School of Rehabilitation Science, McMaster University, Hamilton, Ontario, Canada.
School of Earth, Environment & Society, McMaster University, Hamilton, Ontario, Canada.
PLoS One. 2023 Dec 21;18(12):e0296159. doi: 10.1371/journal.pone.0296159. eCollection 2023.
There is growing interest in identifying valid and reliable methods for detecting early mobility limitations in aging populations. A multi-sensor approach that combines accelerometry with Global Positioning System (GPS) devices could provide valuable insights into late-life mobility decline; however, this innovative approach requires more investigation. We conducted a series of two experiments with 25 older participants (66.2±8.5 years) to determine the validity of a GPS enabled smartwatch (TicWatch S2 and Pro 3 Ultra GPS) and separate accelerometer (ActiGraph wGT3X-BT) to collect movement, navigation and body posture data relevant to mobility. In experiment 1, participants wore the TicWatchS2 and ActiGraph simultaneously on the wrist for 3 days. In experiment 2, participants wore the TicWatch Pro 2 Ultra GPS on the wrist and ActiGraph on the thigh for 3 days. In both experiments participants also carried a Qstarz data logger for trips outside the home. The TicWatch Pro 3 Ultra GPS performed better than the S2 model and was similar to the Qstarz in all tested trip-related measures, and it was able to estimate both passive and active trip modes. Both models showed similar results to the gold standard Qstarz in life-space-related measures. The TicWatch S2 demonstrated good to excellent overall agreement with the ActiGraph algorithms for the time spent in sedentary and non-sedentary activities, with 84% and 87% agreement rates, respectively. Under controlled conditions, the TicWatch Pro 3 Ultra GPS consistently measured step count in line with the participants' self-reported data, with a bias of 0.4 steps. The thigh-worn ActiGraph algorithm accurately classified sitting and lying postures (97%) and standing postures (90%). Our multi-sensor approach to monitoring mobility has the potential to capture both accelerometer-derived movement data and trip/life-space data only available through GPS. In this study, we found that the TicWatch models were valid devices for capturing GPS and raw accelerometer data, making them useful tools for assessing real-life mobility in older adults.
人们越来越关注识别有效和可靠的方法,以检测老龄化人口中的早期移动障碍。一种结合加速度计和全球定位系统(GPS)设备的多传感器方法可以为生命后期的移动能力下降提供有价值的见解;然而,这种创新方法需要更多的研究。我们进行了一系列两项实验,共有 25 名年龄较大的参与者(66.2±8.5 岁)参与,以确定一款带 GPS 的智能手表(TicWatch S2 和 Pro 3 Ultra GPS)和独立的加速度计(ActiGraph wGT3X-BT)的有效性,以收集与移动性相关的运动、导航和身体姿势数据。在实验 1 中,参与者在手腕上同时佩戴 TicWatch S2 和 ActiGraph 佩戴 3 天。在实验 2 中,参与者在手腕上佩戴 TicWatch Pro 2 Ultra GPS,在大腿上佩戴 ActiGraph 佩戴 3 天。在这两项实验中,参与者还携带了一个 Qstarz 数据记录仪,用于记录家庭以外的行程。TicWatch Pro 3 Ultra GPS 的性能优于 S2 型号,在所有测试的与行程相关的测量中与 Qstarz 相似,并且能够估计被动和主动行程模式。两款机型在与生活空间相关的测量中与黄金标准 Qstarz 的结果相似。TicWatch S2 与 ActiGraph 算法在久坐和非久坐活动的时间上表现出良好到极好的总体一致性,分别有 84%和 87%的一致性率。在受控条件下,TicWatch Pro 3 Ultra GPS 始终与参与者的自我报告数据一致地测量步数,偏差为 0.4 步。大腿佩戴的 ActiGraph 算法准确地分类坐姿和躺姿(97%)和站姿(90%)。我们的多传感器移动监测方法有可能捕捉到仅通过 GPS 获得的加速度计衍生运动数据和行程/生活空间数据。在这项研究中,我们发现 TicWatch 型号是捕获 GPS 和原始加速度计数据的有效设备,使其成为评估老年人现实生活中移动能力的有用工具。