Biomechanical Balance Measures During Timed Up and Go Test Improve Prediction of Prospective Falls in Older Adults

Objective: To assess the feasibility of using biomechanical gait balance measures, the frontal and sagittal plane center of mass (COM)-Ankle angles, to prospectively predict recurrent falls in community-dwelling older adults.

Design: A cohort study with a 1-year longitudinal follow-up. Logistic regression was used to test the ability of the COM-Ankle angles to predict prospective falls.

Setting: General community.

Participants: Sixty older adults over the age of 70 years were recruited using a volunteer sample.

Interventions: Not applicable.

Main outcome measure(s): Biomechanical balance parameters: the sagittal and frontal plane COM-Ankle angles during the sit-to-walk and turning phases of the timed Up and Go test. The COM-Ankle angles are the inclination angles of the line formed by the COM and lateral ankle (malleolus) marker of the stance foot in the sagittal and frontal planes. We also included the following clinical balance tests in the analysis: Activity-Specific Balance Confidence, Berg Balance Scale, Fullerton Advanced Balance scale, and timed Up and Go test. Their abilities to predict falls served as a reference for the biomechanical balance parameters.

Results: When the biomechanical gait balance measures were added to all the confounders, the explained variance was increased from 25.3% to 50.2%. Older adults who have a smaller sagittal plane COM-Ankle angle at seat-off, a greater frontal plane COM range of motion during STW and a smaller frontal plane angle during turning were more likely to become recurrent fallers.

Conclusion(s): Our results indicated that dynamic biomechanical balance parameters could provide valuable information about a participant's future fall risks beyond what can be explained by demographics, cognition, depression, strength, and past fall history. Among all biomechanical parameters investigated, frontal plane COM motion measures during STW and turning appear to be the most significant predictors for future falls.