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.

Investigations into children’s perception and acceptance of new technologies, including voice chatbots, robots, and virtual agents, represent an emerging and rapidly expanding field of academic research. These interactive dialogue systems offer new opportunities in various settings (e.g., educational, therapeutic, and health settings) by providing innovative tools to improve the effectiveness of interactions and overall well-being. This study investigates children’s preferences for eight virtual agents: four with natural human voices (one adult male, one adult female, one boy, and one girl) and four with synthetic voices matching the same age and gender distribution. A sample of 132 children aged between 4 and 10 years (excluding 6-year-olds) was divided into four age groups to assess whether preferences change with development. Results suggest that younger children showed no clear preference between natural and synthetic voices in terms of interaction. In contrast, older children displayed a stronger preference for natural voices, likely due to their engaging qualities. While no significant differences in voice quality were noted among younger children, older children favored natural adult voices, which they perceived as clearer and more trustworthy.