Abstract
Frailty is associated with gait variability in several quantitative parameters, including high stride time variability. However, the associations between joint kinematics during walking and increased gait variability with frailty remain unclear. In the current study, principal component analysis was used to identify the key joint kinematics characteristics of gait related to frailty. We analyzed whole kinematic waveforms during the entire gait cycle obtained from the pelvis and lower limb joint angle in 30 older women (frail/prefrail: 15 participants; non-frail: 15 participants). Principal component analysis was conducted using a 60 × 1224 input matrix constructed from participants’ time-normalized pelvic and lower-limb-joint angles along three axes (each leg of 30 participants, 51 time points, four angles, three axes, and two variables). Statistical analyses revealed that only principal component vectors 6 and 9 were related to frailty. Recombining the joint kinematics corresponding to these principal component vectors revealed that frail older women tended to exhibit greater variability of knee- and ankle-joint angles in the sagittal plane while walking compared with non-frail older women. We concluded that greater variability of knee- and ankle-joint angles in the sagittal plane are joint kinematic characteristics of gait related to frailty.
Highlights
Frailty is associated with gait variability in several quantitative parameters, including high stride time variability
Principal component analysis revealed that 26 extracted principal component vectors explained more than 84% of the joint movement patterns
Principal component analysis was used to identify the key characteristic features of joint kinematics of gait related to frailty status
Summary
Frailty is associated with gait variability in several quantitative parameters, including high stride time variability. Detailed investigation of the relationships between frailty status and gait characteristics, including variability in joint kinematics while walking, could be helpful for understanding the mechanisms of increased gait variability and decreased walking ability in frail older adults Elucidating this issue could inform the development of better indicators for early detection of frailty and early preventive measures. The disadvantage of these methods is that they may not be able to detect crucial information in large portions of unanalyzed data[32] In this regard, principal component analysis has recently attracted increasing interest in biomechanical studies because of its usefulness in identifying the movement characteristics of various groups under a range of conditions using waveforms of the entire time series data set in a comprehensive manner[32,33,34,35,36,37,38,39,40,41,42]. The use of this principal component analysis-based approach could enable increased understanding of walking characteristics and joint kinematics among frail older women throughout the entire gait cycle
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