Literature DB >> 30909229

The Digital Tree Drawing Test for Screening of Early Dementia: An Explorative Study Comparing Healthy Controls, Patients with Mild Cognitive Impairment, and Patients with Early Dementia of the Alzheimer Type.

Sibylle Robens1, Petra Heymann2, Regine Gienger2, Andreas Hett2, Stephan Müller3, Christoph Laske4,5, Roland Loy6, Thomas Ostermann1, Ulrich Elbing2.   

Abstract

The digital tree drawing test (dTDT) is a newly developed screening tool for the early detection of Alzheimer's disease. It is performed with a digitizing pen, recording each pen stroke with temporal and spatial precision. It was hypothesized that movement characteristics recorded during the painting process contribute to the identification of patients with mild cognitive impairment (MCI) and early dementia of the Alzheimer type (eDAT). The study population consisted of 187 participants (67 healthy controls, 64 MCI, and 56 eDAT patients) with a mean age of 68.6±10.6 years. Between-group comparisons of the dTDT-variables were conducted with analysis of variance. The diagnostic power of dTDT variables was analyzed with stepwise logistic regressions and areas under curve (AUC) of receiver operating control curves. Cognitively impaired persons used less colors and line widths and changed them less often than healthy subjects (p-values ≤0.05). Compared to control, eDAT patients had larger not-painting periods, were slower, and their pictures had less contrast, image size, and complexity (p-values ≤0.01). Logistic regression models of stepwise selected dTDT variables resulted in an AUC of 0.84 (95% confidence interval (CI) [0.79, 0.90], sensitivity = 0.78, specificity = 0.77) for discriminating healthy subjects from all cognitive impaired, an AUC of 0.77. (95% CI [0.69; 0.85], sensitivity = 0.56, specificity = 0.83) for discriminating healthy controls from MCI patients and an AUC of 0.90 (95% CI [0.84, 0.96], sensitivity = 0.86, specificity = 0.82) for discriminating controls from eDAT patients. The results suggest that digital recording of pen-stroke data during the drawing process can contribute to the screening of cognitive impaired patients.

Entities:  

Keywords:  Digital device; digital tree drawing test; drawing characteristics; early alzheimer’s disease; logistic regression; mild cognitive impairment; neuropsychological drawing test; screening test

Year:  2019        PMID: 30909229     DOI: 10.3233/JAD-181029

Source DB:  PubMed          Journal:  J Alzheimers Dis        ISSN: 1387-2877            Impact factor:   4.472


  4 in total

1.  Electronic Cognitive Screen Technology for Screening Older Adults With Dementia and Mild Cognitive Impairment in a Community Setting: Development and Validation Study.

Authors:  Joyce Y C Chan; Adrian Wong; Brian Yiu; Hazel Mok; Patti Lam; Pauline Kwan; Amany Chan; Vincent C T Mok; Kelvin K F Tsoi; Timothy C Y Kwok
Journal:  J Med Internet Res       Date:  2020-12-18       Impact factor: 5.428

Review 2.  Digital Cognitive Biomarker for Mild Cognitive Impairments and Dementia: A Systematic Review.

Authors:  Zihan Ding; Tsz-Lok Lee; Agnes S Chan
Journal:  J Clin Med       Date:  2022-07-19       Impact factor: 4.964

Review 3.  Evaluation of Digital Drawing Tests and Paper-and-Pencil Drawing Tests for the Screening of Mild Cognitive Impairment and Dementia: A Systematic Review and Meta-analysis of Diagnostic Studies.

Authors:  Joyce Y C Chan; Baker K K Bat; Adrian Wong; Tak Kit Chan; Zhaohua Huo; Benjamin H K Yip; Timothy C Y Kowk; Kelvin K F Tsoi
Journal:  Neuropsychol Rev       Date:  2021-10-16       Impact factor: 6.940

Review 4.  Neuropsychology of posteromedial parietal cortex and conversion factors from Mild Cognitive Impairment to Alzheimer's disease: systematic search and state-of-the-art review.

Authors:  Ciro Rosario Ilardi; Sergio Chieffi; Tina Iachini; Alessandro Iavarone
Journal:  Aging Clin Exp Res       Date:  2021-07-07       Impact factor: 3.636

  4 in total

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