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

Sponsor
Centre Hospitalier Universitaire de Nice (Other)
Overall Status
Recruiting
CT.gov ID
NCT05943834
Collaborator
(none)
100
1
13
7.7

Study Details

Study Description

Brief Summary

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.

Condition or Disease Intervention/Treatment Phase
  • Other: Series of cognitive tasks during a semi-automated call

Study Design

Study Type:
Observational
Anticipated Enrollment :
100 participants
Observational Model:
Cohort
Time Perspective:
Prospective
Official Title:
Early Detection of Alzheimer's Disease and Affective Disorders by Automated Voice and Speech Analysis
Actual Study Start Date :
Jul 13, 2023
Anticipated Primary Completion Date :
Jul 13, 2024
Anticipated Study Completion Date :
Aug 13, 2024

Arms and Interventions

Arm Intervention/Treatment
Patients with a minor or major neurocognitive disorder

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

Other: Series of cognitive tasks during a semi-automated call
Tasks: Verbal learning recall (immediate) or Story Recall task (immediate) Narrative Storytelling /free speech Verbal fluency task Verbal learning recall (delayed) or Story Recall task (delayed)

Outcome Measures

Primary Outcome Measures

  1. Build and validate speech-based machine learning models for relevant Phenotype detection through access to phenotyped patients from reference memory center. [20 minutes]

    Speech biomarker algorithm(s)

Eligibility Criteria

Criteria

Ages Eligible for Study:
50 Years to 100 Years
Sexes Eligible for Study:
All
Accepts Healthy Volunteers:
No
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

Contacts and Locations

Locations

Site City State Country Postal Code
1 CHU de Nice Nice France 06100

Sponsors and Collaborators

  • Centre Hospitalier Universitaire de Nice

Investigators

  • Principal Investigator: Eric ETTORE, MD, Centre Hospitalier Universitaire de Nice

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.
Responsible Party:
Centre Hospitalier Universitaire de Nice
ClinicalTrials.gov Identifier:
NCT05943834
Other Study ID Numbers:
  • 23-PP-03
First Posted:
Jul 13, 2023
Last Update Posted:
Jul 14, 2023
Last Verified:
Jul 1, 2023
Individual Participant Data (IPD) Sharing Statement:
No
Plan to Share IPD:
No
Studies a U.S. FDA-regulated Drug Product:
No
Studies a U.S. FDA-regulated Device Product:
No
Keywords provided by Centre Hospitalier Universitaire de Nice
Additional relevant MeSH terms:

Study Results

No Results Posted as of Jul 14, 2023