Understanding the Factors that Influence the Implementation of AI-Driven Lifestyle Monitoring in Long-Term Care for Older Adults.

IF 3.2 2区 医学 Q1 GERONTOLOGY
S W M Groeneveld, T Dekkers, Jewc van Gemert-Pijnen, R M Verdaasdonk, T J Verveda, R Witteveen, H van Os-Medendorp, M E M den Ouden
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Abstract

Background and objectives: AI-driven lifestyle monitoring systems collect data from ambient, motion, contact, light, and physiological sensors placed in the home, enabling AI algorithms to identify daily routines and detect deviations to support older adults "aging in place." Despite its potential to support several challenges in long-term care for older adults, implementation remains limited. This study explored the facilitators and barriers to implementing AI-driven lifestyle monitoring in long-term care for older adults, as perceived by formal and informal caregivers, as well as management, in both an adopting and non-adopting healthcare organization.

Research design and methods: A qualitative interview study using semi-structured interviews was conducted with 22 participants (5 informal caregivers, 10 formal caregivers, and 7 participants in a management position) from two long-term care organizations. Reflexive thematic analysis, guided by the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework, structured findings into facilitators and barriers.

Results: 12 facilitators and 16 barriers were identified, highlighting AI-driven lifestyle monitoring as a valuable, patient-centred, and unobtrusive tool enhancing care efficiency and caregiver reassurance. However, barriers such as privacy concerns, notification overload, training needs, and organizational alignment must be addressed. Contextual factors, including regulations, partnerships, and financial considerations, further influence implementation.

Discussion and implications: This study showed that to optimize implementation of AI-driven lifestyle monitoring, organizations should address privacy concerns, provide training, engage in system (re)design and create a shared vision. A comprehensive multi-level approach across all levels is essential for successful AI integration in long-term care for older adults.

了解影响在老年人长期护理中实施人工智能驱动的生活方式监测的因素。
背景和目标:人工智能驱动的生活方式监测系统从放置在家中的环境、运动、接触、光线和生理传感器收集数据,使人工智能算法能够识别日常生活并检测偏差,以支持老年人“就地衰老”。尽管它有潜力支持老年人长期护理方面的若干挑战,但实施仍然有限。本研究探讨了在采用和非采用人工智能的医疗机构中,正式和非正式护理人员以及管理人员认为,在老年人长期护理中实施人工智能驱动的生活方式监测的促进因素和障碍。研究设计与方法:采用半结构化访谈法对来自两家长期护理机构的22名参与者(5名非正式护理人员、10名正式护理人员和7名管理人员)进行了定性访谈研究。反思性专题分析在不采用、放弃、扩大、传播和可持续性(NASSS)框架的指导下,将研究结果结构化为促进因素和障碍。结果:确定了12个促进因素和16个障碍,突出了人工智能驱动的生活方式监测是一种有价值的、以患者为中心的、不显眼的工具,可提高护理效率和护理人员的信心。但是,必须解决诸如隐私问题、通知过载、培训需求和组织一致性等障碍。环境因素,包括法规、伙伴关系和财务考虑,进一步影响实施。讨论和启示:本研究表明,为了优化人工智能驱动的生活方式监控的实施,组织应该解决隐私问题,提供培训,参与系统(重新)设计并创建共同愿景。要成功地将人工智能整合到老年人的长期护理中,必须采用跨所有层面的综合多层次方法。
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来源期刊
Gerontologist
Gerontologist GERONTOLOGY-
CiteScore
11.00
自引率
8.80%
发文量
171
期刊介绍: The Gerontologist, published since 1961, is a bimonthly journal of The Gerontological Society of America that provides a multidisciplinary perspective on human aging by publishing research and analysis on applied social issues. It informs the broad community of disciplines and professions involved in understanding the aging process and providing care to older people. Articles should include a conceptual framework and testable hypotheses. Implications for policy or practice should be highlighted. The Gerontologist publishes quantitative and qualitative research and encourages manuscript submissions of various types including: research articles, intervention research, review articles, measurement articles, forums, and brief reports. Book and media reviews, International Spotlights, and award-winning lectures are commissioned by the editors.
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