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Can sit-to-stand lower limb muscle power predict fall status?
Yuan-Yang Cheng1, Shun-Hwa Wei2, Po-Yin Chen2
1Department of Physical Medicine and Rehabilitation, Taichung Veterans General Hospital, Taiwan; Institute of Clinical Medicine, National Yang-Ming University, Taiwan.
Gait & Posture
|June 30, 2014
Summary
This study found that maximal lower limb muscle power and sit-to-stand (STS) stabilization phase predict fall risk in older adults. The developed forceplate can assess balance and monitor fall risk.
Area of Science:
- Biomechanics
- Gerontology
- Rehabilitation Engineering
Background:
- Sit-to-stand (STS) movements are crucial for daily living and mobility in older adults.
- Impaired STS performance is associated with an increased risk of falls, a major concern for elderly health.
- Objective assessment of fall risk requires reliable and sensitive measurement tools.
Purpose of the Study:
- To develop and validate a forceplate system for analyzing STS movements.
- To identify key biomechanical parameters that predict fall status in older adults.
- To investigate the relationship between STS performance, muscle power, and fall history.
Main Methods:
- Developed a forceplate system to measure vertical ground reaction force (VGRF) and STS duration.
- Recruited 105 participants: young adults (20-30 yrs), older non-fallers (>65 yrs), and older fallers (>65 yrs with falls).
- Assessed maximal lower limb muscle power (MP), Modified Falls Efficacy Scale (MFES), and Five Times Sit-to-Stand Test (FSTST) duration.
Main Results:
- Significantly higher MP and MFES scores, and shorter FSTST duration were observed in young adults compared to older adults.
- Fallers exhibited significantly lower MP and longer STS duration compared to non-fallers.
- Regression analysis identified MP and STS stabilization phase as significant predictors of past fall events.
Conclusions:
- The developed forceplate system effectively analyzes STS biomechanics.
- Maximal lower limb muscle power and STS stabilization phase are key indicators for differentiating fallers from non-fallers.
- This technology holds potential for assessing and monitoring balance and fall risk in the elderly population.
