Detection Algorithm for Recurrence or Relapse of Depression Thanks to a Smartwach

NCT ID: NCT06789822

Last Updated: 2025-01-23

Basic Information

• Recruitment Status: ACTIVE_NOT_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 800 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2024-12-19
• Study Completion Date: 2026-02-18

Brief Summary

Globally, 3.8% of the population, or approximately 280 million people, suffer from depression. In France, 12.5% of adults experienced a major depressive episode (MDE) in 2021, with women being twice as affected as men. MDEs often require pharmacological treatment, but only one-third of patients achieve full remission after eight weeks of treatment. Relapse and recurrence are common, especially after the first episode, with the risk increasing with each subsequent episode. Depression significantly impacts morbidity, mortality, and functioning, and is the leading predictor of suicide.

The Dalia mobile application, developed collaboratively with patients and psychiatrists, uses a smartwatch to monitor physiological parameters (e.g., heart activity, sleep quality, moods) to detect early signs of relapse or recurrence. This study aims to identify variations in clinical biomarkers during remission or recovery and validate Dalia's sensitivity in detecting relapse compared to psychiatric diagnosis. Early detection could improve depression management and reduce the burden of the disease.

Detailed Description

Globally, it is estimated that 3.8% of the population suffers from depression, amounting to approximately 280 million people.

In France, 12.5% of individuals aged 18-85 years had a history of a major depressive episode (MDE) within the past 12 months in 2021. In 2017, the prevalence of MDE over the previous 12 months was 8.2% among the actively employed French population. Women exhibited a prevalence rate twice as high as men (11.4% vs. 5.3%). Moderate and severe MDEs accounted for 95% of all MDEs, regardless of gender.

A first depressive episode may be isolated and resolve spontaneously within six to nine months without specific treatment. However, in most cases, pharmacological treatment is necessary. Generally, after eight weeks of well-conducted pharmacological treatment, one-third of patients achieve full remission of symptoms, another third partial remission, and the remaining third do not respond to treatment.

Following a first depressive episode, about half of the patients will experience a relapse (a reactivation of the ongoing depressive episode, not fully resolved during the remission period) or a recurrence (a depressive state occurring after the patient was deemed recovered), with most cases happening within the first six months.

The relapse rate increases further with each decompensation. With each additional episode, the risk of relapse or recurrence rises to up to 90% in individuals with three or more episodes.

Depressive disorder is the main predictor of suicide, with one million lives lost annually. Depressive episodes are associated with an increased risk of suicide, morbidity, and mortality. Chronic depression exacerbates the impact of other diseases such as cancer and cardiovascular diseases, with approximately 60% of individuals experiencing severe and lasting impairment in their functioning. More than half of depressive patients will develop multiple episodes, characterized by emotional suffering and despair. Over the past decades, the efficacy of antidepressant treatments has remained stable. About half of the patients remain depressed after initial treatment with psychological or pharmacological therapies, and treatments reduce only one-third of the disease burden.

Detecting depression as early as possible could improve the management of depressive patients, which is currently not feasible. Over the past decade, the development of digital applications and the market introduction of wearable devices have offered new opportunities to continuously evaluate physical and psychological parameters. Detection algorithms do not aim to identify new risk factors; rather, they combine multiple known predictors to estimate individual risk. During a major depressive episode, several physical and physiological parameters (heart activity, sleep quality, daily moods) are disrupted. A more precise understanding of these parameters and their variations would allow better characterization of depressive patients and early detection of relapse/recurrence through monitoring.

Dalia is a mobile application designed by caregivers in close collaboration with patients and psychiatrists to continuously collect and monitor physiological data via a smartwatch, with the aim of early detection of signs of relapse or recurrence. Early detection of relapse and recurrence risk in MDEs could enable timely management. Although factors for relapse or recurrence have been widely documented, the threshold for variations in physiological (clinical) parameters in a tool detecting relapse or recurrence has not been established.

In this study, a set of clinical variables considered potential factors for detecting relapse or recurrence in MDEs will be measured, and their variations will be assessed. These physiological biomarkers will be collected from patients in remission or recovery from a major depressive episode via a smartwatch. We hypothesize that relapse detected by the Dalia device can be correctly adjusted to relapse diagnosis confirmed by a psychiatrist, with high sensitivity and a low false-negative rate.

Conditions

Depressive Disorder; Depressive Disorder, Major Depressive Disorder; Depressive Disorder Not Otherwise Specified (NOS)

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: PREVENTION
• Blinding Strategy: NONE

Study Groups

Patients included

The Arm of patients invited to wear smartwatch during the study

Group Type: EXPERIMENTAL

Invitation to wear the smartwatch

Intervention Type: DEVICE

During a consultation with the psychiatrist, and after verifying the eligibility criteria, the patient will receive oral information about the study from the psychiatrist.

Interventions

Invitation to wear the smartwatch

During a consultation with the psychiatrist, and after verifying the eligibility criteria, the patient will receive oral information about the study from the psychiatrist.

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

• Age ≥ 18 years;
• Patient in remission or recovery from a major depressive episode (MDE), confirmed by a psychiatrist with a Montgomery and Åsberg Depression Rating Scale (MADRS) score ≤ 7, within a maximum of 6 months after the last MDE;
• Patient speaks French;
• Patient owns a smartphone, computer, or tablet with internet/cellular data access;
• Patient resides in France and is affiliated with a social security scheme.

Exclusion Criteria

• Patient taking non-cardioselective beta-blockers (carvedilol, labetalol, propranolol, pindolol) and beta-mimetics (salbutamol, terbutaline);
• Patient with a pacemaker or known cardiac rhythm disorder;
• Patient with substance use disorders in the past six months (alcohol or drug abuse);
• Patient with dementia, mental disorders, cognitive impairments, or psychiatric conditions that could compromise their participation in the study and/or adherence to the study protocol: suicide risk, dementia, schizophrenia or other psychotic disorders, bipolar disorder, anxiety disorders including panic disorder, generalized anxiety disorder, obsessive-compulsive disorder, and post-traumatic stress disorder, psychotic depression, or depression secondary to brain disorders;
• Patient under guardianship, curatorship, or any other administrative or judicial measure restricting rights and freedom;
• Patient deemed non-autonomous by the investigator;
• Patient unable to wear the smartwatch for the duration of the study.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Digital Medical Hub

OTHER

Sponsor Role: collaborator

Dalia Care

INDUSTRY

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Hôpital Saint-Antoine

Paris, , France

Countries

France

Related Links

https://www.daliacare.com

Related Info

Other Identifiers

2024-A01982-45

Identifier Type: -

Identifier Source: org_study_id