Department of Kinesiology & Community Health, University of Illinois at Urbana-Champaign, 906 S. Goodwin Ave, Urbana, IL 61801, USA; Illinois Multiple Sclerosis Research Collaborative, University of Illinois at Urbana-Champaign, 901 W. University Avenue, Suite 201 Urbana, IL 61801, USA; Department of Internal Medicine-Geriatrics and Gerontology, Wake Forest School of Medicine, Winston-Salem, NC 27109, USA.
Department of Kinesiology & Community Health, University of Illinois at Urbana-Champaign, 906 S. Goodwin Ave, Urbana, IL 61801, USA; Illinois Multiple Sclerosis Research Collaborative, University of Illinois at Urbana-Champaign, 901 W. University Avenue, Suite 201 Urbana, IL 61801, USA.
Gait Posture. 2021 Feb;84:114-119. doi: 10.1016/j.gaitpost.2020.11.011. Epub 2020 Nov 15.
Falls are a major health concern for people with Multiple Sclerosis (pwMS), and impaired postural control is an important predictor of falls. Lab-based technology to measure posture is precise but expensive, and clinical tests may not capture underlying impairments. An alternative solution is to leverage smartphone accelerometry as it is affordable, ubiquitous, and portable.
Can smartphone accelerometry measure postural control compared to a force plate and research grade accelerometer in pwMS, and can smartphone accelerometry discriminate between assisted device and non-assisted device users?
27 pwMS (12 assisted device users, 15 non-assisted device users) stood on a force plate while holding a smartphone with an attached research grade accelerometer against their chest. Participants performed two, 30 s trials of: eyes open, eyes closed, semi-tandem, tandem, and single leg. Acceleration and center of pressure were extracted, and Root Mean Square (RMS) and 95 % confidence ellipse were calculated. Spearman's correlations were performed, and receiving operating characteristic (ROC) curves and the Area Under the Curve (AUC) were calculated.
There were moderate to high correlations between the smartphone and accelerometer for RMS (ρ = 0.85 - 1.0; p = 0.001 - <0.001) and 95 % area ellipse (ρ = 0.92 - 0.99; p = <0.001). There were weak to moderate correlations between the smartphone and force plate for RMS (ρ = 0.38 - 0.92; p = 0.06 - <0.001) and 95 % area ellipse (ρ = 0.69 - 0.90 p = 0.002 - <0.001). To discriminate between assisted device usage, ROC curves for smartphone outputs were constructed, the AUC was high and statistically significant (p < 0.001 - 0.02).
There is potential to leverage smartphone accelerometery to measure postural control in pwMS. These finding provide preliminary results to support the development of a mobile health application to measure fall risk for pwMS.
对于多发性硬化症（pwMS）患者来说，跌倒仍是一个严重的健康问题，姿势控制受损是跌倒的一个重要预测因素。基于实验室的技术可以精确测量姿势，但价格昂贵，而临床测试可能无法捕捉到潜在的损伤。一种替代方法是利用智能手机加速度计，因为它价格实惠、无处不在且便于携带。
在 pwMS 中，智能手机加速度计能否与力板和研究级加速度计相比来测量姿势控制，智能手机加速度计能否区分辅助设备和非辅助设备使用者？
27 名 pwMS（12 名辅助设备使用者，15 名非辅助设备使用者）站在力板上，同时将智能手机用研究级加速度计固定在胸前。参与者进行了两次各 30 秒的测试：睁眼、闭眼、半串联、串联和单腿站立。提取加速度和中心压力，并计算均方根（RMS）和 95%置信椭圆。进行 Spearman 相关分析，并计算接收者操作特征（ROC）曲线和曲线下面积（AUC）。
智能手机与加速度计的 RMS（ρ=0.85-1.0；p=0.001-<0.001）和 95%面积椭圆（ρ=0.92-0.99；p=<0.001）的相关性为中等到高度相关。智能手机与力板的 RMS（ρ=0.38-0.92；p=0.06-<0.001）和 95%面积椭圆（ρ=0.69-0.90；p=0.002-<0.001）的相关性为弱到中度相关。为了区分辅助设备的使用，构建了智能手机输出的 ROC 曲线，AUC 较高且具有统计学意义（p<0.001-0.02）。
利用智能手机加速度计来测量 pwMS 的姿势控制具有一定的潜力。这些发现为开发用于测量 pwMS 跌倒风险的移动健康应用程序提供了初步结果。
