Developing Smartphone-Based Objective Assessments of Physical Function in Rheumatoid Arthritis Patients: The PARADE Study.
Developing Smartphone-Based Objective Assessments of Physical Function in Rheumatoid Arthritis Patients: The PARADE Study.
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DOI：
10.1159/000506860
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发表时间：
2020-01-01
影响因子：
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通讯作者：
Crouthamel, Michelle
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文献类型：
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作者：
Hamy, Valentin;Garcia-Gancedo, Luis;Crouthamel, Michelle
背景技术背景：测量身体活动和移动性的数字生物标志物在评估慢性疾病（如类风湿性关节炎）方面具有很大的意义，因为它提供了关于患者生活质量的见解，可以在整个人群中进行可靠的比较。目的：研究通过移动的软件应用程序分析远程收集的iPhone传感器数据的可行性，以获得有关功能能力的有意义信息，方法：向研究参与者提供两个客观、主动的任务：腕关节运动测试和步行测试，两者都是远程进行的，没有任何医疗监督。在这些任务中，捕获了陀螺仪和加速度计的时间序列数据。使用机器学习技术（如逻辑回归）以及明确编程的算法开发处理方案，以评估两项任务中的数据质量。从高质量的数据中提取运动特定的功能，包括腕关节的活动范围（ROM）在屈曲-伸展（手腕运动测试）和步态参数（步行测试），并与主观疼痛和移动性参数进行比较，分别通过application.RESULTS：收集的646个腕关节运动样本，289（45%）是高质量的。步行测试收集的数据包括2，583个样本（通过867次测试），其中651个（25%）是高质量的。对高质量数据的进一步分析强调了活动减少和症状严重程度增加之间的联系。ANOVA检验显示，轻中度组之间的腕关节ROM存在统计学显著差异，（220例受试者）与重度（36名参与者）腕关节疼痛（p < 0.001）以及轻度与中度行走问题组之间的平均步行时间（p < 0.03）。这些发现证明了使用iPhone传感器远程捕获和量化有意义的客观临床信息的潜力，并代表了早期步骤为类风湿关节炎临床试验开发以患者为中心的数字终点。
BACKGROUND: Digital biomarkers that measure physical activity and mobility are of great interest in the assessment of chronic diseases such as rheumatoid arthritis, as it provides insights on patients' quality of life that can be reliably compared across a whole population.OBJECTIVE: To investigate the feasibility of analyzing iPhone sensor data collected remotely by means of a mobile software application in order to derive meaningful information on functional ability in rheumatoid arthritis patients.METHODS: Two objective, active tasks were made available to the study participants: a wrist joint motion test and a walk test, both performed remotely and without any medical supervision. During these tasks, gyroscope and accelerometer time-series data were captured. Processing schemes were developed using machine learning techniques such as logistic regression as well as explicitly programmed algorithms to assess data quality in both tasks. Motion-specific features including wrist joint range of motion (ROM) in flexion-extension (for the wrist motion test) and gait parameters (for the walk test) were extracted from high quality data and compared with subjective pain and mobility parameters, separately captured via the application.RESULTS: Out of 646 wrist joint motion samples collected, 289 (45%) were high quality. Data collected for the walk test included 2,583 samples (through 867 executions of the test) from which 651 (25%) were high quality. Further analysis of high-quality data highlighted links between reduced mobility and increased symptom severity. ANOVA testing showed statistically significant differences in wrist joint ROM between groups with light-moderate (220 participants) versus severe (36 participants) wrist pain (p < 0.001) as well as in average step times between groups with slight versus moderate problems walking about (p < 0.03).CONCLUSION: These findings demonstrate the potential to capture and quantify meaningful objective clinical information remotely using iPhone sensors and represent an early step towards the development of patient-centric digital endpoints for clinical trials in rheumatoid arthritis.