Improved Prediction of Falls in Community-Dwelling Older Adults Through Phase-Dependent Entropy of Daily-Life Walking

Abstract

Age and age-related diseases have been suggested to decrease entropy of human gait kinematics, which is thought to make older adults more susceptible to falls. In this study we introduce a new entropy measure, called phase-dependent generalized multiscale entropy (PGME), and test whether this measure improves fall-risk prediction in community-dwelling older adults. PGME can assess phase-dependent changes in the stability of gait dynamics that result from kinematic changes in events such as heel strike and toe-off. PGME was assessed for trunk acceleration of 30 s walking epochs in a re-analysis of 1 week of daily-life activity data from the FARAO study, originally described by van Schooten et al. (2016). The re-analyzed data set contained inertial sensor data from 52 single- and 46 multiple-time prospective fallers in a 6 months follow-up period, and an equal number of non-falling controls matched by age, weight, height, gender, and the use of walking aids. The predictive ability of PGME for falls was assessed using a partial least squares regression. PGME had a superior predictive ability of falls among single-time prospective fallers when compared to the other gait features. The single-time fallers had a higher PGME (p < 0.0001) of their trunk acceleration at 60% of their step cycle when compared with non-fallers. No significant differences were found between PGME of multiple-time fallers and non-fallers, but PGME was found to improve the prediction model of multiple-time fallers when combined with other gait features. These findings suggest that taking into account phase-dependent changes in the stability of the gait dynamics has additional value for predicting falls in older people, especially for single-time prospective fallers.

The validation of new phase-dependent gait stability measures: a modeling approach

“…By and large, they are based on a limit cycle's local stability quantified by the aforementioned responses to infinitesimal perturbations, but applied at different phases of the cycle. When it comes to human gait, phase-dependent stability metrics have, in fact, been shown to potentially improve the accuracy of fall prediction over cycle-averaged parameters (Ihlen et al, 2018).…”

Reliability and concurrent validity of spatiotemporal stride characteristics measured with an ankle-worn sensor among older individuals

“…Additional insight into the functional status of an individual may be gained by exploring the stepto-step or stride-to-stride variation (gait variability) [3,4], which may be an even more sensitive prognostic indicator of future health outcomes than the mean stride characteristics [5,6].…”

“…However, these are laboratory-bound methods and limit the number of captured strides in overground walking to only the instrumented part/capture volume. Accordingly, much recent effort has been devoted to affordable wearable inertial measurement units (IMU) [7][8][9][10][11][12][13], which capture strides independent of a pre-determined capture area [5,6,14,15]. Most of the validation studies of IMU-based gait assessments have been conducted for mean gait characteristics.…”

Acceleration Gait Measures as Proxies for Motor Skill of Walking: A Narrative Review

“…• Newer phase-dependent Lyapunov exponents and entropy measures are used to improve fall-risk prediction. For example, phase-dependent generalized multiscale entropy was used to differentiate between fallers and non-fallers; this type of analysis can capture nonlinearity and involves both spectral decomposition analysis and phase-dependent analysis [194]. Whereas, phasedependent Lyapunov exponents can be used to analyse local dynamic stability, such which cueing strategies affect gait stability in different gait phases [195].…”