背景:步数越来越多地用于公共卫生和临床研究以评估健康状况,生活方式,和健康状况。然而,使用商业活动跟踪器估计步数有几个限制,包括缺乏可重复性,概括性,和可扩展性。智能手机是一个潜在的有希望的替代品,但是他们的计步算法需要强大的验证,以说明时间传感器的身体位置,个体步态特征,和异质健康状态。
目标:我们的目标是评估一个开源的,根据从不同身体位置同时收集的数据估计的步数,在各种测量条件下智能手机的步数计数方法(“跨身体”验证),手动确定的地面实况(“视觉评估”验证),和步骤计数从商业活动跟踪器(Fitbit收费2)在晚期癌症患者(“商业可穿戴”验证)。
方法:我们使用了8个独立的数据集,半控制,以及在典型身体位置携带不同设备(主要是Android智能手机和可穿戴加速度计)的自由生活环境。共有5个数据集(n=103)用于跨体验证,2个数据集(n=107)用于视觉评估验证,1个数据集(n=45)用于商业可穿戴验证。在每个场景中,步数是使用先前发布的智能手机步数计数方法估算的,该方法使用原始的亚秒级加速度计数据。我们使用Bland-Altman分析计算了步数估计和验证标准之间的平均偏差和一致极限(LoA)。
结果:在跨体验证数据集中,参与者执行了751.7(SD581.2)步骤,平均偏差为-7.2(LoA-47.6,33.3)步,或-0.5%。在视觉评估的验证数据集中,地面真相步数为367.4(SD359.4)步,而平均偏差为-0.4(LoA-75.2,74.3)步,或0.1%。在商业可穿戴验证数据集中,Fitbit设备显示平均步数为1931.2(SD2338.4),而计算出的偏差等于-67.1(LoA-603.8,469.7)步,或相差3.4%。
结论:这项研究表明,我们的开源,智能手机数据的计步方法提供了跨传感器位置的可靠计步,测量场景,和人口,包括健康的成年人和癌症患者。
BACKGROUND: Step counts are increasingly used in public health and clinical research to assess well-being, lifestyle, and health status. However, estimating step counts using commercial activity trackers has several limitations, including a lack of reproducibility, generalizability, and scalability. Smartphones are a potentially promising alternative, but their step-counting algorithms require robust validation that accounts for temporal sensor body location, individual gait characteristics, and heterogeneous health states.
OBJECTIVE: Our goal was to evaluate an open-source, step-counting method for smartphones under various measurement conditions against step counts estimated from data collected simultaneously from different body locations (\"cross-body\" validation), manually ascertained ground truth (\"visually assessed\" validation), and step counts from a commercial activity tracker (Fitbit Charge 2) in patients with advanced cancer (\"commercial wearable\" validation).
METHODS: We used 8 independent data sets collected in controlled, semicontrolled, and free-living environments with different devices (primarily Android smartphones and wearable accelerometers) carried at typical body locations. A total of 5 data sets (n=103) were used for cross-body validation, 2 data sets (n=107) for visually assessed validation, and 1 data set (n=45) was used for commercial wearable validation. In each scenario, step counts were estimated using a previously published step-counting method for smartphones that uses raw subsecond-level accelerometer data. We calculated the mean bias and limits of agreement (LoA) between step count estimates and validation criteria using Bland-Altman analysis.
RESULTS: In the cross-body validation data sets, participants performed 751.7 (SD 581.2) steps, and the mean bias was -7.2 (LoA -47.6, 33.3) steps, or -0.5%. In the visually assessed validation data sets, the ground truth step count was 367.4 (SD 359.4) steps, while the mean bias was -0.4 (LoA -75.2, 74.3) steps, or 0.1%. In the commercial wearable validation data set, Fitbit devices indicated mean step counts of 1931.2 (SD 2338.4), while the calculated bias was equal to -67.1 (LoA -603.8, 469.7) steps, or a difference of 3.4%.
CONCLUSIONS: This
study demonstrates that our open-source, step-counting method for smartphone data provides reliable step counts across sensor locations, measurement scenarios, and populations, including healthy adults and patients with cancer.