基于手机应用评分量表和k均值聚类的药物-药物交互管理手机应用质量调查:应用商店系统搜索

IF 6.2 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Ayush Bhattacharya, Jose Fernando Florez-Arango
{"title":"基于手机应用评分量表和k均值聚类的药物-药物交互管理手机应用质量调查:应用商店系统搜索","authors":"Ayush Bhattacharya, Jose Fernando Florez-Arango","doi":"10.2196/65927","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Drug-drug interactions (DDIs) pose a significant risk to patient safety and increase health care costs. Mobile apps offer potential solutions for managing DDIs, yet their quality and effectiveness from the user's perspective remain unclear.</p><p><strong>Objective: </strong>The aim is to evaluate the quality of publicly available mobile apps for DDI management in the US using the Mobile App Rating Scale (MARS) and to identify patterns that reflect user satisfaction and preferences.</p><p><strong>Methods: </strong>A structured review was conducted to identify mobile apps for DDI management, resulting in 19 eligible apps. Two health care-affiliated evaluators independently assessed each app using the mobile app rating scale (MARS). Dimensionality scores were calculated, and correlation analysis was conducted to examine relationships among dimensions. K-means clustering was applied to group apps based on their MARS scores. Scatter plots visualized app distributions across clusters. To validate the clustering model and assess alignment with user satisfaction, mean weighted user ratings were compared with mean MARS scores per cluster. Correlation analysis was also performed between individual MARS dimensions and user ratings within each cluster.</p><p><strong>Results: </strong>The mean MARS score was 3.54 out of 5, with the Information dimension scoring the highest (mean 3.68, SD 0.51) and Engagement the lowest (mean 3.42, SD 0.80). The Kruskal-Wallis test revealed no significant differences in median scores across the four dimensions (χ²3=2.109, P=.55). All MARS dimensions were positively correlated (r=0.65 to 0.92), indicating interrelated quality characteristics. K-means clustering identified three app groups with varying quality profiles: Cluster 1 (n=7, mean MARS=2.86), Cluster 2 (n=7, mean=3.57), and Cluster 3 (n=5, mean=4.44). Cluster 1 apps showed strongest correlations between user satisfaction and functionality (r=0.74) and engagement (r=0.53). Cluster 2 users prioritized information (r=0.41) and aesthetics (r=0.58), and Cluster 3 exhibited balanced influence from information (r=0.62), aesthetics (r=0.58), and functionality (r=0.39). Scatter plots indicated that engagement, functionality, and aesthetics were key drivers of user perception, while information, though consistently strong, played a lesser role in differentiating the apps. The weighted user ratings aligned with MARS scores, supporting the validity of the clustering model.</p><p><strong>Conclusions: </strong>This study assesses the quality of mobile apps for DDI management by integrating MARS with K-means Clustering. This approach enabled a structured classification of apps based on the MARS scores, identifying distinct clusters that reflect overall app quality profiles across key usability dimensions. The study revealed that the influence of MARS dimensions on app ratings varies by cluster, highlighting that the significance of these dimensions shifts according to the specific needs and preferences of different user groups.</p>","PeriodicalId":14756,"journal":{"name":"JMIR mHealth and uHealth","volume":"13 ","pages":"e65927"},"PeriodicalIF":6.2000,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12516926/pdf/","citationCount":"0","resultStr":"{\"title\":\"Investigating the Quality of Mobile Apps for Drug-Drug Interaction Management Using the Mobile App Rating Scale and K-Means Clustering: Systematic Search of App Stores.\",\"authors\":\"Ayush Bhattacharya, Jose Fernando Florez-Arango\",\"doi\":\"10.2196/65927\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Drug-drug interactions (DDIs) pose a significant risk to patient safety and increase health care costs. Mobile apps offer potential solutions for managing DDIs, yet their quality and effectiveness from the user's perspective remain unclear.</p><p><strong>Objective: </strong>The aim is to evaluate the quality of publicly available mobile apps for DDI management in the US using the Mobile App Rating Scale (MARS) and to identify patterns that reflect user satisfaction and preferences.</p><p><strong>Methods: </strong>A structured review was conducted to identify mobile apps for DDI management, resulting in 19 eligible apps. Two health care-affiliated evaluators independently assessed each app using the mobile app rating scale (MARS). Dimensionality scores were calculated, and correlation analysis was conducted to examine relationships among dimensions. K-means clustering was applied to group apps based on their MARS scores. Scatter plots visualized app distributions across clusters. To validate the clustering model and assess alignment with user satisfaction, mean weighted user ratings were compared with mean MARS scores per cluster. Correlation analysis was also performed between individual MARS dimensions and user ratings within each cluster.</p><p><strong>Results: </strong>The mean MARS score was 3.54 out of 5, with the Information dimension scoring the highest (mean 3.68, SD 0.51) and Engagement the lowest (mean 3.42, SD 0.80). The Kruskal-Wallis test revealed no significant differences in median scores across the four dimensions (χ²3=2.109, P=.55). All MARS dimensions were positively correlated (r=0.65 to 0.92), indicating interrelated quality characteristics. K-means clustering identified three app groups with varying quality profiles: Cluster 1 (n=7, mean MARS=2.86), Cluster 2 (n=7, mean=3.57), and Cluster 3 (n=5, mean=4.44). Cluster 1 apps showed strongest correlations between user satisfaction and functionality (r=0.74) and engagement (r=0.53). Cluster 2 users prioritized information (r=0.41) and aesthetics (r=0.58), and Cluster 3 exhibited balanced influence from information (r=0.62), aesthetics (r=0.58), and functionality (r=0.39). Scatter plots indicated that engagement, functionality, and aesthetics were key drivers of user perception, while information, though consistently strong, played a lesser role in differentiating the apps. The weighted user ratings aligned with MARS scores, supporting the validity of the clustering model.</p><p><strong>Conclusions: </strong>This study assesses the quality of mobile apps for DDI management by integrating MARS with K-means Clustering. This approach enabled a structured classification of apps based on the MARS scores, identifying distinct clusters that reflect overall app quality profiles across key usability dimensions. The study revealed that the influence of MARS dimensions on app ratings varies by cluster, highlighting that the significance of these dimensions shifts according to the specific needs and preferences of different user groups.</p>\",\"PeriodicalId\":14756,\"journal\":{\"name\":\"JMIR mHealth and uHealth\",\"volume\":\"13 \",\"pages\":\"e65927\"},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2025-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12516926/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JMIR mHealth and uHealth\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2196/65927\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR mHealth and uHealth","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2196/65927","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

摘要

背景:药物-药物相互作用(ddi)对患者安全构成重大风险,并增加医疗保健费用。移动应用程序为管理ddi提供了潜在的解决方案,但从用户的角度来看,它们的质量和有效性尚不清楚。目的:目的是使用移动应用评级量表(MARS)评估美国DDI管理公开可用移动应用的质量,并确定反映用户满意度和偏好的模式。方法:对DDI管理的移动应用程序进行结构化审查,获得19个符合条件的应用程序。两名医疗保健相关的评估人员使用移动应用程序评级量表(MARS)独立评估每个应用程序。计算各维度得分,并进行相关分析,考察各维度之间的关系。根据应用程序的MARS分数,将K-means聚类应用于分组应用程序。散点图显示了应用程序在集群中的分布。为了验证聚类模型并评估与用户满意度的一致性,将平均加权用户评分与每个聚类的平均MARS分数进行比较。还对每个集群中的单个MARS维度与用户评分之间进行了相关分析。结果:MARS平均得分为3.54分(满分5分),其中信息维度得分最高(平均3.68分,SD 0.51分),敬业度得分最低(平均3.42分,SD 0.80分)。Kruskal-Wallis检验显示,四个维度的中位数得分无显著差异(χ 2 3=2.109, P= 0.55)。所有MARS维度呈正相关(r=0.65 ~ 0.92),表明质量特征相互关联。K-means聚类确定了三个具有不同质量概况的应用组:集群1 (n=7,平均MARS=2.86),集群2 (n=7,平均3.57)和集群3 (n=5,平均4.44)。集群1应用在用户满意度、功能(r=0.74)和用户粘性(r=0.53)之间表现出最强的相关性。集群2用户优先考虑信息(r=0.41)和美学(r=0.58),集群3显示出信息(r=0.62)、美学(r=0.58)和功能(r=0.39)的平衡影响。散点图显示,用户粘性、功能和美感是用户感知的关键驱动因素,而信息虽然一直很重要,但在区分应用方面的作用较小。加权用户评分与MARS分数一致,支持聚类模型的有效性。结论:本研究通过将MARS与K-means聚类相结合来评估DDI管理移动应用程序的质量。这种方法能够基于MARS分数对应用进行结构化分类,识别出反映关键可用性维度的整体应用质量概况的不同集群。研究显示,MARS维度对应用评级的影响因集群而异,突出表明这些维度的重要性根据不同用户群体的特定需求和偏好而变化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigating the Quality of Mobile Apps for Drug-Drug Interaction Management Using the Mobile App Rating Scale and K-Means Clustering: Systematic Search of App Stores.

Background: Drug-drug interactions (DDIs) pose a significant risk to patient safety and increase health care costs. Mobile apps offer potential solutions for managing DDIs, yet their quality and effectiveness from the user's perspective remain unclear.

Objective: The aim is to evaluate the quality of publicly available mobile apps for DDI management in the US using the Mobile App Rating Scale (MARS) and to identify patterns that reflect user satisfaction and preferences.

Methods: A structured review was conducted to identify mobile apps for DDI management, resulting in 19 eligible apps. Two health care-affiliated evaluators independently assessed each app using the mobile app rating scale (MARS). Dimensionality scores were calculated, and correlation analysis was conducted to examine relationships among dimensions. K-means clustering was applied to group apps based on their MARS scores. Scatter plots visualized app distributions across clusters. To validate the clustering model and assess alignment with user satisfaction, mean weighted user ratings were compared with mean MARS scores per cluster. Correlation analysis was also performed between individual MARS dimensions and user ratings within each cluster.

Results: The mean MARS score was 3.54 out of 5, with the Information dimension scoring the highest (mean 3.68, SD 0.51) and Engagement the lowest (mean 3.42, SD 0.80). The Kruskal-Wallis test revealed no significant differences in median scores across the four dimensions (χ²3=2.109, P=.55). All MARS dimensions were positively correlated (r=0.65 to 0.92), indicating interrelated quality characteristics. K-means clustering identified three app groups with varying quality profiles: Cluster 1 (n=7, mean MARS=2.86), Cluster 2 (n=7, mean=3.57), and Cluster 3 (n=5, mean=4.44). Cluster 1 apps showed strongest correlations between user satisfaction and functionality (r=0.74) and engagement (r=0.53). Cluster 2 users prioritized information (r=0.41) and aesthetics (r=0.58), and Cluster 3 exhibited balanced influence from information (r=0.62), aesthetics (r=0.58), and functionality (r=0.39). Scatter plots indicated that engagement, functionality, and aesthetics were key drivers of user perception, while information, though consistently strong, played a lesser role in differentiating the apps. The weighted user ratings aligned with MARS scores, supporting the validity of the clustering model.

Conclusions: This study assesses the quality of mobile apps for DDI management by integrating MARS with K-means Clustering. This approach enabled a structured classification of apps based on the MARS scores, identifying distinct clusters that reflect overall app quality profiles across key usability dimensions. The study revealed that the influence of MARS dimensions on app ratings varies by cluster, highlighting that the significance of these dimensions shifts according to the specific needs and preferences of different user groups.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
JMIR mHealth and uHealth
JMIR mHealth and uHealth Medicine-Health Informatics
CiteScore
12.60
自引率
4.00%
发文量
159
审稿时长
10 weeks
期刊介绍: JMIR mHealth and uHealth (JMU, ISSN 2291-5222) is a spin-off journal of JMIR, the leading eHealth journal (Impact Factor 2016: 5.175). JMIR mHealth and uHealth is indexed in PubMed, PubMed Central, and Science Citation Index Expanded (SCIE), and in June 2017 received a stunning inaugural Impact Factor of 4.636. The journal focusses on health and biomedical applications in mobile and tablet computing, pervasive and ubiquitous computing, wearable computing and domotics. JMIR mHealth and uHealth publishes since 2013 and was the first mhealth journal in Pubmed. It publishes even faster and has a broader scope with including papers which are more technical or more formative/developmental than what would be published in the Journal of Medical Internet Research.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信