.png){kind=icon}
返回
Digitally generated Trail Making Test data: Analysis using hidden Markov modeling
.png){kind=icon}
OA
AI
DOI：10.1002/dad2.12292.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The Trail Making Test (TMT) is a neuropsychological test used to assess cognitive dysfunction. The TMT consists of two parts: TMT-A requires connecting numbers 1 to 25 sequentially; TMT-B requires connecting numbers 1 to 12 and letters A to L sequentially, alternating between numbers and letters. We propose using a digitally recorded version of TMT to capture cognitive or physical functions underlying test performance. We analyzed digital versions of TMT-A and -B to derive time metrics and used Bayesian hidden Markov models to extract additional metrics. We correlated these derived metrics with cognitive and physical function scores using regression. On both TMT-A and -B, digital metrics associated with graphomotor processing test scores and gait speed. Digital metrics on TMT-B were additionally associated with episodic memory test scores and grip strength. These metrics provide additional information of cognitive state and can differentiate cognitive and physical factors affecting test performance.
Keyword:
aging
Bayesian hidden Markov models
cognitive function
digital biomarker
Trail Making Test
期刊
A
IF:
4.4
论文数:
815
被引数:
3.0K
机构
引用论文
.png){kind=icon}
A prison-model of CoSA: the potential to offer “through the gate” support and accountabilityCoSA的监狱模型：提供“门后”支持和问责制的潜力
OAAI
OAAI分享收藏
查看更多内容