TY - JOUR AU - Shaw Jr, George AU - Nadkarni, Devaki AU - Phann, Eric AU - Sielaty, Rachel AU - Ledenyi, Madeleine AU - Abnowf, Razaan AU - Xu, Qian AU - Arredondo, Paul AU - Chen, Shi PY - 2022 DA - 2022/10/11 TI -在疫苗接种应用程序中分离功能:计算分析乔- Res JMIR形式SP - e36818六世- 6 - 10 KW -疫苗KW -移动健康KW - mHealth KW -主成分分析KW - PCA KW - k - means聚类KW -信息交换KW -手机AB -背景:一些最新估计显示,大约有95%的美国人拥有智能手机有很多的功能,比如SMS短信,能够拍摄高分辨率的图像,和移动软件应用。专注于疫苗接种和免疫的移动健康应用程序在数字健康信息技术市场上激增。移动卫生应用程序有可能对疫苗接种覆盖率产生积极影响。然而，它们的一般功能、用户和疾病覆盖范围以及信息交换尚未得到全面研究或计算评估。目的:本研究的主要目的是开发一种计算方法来探索疫苗接种应用程序的描述性、可用性、信息交换和隐私特性，为疫苗接种应用程序的设计提供参考。此外，我们还试图找出应用程序在设计、可读性和信息交换能力方面的潜在限制和缺陷。方法:编写全面的代码本，对疫苗接种应用程序的描述性、可用性、信息交换和隐私功能进行内容分析。疫苗接种相关应用程序的搜索和选择过程于2019年3月至5月进行。我们一共发现了211款应用，其中iOS和Android分别占62.1%(131/211)和37.9%(80/211)。 Of the 211 apps, 119 (56.4%) were included in the final study analysis, with 42 features evaluated according to the developed codebook. The apps selected were a mix of apps used in the United States and internationally. Principal component analysis was used to reduce the dimensionality of the data. Furthermore, cluster analysis was used with unsupervised machine learning to determine patterns within the data to group the apps based on preselected features. Results: The results indicated that readability and information exchange were highly correlated features based on principal component analysis. Of the 119 apps, 53 (44.5%) were iOS apps, 55 (46.2%) were for the Android operating system, and 11 (9.2%) could be found on both platforms. Cluster 1 of the k-means analysis contained 22.7% (27/119) of the apps; these were shown to have the highest percentage of features represented among the selected features. Conclusions: We conclude that our computational method was able to identify important features of vaccination apps correlating with end user experience and categorize those apps through cluster analysis. Collaborating with clinical health providers and public health officials during design and development can improve the overall functionality of the apps. SN - 2561-326X UR - https://formative.www.mybigtv.com/2022/10/e36818 UR - https://doi.org/10.2196/36818 UR - http://www.ncbi.nlm.nih.gov/pubmed/36222791 DO - 10.2196/36818 ID - info:doi/10.2196/36818 ER -