Cognitive impairment assessment through handwriting (COGITAT) score: a novel tool that predicts cognitive state from handwriting for forensic and clinical applications

Front Psychol. 2024 Mar 28:15:1275315. doi: 10.3389/fpsyg.2024.1275315. eCollection 2024.

Abstract

Introduction: Handwriting deteriorates proportionally to the writer's cognitive state. Such knowledge is of special importance in the case of a contested will, where dementia of the testator is claimed, but medical records are often insufficient to decide what the testator's cognitive state really was. By contrast, if the will is handwritten, handwriting analysis allows us to gauge the testator's cognitive state at the precise moment when he/she was writing the will. However, quantitative methods are needed to precisely evaluate whether the writer's cognitive state was normal or not. We aim to provide a test that quantifies handwriting deterioration to gauge a writer's cognitive state.

Methods: We consecutively enrolled patients who came for the evaluation of cognitive impairment at the Outpatient Clinic for Cognitive Impairment of the Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal and Child Sciences (DINOGMI) of the University of Genoa, Italy. Additionally, we enrolled their caregivers. We asked them to write a short text by hand, and we administered the Mini Mental State Examination (MMSE). Then, we investigated which handwriting parameters correlated with cognitive state as gauged by the MMSE.

Results: Our study found that a single score, which we called the COGnitive Impairment Through hAndwriTing (COGITAT) score, reliably allows us to predict the writer's cognitive state.

Conclusion: The COGITAT score may be a valuable tool to gage the cognitive state of the author of a manuscript. This score may be especially useful in contested handwritten wills, where clinical examination of the writer is precluded.

Keywords: cognitive impairment; dementia; forensic science; handwriting analysis; posthumous; will challenge.

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This research was supported by grants from the Italian Ministry of Health (Ricerca Corrente) and by the IRCCS Ospedale Policlinico San Martino (Genoa, Italy). This work was carried out within the framework of the project “RAISE - Robotics and AI for Socioeconomic Empowerment” and has been supported by European Union - NextGeneration.