Geriatrics Section, Department of Medicine, Boston University School of Medicine, Boston, MA, USA.
Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA.
J Alzheimers Dis. 2021;82(1):17-32. doi: 10.3233/JAD-201119.
Coupling digital technology with traditional neuropsychological test performance allows collection of high-precision metrics that can clarify and/or define underlying constructs related to brain and cognition.
To identify graphomotor and information processing trajectories using a digitally administered version of the Digit Symbol Substitution Test (DSST).
A subset of Long Life Family Study participants (n = 1,594) completed the DSST. Total time to draw each symbol was divided into 'writing' and non-writing or 'thinking' time. Bayesian clustering grouped participants by change in median time over intervals of eight consecutively drawn symbols across the 90 s test. Clusters were characterized based on sociodemographic characteristics, health and physical function data, APOE genotype, and neuropsychological test scores.
Clustering revealed four 'thinking' time trajectories, with two clusters showing significant changes within the test. Participants in these clusters obtained lower episodic memory scores but were similar in other health and functional characteristics. Clustering of 'writing' time also revealed four performance trajectories where one cluster of participants showed progressively slower writing time. These participants had weaker grip strength, slower gait speed, and greater perceived physical fatigability, but no differences in cognitive test scores.
Digital data identified previously unrecognized patterns of 'writing' and 'thinking' time that cannot be detected without digital technology. These patterns of performance were differentially associated with measures of cognitive and physical function and may constitute specific neurocognitive biomarkers signaling the presence of subtle to mild dysfunction. Such information could inform the selection and timing of in-depth neuropsychological assessments and help target interventions.
将数字技术与传统神经心理学测试表现相结合，可以收集高精度的指标，从而阐明和/或定义与大脑和认知相关的潜在结构。
使用数字版数字符号替代测试（DSST）来识别笔迹和信息处理轨迹。
从长寿家族研究的一部分参与者（n = 1594）中选择了一些人来完成 DSST。绘制每个符号的总时间分为“书写”和非书写或“思考”时间。贝叶斯聚类根据 90 秒测试中连续 8 个符号的中位数时间变化将参与者分组。根据社会人口统计学特征、健康和身体功能数据、APOE 基因型和神经心理学测试分数对聚类进行特征描述。
聚类结果显示了四种“思考”时间轨迹，其中两个轨迹在测试中发生了显著变化。这些聚类中的参与者在情景记忆测试中得分较低，但在其他健康和功能特征方面相似。“书写”时间的聚类也揭示了四个表现轨迹，其中一个参与者的书写时间逐渐变慢。这些参与者的握力较弱，步态速度较慢，身体疲劳感更强，但认知测试分数没有差异。
数字数据识别了以前无法通过数字技术检测到的“书写”和“思考”时间的新模式。这些表现模式与认知和身体功能的测量指标差异相关，可能构成特定的神经认知生物标志物，表明存在轻微至轻度的功能障碍。这些信息可以为深入的神经心理学评估的选择和时机提供信息，并有助于确定干预措施的目标。
