.edu.
Objective measures from the Instrumented Stand and Walk Test (ISAW) improve prediction of future falls in older men. This advanced gait and balance assessment surpasses traditional fall history for identifying individuals at risk.
Area of Science:
- Gerontology
- Biomechanics
- Clinical Biomechanics
Background:
- Balance and gait impairments are common precursors to falls in older adults.
- Objective measures offer potential for improved prediction of future fallers.
- The Instrumented Stand and Walk Test (ISAW) utilizes wearable sensors to comprehensively assess balance and gait domains.
Purpose of the Study:
- To evaluate if ISAW objective measures enhance fall prediction in high-functioning older men.
- To compare the predictive power of ISAW metrics against traditional fall history, gait speed, and dual-task cost.
- To identify key ISAW metrics contributing to accurate fall risk assessment.
Main Methods:
- Recruited 214 high-functioning older men (mean age 82 years).
- Administered the ISAW test, including 30s stance, 7m walk, turn, and return, using inertial sensors.
- Analyzed 17 ISAW metrics using regression models to predict falls within 12 months, comparing models with and without age and fall history.
Main Results:
- The ISAW test improved faller prediction compared to age and fall history alone (AUC increased from 0.69 to 0.75).
- Top models incorporated postural sway, turning, gait variability, and gait initiation measures.
- Gait speed and dual-task cost did not significantly distinguish fallers from non-fallers in this cohort.
Conclusions:
- Objective ISAW measures significantly enhance the prediction of future falls in high-functioning older men.
- A multi-domain approach, assessing sway, turning, variability, and initiation, is crucial for accurate fall risk identification.
- Simple measures like gait speed are insufficient for predicting falls in this population, highlighting the complexity of fall etiology.
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