Merschel Steve, Reinhardt Lars
Preventicus GmbH, Jena, Germany.
Institute for Applied Training Science, Leipzig, Germany.
JMIR Form Res. 2022 Mar 28;6(3):e29479. doi: 10.2196/29479.
Continuous heart rate monitoring via mobile health technologies based on photoplethysmography (PPG) has great potential for the early detection of sustained cardiac arrhythmias such as atrial fibrillation. However, PPG measurements are impaired by motion artifacts.
The aim of this investigation was to evaluate the analyzability of smartwatch-derived PPG data during everyday life and to determine the relationship between the analyzability of the data and the activity level of the participant.
A total of 41 (19 female and 22 male) adults in good cardiovascular health (aged 19-79 years) continuously wore a smartwatch equipped with a PPG sensor and a 3D accelerometer (Cardio Watch 287, Corsano Health BV) for a period of 24 hours that represented their individual daily routine. For each participant, smartwatch data were analyzed on a 1-minute basis by an algorithm designed for heart rhythm analysis (Preventicus Heartbeats, Preventicus GmbH). As outcomes, the percentage of analyzable data (PAD) and the mean acceleration (ACC) were calculated. To map changes of the ACC and PAD over the course of one day, the 24-hour period was divided into 8 subintervals comprising 3 hours each.
Univariate analysis of variance showed a large effect (η> 0.6; P<.001) of time interval (phase) on the ACC and PAD. The PAD ranged between 34% and 100%, with an average of 71.5% for the whole day, which is equivalent to a period of 17.2 hours. Between midnight and 6 AM, the mean values were the highest for the PAD (>94%) and the lowest for the ACC (<6×10 m/s). Regardless of the time of the day, the correlation between the PAD and ACC was strong (r=-0.64). A linear regression analysis for the averaged data resulted in an almost perfect coefficient of determination (r=0.99).
This study showed a large relationship between the activity level and the analyzability of smartwatch-derived PPG data. Given the high yield of analyzable data during the nighttime, continuous arrhythmia screening seems particularly effective during sleep phases.
通过基于光电容积脉搏波描记法（PPG）的移动健康技术进行连续心率监测，对于早期检测持续性心律失常（如心房颤动）具有巨大潜力。然而，PPG测量会受到运动伪影的影响。
本研究旨在评估日常生活中智能手表获取的PPG数据的可分析性，并确定数据可分析性与参与者活动水平之间的关系。
共有41名（19名女性和22名男性）心血管健康状况良好的成年人（年龄在19 - 79岁之间）连续佩戴一块配备PPG传感器和3D加速度计的智能手表（Cardio Watch 287，Corsano Health BV）24小时，以反映他们各自的日常活动。对于每位参与者，通过一种专为心律分析设计的算法（Preventicus Heartbeats，Preventicus GmbH）对智能手表数据进行每分钟分析。作为结果，计算可分析数据百分比（PAD）和平均加速度（ACC）。为了描绘一天中ACC和PAD的变化情况，将24小时时间段分为8个各包含3小时的子区间。
单因素方差分析显示，时间间隔（阶段）对ACC和PAD有较大影响（η> 0.6；P <.001）。PAD范围在34%至100%之间，全天平均为71.5%，相当于17.2小时的时长。在午夜至凌晨6点之间，PAD的平均值最高（> 94%），ACC的平均值最低（< 6×10 m/s）。无论一天中的什么时间，PAD与ACC之间的相关性都很强（r = -0.64）。对平均数据进行线性回归分析得到了几乎完美的决定系数（r = 0.99）。
本研究表明活动水平与智能手表获取的PPG数据的可分析性之间存在密切关系。鉴于夜间可分析数据的高产出率，连续心律失常筛查在睡眠阶段似乎特别有效。
