TY - JOUR AU - Nagpal, Meghan S AU - Barbaric, Antonia AU - Sherifali, Diana AU - Morita, Plinio P AU - Cafazzo, Joseph A PY - 2021 DA - 201/12/20 TI - 2型糖尿病患者健康行为的患者生成数据分析:范围综述JO - JMIR糖尿病SP - e29027 VL - 6 IS - 4kw - 2型糖尿病KW -肥胖管理KW -健康行为KW -机器学习KW -人工智能KW -大数据KW -数据科学KW -患者生成的健康数据KW -手机AB -背景:由于2型糖尿病(T2D)的并发症可以通过适当的自我管理减轻，可以积极改变健康行为。现有技术工具可以帮助患有T2D或有患T2D风险的人管理他们的病情，这些工具提供了一个大型的患者生成健康数据储存库(PGHD)。分析可以深入了解T2D患者的健康行为。目的:本综述的目的是调查通过PGHD分析可以了解到哪些患有T2D或有发展T2D风险的人的健康行为。方法:采用Arksey和O 'Malley框架进行范围综述，由2名审稿人对文献进行全面检索。总共使用与糖尿病、行为和分析相关的关键词搜索了3个电子数据库(PubMed、IEEE Xplore和ACM数字图书馆)。采用预先确定的纳入和排除标准进行了几轮筛选，之后选择研究。通过描述-分析叙事方法进行批判性检查，从研究中提取的数据被分类为主题类别。根据我们的目标，这些类别反映了本研究的结果。 Results: We identified 43 studies that met the inclusion criteria for this review. Although 70% (30/43) of the studies examined PGHD independently, 30% (13/43) combined PGHD with other data sources. Most of these studies used machine learning algorithms to perform their analysis. The themes identified through this review include predicting diabetes or obesity, deriving factors that contribute to diabetes or obesity, obtaining insights from social media or web-based forums, predicting glycemia, improving adherence and outcomes, analyzing sedentary behaviors, deriving behavior patterns, discovering clinical correlations from behaviors, and developing design principles. Conclusions: The increased volume and availability of PGHD have the potential to derive analytical insights into the health behaviors of people living with T2D. From the literature, we determined that analytics can predict outcomes and identify granular behavior patterns from PGHD. This review determined the broad range of insights that can be examined through PGHD, which constitutes a unique source of data for these applications that would not be possible through the use of other data sources. SN - 2371-4379 UR - https://diabetes.www.mybigtv.com/2021/4/e29027 UR - https://doi.org/10.2196/29027 UR - http://www.ncbi.nlm.nih.gov/pubmed/34783668 DO - 10.2196/29027 ID - info:doi/10.2196/29027 ER -