Association Between the Digital Clock Drawing Test and Neuropsychological Test Performance: Large Community-Based Prospective Cohort (Framingham Heart Study)

Yuan, Jing; Libon, David J.; Karjadi, Cody; Ang, Ting Fang Alvin; Devine, Sherral; Auerbach, Sanford; Au, Rhoda; Lin, Honghuang

doi:10.2196/27407

Cited by 22 publications

(21 citation statements)

References 25 publications

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“…of DeepN, the reference number sequences S i , i = 1, 2, 3, and the predefined number positions 12 of the number digits from 0 to 12. There are four criteria on the numbers parameter, such as the presence of all the numbers and no additional numbers, the correctness of the order of the numbers, the correctness of the position of the numbers, and the positioning of the numbers within the contour.…”

Section: Assignment Of Scoresmentioning

confidence: 99%

“…Therefore, the correctness of the order of the numbers was evaluated to be one, since the value 85.71 of the maximum ratio R seq was greater than 65.00, the heuristically given value of θ n . The ratio n( 12 within 100.00 pixels, a given limit dc , was less than 0.65, a heuristically given value θ dc , where the evaluated distance d cn [j] for each of the digit numbers k in D[j] was specifically 497.8, 462.5, 350.7, 399.3, 415.1, 254.5, 208.5, 1037.8, 836.2, 792.1, 743.6 and 952.1. Therefore, the correctness of the position of the numbers was evaluated to be zero.…”

Section: Scoring On Criteria Of Hand Parametermentioning

confidence: 99%

“…Hence, in CDT, the test subject uses various cortical areas at the same time for the task being performed; these include use of the frontal, parietal, and temporal lobes [ 9 , 10 ]. As such, various cognitive skills such as selective, sustained attention, visuospatial skills, verbal working memory, visual memory and reconstruction, on-demand motor execution (praxis), auditory comprehension, numerical knowledge, and executive function can be tested [ 9 , 11 , 12 ]. It was Shulman and collaborators who employed the CDT as a screening tool for older patients for cognitive disorders and, since then, multiple studies have affirmed the utility of the CDT for screening and diagnosis of cognitive impairment for suspected pathologies such as Huntington’s disease, schizophrenia, unilateral neglect, delirium, multiple sclerosis, etc.…”

Section: Introductionmentioning

confidence: 99%

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Automatic, Qualitative Scoring of the Clock Drawing Test (CDT) Based on U-Net, CNN and Mobile Sensor Data

Park

Lee

2021

Sensors

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The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular screening tool for cognitive functions. In spite of its qualitative capabilities in diagnosis of neurological diseases, the assessment of the CDT has depended on quantitative methods as well as manual paper based methods. Furthermore, due to the impact of the advancement of mobile smart devices imbedding several sensors and deep learning algorithms, the necessity of a standardized, qualitative, and automatic scoring system for CDT has been increased. This study presents a mobile phone application, mCDT, for the CDT and suggests a novel, automatic and qualitative scoring method using mobile sensor data and deep learning algorithms: CNN, a convolutional network, U-Net, a convolutional network for biomedical image segmentation, and the MNIST (Modified National Institute of Standards and Technology) database. To obtain DeepC, a trained model for segmenting a contour image from a hand drawn clock image, U-Net was trained with 159 CDT hand-drawn images at 128 × 128 resolution, obtained via mCDT. To construct DeepH, a trained model for segmenting the hands in a clock image, U-Net was trained with the same 159 CDT 128 × 128 resolution images. For obtaining DeepN, a trained model for classifying the digit images from a hand drawn clock image, CNN was trained with the MNIST database. Using DeepC, DeepH and DeepN with the sensor data, parameters of contour (0–3 points), numbers (0–4 points), hands (0–5 points), and the center (0–1 points) were scored for a total of 13 points. From 219 subjects, performance testing was completed with images and sensor data obtained via mCDT. For an objective performance analysis, all the images were scored and crosschecked by two clinical experts in CDT scaling. Performance test analysis derived a sensitivity, specificity, accuracy and precision for the contour parameter of 89.33, 92.68, 89.95 and 98.15%, for the hands parameter of 80.21, 95.93, 89.04 and 93.90%, for the numbers parameter of 83.87, 95.31, 87.21 and 97.74%, and for the center parameter of 98.42, 86.21, 96.80 and 97.91%, respectively. From these results, the mCDT application and its scoring system provide utility in differentiating dementia disease subtypes, being valuable in clinical practice and for studies in the field.

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Section: Assignment Of Scoresmentioning

confidence: 99%

Section: Scoring On Criteria Of Hand Parametermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic, Qualitative Scoring of the Clock Drawing Test (CDT) Based on U-Net, CNN and Mobile Sensor Data

Park

Lee

2021

Sensors

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“…In our prior study [ 15 ], we validated the psychometric characteristics of the dCDT against standard paper-pencil neuropsychological (NP) tests in the community-based Framingham Heart Study (FHS) [ 15 ]. The composite scores derived from dCDT features were significantly associated with both NP test results and mild cognitive impairment [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Associations Between the Digital Clock Drawing Test and Brain Volume: Large Community-Based Prospective Cohort (Framingham Heart Study)

Yuan¹,

Au²,

Karjadi³

et al. 2022

J Med Internet Res

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Background The digital Clock Drawing Test (dCDT) has been recently used as a more objective tool to assess cognition. However, the association between digitally obtained clock drawing features and structural neuroimaging measures has not been assessed in large population-based studies. Objective We aimed to investigate the association between dCDT features and brain volume. Methods This study included participants from the Framingham Heart Study who had both a dCDT and magnetic resonance imaging (MRI) scan, and were free of dementia or stroke. Linear regression models were used to assess the association between 18 dCDT composite scores (derived from 105 dCDT raw features) and brain MRI measures, including total cerebral brain volume (TCBV), cerebral white matter volume, cerebral gray matter volume, hippocampal volume, and white matter hyperintensity (WMH) volume. Classification models were also built from clinical risk factors, dCDT composite scores, and MRI measures to distinguish people with mild cognitive impairment (MCI) from those whose cognition was intact. Results A total of 1656 participants were included in this study (mean age 61 years, SD 13 years; 50.9% women), with 23 participants diagnosed with MCI. All dCDT composite scores were associated with TCBV after adjusting for multiple testing (P value <.05/18). Eleven dCDT composite scores were associated with cerebral white matter volume, but only 1 dCDT composite score was associated with cerebral gray matter volume. None of the dCDT composite scores was associated with hippocampal volume or WMH volume. The classification model for differentiating MCI and normal cognition participants, which incorporated age, sex, education, MRI measures, and dCDT composite scores, showed an area under the curve of 0.897. Conclusions dCDT composite scores were significantly associated with multiple brain MRI measures in a large community-based cohort. The dCDT has the potential to be used as a cognitive assessment tool in the clinical diagnosis of MCI.

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“…Owing to research-grade accelerometry, it is now possible to remotely track PA behavior, whereas smartphone-based technology can assess cognitive function in real-world contexts using ecological momentary cognitive tests (EMCTs). Evidence suggests that EMCTs have relatively high completion rates of 60% to 85% [17], and performance on these tests correlates with standard neuropsychological testing scores [18][19][20][21][22]. With 81% of adults aged 60 to 69 years and 62% of those aged ≥70 years owning a smartphone [23,24], EMCTs can be deployed to increase our understanding of whether acute fluctuations in real-world PA lead to immediate improvements in cognition in a variety of everyday contexts.…”

Section: Introductionmentioning

confidence: 99%

Daily Level Association of Physical Activity and Performance on Ecological Momentary Cognitive Tests in Free-living Environments: A Mobile Health Observational Study

Zlatar

Campbell

Tang

et al. 2022

JMIR Mhealth Uhealth

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Background Research suggests that physical activity (PA) has both acute and chronic beneficial effects on cognitive function in laboratory settings and under supervised conditions. Mobile health technologies make it possible to reliably measure PA and cognition in free-living environments, thus increasing generalizability and reach. Research is needed to determine whether the benefits of PA on cognitive function extend from the laboratory to real-world contexts. Objective This observational study aims to examine the association between daily fluctuations in PA and cognitive performance using mobile health technologies in free-living environments. Methods A total of 90 adults (mean age 59, SD 6.3 years; 65/90, 72% men) with various comorbidities (eg, cardiovascular risk and HIV) and different levels of baseline cognition (ranging from cognitively normal to impaired) completed ecological momentary cognitive tests (EMCTs) on a smartphone twice daily while wearing an accelerometer to capture PA levels for 14 days. Linear mixed-effects models examined the daily associations of PA with executive function and verbal learning EMCTs. Moderation analyses investigated whether the relationship between daily PA and daily performance on EMCTs changed as a function of baseline cognition, cardiovascular risk, and functional status (independent vs dependent). Results Days with greater PA were associated with better (faster) performance on an executive function EMCT after covariate adjustment (estimate −0.013; β=−.16; P=.04). Moderation analyses (estimate 0.048; β=.58; P=.001) indicated that days with greater PA were associated with better (faster) executive function performance in individuals who were functionally dependent (effect size −0.53; P<.001) and not in functionally independent adults (effect size −0.01; P=.91). Conclusions EMCTs may be a sensitive tool for capturing daily-level PA-related fluctuations in cognitive performance in real-world contexts and could be a promising candidate for tracking cognitive performance in digital health interventions aimed at increasing PA. Further research is needed to determine individual characteristics that may moderate the association between daily PA and EMCT performance in free-living environments.

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Association Between the Digital Clock Drawing Test and Neuropsychological Test Performance: Large Community-Based Prospective Cohort (Framingham Heart Study)

Cited by 22 publications

References 25 publications

Automatic, Qualitative Scoring of the Clock Drawing Test (CDT) Based on U-Net, CNN and Mobile Sensor Data

Automatic, Qualitative Scoring of the Clock Drawing Test (CDT) Based on U-Net, CNN and Mobile Sensor Data

Associations Between the Digital Clock Drawing Test and Brain Volume: Large Community-Based Prospective Cohort (Framingham Heart Study)

Daily Level Association of Physical Activity and Performance on Ecological Momentary Cognitive Tests in Free-living Environments: A Mobile Health Observational Study

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