痴呆症护理研究和心理社会因素
Alexandra König1,2, Janna Herrmann3, Nicklas Linz4
1ki elements UG, Saarbrücken, Germany.
Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 25, 2025
概括
数字语音评估对低收入和中等收入国家 (LMICs) 的阿尔茨海默病检测有希望. 将技术适应当地语言和教育水平对于准确的认知障碍诊断至关重要.
科学领域:
- 神经学与公共卫生
- 数字卫生技术数字卫生技术
- 全球卫生平等全球卫生平等
背景情况:
- 阿尔茨海默病 (AD) 的患病率在低收入和中等收入国家 (LMICs) 正在增加.
- 基于数字语音的认知评估为早期AD检测提供了可扩展的解决方案.
- 在LMICs的实施面临挑战:基础设施,语言多样性和数字素养.
研究的目的:
- 在LMICs中概述数字语音认知评估的挑战.
- 确定在LMICs中部署这些评估的最佳实践.
- 适应和验证基于语音的认知评估技术,用于各种LMIC设置.
主要方法:
- 在四个LMIC (肯尼亚,智利,埃及,印度) 地区利用ki:elements的语音技术,共有11800名参与者.
- 适应当地语言的技术,包括口头记忆和语义流任务.
- 分析语音数据与人口统计/临床信息,以找到认知障碍的语音生物标志物,考虑教育和文化因素.
主要成果:
- 数字语音评估在LMICs中是可行的和被接受的,与传统测试相关.
- 挑战包括缺乏当地规范,多语言效应,教育影响较低以及连接问题.
- 解决方案:放松单语言限制,教育匹配,线下数据捕获和跨队列通信.
结论:
- 数字语音认知评估是对低收入国家阿尔茨海默氏症的有希望的工具.
- 成功实施需要关注当地基础设施,文化/语言多样性和教育.
- 适应性策略可以提高认知障碍检测在全球不同环境中的准确性.
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