@Article{info:doi/10.2196/31618，作者=“Cho, Sylvia and Weng, Chunhua and Kahn, Michael G and Natarajan, Karthik”，标题=“识别个人可穿戴设备数据的数据质量维度:多方法研究”，期刊=“JMIR Mhealth Uhealth”，年=“2021”，月=“Dec”，日=“23”，卷=“9”，号=“12”，页=“e31618”，关键词=“患者生成的健康数据;数据的准确性;数据质量;可穿戴设备;健身追踪;背景:人们对使用个人生成的可穿戴设备数据进行生物医学研究越来越感兴趣，但也存在对数据质量的担忧，例如数据缺失或数据不正确。这强调了在进行研究之前评估数据质量的重要性。为了进行数据质量评估，必须通过识别数据质量维度来定义个人生成的可穿戴设备数据的数据质量意味着什么。目的:本研究旨在确定个人生成的可穿戴设备数据的数据质量维度，用于研究目的。方法:本研究分为文献综述、问卷调查和焦点小组讨论三个阶段。 The literature review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline to identify factors affecting data quality and its associated data quality challenges. In addition, we conducted a survey to confirm and complement results from the literature review and to understand researchers' perceptions on data quality dimensions that were previously identified as dimensions for the secondary use of electronic health record (EHR) data. We sent the survey to researchers with experience in analyzing wearable device data. Focus group discussion sessions were conducted with domain experts to derive data quality dimensions for person-generated wearable device data. On the basis of the results from the literature review and survey, a facilitator proposed potential data quality dimensions relevant to person-generated wearable device data, and the domain experts accepted or rejected the suggested dimensions. Results: In total, 19 studies were included in the literature review, and 3 major themes emerged: device- and technical-related, user-related, and data governance--related factors. The associated data quality problems were incomplete data, incorrect data, and heterogeneous data. A total of 20 respondents answered the survey. The major data quality challenges faced by researchers were completeness, accuracy, and plausibility. The importance ratings on data quality dimensions in an existing framework showed that the dimensions for secondary use of EHR data are applicable to person-generated wearable device data. There were 3 focus group sessions with domain experts in data quality and wearable device research. The experts concluded that intrinsic data quality features, such as conformance, completeness, and plausibility, and contextual and fitness-for-use data quality features, such as completeness (breadth and density) and temporal data granularity, are important data quality dimensions for assessing person-generated wearable device data for research purposes. Conclusions: In this study, intrinsic and contextual and fitness-for-use data quality dimensions for person-generated wearable device data were identified. The dimensions were adapted from data quality terminologies and frameworks for the secondary use of EHR data with a few modifications. Further research on how data quality can be assessed with respect to each dimension is needed. ", issn="2291-5222", doi="10.2196/31618", url="https://mhealth.www.mybigtv.com/2021/12/e31618", url="https://doi.org/10.2196/31618", url="http://www.ncbi.nlm.nih.gov/pubmed/34941540" }