在洛杉矶县的临床试验中，使用来自Twitter的患者生成的健康数据来识卡塔尔世界杯8强波胆分析别、参与和招募癌症幸存者:可行性研究评估% a路透，Katja % a Angyan,Praveen % a Le,NamQuyen % a Buchanan,Thomas公共卫生和预防医学系，纽约州立大学北部医科大学，美国纽约州锡拉丘兹欧文大道766号，13210,1 3154641520，reuterk@upstate.edu %K乳腺癌%K癌症%K临床研究%K临床试验%K结肠癌%K信息监测%K肾癌%K肺癌%K淋巴瘤%K患者参与%K前列腺癌%K招募%K推特%K社交媒体%D 2021 %7 26.11.2021 %9原始论文%J JMIR表格Res %G英文%X背景:未能找到和吸引临床试验参与者仍然是临床研究的持久障碍。研究人员越来越多地利用基于社交媒体的方法来补充招聘方法。我们假设，来自癌症幸存者及其家人和朋友在社交网络Twitter上的用户生成的数据可以用于识别、参与和招募癌症幸存者进行癌症试验。目的:本试点研究旨在研究在洛杉矶县的Twitter上使用来自癌症幸存者及其家人和朋友的用户报告的健康数据来加强临床试验招募的可行性。我们关注6种癌症(乳腺癌、结肠癌、肾癌、淋巴瘤、肺癌和前列腺癌)。方法:社交媒体干预包括监测洛杉矶县推特用户关于6种癌症病症的特定癌症帖子，以确定癌症幸存者及其家人和朋友，并与符合条件的推特用户联系，提供南加州大学诺里斯综合癌症中心的公开癌症试验信息。我们回顾了2017年7月28日至2018年11月29日期间洛杉矶县推特用户发布的回顾性和前瞻性数据。这项研究在南加州大学诺里斯分校招募了124项公开临床试验。 We used descriptive statistics to report the proportion of Twitter users who were identified, engaged, and enrolled. Results: We analyzed 107,424 Twitter posts in English by 25,032 unique Twitter users in LA County for the 6 cancer conditions. We identified and contacted 1.73% (434/25,032) of eligible Twitter users (127/434, 29.3% cancer survivors; 305/434, 70.3% family members and friends; and 2/434, 0.5% Twitter users were excluded). Of them, 51.4% (223/434) were female and approximately one-third were male. About one-fifth were people of color, whereas most of them were White. Approximately one-fifth (85/434, 19.6%) engaged with the outreach messages (cancer survivors: 33/85, 38% and family members and friends: 52/85, 61%). Of those who engaged with the messages, one-fourth were male, the majority were female, and approximately one-fifth were people of color, whereas the majority were White. Approximately 12% (10/85) of the contacted users requested more information and 40% (4/10) set up a prescreening. Two eligible candidates were transferred to USC Norris for further screening, but neither was enrolled. Conclusions: Our findings demonstrate the potential of identifying and engaging cancer survivors and their family members and friends on Twitter. Optimization of downstream recruitment efforts such as screening for digital populations on social media may be required. Future research could test the feasibility of the approach for other diseases, locations, languages, social media platforms, and types of research involvement (eg, survey research). Computer science methods could help to scale up the analysis of larger data sets to support more rigorous testing of the intervention. Trial Registration: ClinicalTrials.gov NCT03408561; https://clinicaltrials.gov/ct2/show/NCT03408561 %M 34842538 %R 10.2196/29958 %U https://formative.www.mybigtv.com/2021/11/e29958 %U https://doi.org/10.2196/29958 %U http://www.ncbi.nlm.nih.gov/pubmed/34842538