Evaluating Older Adults' Engagement and Usability With AI-Driven Interventions: Randomized Pilot Study.

IF 2 Q3 HEALTH CARE SCIENCES & SERVICES JMIR Formative Research Pub Date : 2025-01-24 DOI:10.2196/64763
Marcia Shade, Changmin Yan, Valerie K Jones, Julie Boron
{"title":"Evaluating Older Adults' Engagement and Usability With AI-Driven Interventions: Randomized Pilot Study.","authors":"Marcia Shade, Changmin Yan, Valerie K Jones, Julie Boron","doi":"10.2196/64763","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Technologies that serve as assistants are growing more popular for entertainment and aiding in daily tasks. Artificial intelligence (AI) in these technologies could also be helpful to deliver interventions that assist older adults with symptoms or self-management. Personality traits may play a role in how older adults engage with AI technologies. To ensure the best intervention delivery, we must understand older adults' engagement with and usability of AI-driven technologies.</p><p><strong>Objective: </strong>This study aimed to describe how older adults engaged with routines facilitated by a conversational AI assistant.</p><p><strong>Methods: </strong>A randomized pilot trial was conducted for 12-weeks in adults aged 60 years or older, self-reported living alone, and having chronic musculoskeletal pain. Participants (N=50) were randomly assigned to 1 of 2 intervention groups (standard vs enhanced) to engage with routines delivered by the AI assistant Alexa (Amazon). Participants were encouraged to interact with prescribed routines twice daily (morning and evening) and as needed. Data were collected and analyzed on routine engagement characteristics and perceived usability of the AI assistant. An analysis of the participants' personality traits was conducted to describe how personality may impact engagement and usability of AI technologies as interventions.</p><p><strong>Results: </strong>The participants had a mean age of 79 years, with moderate to high levels of comfort and trust in technology, and were predominately White (48/50, 96%) and women (44/50, 88%). In both intervention groups, morning routines (n=62, 74%) were initiated more frequently than evening routines (n=52, 62%; z=-2.81, P=.005). Older adult participants in the enhanced group self-reported routine usability as good (mean 74.50, SD 11.90), and those in the standard group reported lower but acceptable usability scores (mean 66.29, SD 6.94). Higher extraversion personality trait scores predicted higher rates of routine initiation throughout the whole day and morning in both groups (standard day: B=0.47, P=.004; enhanced day: B=0.44, P=.045; standard morning: B=0.50, P=.03; enhanced morning: B=0.53, P=.02). Higher agreeableness (standard: B=0.50, P=.02; enhanced B=0.46, P=.002) and higher conscientiousness (standard: B=0.33, P=.04; enhanced: B=0.38, P=.006) personality trait scores predicted better usability scores in both groups.</p><p><strong>Conclusions: </strong>he prescribed interactive routines delivered by an AI assistant were feasible to use as interventions with older adults. Engagement and usability by older adults may be influenced by personality traits such as extraversion, agreeableness, and conscientiousness. While integrating AI-driven interventions into health care, it is important to consider these factors to promote positive outcomes.</p>","PeriodicalId":14841,"journal":{"name":"JMIR Formative Research","volume":"9 ","pages":"e64763"},"PeriodicalIF":2.0000,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11784632/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Formative Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2196/64763","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Technologies that serve as assistants are growing more popular for entertainment and aiding in daily tasks. Artificial intelligence (AI) in these technologies could also be helpful to deliver interventions that assist older adults with symptoms or self-management. Personality traits may play a role in how older adults engage with AI technologies. To ensure the best intervention delivery, we must understand older adults' engagement with and usability of AI-driven technologies.

Objective: This study aimed to describe how older adults engaged with routines facilitated by a conversational AI assistant.

Methods: A randomized pilot trial was conducted for 12-weeks in adults aged 60 years or older, self-reported living alone, and having chronic musculoskeletal pain. Participants (N=50) were randomly assigned to 1 of 2 intervention groups (standard vs enhanced) to engage with routines delivered by the AI assistant Alexa (Amazon). Participants were encouraged to interact with prescribed routines twice daily (morning and evening) and as needed. Data were collected and analyzed on routine engagement characteristics and perceived usability of the AI assistant. An analysis of the participants' personality traits was conducted to describe how personality may impact engagement and usability of AI technologies as interventions.

Results: The participants had a mean age of 79 years, with moderate to high levels of comfort and trust in technology, and were predominately White (48/50, 96%) and women (44/50, 88%). In both intervention groups, morning routines (n=62, 74%) were initiated more frequently than evening routines (n=52, 62%; z=-2.81, P=.005). Older adult participants in the enhanced group self-reported routine usability as good (mean 74.50, SD 11.90), and those in the standard group reported lower but acceptable usability scores (mean 66.29, SD 6.94). Higher extraversion personality trait scores predicted higher rates of routine initiation throughout the whole day and morning in both groups (standard day: B=0.47, P=.004; enhanced day: B=0.44, P=.045; standard morning: B=0.50, P=.03; enhanced morning: B=0.53, P=.02). Higher agreeableness (standard: B=0.50, P=.02; enhanced B=0.46, P=.002) and higher conscientiousness (standard: B=0.33, P=.04; enhanced: B=0.38, P=.006) personality trait scores predicted better usability scores in both groups.

Conclusions: he prescribed interactive routines delivered by an AI assistant were feasible to use as interventions with older adults. Engagement and usability by older adults may be influenced by personality traits such as extraversion, agreeableness, and conscientiousness. While integrating AI-driven interventions into health care, it is important to consider these factors to promote positive outcomes.

Abstract Image

Abstract Image

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
评估老年人参与和可用性与人工智能驱动的干预:随机试点研究。
背景:作为助手的技术在娱乐和协助日常工作方面越来越受欢迎。这些技术中的人工智能(AI)也有助于提供干预措施,帮助有症状或自我管理的老年人。性格特征可能在老年人如何使用人工智能技术方面发挥作用。为确保提供最佳干预措施,我们必须了解老年人对人工智能驱动技术的参与程度和可用性。目的:本研究旨在描述老年人如何通过会话人工智能助手进行日常工作。方法:一项为期12周的随机先导试验在60岁或以上、自我报告独居、患有慢性肌肉骨骼疼痛的成年人中进行。参与者(N=50)被随机分配到2个干预组(标准组和增强组)中的1个,参与人工智能助手Alexa(亚马逊)提供的日常工作。参与者被鼓励按照规定的日常活动每天进行两次(早上和晚上),并根据需要进行互动。收集并分析了人工智能助手的日常参与特征和感知可用性的数据。对参与者的人格特征进行了分析,以描述人格如何影响人工智能技术作为干预措施的参与度和可用性。结果:参与者的平均年龄为79岁,对技术的舒适度和信任度中高,主要是白人(48/50,96%)和女性(44/50,88%)。在两个干预组中,晨间活动(n=62, 74%)比晚间活动(n=52, 62%;z = -2.81, P = .005)。增强组的老年人自我报告日常可用性良好(平均74.50,SD 11.90),标准组的老年人报告较低但可接受的可用性得分(平均66.29,SD 6.94)。外向性人格特质得分越高,两组的日常开始率越高(标准日:B=0.47, P= 0.004;增强日:B=0.44, P= 0.045;标准早晨:B=0.50, P=.03;增强早晨:B=0.53, P= 0.02)。较高的亲和性(标准:B=0.50, P= 0.02;增强的B=0.46, P= 0.002)和更高的责任心(标准:B=0.33, P= 0.04;增强:B=0.38, P= 0.006)人格特质得分预测两组可用性得分更高。结论:他所规定的由人工智能助手提供的互动程序是可行的,可以作为老年人的干预措施。老年人的参与和可用性可能受到性格特征的影响,如外向性、宜人性和责任心。在将人工智能驱动的干预措施纳入卫生保健的同时,重要的是要考虑这些因素,以促进积极成果。
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
JMIR Formative Research
JMIR Formative Research Medicine-Medicine (miscellaneous)
CiteScore
2.70
自引率
9.10%
发文量
579
审稿时长
12 weeks
期刊最新文献
Development of a Contextualized, Research-Based Flemish Assessment Framework for Digital Care, Assistance, and Support: Delphi Study. Public Perceptions of AI in Medicine and Implications for Future Medical Education: Cross-Sectional Survey. Fact-Checking Large Language Model Responses to a Health Care Prompt: Comparative Study. The Influence of the COVID-19 Pandemic on Current Teaching Methods, Training, and Perception Among Romanian Surgery-Oriented Students: Cross-Sectional Study. Development of Virtual Mental Health Stepped Care Service for a Heart Failure Remote Management Program: Qualitative Descriptive Study.
×
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:604180095
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