Division of Vascular Surgery, Department of Surgery, Stanford University, Stanford, California, United States of America.
Graduate School of Business, Stanford University, Stanford, California, United States of America.
PLoS One. 2021 Mar 24;16(3):e0247834. doi: 10.1371/journal.pone.0247834. eCollection 2021.
Smartphone and wearable-based activity data provide an opportunity to remotely monitor functional capacity in patients. In this study, we assessed the ability of a home-based 6-minute walk test (6MWT) as well as passively collected activity data to supplement or even replace the in-clinic 6MWTs in patients with cardiovascular disease.
We enrolled 110 participants who were scheduled for vascular or cardiac procedures. Each participant was supplied with an iPhone and an Apple Watch running the VascTrac research app and was followed for 6 months. Supervised 6MWTs were performed during clinic visits at scheduled intervals. Weekly at-home 6MWTs were performed via the VascTrac app. The app passively collected activity data such as daily step counts. Logistic regression with forward feature selection was used to assess at-home 6MWT and passive data as predictors for "frailty" as measured by the gold-standard supervised 6MWT. Frailty was defined as walking <300m on an in-clinic 6MWT.
Under a supervised in-clinic setting, the smartphone and Apple Watch with the VascTrac app were able to accurately assess 'frailty' with sensitivity of 90% and specificity of 85%. Outside the clinic in an unsupervised setting, the home-based 6MWT is 83% sensitive and 60% specific in assessing "frailty." Passive data collected at home were nearly as accurate at predicting frailty on a clinic-based 6MWT as was a home-based 6MWT, with area under curve (AUC) of 0.643 and 0.704, respectively.
In this longitudinal observational study, passive activity data acquired by an iPhone and Apple Watch were an accurate predictor of in-clinic 6MWT performance. This finding suggests that frailty and functional capacity could be monitored and evaluated remotely in patients with cardiovascular disease, enabling safer and higher resolution monitoring of patients.
基于智能手机和可穿戴设备的活动数据为远程监测患者的功能能力提供了机会。在这项研究中，我们评估了家庭 6 分钟步行测试（6MWT）以及被动收集的活动数据补充甚至替代心血管疾病患者门诊 6MWT 的能力。
我们招募了 110 名计划接受血管或心脏手术的患者。每位患者都配备了一部 iPhone 和一部运行 VascTrac 研究应用程序的 Apple Watch，并随访了 6 个月。在预约的间隔时间内，在诊所进行了监督 6MWT。通过 VascTrac 应用程序每周在家进行 6MWT。该应用程序被动收集日常步数等活动数据。使用向前特征选择的逻辑回归评估家庭 6MWT 和被动数据作为金标准监督 6MWT 测量的“虚弱”的预测指标。虚弱定义为在门诊 6MWT 中行走 <300m。
在监督的门诊环境下，配备 VascTrac 应用程序的智能手机和 Apple Watch 能够准确评估“虚弱”，灵敏度为 90%，特异性为 85%。在非监督的门诊外环境下，家庭 6MWT 评估“虚弱”的灵敏度为 83%，特异性为 60%。在家中收集的被动数据在预测基于门诊的 6MWT 的虚弱方面与家庭 6MWT 一样准确，曲线下面积（AUC）分别为 0.643 和 0.704。
在这项纵向观察性研究中，iPhone 和 Apple Watch 获得的被动活动数据是门诊 6MWT 表现的准确预测指标。这一发现表明，虚弱和功能能力可以在心血管疾病患者中远程监测和评估，从而更安全、更具分辨率地监测患者。
