KU Leuven, Department of Movement Sciences, B-3000 Leuven, Belgium.
KU Leuven, Faculty of Rehabilitation and Movement Sciences, B-3000 Leuven, Belgium.
Gait Posture. 2021 Feb;84:17-23. doi: 10.1016/j.gaitpost.2020.11.017. Epub 2020 Nov 17.
Identifying older adults with increased fall risk due to poor postural control on a large scale is only possible through omnipresent and low cost measuring devices such as the inertial measurement units (IMU) embedded in smartphones. However, the correlation between smartphone measures of postural stability and state-of-the-art force plate measures has never been assessed in a large sample allowing us to take into account age as a covariate.
How reliably can postural stability be measured with a smartphone embedded IMU in comparison to a force plate?
We assessed balance in 97 adults aged 50-90 years in four different conditions (eyes open, eyes closed, semi-tandem and dual-task) in the anterio-posterior and medio-lateral directions. We used six different parameters (root mean square and average absolute value of COP displacement, velocity and acceleration) for the force plate and two different parameters (root mean square and average absolute value of COM acceleration) for the smartphone.
Test-retest reliability was smaller for the smartphone than for the force plate (intra class correlation) but both devices could equally well detect differences between conditions (similar Cohen's d). Parameters from the smartphone and the force plate, with age regressed out, were moderately correlated (robust correlation coefficients of around 0.5).
This study comprehensively documents test-retest reliability and effect sizes for stability measures obtained with a force plate and smartphone as well as correlations between force plate and smartphone measures based on a large sample of older adults. Our large sample size allowed us to reliably determine the strength of the correlations between force plate and smartphone measures. The most important practical implication of our results is that more repetitions or longer trials are required when using a smartphone instead of a force plate to assess balance.
只有通过无处不在且成本低廉的测量设备（如智能手机中嵌入的惯性测量单元 (IMU)）才能大规模识别出因姿势控制不佳而增加跌倒风险的老年人。然而，智能手机对姿势稳定性的测量与最先进的测力板测量之间的相关性从未在大样本中进行过评估，使我们能够将年龄作为协变量考虑在内。
与测力板相比，智能手机嵌入式 IMU 能在多大程度上可靠地测量姿势稳定性？
我们评估了 97 名年龄在 50-90 岁的成年人在四种不同条件下（睁眼、闭眼、半串联和双重任务）的平衡，包括前-后和中-侧方向。我们使用了六个不同的参数（COP 位移、速度和加速度的均方根和平均值绝对值）来描述测力板，以及智能手机的两个不同参数（COM 加速度的均方根和平均值绝对值）。
与测力板相比，智能手机的测试-重测可靠性较小（组内相关系数），但两种设备都能很好地检测到条件之间的差异（相似的 Cohen's d）。从智能手机和测力板中获得的参数，随着年龄的回归，与年龄相关，具有中等相关性（稳健相关系数约为 0.5）。
这项研究全面记录了基于大量老年人样本，测力板和智能手机获得的稳定性测量的测试-重测可靠性和效应大小，以及测力板和智能手机测量之间的相关性。我们的大样本量使我们能够可靠地确定测力板和智能手机测量之间的相关性强度。我们研究结果的最重要实际意义是，当使用智能手机而不是测力板来评估平衡时，需要更多的重复或更长的试验。
