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.

中文翻译：

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开发基于智能手机的类风湿性关节炎患者身体功能客观评估：PARADE 研究

背景：测量身体活动和活动能力的数字生物标志物在评估类风湿性关节炎等慢性疾病方面非常重要，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：研究分析通过移动软件应用程序远程收集的 iPhone 传感器数据的可行性，以获取有关类风湿关节炎患者功能能力的有意义的信息。方法：为研究参与者提供了两项客观、主动的任务：腕关节运动测试和步行测试，均远程执行且无需任何医疗监督。在这些任务中，陀螺仪和加速度计的时间序列数据被捕获。处理方案是使用逻辑回归等机器学习技术以及明确编程的算法开发的，以评估这两个任务中的数据质量。从高质量数据中提取运动特定特征，包括屈伸腕关节运动范围 (ROM)（用于手腕运动测试）和步态参数（用于步行测试），并与主观疼痛和活动参数进行比较，分别捕获通过应用程序。结果：在收集的 646 个腕关节运动样本中，有 289 个（45%）是高质量的。为步行测试收集的数据包括 2,583 个样本（通过 867 次测试执行），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行动不便与症状严重程度增加之间的联系。ANOVA 测试显示，轻度中度（220 名参与者）与重度（36 名参与者）腕部疼痛（p < 0.001）组之间的腕关节 ROM 以及轻度与中度行走问题组之间的平均步数差异具有统计学意义。 p < 0.03)。结论：这些研究结果证明了使用 iPhone 传感器远程捕获和量化有意义的客观临床信息的潜力，并代表了为类风湿性关节炎临床试验开发以患者为中心的数字端点的早期步骤。