Al Abiad Nahime, van Schooten Kimberley S, Renaudin Valerie, Delbaere Kim, Robert Thomas
Laboratoire de Biomécanique et Mécanique des Chocs, Université Gustave Eiffel and Université Claude Bernard Lyon 1, Lyon, France.
Laboratoire de Géolocalisation, Université Gustave Eiffel, Bouguenais, France.
JMIR Aging. 2023 Nov 24;6:e49587. doi: 10.2196/49587.
In recent years, researchers have been advocating for the integration of ambulatory gait monitoring as a complementary approach to traditional fall risk assessments. However, current research relies on dedicated inertial sensors that are fixed on a specific body part. This limitation impacts the acceptance and adoption of such devices.
Our study objective is twofold: (1) to propose a set of step-based fall risk parameters that can be obtained independently of the sensor placement by using a ubiquitous step detection method and (2) to evaluate their association with prospective falls.
A reanalysis was conducted on the 1-week ambulatory inertial data from the StandingTall study, which was originally described by Delbaere et al. The data were from 301 community-dwelling older people and contained fall occurrences over a 12-month follow-up period. Using the ubiquitous and robust step detection method Smartstep, which is agnostic to sensor placement, a range of step-based fall risk parameters can be calculated based on walking bouts of 200 steps. These parameters are known to describe different dimensions of gait (ie, variability, complexity, intensity, and quantity). First, the correlation between parameters was studied. Then, the number of parameters was reduced through stepwise backward elimination. Finally, the association of parameters with prospective falls was assessed through a negative binomial regression model using the area under the curve metric.
The built model had an area under the curve of 0.69, which is comparable to models exclusively built on fixed sensor placement. A higher fall risk was noted with higher gait variability (coefficient of variance of stride time), intensity (cadence), and quantity (number of steps) and lower gait complexity (sample entropy and fractal exponent).
These findings highlight the potential of our method for comprehensive and accurate fall risk assessments, independent of sensor placement. This approach has promising implications for ambulatory gait monitoring and fall risk monitoring using consumer-grade devices.
近年来，研究人员一直主张将动态步态监测作为传统跌倒风险评估的一种补充方法。然而，目前的研究依赖于固定在身体特定部位的专用惯性传感器。这一局限性影响了此类设备的接受度和采用率。
我们的研究目标有两个：（1）提出一组基于步数的跌倒风险参数，这些参数可以通过使用一种普遍适用的步检测方法独立于传感器位置获得；（2）评估它们与未来跌倒的关联。
对StandingTall研究的1周动态惯性数据进行了重新分析，该研究最初由德尔巴埃雷等人描述。数据来自301名居住在社区的老年人，包含了12个月随访期内的跌倒事件。使用普遍适用且稳健的步检测方法Smartstep（该方法与传感器位置无关），可以基于200步的行走片段计算一系列基于步数的跌倒风险参数。已知这些参数描述了步态的不同维度（即变异性、复杂性、强度和数量）。首先，研究了参数之间的相关性。然后，通过逐步向后消除减少参数数量。最后，使用曲线下面积指标通过负二项回归模型评估参数与未来跌倒的关联。
所构建模型的曲线下面积为0.69，与仅基于固定传感器位置构建的模型相当。步态变异性（步幅时间的方差系数）、强度（步频）和数量（步数）越高，步态复杂性（样本熵和分形指数）越低，跌倒风险越高。
这些发现突出了我们的方法在独立于传感器位置进行全面准确的跌倒风险评估方面的潜力。这种方法对使用消费级设备的动态步态监测和跌倒风险监测具有广阔的应用前景。
