Developing an integrated medication adherence system: Exploring the potential of i-Ware's augmented reality goggles and mobile application

IF 2.1 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Smart Cities Pub Date : 2023-08-11 DOI:10.1049/smc2.12062
Aya Taghian, Ahmed H. Abd El-Malek, Mohammed S. Sayed, Mohammed Abo-Zahhad
{"title":"Developing an integrated medication adherence system: Exploring the potential of i-Ware's augmented reality goggles and mobile application","authors":"Aya Taghian,&nbsp;Ahmed H. Abd El-Malek,&nbsp;Mohammed S. Sayed,&nbsp;Mohammed Abo-Zahhad","doi":"10.1049/smc2.12062","DOIUrl":null,"url":null,"abstract":"<p>Medical therapists often manage medications to improve therapeutic outcomes for their patients. For senior patients who take multiple drugs to manage various conditions, medication adherence is critical. To provide an immersive and engaging medication reminder experience, the authors propose i-Ware, a smart wearable m-Health (mobile health) device. The system's hardware and software were co-designed to meet non-functional requirements. The model reminds patients to take their medication, and the augmented reality goggles aid those who struggle to manage their medicine. The navigation features help users find their way home, and the audio feature reads out the date and time, useful for patients with low vision. The i-Ware system has the potential for real-world application and can significantly improve medication adherence. As an AR-enabled medicine reminder, i-Ware is an innovative solution for medication management in senior patients.</p>","PeriodicalId":34740,"journal":{"name":"IET Smart Cities","volume":"5 3","pages":"230-242"},"PeriodicalIF":2.1000,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/smc2.12062","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Smart Cities","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/smc2.12062","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Medical therapists often manage medications to improve therapeutic outcomes for their patients. For senior patients who take multiple drugs to manage various conditions, medication adherence is critical. To provide an immersive and engaging medication reminder experience, the authors propose i-Ware, a smart wearable m-Health (mobile health) device. The system's hardware and software were co-designed to meet non-functional requirements. The model reminds patients to take their medication, and the augmented reality goggles aid those who struggle to manage their medicine. The navigation features help users find their way home, and the audio feature reads out the date and time, useful for patients with low vision. The i-Ware system has the potential for real-world application and can significantly improve medication adherence. As an AR-enabled medicine reminder, i-Ware is an innovative solution for medication management in senior patients.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
开发一个综合的药物依从系统:探索i‐Ware增强现实护目镜和移动应用程序的潜力
医学治疗师经常管理药物来改善患者的治疗效果。对于服用多种药物来治疗各种疾病的老年患者,药物依从性至关重要。为了提供一种身临其境的、引人入胜的药物提醒体验,作者提出了i - Ware,一种智能可穿戴的移动健康设备。该系统的硬件和软件是共同设计的,以满足非功能需求。这个模型提醒病人吃药,增强现实护目镜帮助那些难以管理药物的人。导航功能可以帮助用户找到回家的路,音频功能可以读出日期和时间,这对弱视患者很有用。i - Ware系统具有实际应用的潜力,可以显著提高药物依从性。i - Ware是一款基于AR功能的用药提醒系统,为老年患者的用药管理提供了创新的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
IET Smart Cities
IET Smart Cities Social Sciences-Urban Studies
CiteScore
7.70
自引率
3.20%
发文量
25
审稿时长
21 weeks
期刊最新文献
Guest Editorial: Smart cities 2.0: How Artificial Intelligence and Internet of Things are transforming urban living Smart city fire surveillance: A deep state-space model with intelligent agents Securing smart cities through machine learning: A honeypot-driven approach to attack detection in Internet of Things ecosystems Smart resilience through IoT-enabled natural disaster management: A COVID-19 response in São Paulo state Optimising air quality prediction in smart cities with hybrid particle swarm optimization-long-short term memory-recurrent neural network model
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1