NCT04370002

Sensor-based Characterization of Depression

Leveraging Artificial Intelligence for the Assessment of Severity of Depressive Symptoms

• Status: Unknown
• Phase: —
• Study type: Observational
• Enrollment: 100 (estimated)
• Sponsor: Massachusetts General Hospital · Academic / Other
• Sex: All
• Age: 18 Years – 75 Years
• Healthy volunteers: Not accepted

Summary

This is a longitudinal study where individual with Major Depressive Disorder (MDD) will be monitored for 12 weeks. The study aims to develop an objective, sensor-based, algorithm able to detect the presence of depression as well as predict treatment response. Measurement-based treatment is considered optimal and the development of a valid passive, objective, behavioral and biological assessment of depressive symptoms that does not rely on clinician interviews will improve monitoring and ultimately improve treatment significantly.

Detailed description

In this longitudinal study 100 individuals with Major Depressive Disorder (MDD) will be monitored for 12 weeks. Data will include self-report surveys, in-person assessments, physiological features derived by wearable devices and socialization and activity data derived by mobile applications. The study will utilize advanced statistical methods to integrate different sources of passive sensor-based behavioral and physiological data to develop models able to detect depression and predict treatment response.

Conditions

• Unipolar Depression

Timeline

• Start date: 2020-01-28
• Primary completion: 2024-09-01
• Completion: 2024-11-30
• First posted: 2020-04-30
• Last updated: 2023-11-03

Locations

2 sites across 1 country: United States