@文章{信息:doi/10.2196/36818，作者=“Shaw Jr, George and Nadkarni, Devaki and Phann, Eric and Sielaty, Rachel and Ledenyi, Madeleine and Abnowf, Razaan and Xu, Qian and Arredondo, Paul and Chen, Shi”，标题=“在疫苗接种应用程序中从功能中分离特征:计算分析”，期刊=“JMIR Form Res”，年=“2022”，月=“Oct”，日=“11”，卷=“6”，数=“10”，页=“e36818”，关键词=“疫苗;移动健康;移动健康;主成分分析;主成分分析;k - means聚类;信息交换;背景:一些最新的估计显示，大约95%的美国人拥有一部具有多种功能的智能手机，如短信、高分辨率照片和移动软件应用程序。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。 However, their general functionality, user and disease coverage, and exchange of information have not been comprehensively studied or evaluated computationally. Objective: The primary aim of this study is to develop a computational method to explore the descriptive, usability, information exchange, and privacy features of vaccination apps, which can inform vaccination app design. Furthermore, we sought to identify potential limitations and drawbacks in the apps' design, readability, and information exchange abilities. Methods: A comprehensive codebook was developed to conduct a content analysis on vaccination apps' descriptive, usability, information exchange, and privacy features. The search and selection process for vaccination-related apps was conducted from March to May 2019. We identified a total of 211 apps across both platforms, with iOS and Android representing 62.1{\%} (131/211) and 37.9{\%} (80/211) of the apps, respectively. 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. ", issn="2561-326X", doi="10.2196/36818", url="https://formative.www.mybigtv.com/2022/10/e36818", url="https://doi.org/10.2196/36818", url="http://www.ncbi.nlm.nih.gov/pubmed/36222791" }