@Article{信息:doi 10.2196 / /公共健康。5880，作者=“Kesse-Guyot, Emmanuelle和Assmann, Karen和Andreeva, Valentina和Castetbon, Katia和M{\'e}jean, Caroline和Touvier, Mathilde和Salanave, Beno和Deschamps, Val{\'e}rie和P{\'e}neau, Sandrine和Fezeu, L{\'e} old和Julia, Chantal和All{\ 'e} s, Benjamin和Galan, Pilar和Hercberg, Serge”，标题=“e-流行病学方法验证研究的教训”，期刊=“JMIR公共卫生监测”，年=“2016”，月=“10月”，日=“18”，Volume ="2"， number="2"， pages="e160"， keywords="队列研究;背景:传统的流行病学研究方法存在局限性，导致物流、人力和财力负担高。创新数字工具的持续发展有可能克服许多现有的方法问题。尽管如此，基于网络的研究仍然相对少见，部分原因是对有效性和可泛化性的持续担忧。目的:本观点的目的是总结法国基于网络的队列研究NutriNet-Sant研究的方法学研究结果。方法:基于NutriNet-Sant队列(目前包括>万名参与者)之前的发现，我们综合了关于样本代表性、有利的招募策略和数据质量的电子流行病学知识。结果:总体而言，报告的结果支持基于网络的研究在克服流行病学研究中常见方法缺陷方面的有用性，特别是在数据质量方面(例如，体重指数[BMI]分类的一致性为93{\%})，减少社会可取性偏差，以及获得广泛的参与者资料，包括难以触及的亚群体，如年轻人(12.30{\%}[15,118/122,912]，<25岁)和老年人(6.60{\%}[8112/122,912]， ≥65 years), unemployed or homemaker (12.60{\%} [15,487/122,912]), and low educated (38.50{\%} [47,312/122,912]) people. However, some selection bias remained (78.00{\%} (95,871/122,912) of the participants were women, and 61.50{\%} (75,590/122,912) had postsecondary education), which is an inherent aspect of cohort study inclusion; other specific types of bias may also have occurred. Conclusions: Given the rapidly growing access to the Internet across social strata, the recruitment of participants with diverse socioeconomic profiles and health risk exposures was highly feasible. Continued efforts concerning the identification of specific biases in e-cohorts and the collection of comprehensive and valid data are still needed. This summary of methodological findings from the NutriNet-Sant{\'e} cohort may help researchers in the development of the next generation of high-quality Web-based epidemiological studies. ", issn="2369-2960", doi="10.2196/publichealth.5880", url="http://publichealth.www.mybigtv.com/2016/2/e160/", url="https://doi.org/10.2196/publichealth.5880", url="http://www.ncbi.nlm.nih.gov/pubmed/27756715" }