Acquisition and Analysis of Relationships Between Longitudinal Emotional Signals Produced by an Artificial Intelligence Algorithm and Self-questionnaires Used in the Psychiatric Follow-up of Patients With Mood and/or Anxiety Disorders: a Real-Environment Study. (EMOACQ-1)

• ClinicalTrials.gov Identifier: NCT05988840
• Recruitment Status: Not yet recruiting
• First Posted: August 14, 2023
• Last Update Posted: August 15, 2023
• Sponsor: Emobot
• Information provided by (Responsible Party): Emobot

Study Description

Brief Summary:

The worldwide prevalence of anxiety and depression increased massively during the pandemic, with a 25% rise in the number of patients suffering from psychological distress. Psychiatrists, and even more so general practitioners, need measurement tools that enable them to remotely monitor their patients' psychological state of health, and to be automatically alerted in the event of a break in behavior.

In this study, the investigators propose to collect clinical data along with longitudinal measurement of patients' emotions. Emobot proposes to analyze the evolution of mood disorders over time by passively studying people's emotional behavior. The aim of EMOACQ-1 is to acquire knowledge and produce a quantitative link between emotional expression and mood disorders, ultimately facilitating the understanding and management of these disorders.

Through this study, could be developed a technological solution to support healthcare professionals and patients in psychiatry, a field known as the "poor relation of medicine" and lacking in resources. Such a solution would enable better understanding, disorders remote & continuous monitoring and, ultimately, better treatment of these disorders.

The investigators will process the data by carrying out a number of analyses, including descriptive, comparative and correlation studies of the data from the self-questionnaire results and the emotional signals captured by the devices.

Finally, the aim will be to predict questionnaire scores from the emotional signals produced.

Condition or disease	Intervention/treatment
Anxiety Disorders; Major Depressive Disorder	Other: Acquisition and analysis of relationships between Longitudinal Emotional Signals produced by a software and Self-questionnaires.

Study Design

• Study Type: Observational
• Estimated Enrollment: 50 participants
• Observational Model: Cohort
• Time Perspective: Prospective
• Official Title: Acquisition and Analysis of Relationships Between Longitudinal Emotional Signals Produced by an Artificial Intelligence Algorithm and Self-questionnaires Used in the Psychiatric Follow-up of Patients With Mood and/or Anxiety Disorders: a Real-Environment Study.
• Estimated Study Start Date: October 17, 2023
• Estimated Primary Completion Date: August 17, 2024
• Estimated Study Completion Date: October 17, 2024

Groups and Cohorts

Group/Cohort	Intervention/treatment
The hardware group (on-board camera); A physical device equipped with a camera and embedding the acquisition/monitoring software. Positioned in the living space, it will be possible to capture the facial expressions of the person in ecology, for example when watching a TV program or reading.	Other: Acquisition and analysis of relationships between Longitudinal Emotional Signals produced by a software and Self-questionnaires.; Using the tool developed by Emobot, EMOACQ-1 is a study that passively and non-interventionaly collects data by capturing patients' facial expressions throughout the day, and then measures the correlation between emotional signals and the results of measurement questionnaires used in psychiatry.
The software-only group (running on a PC or tablet and using the available webcam); Software running on a computer, connected to the computer's camera (webcam). If the person is teleworking on a PC, it is expected that images will be captured during videoconferencing-type interactions.	Other: Acquisition and analysis of relationships between Longitudinal Emotional Signals produced by a software and Self-questionnaires.; Using the tool developed by Emobot, EMOACQ-1 is a study that passively and non-interventionaly collects data by capturing patients' facial expressions throughout the day, and then measures the correlation between emotional signals and the results of measurement questionnaires used in psychiatry.

Outcome Measures

Primary Outcome Measures :

1. Repeated measurements Correlations between emotional signals and studied disorders standardized tests. [ Time Frame: 10 months ]
   Repeated measurements Correlations between emotional signals and studied disorders standardized tests.

Eligibility Criteria

• Ages Eligible for Study: 18 Years and older (Adult, Older Adult)
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: Yes
• Sampling Method: Non-Probability Sample

Study Population

The first cohort of 20 participants will be provided with the 20 measuring embedded device, we will be able to collect specific data at targeted times and for specific activities.

The second cohort formed with the remaining participants will receive the software on their personal computer, enabling us to capture more general data and analyze behavior patterns in less controlled contexts.

Criteria

Inclusion Criteria:

• Persons over the age of 18 who volunteer to take part in research
• Must have access to a computer with an Internet connection,
• Written comprehension of French.

Exclusion Criteria:

• N/A

Contacts and Locations

Contacts

Contact: Tanel Petelot; +33 51 44 26 67 ext +33; tanel.petelot@emobot.fr

Sponsors and Collaborators

Emobot

More Information

Additional Information:

D. Agarwal et al. From Multimodal to Unimodal Attention in Transformers using Knowledge Distillation. Nov 2021, Virtual, United States. 

Mamadou Dia et al. A Novel Stochastic Transformer-based Approach for Post-Traumatic Stress Disorder Detection using Audio Recording of Clinical Interviews, CBMS, June 2023. 

E. Garcia-Ceja et al. Depression: A Motor Activity Database of Depression Episodes in Unipolar and Bipolar Patients 

J. Gratch et al. The Distress Analysis Interview Corpus of Human and Computer Interviews. 

X. Kong et al. Automatic Identification of Depression Using Facial Images with Deep Convolutional Neural Network. 

Mundt JC, Vogel AP, Feltner DE, Lenderking WR. Vocal acoustic biomarkers of depression severity and treatment response. 

• Responsible Party: Emobot
• ClinicalTrials.gov Identifier: NCT05988840
• Other Study ID Numbers: 2023-A01589-36
• First Posted: August 14, 2023
• Last Update Posted: August 15, 2023
• Last Verified: August 2023

Individual Participant Data (IPD) Sharing Statement:

• Plan to Share IPD: No

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No

Additional relevant MeSH terms:

Anxiety Disorders; Depressive Disorder; Depressive Disorder, Major; Mental Disorders; Mood Disorders