Effect of age on the variability and stability of gait: A cross-sectional treadmill study in healthy individuals between 20 and 69 years of age

Abstract

Falls during walking are a major health issue in the elderly population. Older individuals are usually more cautious, walk more slowly, take shorter steps, and exhibit increased step-to-step variability. They often have impaired dynamic balance, which explains their increased falling risk. Those locomotor characteristics might be the result of the neurological/musculoskeletal degenerative processes typical of advanced age or of a decline that began earlier in life. In order to help determine between the two possibilities, we analyzed the relationship between age and gait features among 100 individuals aged 20-69. Trunk acceleration was measured during a 5-min treadmill session using a 3D accelerometer. The following dependent variables were assessed: preferred walking speed, walk ratio (step length normalized by step frequency), gait instability (local dynamic stability, Lyapunov exponent method), and acceleration variability (root mean square [RMS]). Using age as a predictor, linear regressions were performed for each dependent variable. The results indicated that walking speed, walk ratio and trunk acceleration variability were not dependent on age (R²<2%). However, there was a significant quadratic association between age and gait instability in the mediolateral direction (R²=15%). We concluded that most of the typical gait features of older age do not result from a slow evolution over the life course. On the other hand, gait instability likely begins to increase at an accelerated rate as early as age 40-50. This finding supports the premise that local dynamic stability is likely a relevant early indicator of falling risk.

Evaluation of hip abductor and adductor strength in the elderly: a reliability study

“…An increasing interest in lateral control is emerging, given that impairments in this domain are thought to increase falls risk [19, 20]. Recent findings in nonlinear gait analysis even suggest that gait stability in the frontal plane is of interest for predicting fall risk [21, 22]. Finally, lateral and postero-lateral falls have greater hip injury potential, including hip fractures, than falls in other directions [23–25].…”

The relationship between trunk and foot acceleration variability during walking shows minor changes in persons with multiple sclerosis

“…Compared to healthy controls, PwMS display greater variability in step width and single support time based on coefficient variation (Sosnoff et al, 2012). Linear measures of variability such as coefficient of variation or standard deviation provide information about the magnitude of variability within a time series (O’Connor et al, 2012; Terrier and Reynard, 2015). Nonlinear variability measures provide information on the temporal structure of variability within a time series (Harbourne and Stergiou, 2009; Stergiou and Decker, 2011).…”

The Maximum Lyapunov Exponent During Walking and Running: Reliability Assessment of Different Marker-Sets

“…It is thus surmised that more trials spread over more than 1 day can significantly improve the reliability of the measurement. To present, studies employing the Lyapunov analysis for examining the stability of gait have focused on differences between groups of young and older age (Buzzi et al, 2003; Kang and Dingwell, 2008; Hamacher et al, 2011, 2015; Terrier and Reynard, 2015; Mehdizadeh, 2017) or health and pathology (Moraiti et al, 2007, 2010; Lamoth et al, 2010; Look et al, 2013; Wurdeman et al, 2013; Kao et al, 2014; Hoogkamer et al, 2015). However, interventional or prospective study designs examining the resulting MLE might require higher degrees of accuracy and thus more than one measurement trial and day.…”

“…Previous studies in walking conditions reported good intrasession reliability (Kang and Dingwell, 2006b; van Schooten et al, 2013; Reynard and Terrier, 2014; Reynard et al, 2014; Rábago et al, 2015). Based on this, it was argued that differences between young and older adults (Buzzi et al, 2003; Kang and Dingwell, 2008; Terrier and Reynard, 2015; Mehdizadeh, 2017) as well as patients with moderate neurological gait disorders (Reynard et al, 2014) where instability is expected, can be discovered with the MLE. However, the reliability of the MLE is decreased between days (van Schooten et al, 2013; Reynard and Terrier, 2014).…”

“…As such, while the trunk is suggested to be representative of the stability of the human system (Kang and Dingwell, 2009; Beurskens et al, 2014), previous studies examining the MLE employed diverse placements and quantities of markers or accelerometers. For instance, the sternum (Terrier and Reynard, 2015), the first (Dingwell and Marin, 2006), and sixth (Bruijn et al, 2009a; van Schooten et al, 2011) thoracic vertebrae, the second (Sejdić et al, 2013) and fifth (Terrier and Dériaz, 2011; van Schooten et al, 2013) lumbar vertebrae have been used, while clusters of two (Wurdeman and Stergiou, 2013) or six markers (Kang and Dingwell, 2006a,b, 2008) have also been employed for acquiring of data and subsequent analysis of dynamic stability. However, through time series analysis we compute a few characteristic numbers from a large sample of data (Kantz and Schreiber, 2004; Bradley and Kantz, 2015), and data collected from different parts of the system can contain different information regarding its states.…”