Mild Dementia Decision Support from AI-based Digital Biomarkers using Mobile Playful Exercises with High Adherence

M. Pszeida, L. Paletta, S. Russegger, T. Orgel, S. Draxler, M. Koini, Martin Berger, M. Fellner, S. Spat, Sandra Schuessler, J. Zuschnegg, Bernhard Strobl, Karin Ploder, Maria M Hofmarcher Holzhacker
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Abstract

Early detection of cognitive decline and monitoring of cognitive functioning in mild dementia are fundamental for timely adaptation of lifestyle and intervention strategies. The development of digital dementia biomarkers through playful exercises with high adherence rate was a key objective of the national project multimodAAL (no. FFG 875345). The results of a study on computer-based cognitive and physical training (CCPT) in persons diagnosed with mild Alzheimer’s disease (PwAD) are presented.Method: Tablet-PC-based intervention was applied within 6 months in Austria, engaging PwADs living at home by means of playful multimodal training and activation (n=11; female N=8, male N=3; age M=76.6 / SD=9.2 years, MMSE score M=21.50 / SD=4.41). PwADs interacted with a prototypical version of the BRAINMEE app that included a suite of cognitive exercises (puzzle, pairs, text gap filling) based on audiovisual information. The playful training app was introduced and assisted by mobile care professionals with weekly visits, however, PwDs played alone between these visits.Result: PwADs applied training with high adherence, finalizing M=72 (M=32) digital exercises per day within the first (last) month of the study. Duration of using exercise type ‘outsider’(p=.028*) and ‘quiz’ (p=.001**), averaged about 2 week figures, each provided statistically significant correlations (Spearman) with MMSE test scores, as well as ‘spot-the-difference’ (p=.003**) with Trail Making Test A, ‘outsider’ (p=.005**) with Auditory Verbal Learning Test (AVLT), respectively. A neural network (Support Vector Machine, linear kernel, 11-fold cross validation) using duration of use of ‘quiz’, ’outsider’ and ‘hearing’ (guessing animal sounds) as input data resulted in M=2.16 absolute error in MMSE score estimation on test data.Conclusion: The work outlined within the Austrian study on digital biomarker development indicates successful steps towards daily use of cognitive assessment using highly adherent playful training. The playful training app is applied in the European project MARA (no. FFG 886427) to enable continuous estimates of MCI’s mental state over time. The app was very well accepted by both PwADs and persons with MCI. It offers with its pervasive mental assessment tool a large potential for future long-term monitoring in dementia prevention, early detection as well as in numerous dementia care services.
使用高依从性移动游戏练习的基于ai的数字生物标志物的轻度痴呆决策支持
早期发现认知能力下降和监测轻度痴呆患者的认知功能是及时适应生活方式和干预策略的基础。通过高依从率的有趣练习开发数字痴呆症生物标志物是国家项目multimodAAL的关键目标。FFG 875345)。一项基于计算机的认知和体能训练(CCPT)在轻度阿尔茨海默病(PwAD)诊断的人的研究结果。方法:在奥地利6个月内采用基于平板电脑的干预措施,通过有趣的多模式训练和激活来吸引生活在家中的pwad (n=11;女性N=8,男性N=3;年龄M=76.6 / SD=9.2岁,MMSE评分M=21.50 / SD=4.41)。pwad与BRAINMEE应用程序的原型版本进行交互,该应用程序包括一套基于视听信息的认知练习(拼图、配对、文本空白填充)。这款有趣的训练应用程序是由每周来访的移动护理专业人员介绍和协助的,然而,在这些访问之间,残疾人独自玩耍。结果:PwADs应用训练具有高依从性,在研究的第一个(最后一个)月内每天完成M=72 (M=32)个数字练习。使用运动类型“局外人”(p= 0.028 *)和“测验”(p= 0.001 **)的持续时间平均约为2周,分别与MMSE测试成绩(Spearman)以及与Trail Making test A的“发现差异”(p= 0.003 **)和听觉言语学习测试(AVLT)的“局外人”(p= 0.005 **)具有统计学显著相关性。使用“测验”、“局外人”和“听力”(猜测动物声音)作为输入数据的持续时间的神经网络(支持向量机、线性核、11倍交叉验证)导致测试数据的MMSE分数估计的M=2.16绝对误差。结论:奥地利关于数字生物标志物发展的研究概述的工作表明,通过高度依附性的有趣训练,认知评估的日常使用取得了成功。这款有趣的培训应用程序应用于欧洲项目MARA (no. 6)。FFG 886427),使MCI的精神状态随时间的持续估计。这个应用程序被pwad和MCI患者很好地接受。它通过其普及的精神评估工具,为今后在痴呆症预防、早期发现以及众多痴呆症护理服务方面的长期监测提供了巨大潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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