Medication adherence management for in-home geriatric care with a companion robot and a wearable device

Q2 Health Professions Smart Health Pub Date : 2023-11-04 DOI:10.1016/j.smhl.2023.100434
Fei Liang , Zhidong Su , Weihua Sheng , Alex Bishop , Barbara Carlson
{"title":"Medication adherence management for in-home geriatric care with a companion robot and a wearable device","authors":"Fei Liang ,&nbsp;Zhidong Su ,&nbsp;Weihua Sheng ,&nbsp;Alex Bishop ,&nbsp;Barbara Carlson","doi":"10.1016/j.smhl.2023.100434","DOIUrl":null,"url":null,"abstract":"<div><p><span>Older adults are prone to forgetfulness and varying degrees of cognitive impairment, which can lead to not taking medication on time, taking the wrong medication or the wrong dose, all of which can negatively affect a person’s health and recovery from illness. Existing medication reminders, like mobile apps and pill boxes, are neither age-friendly nor designed to minimize the burden of documenting medication adherence. In this paper, we present a Medication Adherence </span>Management System<span> (MAMS) for elders, which is based on a companion robot and a wearable device<span>. The MAMS addresses the key issues of safe medication management: medication reminders, medication confirmation, and medication history recording. Human subject tests were conducted to evaluate the performance, acceptability and usability of the MAMS. Results from 35 human subjects showed that the average scores of the convenience, usefulness, and adoptability of the proposed MAMS were 8.17, 8.49, and 8.23 out of 10, respectively. The System Usability Scale<span> (SUS) scores for the MAMS, the robot, and the wearable device are 75.29, 78.60 and 76.40, respectively. We believe the MAMS has potential use in future in-home geriatric care.</span></span></span></p></div>","PeriodicalId":37151,"journal":{"name":"Smart Health","volume":"30 ","pages":"Article 100434"},"PeriodicalIF":0.0000,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart Health","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352648323000624","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Health Professions","Score":null,"Total":0}
引用次数: 0

Abstract

Older adults are prone to forgetfulness and varying degrees of cognitive impairment, which can lead to not taking medication on time, taking the wrong medication or the wrong dose, all of which can negatively affect a person’s health and recovery from illness. Existing medication reminders, like mobile apps and pill boxes, are neither age-friendly nor designed to minimize the burden of documenting medication adherence. In this paper, we present a Medication Adherence Management System (MAMS) for elders, which is based on a companion robot and a wearable device. The MAMS addresses the key issues of safe medication management: medication reminders, medication confirmation, and medication history recording. Human subject tests were conducted to evaluate the performance, acceptability and usability of the MAMS. Results from 35 human subjects showed that the average scores of the convenience, usefulness, and adoptability of the proposed MAMS were 8.17, 8.49, and 8.23 out of 10, respectively. The System Usability Scale (SUS) scores for the MAMS, the robot, and the wearable device are 75.29, 78.60 and 76.40, respectively. We believe the MAMS has potential use in future in-home geriatric care.

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
家庭老年护理的药物依从性管理与同伴机器人和可穿戴设备
老年人容易健忘和不同程度的认知障碍,这可能导致不按时服药,服用错误的药物或错误的剂量,所有这些都会对一个人的健康和疾病恢复产生负面影响。现有的药物提醒,如移动应用程序和药盒,既不适合年龄,也没有设计成尽量减少记录药物依从性的负担。在本文中,我们提出了一个基于陪伴机器人和可穿戴设备的老年人药物依从性管理系统(MAMS)。MAMS解决了安全用药管理的关键问题:用药提醒、用药确认和用药历史记录。进行人体受试者测试以评估MAMS的性能、可接受性和可用性。35名受试者的结果表明,MAMS的便捷性、有用性和可接受性的平均得分分别为8.17、8.49和8.23(满分为10分)。MAMS、机器人和可穿戴设备的系统可用性量表(System Usability Scale, SUS)得分分别为75.29、78.60和76.40。我们相信MAMS在未来的家庭老年护理中有潜在的用途。
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
Smart Health
Smart Health Computer Science-Computer Science Applications
CiteScore
6.50
自引率
0.00%
发文量
81
期刊最新文献
Editorial Board Smart health practices: Strategies to improve healthcare efficiency through digital twin technology Human knowledge-based artificial intelligence methods for skin cancer management: Accuracy and interpretability study SAFE: Sound Analysis for Fall Event detection using machine learning Latent Space Representation of Adversarial AutoEncoder for Human Activity Recognition: Application to a low-cost commercial force plate and inertial measurement units
×
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