AccScience Publishing / JCTR / Online First / DOI: 10.36922/JCTR025380063
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ORIGINAL ARTICLE

Task-related handwriting and drawing features for early detection of Alzheimer’s disease: A pilot study

Maria Santina Ler1* ,  Miriam Veneziano1 ,  Alfonsina D’Iorio1 ,  Gennaro Cordasco2 ,  Gabriella Santangelo1 ,  Anna Esposito1*
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1 Department of Psychology, Faculty of Psychology, University of Campania Luigi Vanvitelli, Caserta, Italy
2 Department of Computer Science, Faculty of Computer Science, University of Salerno, Salerno, Italy
JCTR, 025380063 https://doi.org/10.36922/JCTR025380063
Received: 18 September 2025 | Revised: 30 October 2025 | Accepted: 19 November 2025 | Published online: 4 December 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Background: Dementia causes significant disability worldwide and has no cure. The only way to improve the quality of life of those affected is through early intervention. For this reason, the development of effective diagnostic tools is a priority for healthcare systems and researchers. Handwriting and drawing, which engage multiple cognitive and motor areas, have shown promise in detecting early signs of dementia. However, findings in this field remain inconsistent, largely due to a lack of standardized protocols. Aim: This study aims to investigate the discriminatory power of graphomotor analysis in distinguishing individuals with Alzheimer’s disease (AD) from healthy controls (HC) by examining the contribution of dynamic handwriting features and task-related characteristics within an easy-to-use and multi-task protocol. Methods: Patients with AD (n = 14) and HC (n = 25) were asked to complete five drawing and two writing tasks, and their online data were recorded using a digital tablet. Results: Significant differences (p<0.05) between groups were observed for time- and ductus-related features in almost all tasks, while pressure, space, and inclination features did not differ significantly. Conclusion: Although certain graphomotor characteristics are more sensitive than others, analyzing them together yields a detailed functional profile of patients. Overall, the study provides evidence of the effectiveness of handwriting analysis in identifying several symptoms associated with dementia. The protocol warrants further validation with a larger sample. Relevance for patients: The proposed protocol highlights the potential of a handwriting-based tool as an ecologically valid, objective, and accessible method for assessing and monitoring dementia. Adopting up-to-date digital approaches responds to the need for more sensitive tools that align with technological and cultural changes within the population. This could consequently simplify screening, improve access to treatment, and enhance the quality of life for patients and their caregivers.

Keywords
Dementia screening
Alzheimer’s disease
Handwriting analysis
Online feature
Kinematic parameters
Funding
This study received funding from the EU-H2020 program (grant no. 101182965, CRYSTAL), the EU NextGenerationE PNRR Mission 4 Component 2 Investment 1.1 (D.D 1409 del 14-09-2022) PRIN 2022 under the IRRESPECTIVE project (code P20222MYKE, CUP: B53D23025980001), and PNRR MUR under AI-PATTERNS FAIR Project (E63C22002150007).
Conflict of interest
The authors declare that they have no competing interests.
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Journal of Clinical and Translational Research, Electronic ISSN: 2424-810X Print ISSN: 2382-6533, Published by AccScience Publishing
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