Collecting Symptoms and Sensor Data With Consumer Smartwatches (the Knee OsteoArthritis, Linking Activity and Pain Study): Protocol for a Longitudinal, Observational Feasibility Study
Collecting Symptoms and Sensor Data With Consumer Smartwatches (the Knee OsteoArthritis, Linking Activity and Pain Study): Protocol for a Longitudinal, Observational Feasibility Study
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DOI：
10.2196/10238
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发表时间：
2019-01-01
影响因子：
1.7
通讯作者：
Dixon, William G.
中科院分区：
文献类型：
--
作者：
Beukenhorst, Anna L.;Parkes, Matthew J.;Dixon, William G.
背景：膝关节骨性关节炎、连接活动和疼痛（KOALAP）研究首次测试了将消费级蜂窝智能手表用于医疗保健研究的可行性。目的：总体目的是研究使用消费级蜂窝智能手表作为捕获膝关节骨关节炎患者疼痛（每天多次）和身体活动（连续）数据的新工具的可行性。此外，KOALAP旨在调查智能手表传感器数据的质量，并评估参与度、可接受性和用户体验是否足以用于未来的大规模观察和干预性研究。方法：2017年9月共招募26名自诊断膝关节骨关节炎的参与者。所有参与者年龄都在50岁或以上，要么住在大曼彻斯特地区，要么愿意去大曼彻斯特地区。参与者收到了一块智能手表（华为Watch 2），上面安装了一个定制的应用程序，该应用程序通过问卷调查和连续的手表传感器数据收集患者报告的结果。所有数据每天收集，连续90 d。通过访谈（基线和随访）以及基线和研究结束问卷收集其他数据。本研究经过了曼彻斯特大学研究伦理委员会（#0165）和大学信息治理（#IGRR000060）的全面审查。为了进行定性数据分析，我们与大学信息治理办公室合作制定了系统级安全政策。此外，该项目在b谷歌经历了一个内部审查过程，包括对可访问性、产品工程、隐私、安全、法律和保护法规遵从性的单独审查。结果：参与者于2017年9月招募。通过手表收集的数据于2018年1月完成。通过患者访谈收集定性数据仍在进行中。数据分析将在收集所有数据后开始；结果预计将于2019年公布。结论：KOALAP是首个使用消费者蜂窝智能手表收集自我报告症状和肌肉骨骼疾病传感器数据的健康研究。本研究的结果将用于未来移动健康研究的设计。可行性和参与者动机的结果将告知未来的研究人员，蜂窝智能手表是否或在何种条件下是收集患者报告结果和被动测量患者行为的有用工具。探索不同时刻自我报告症状之间的关联将有助于我们理解更频繁地收集症状数据是否有价值。传感器数据质量测量将表明使用蜂窝智能手表获取传感器数据是否可行。数据质量评估方法和数据处理方法可以重复使用，但应进一步研究其在其他临床领域的普遍性。
Background: The Knee OsteoArthritis, Linking Activity and Pain (KOALAP) study is the first to test the feasibility of using consumer-grade cellular smartwatches for health care research.Objective: The overall aim was to investigate the feasibility of using consumer-grade cellular smartwatches as a novel tool to capture data on pain (multiple times a day) and physical activity (continuously) in patients with knee osteoarthritis. Additionally, KOALAP aimed to investigate smartwatch sensor data quality and assess whether engagement, acceptability, and user experience are sufficient for future large-scale observational and interventional studies.Methods: A total of 26 participants with self-diagnosed knee osteoarthritis were recruited in September 2017. All participants were aged 50 years or over and either lived in or were willing to travel to the Greater Manchester area. Participants received a smartwatch (Huawei Watch 2) with a bespoke app that collected patient-reported outcomes via questionnaires and continuous watch sensor data. All data were collected daily for 90 days. Additional data were collected through interviews (at baseline and follow-up) and baseline and end-of-study questionnaires. This study underwent full review by the University of Manchester Research Ethics Committee (#0165) and University Information Governance (#IGRR000060). For qualitative data analysis, a system-level security policy was developed in collaboration with the University Information Governance Office. Additionally, the project underwent an internal review process at Google, including separate reviews of accessibility, product engineering, privacy, security, legal, and protection regulation compliance.Results: Participants were recruited in September 2017. Data collection via the watches was completed in January 2018. Collection of qualitative data through patient interviews is still ongoing. Data analysis will commence when all data are collected; results are expected in 2019.Conclusions: KOALAP is the first health study to use consumer cellular smartwatches to collect self-reported symptoms alongside sensor data for musculoskeletal disorders. The results of this study will be used to inform the design of future mobile health studies. Results for feasibility and participant motivations will inform future researchers whether or under which conditions cellular smartwatches are a useful tool to collect patient-reported outcomes alongside passively measured patient behavior. The exploration of associations between self-reported symptoms at different moments will contribute to our understanding of whether it may be valuable to collect symptom data more frequently. Sensor data-quality measurements will indicate whether cellular smartwatch usage is feasible for obtaining sensor data. Methods for data-quality assessment and data-processing methods may be reusable, although generalizability to other clinical areas should be further investigated.