TY -的非盟- Cho,西尔维娅AU -翁,Chunhua AU - Kahn,迈克尔·G AU - Natarajan Karthik PY - 2021 DA - 2021/12/23 TI -识别数据质量维Person-Generated可穿戴设备数据:多方法研究乔——JMIR Mhealth Uhealth SP - e31618六世- 9 - 12 KW -我们相信健康数据KW -数据的准确性KW -数据质量KW -可穿戴设备KW -健身追踪KW -定性研究AB -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，例如数据丢失或不正确。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。文献综述遵循PRISMA(系统评价和荟萃分析首选报告项目)指南进行，以确定影响数据质量的因素及其相关的数据质量挑战。此外，我们进行了一项调查，以确认和补充文献综述的结果，并了解研究人员对数据质量维度的看法，这些维度以前被确定为电子健康记录(EHR)数据二次使用的维度。我们将调查发给了具有分析可穿戴设备数据经验的研究人员。与领域专家进行焦点小组讨论，得出个人生成的可穿戴设备数据的数据质量维度。 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. SN - 2291-5222 UR - https://mhealth.www.mybigtv.com/2021/12/e31618 UR - https://doi.org/10.2196/31618 UR - http://www.ncbi.nlm.nih.gov/pubmed/34941540 DO - 10.2196/31618 ID - info:doi/10.2196/31618 ER -