Handwriting Analysis in Movement Disorders

NCT05355480 | Unknown

• 1 other identifier: NEUR_04
• Study Type: observational
• Target: 40
• Locations: 1 country
• Sites: 1

Brief Summary

Handwriting is a complex cognitive prowess that deteriorates in patients affected by neurodegenerative diseases, including movement disorders. More in detail, patients with Parkinson's disease (PD) may manifest prominent handwriting abnormalities which have been collectively identified as parkinsonian micrographia. MIcrographia may manifest at the onset of the disease and then worsens progressively with time. Previous techniques released to investigate micrographia in PD relied on perceptual analysis of simple tasks or were based on expensive technological tools, including tablets. However, handwriting can be promptly collected in an ecological scenario, through safe, cheap, and largely available tools. Also, the objective handwriting analysis through artificial intelligence would represent an innovative strategy even superior to previous techniques, since it allows for the analysis of large amounts of data. In this experimental project, the investigators apply a specific machine learning algorithm to analyze handwriting samples recorded in healthy controls and PD patients. The study aims to verify whether the technique proposed by the investigators would be able to detect parkinsonian micrographia objectively, monitor the evolution of handwriting abnormalities and assess the symptomatic improvement of handwriting following L-Dopa administration in PD patients.

Conditions

Movement Disorders; Parkinson Disease; Writing Disorder

Outcome Measures

Primary Outcomes (1)

• Stroke size of handwriting characters

  height of single letters

  through study completion, an average of 1 year

Study Arms (2)

Healthy Subjects

Collection of handwriting samples

Patients with Parkinson's disease

Collection of handwriting samples

Eligibility Criteria

• Age: 18 Years - 90 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Healthy subjects and patients with idiopathic PD

You may qualify if:

• Healthy conditions
• clinical diagnosis of Parkinson's disease

You may not qualify if:

• cognitive decline

Sponsors & Collaborators

• Neuromed IRCCS: lead

Study Sites (1)

IRCCS Neuromed

Pozzilli, 86077, Italy

RECRUITING

MeSH Terms

Conditions

Movement Disorders; Parkinson Disease

Condition Hierarchy (Ancestors)

Central Nervous System Diseases; Nervous System Diseases; Parkinsonian Disorders; Basal Ganglia Diseases; Brain Diseases; Synucleinopathies; Neurodegenerative Diseases

Central Study Contacts

Antonio Suppa, MD

CONTACT

+00390649914074; antonio.suppa@uniroma1.it

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Prof.

Study Record Dates

• First Submitted: April 20, 2022
• First Posted: May 2, 2022
• Study Start: December 1, 2022
• Primary Completion: December 31, 2022
• Study Completion: December 31, 2023
• Last Updated: March 23, 2023

Record last verified: 2023-03

Locations

IT (1)