公共卫生 公共卫生
Wee Shiong Lim1,2,3, Audrey Yeo1, Kalene Pek1
1Institute of Geriatrics and Active Ageing, Tan Tock Seng Hospital, Singapore, Singapore.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 23, 2025
概括
这项研究概述了一个基于社区的数字干预 (ADL+2.0) 以防止老年人认知能力下降. 它使用人工智能和实施科学来测试有效性并优化现实世界的部署,以减少更广泛的痴呆风险.
科学领域:
- 老年学是一门学科.
- 数字健康数字健康
- 实施科学 实施科学
背景情况:
- 改变生活方式已被证明可以降低痴呆风险,但社区对预防策略的接受程度较低.
- 数字健康干预提供了可扩展的解决方案,但它们在老年人中实际实施需要进一步探索.
- 了解实施因素 (什么有效,如何,为什么,为谁) 对于在痴呆症预防中有效部署数字健康至关重要.
研究的目的:
- 描述ADL+ 2.0的第1阶段 (试验) 协议,这是一个基于社区的,以理论为导向的,人工智能辅助的多域数字干预.
- 利用实施科学方法来评估干预的有效性并优化其实际应用.
- 确定在老年人群中实施数字健康干预措施的障碍和促进因素,以预防认知衰退.
主要方法:
- 一种类型1混合实施-有效性研究设计,使用一个集群随机试验,比较三个组:远程和现场,仅远程和控制.
- ADL+ 2.0干预集成了远程 (基于应用程序) 和现场 (基于组) 组件,结合行为理论和人工智能驱动的建议.
- 数据收集包括认知,功能,社会和生活质量的结果,以及对实施决定因素的定性数据,这些数据被映射到实施研究综合框架 (CFIR) 中.
主要成果:
- 通过认知,功能,社会和生活质量结果的变化来评估有效性,主要结果测量到干预后9个月.
- 采访和重点小组的定性数据将确定实施决定因素 (上下文,促进者,障碍).
- 关键实施成果 (可接受性,适当性,可行性) 的定量评估将在各个集群中进行.
结论:
- 研究协议详细介绍了一种以理论为指导,人工智能辅助的数字干预,旨在防止认知衰退.
- 第1阶段的发现将为第2阶段的扩展提供信息,优化实施策略,以便更广泛的社区采用.
- 该干预旨在克服先前多领域研究的局限性,并支持有效,可行和相关的痴呆症预防策略.
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