Early Detection of Alzheimer's Disease and Affective Disorders by Automated Voice and Speech Analysis (PLATA)

Centre Hospitalier Universitaire de Nice

Status: Enrolling

Conditions: Neurocognitive Disorders

Treatments: Other: Series of cognitive tasks during a semi-automated call

Study type: Observational

Funder types: Other

Identifiers

NCT05943834

23-PP-03

Details and patient eligibility

About

PLATA aims to develop an algorithm to identify vocal biomarkers of Alzheimer's dementia.

Using data collected as part of routine care, speech patterns will be compared to known biomarkers of Alzheimer's disease, such as amyloid 1-42 and p-Tau in CSF (cerebrospinal fluid).

If biomarkers of speech can be identified in Alzheimer's disease, it is possible that patients and research participants will no longer need to undergo need to undergo the intensive and invasive baseline biomarker methods currently used, such as lumbar punctures and PET scans.

Enrollment: 100 estimated patients

Sex: All

Ages: 50 to 100 years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Age ≥ 50 years
• Diagnosis relevant biomarker and neuropsychological data already available
• Cognitively healthy to very mild dementia (CDR score max. 0.5)
• Sufficient knowledge of the study language to understand study information, non opposition form,and questionnaires
• Expression of non opposition

Exclusion criteria

• Hearing problems
• Patient protected by law, under guardianship or curator ship, or not able to participate in a clinical study according to the article L.1121-16 of the French Public Health Code

Trial design

100 participants in 1 patient group

Patients with a minor or major neurocognitive disorder

Description:

Every patient will receive one semi-automated phone call, during the call a series of cognitive tasks will be performed. Each task will be recorded in a secondary audio stream which records the participant responses to allow for deep speech analysis of performance on these tasks

Treatment:

Other: Series of cognitive tasks during a semi-automated call