@Article{信息:doi 10.2196 / / jmir。2369，作者=“Dickerson, Justin B和McNeal, Catherine J和Tsai, Ginger和Rivera, kathleen M和Smith, Matthew Lee和Ohsfeldt, Robert L和Ory, Marcia G”，标题=“基于互联网的健康风险评估能否突出心脏病风险因素意识的问题?”横断面分析”，期刊=“J Med Internet Res”，年=“2014”，月=“4”，日=“18”，量=“16”，数=“4”，页数=“e106”，关键词=“健康风险评估”;互联网;风险因素;卫生疾病;背景:健康风险评估作为一种工具越来越受欢迎，它可以方便有效地接触到那些可能患有严重慢性疾病(如冠心病)的社区成年人。使用这些工具来提高成年人的风险因素意识，并与临床测量的风险因素值保持一致，可能是提高公共卫生知识和建立有效干预措施的一个机会。目的:本研究的目的是确定基于互联网的健康风险评估是否可以突出受访者自我报告和临床测量的可能有冠心病风险的社区成年人的冠心病风险因素之间的重要一致性。方法:分析来自127个临床地点社区居民的基于互联网的心血管健康风险评估(Heart Aware)的数据。 Respondents were recruited through individual hospital marketing campaigns, such as media advertising and print media, found throughout inpatient and outpatient facilities. CHD risk factors from the Framingham Heart Study were examined. Weighted kappa statistics were calculated to measure interrater agreement between respondents' self-reported and clinically measured CHD risk factors. Weighted kappa statistics were then calculated for each sample by strata of overall 10-year CHD risk. Three samples were drawn based on strategies for treating missing data: a listwise deleted sample, a pairwise deleted sample, and a multiple imputation (MI) sample. Results: The MI sample (n=16,879) was most appropriate for addressing missing data. No CHD risk factor had better than marginal interrater agreement ($\kappa$>.60). High-density lipoprotein cholesterol (HDL-C) exhibited suboptimal interrater agreement that deteriorated (eg, $\kappa$<.30) as overall CHD risk increased. Conversely, low-density lipoprotein cholesterol (LDL-C) interrater agreement improved (eg, up to $\kappa$=.25) as overall CHD risk increased. Overall CHD risk of the sample was lower than comparative population-based CHD risk (ie, no more than 15{\%} risk of CHD for the sample vs up to a 30{\%} chance of CHD for the population). Conclusions: Interventions are needed to improve knowledge of CHD risk factors. Specific interventions should address perceptions of HDL-C and LCL-C. Internet-based health risk assessments such as Heart Aware may contribute to public health surveillance, but they must address selection bias of Internet-based recruitment methods. ", issn="14388871", doi="10.2196/jmir.2369", url="//www.mybigtv.com/2014/4/e106/", url="https://doi.org/10.2196/jmir.2369", url="http://www.ncbi.nlm.nih.gov/pubmed/24760950" }