背景:早期发现认知障碍或痴呆对于降低严重神经退行性疾病的发生率至关重要。然而,目前可用的诊断工具用于检测轻度认知障碍(MCI)或痴呆是耗时的,贵,或无法广泛使用。因此,探索更有效的方法来帮助临床医生检测MCI是必要的。
目的:在本研究中,我们旨在探讨在基于平板电脑的“绘图和拖动”任务下通过运动动力学评估MCI的可行性和效率。
方法:我们通过举办座谈会,迭代地设计“绘制和拖动”任务,编程,以及与利益相关者的访谈(神经学家,护士,工程师,MCI患者,健康的老年人,和护理人员)。随后,通过比较与手部运动功能相关的5类特征,在健康对照组和MCI组中评估了中风模式和运动动力学(即,时间,中风,频率,得分,和顺序)。最后,使用结构化问卷和非结构化访谈调查了用户对整体认知筛选系统的体验,他们的建议被记录下来。
结果:“绘制和拖动”任务可以有效地检测MCI,平均准确率为85%(SD2%)。使用运动动力学的统计比较,我们发现基于时间和分数的特征是所有特征中最有效的。具体来说,与健康对照组相比,MCI组显示手从一个笔划切换到下一个笔划的时间显着增加,绘图时间较长,缓慢拖动,和较低的分数。此外,MCI患者对绘制序列特征的决策策略和视觉感知较差,通过添加辅助信息和在图纸中丢失更多本地细节来证明。来自用户体验的反馈表明,我们的系统是用户友好的,有助于筛查自我感知的缺陷。
结论:基于片剂的MCI检测系统定量评估了老年人的手运动功能,并进一步阐明了MCI患者的认知和行为下降现象。这种创新的方法用于识别和测量与MCI或阿尔茨海默痴呆症相关的数字生物标志物。随着疾病的进展,能够监测患者执行功能和视觉感知能力的变化。
BACKGROUND: Early detection of cognitive impairment or dementia is essential to reduce the incidence of severe neurodegenerative diseases. However, currently available diagnostic tools for detecting mild cognitive impairment (MCI) or dementia are time-consuming, expensive, or not widely accessible. Hence, exploring more effective methods to assist clinicians in detecting MCI is necessary.
OBJECTIVE: In this
study, we aimed to explore the feasibility and efficiency of assessing MCI through movement kinetics under tablet-based \"drawing and dragging\" tasks.
METHODS: We iteratively designed \"drawing and dragging\" tasks by conducting symposiums, programming, and interviews with stakeholders (neurologists, nurses, engineers, patients with MCI, healthy older adults, and caregivers). Subsequently, stroke patterns and movement kinetics were evaluated in healthy control and MCI groups by comparing 5 categories of features related to
hand motor function (ie, time, stroke, frequency, score, and sequence). Finally, user experience with the overall cognitive screening system was investigated using structured questionnaires and unstructured interviews, and their suggestions were recorded.
RESULTS: The \"drawing and dragging\" tasks can detect MCI effectively, with an average accuracy of 85% (SD 2%). Using statistical comparison of movement kinetics, we discovered that the time- and score-based features are the most effective among all the features. Specifically, compared with the healthy control group, the MCI group showed a significant increase in the time they took for the
hand to switch from one stroke to the next, with longer drawing times, slow dragging, and lower scores. In addition, patients with MCI had poorer decision-making strategies and visual perception of drawing sequence features, as evidenced by adding auxiliary information and losing more local details in the drawing. Feedback from user experience indicates that our system is user-friendly and facilitates screening for deficits in self-perception.
CONCLUSIONS: The tablet-based MCI detection system quantitatively assesses
hand motor function in older adults and further elucidates the cognitive and behavioral decline phenomenon in patients with MCI. This innovative approach serves to identify and measure digital biomarkers associated with MCI or Alzheimer dementia, enabling the monitoring of changes in patients\' executive function and visual perceptual abilities as the disease advances.