Digital Clock Drawing Test metrics in older patients before and after endoscopy with sedation: an exploratory analysis

2021 ◽  
Author(s):  
Richard A. Buckley ◽  
Kelly J. Atkins ◽  
Brendan Silbert ◽  
David A. Scott ◽  
Lisbeth Evered
Keyword(s):  
Older Patients ◽  
Exploratory Analysis ◽  
Clock Drawing Test ◽  
Digital Clock ◽  
Clock Drawing ◽  
Drawing Test ◽  
Before And After

scholarly journals Classifying Non-Dementia and Alzheimer’s Disease/Vascular Dementia Patients Using Kinematic, Time-Based, and Visuospatial Parameters: The Digital Clock Drawing Test

2021 ◽  
Vol 82 (1) ◽  
pp. 47-57 ◽  
Author(s):  
Anis Davoudi ◽  
Catherine Dion ◽  
Shawna Amini ◽  
Patrick J. Tighe ◽  
Catherine C. Price ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Vascular Dementia ◽  
Area Under The Curve ◽  
Clock Drawing Test ◽  
Digital Clock ◽  
Clock Drawing ◽  
Drawing Test ◽  
Dementia Patients ◽  
Optimal Area

Background: Advantages of digital clock drawing metrics for dementia subtype classification needs examination. Objective: To assess how well kinematic, time-based, and visuospatial features extracted from the digital Clock Drawing Test (dCDT) can classify a combined group of Alzheimer’s disease/Vascular Dementia patients versus healthy controls (HC), and classify dementia patients with Alzheimer’s disease (AD) versus vascular dementia (VaD). Methods: Healthy, community-dwelling control participants (n = 175), patients diagnosed clinically with Alzheimer’s disease (n = 29), and vascular dementia (n = 27) completed the dCDT to command and copy clock drawing conditions. Thirty-seven dCDT command and 37 copy dCDT features were extracted and used with Random Forest classification models. Results: When HC participants were compared to participants with dementia, optimal area under the curve was achieved using models that combined both command and copy dCDT features (AUC = 91.52%). Similarly, when AD versus VaD participants were compared, optimal area under the curve was, achieved with models that combined both command and copy features (AUC = 76.94%). Subsequent follow-up analyses of a corpus of 10 variables of interest determined using a Gini Index found that groups could be dissociated based on kinematic, time-based, and visuospatial features. Conclusion: The dCDT is able to operationally define graphomotor output that cannot be measured using traditional paper and pencil test administration in older health controls and participants with dementia. These data suggest that kinematic, time-based, and visuospatial behavior obtained using the dCDT may provide additional neurocognitive biomarkers that may be able to identify and tract dementia syndromes.

scholarly journals Digital Clock Drawing: Differentiating “Thinking” versus “Doing” in Younger and Older Adults with Depression

2014 ◽  
Vol 20 (9) ◽  
pp. 920-928 ◽  
Author(s):  
Jamie Cohen ◽  
Dana L. Penney ◽  
Randall Davis ◽  
David J. Libon ◽  
Rodney A. Swenson ◽  
...  
Keyword(s):  
Motor Performance ◽  
Psychomotor Speed ◽  
Psychomotor Slowing ◽  
Younger Adults ◽  
Clock Drawing Test ◽  
Digital Clock ◽  
Clock Drawing ◽  
Time To Completion ◽  
Drawing Test ◽  
Depressed Group

AbstractPsychomotor slowing has been documented in depression. The digital Clock Drawing Test (dCDT) provides: (i) a novel technique to assess both cognitive and motor aspects of psychomotor speed within the same task and (ii) the potential to uncover subtleties of behavior not previously detected with non-digitized modes of data collection. Using digitized pen technology in 106 participants grouped by Age (younger/older) and Affect (euthymic/unmedicated depressed), we recorded cognitive and motor output by capturing how the clock is drawn rather than focusing on the final product. We divided time to completion (TTC) for Command and Copy conditions of the dCDT into metrics of percent of drawing (%Ink) versus non-drawing (%Think) time. We also obtained composite Z-scores of cognition, including attention/information processing (AIP), to explore associations of %Ink and %Think times to cognitive and motor performance. Despite equivalent TTC, %Ink and %Think Command times (Copy n.s.) were significant (AgeXAffect interaction: p=.03)—younger depressed spent a smaller proportion of time drawing relative to thinking compared to the older depressed group. Command %Think time negatively correlated with AIP in the older depressed group (r=−.46; p=.02). Copy %Think time negatively correlated with AIP in the younger depressed (r=−.47; p=.03) and older euthymic groups (r=−.51; p=.01). The dCDT differentiated aspects of psychomotor slowing in depression regardless of age, while dCDT/cognitive associates for younger adults with depression mimicked patterns of older euthymics. (JINS, 2014, 20, 1–9)

scholarly journals Learning classification models of cognitive conditions from subtle behaviors in the digital Clock Drawing Test

2015 ◽  
Vol 102 (3) ◽  
pp. 393-441 ◽  
Author(s):  
William Souillard-Mandar ◽  
Randall Davis ◽  
Cynthia Rudin ◽  
Rhoda Au ◽  
David J. Libon ◽  
...  
Keyword(s):  
Classification Models ◽  
Clock Drawing Test ◽  
Digital Clock ◽  
Clock Drawing ◽  
Drawing Test

A novel digital clock drawing test as a screening tool for perioperative neurocognitive disorders: A feasibility study

2020 ◽  
Author(s):  
Richard A. Buckley ◽  
Kelly J. Atkins ◽  
Erika Fortunato ◽  
Brendan Silbert ◽  
David A. Scott ◽  
...  
Keyword(s):  
Feasibility Study ◽  
Screening Tool ◽  
Neurocognitive Disorders ◽  
Clock Drawing Test ◽  
Digital Clock ◽  
Clock Drawing ◽  
Drawing Test

scholarly journals Longitudinal Changes in Clock Drawing Test (CDT) Performance before and after Cognitive Decline

PLoS ONE ◽  
2014 ◽  
Vol 9 (5) ◽  
pp. e97873 ◽  
Author(s):  
Ping Wang ◽  
Langfeng Shi ◽  
Qianhua Zhao ◽  
Zhen Hong ◽  
Qihao Guo
Keyword(s):  
Cognitive Decline ◽  
Longitudinal Changes ◽  
Clock Drawing Test ◽  
Clock Drawing ◽  
Drawing Test ◽  
Before And After

O4-12-03: Using the Digital Clock Drawing Test and Machine Learning to Improve Accuracy of Cognitive Screening

2016 ◽  
Vol 12 ◽  
pp. P363-P364
Author(s):  
William Souillard-Mandar ◽  
Randall Davis ◽  
Cynthia Rudin ◽  
Rhoda Au ◽  
David J. Libon ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cognitive Screening ◽  
Clock Drawing Test ◽  
Digital Clock ◽  
Clock Drawing ◽  
Drawing Test ◽  
Improve Accuracy
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