Modeling and Predicting Real World Behavior Using Mobile Sensor Data on Patients With Major Depressive Disorder

NCT ID: NCT02499094

Last Updated: 2015-07-15

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 1004 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2014-12-31

Brief Summary

The purpose of this study is to validate the effectiveness of using an integrated mobile sensing platform to deliver large-scale data-driven interventions to patients with depression.

Detailed Description

This study is a smartphone-based, randomized, single-blind, controlled parallel-

design study with two intervention arms and one control arm. The two intervention arms

will receive in-app messages and phone-based support, which will be triggered by

participant's self-reported surveys and passive behavioral data gathered through a

smartphone app. The study will include a nationwide sample of adult (18 years or older)

smartphone users, who are currently experiencing depressive symptoms. The primary

outcome will be decrease in depression symptom severity, as measured by the 9-item

Patient Health Questionnaire, over 6 months.

Conditions

Depressive Disorder

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: SUPPORTIVE_CARE
• Blinding Strategy: DOUBLE

Study Groups

Intervention A- Heuristic based

Behavioral-data driven support, both mobile phone application and phone-based, informed by their self-reported symptom assessment as well as simple heuristic-based behavioral measures

Group Type: EXPERIMENTAL

Behavioral-data driven support

Intervention Type: OTHER

Phone-based support (e.g. triaging of the situation, emotional support, and guidance on next steps for the patient) and in-app support (health education content based on cognitive behavioral therapies, mindfulness, and behavioral activation)

Intervention B- Machine Learning Based

Behavioral-data driven support, both mobile phone application and phone-based, informed by their self-reported symptom assessment as well as machine learning model-based behavioral measures

Group Type: EXPERIMENTAL

Behavioral-data driven support

Intervention Type: OTHER

Phone-based support (e.g. triaging of the situation, emotional support, and guidance on next steps for the patient) and in-app support (health education content based on cognitive behavioral therapies, mindfulness, and behavioral activation)

Control

No intervention

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

Behavioral-data driven support

Phone-based support (e.g. triaging of the situation, emotional support, and guidance on next steps for the patient) and in-app support (health education content based on cognitive behavioral therapies, mindfulness, and behavioral activation)

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Currently suffering from depression (as measured by a PHQ-9 score of 10 or more at the time of screening)
• Own an iPhone or Android smartphone with a mobile voice calling plan with a US carrier
• Fluency in English

Exclusion Criteria

• Participants with visual or hearing impairment
• Recent history of pregnancy (currently pregnant or those who have given birth within the past four months at the time of screening)
• Recent loss of a loved one (within the past two months at the time of screening)
• Unable or unwilling to accept End User License Agreement, or to provide information regarding their demographic characteristics and mental health history

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Ginger.io

INDUSTRY

Sponsor Role: lead

Responsible Party

Gourab De

Data Scientist

Responsibility Role: PRINCIPAL_INVESTIGATOR

Other Identifiers

GIO-001

Identifier Type: -

Identifier Source: org_study_id