The events of the past 2 years related to the pandemic have shown that it is increasingly important to find new tools to help mental health experts in diagnosing mood disorders. Leaving aside the longcovid cognitive (e.g., difficulty in concentration) and bodily (e.g., loss of smell) effects, the short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms. The aim of this study is to use a new tool, the online handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients. To this aim, patients with clinical depression (n = 14), individuals with high sub-clinical (diagnosed by a test rather than a doctor) depressive traits (n = 15) and healthy individuals (n = 20) were recruited and asked to perform four online drawing /handwriting tasks using a digitizing tablet and a special writing device. From the raw collected online data, seventeen drawing/writing features (categorized into five categories) were extracted, and compared among the three groups of the involved participants, through ANOVA repeated measures analyses. Results shows that Time features are more effective in discriminating between healthy and participants with sub-clinical depressive characteristics. On the other hand, Ductus and Pressure features are more effective in discriminating between clinical depressed and healthy participants.

翻译：过去两年与大流行相关的事件表明，寻找新工具来帮助心理健康专家诊断情绪障碍日益重要。抛开长新冠的认知（如注意力难以集中）和身体（如嗅觉丧失）效应不谈，新冠对心理健康的短期影响表现为焦虑和抑郁症状显著增加。本研究旨在利用一种新工具——在线手写与绘画分析——来区分健康个体与抑郁患者。为此，我们招募了临床抑郁症患者（n=14）、具有较高亚临床（经测试而非医生诊断）抑郁特征的个体（n=15）以及健康个体（n=20），并要求他们使用数字化绘图板和专用书写设备完成四项在线绘画/手写任务。从原始收集的在线数据中，提取了十七个绘画/书写特征（分为五类），并通过重复测量方差分析（ANOVA）对三组参与者的特征进行比较。结果显示，时间特征在区分健康个体与具有亚临床抑郁特征的参与者方面更为有效；另一方面，笔迹与压力特征在区分临床抑郁患者与健康个体方面更为有效。