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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.

已有研究表明，年龄与年龄相关性疾病会降低人类步态运动学的熵值，该现象被认为会使老年人更易发生跌倒。本研究提出了一种全新的熵值度量方法——相位依赖性广义多尺度熵（phase-dependent generalized multiscale entropy, PGME），并验证该方法能否提升社区居住老年人的跌倒风险预测能力。PGME可评估由足跟触地、足趾离地等步态事件的运动学变化所引发的步态动力学稳定性的相位依赖性变化。本研究对van Schooten等人（2016）最初报道的FARAO研究中的1周日常生活活动数据进行二次分析，针对30秒步行时段的躯干加速度计算PGME值。本次二次分析的数据集包含6个月随访期内的52名单次前瞻性跌倒者、46名多次前瞻性跌倒者的惯性传感器数据，以及按年龄、体重、身高、性别和助行器使用情况匹配的同等数量的非跌倒对照组样本。本研究采用偏最小二乘回归分析评估PGME的跌倒预测能力。相较于其他步态特征，PGME对单次前瞻性跌倒者的跌倒预测能力更优。与非跌倒者相比，单次前瞻性跌倒者在步态周期60%时段的躯干加速度PGME值更高（p < 0.0001）。多次前瞻性跌倒者与非跌倒者的PGME值无显著差异，但当与其他步态特征结合使用时，PGME可提升多次前瞻性跌倒者的跌倒预测模型性能。上述研究结果表明，纳入步态动力学稳定性的相位依赖性变化信息，可为老年人跌倒预测提供额外价值，尤其针对单次前瞻性跌倒者。