  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.
