首页> 外文期刊>JMIR Research Protocols >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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Collecting Symptoms and Sensor Data With Consumer Smartwatches (the Knee OsteoArthritis, Linking Activity and Pain Study): Protocol for a Longitudinal, Observational Feasibility Study

机译:使用消费者智能手表收集症状和传感器数据(膝骨关节炎,链接活动和疼痛研究):纵向观察可行性研究的方案

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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.
机译:背景技术膝关节骨关节炎,链接活动和疼痛(KOALAP)研究是第一个测试使用消费者级蜂窝智能手表进行保健研究的可行性的研究。目的总体目的是研究使用消费级蜂窝智能手表作为新颖工具来捕获膝骨关节炎患者的疼痛(每天多次)和身体活动(连续)数据的可行性。此外,KOALAP旨在调查智能手表传感器的数据质量,并评估参与度,可接受性和用户体验是否足以满足未来的大规模观察和干预研究需求。方法2017年9月,共招募了26位自我诊断为膝骨关节炎的参与者。所有参与者年龄均在50岁以上,并且居住或愿意前往大曼彻斯特地区。参与者收到了带有定制应用程序的智能手表(华为手表2),该应用程序通过问卷和连续的手表传感器数据收集了患者报告的结果。每天收集90天的所有数据。通过访谈(基线和随访)以及基线和研究结束时的问卷调查收集了其他数据。曼彻斯特大学研究道德委员会(#0165)和大学信息治理(#IGRR000060)对此研究进行了全面审查。为了进行定性数据分析,与大学信息治理办公室合作开发了系统级安全策略。此外,该项目在Google进行了内部审核,包括对可访问性,产品工程,隐私,安全性,法律和保护法规遵从性的单独审核。结果参与者于2017年9月被招募。通过手表的数据收集于2018年1月完成。通过患者访谈的定性数据收集仍在进行中。收集所有数据后将开始数据分析;预计将在2019年获得结果。结论KOALAP是第一项使用消费类智能手表来收集自我报告症状以及肌肉骨骼疾病传感器数据的健康研究。这项研究的结果将用于设计未来的移动健康研究。可行性和参与者动机的结果将告知未来的研究人员,无论是否在何种条件下蜂窝智能手表都是一种有用的工具,可以收集患者报告的结果以及被动测量的患者行为。探索不同时刻自我报告的症状之间的关联将有助于我们了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。数据质量评估和数据处理方法可以重用,尽管应进一步研究对其他临床领域的普遍性。

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