Psaltos Dimitrios, Chappie Kara, Karahanoglu Fikret Isik, Chasse Rachel, Demanuele Charmaine, Kelekar Amey, Zhang Hao, Marquez Vanessa, Kangarloo Tairmae, Patel Shyamal, Czech Matthew, Caouette David, Cai Xuemei
Early Clinical Development, Pfizer Inc., Cambridge, Massachusetts, USA.
Tufts Medical Center, Boston, Massachusetts, USA.
Digit Biomark. 2019 Oct 29;3(3):133-144. doi: 10.1159/000503282. eCollection 2019 Sep-Dec.
Traditional measurement systems utilized in clinical trials are limited because they are episodic and thus cannot capture the day-to-day fluctuations and longitudinal changes that frequently affect patients across different therapeutic areas.
The aim of this study was to collect and evaluate data from multiple devices, including wearable sensors, and compare them to standard lab-based instruments across multiple domains of daily tasks.
Healthy volunteers aged 18-65 years were recruited for a 1-h study to collect and assess data from wearable sensors. They performed walking tasks on a gait mat while instrumented with a watch, phone, and sensor insoles as well as several speech tasks on multiple recording devices.
Step count and temporal gait metrics derived from a single lumbar accelerometer are highly precise; spatial gait metrics are consistently 20% shorter than gait mat measurements. The insole's algorithm only captures about 72% of steps but does have precision in measuring temporal gait metrics. Mobile device voice recordings provide similar results to traditional recorders for average signal pitch and sufficient signal-to-noise ratio for analysis when hand-held. Lossless compression techniques are advised for signal processing.
Gait metrics from a single lumbar accelerometer sensor are in reasonable concordance with standard measurements, with some variation between devices and across individual metrics. Finally, participants in this study were familiar with mobile devices and had high acceptance of potential future continuous wear for clinical trials.
临床试验中使用的传统测量系统存在局限性，因为它们是间歇性的，因此无法捕捉经常影响不同治疗领域患者的日常波动和纵向变化。
本研究的目的是收集和评估来自多个设备（包括可穿戴传感器）的数据，并将其与基于实验室的标准仪器在日常任务的多个领域进行比较。
招募了18 - 65岁的健康志愿者参加一项为期1小时的研究，以收集和评估来自可穿戴传感器的数据。他们在配备手表、手机和传感器鞋垫的情况下在步态垫上执行步行任务，并在多个录音设备上执行多项语音任务。
从单个腰部加速度计得出的步数和时间步态指标非常精确；空间步态指标始终比步态垫测量结果短20%。鞋垫算法仅能捕捉约72%的步数，但在测量时间步态指标方面具有精度。移动设备语音记录在平均信号音高方面提供了与传统记录器相似的结果，并且手持时具有足够的信噪比用于分析。建议对信号处理采用无损压缩技术。
单个腰部加速度计传感器的步态指标与标准测量结果具有合理的一致性，但不同设备之间以及各个指标之间存在一些差异。最后，本研究中的参与者熟悉移动设备，并且对未来临床试验中潜在的持续佩戴接受度很高。
