Modeling and Predicting Real World Behavior Using Mobile Sensor Data on Patients With Major Depressive Disorder
Primary Purpose
Depressive Disorder
Status
Completed
Phase
Not Applicable
Locations
Study Type
Interventional
Intervention
Behavioral-data driven support
Sponsored by
About this trial
This is an interventional supportive care trial for Depressive Disorder
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
Sites / Locations
Arms of the Study
Arm 1
Arm 2
Arm 3
Arm Type
Experimental
Experimental
No Intervention
Arm Label
Intervention A- Heuristic based
Intervention B- Machine Learning Based
Control
Arm Description
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
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
No intervention
Outcomes
Primary Outcome Measures
Change in depression symptom severity
Change in the 9-item Patient Health Questionnaire (PHQ-9) score from baseline
Secondary Outcome Measures
Change in patient activation
Change in the 13-item Patient Activation Measure score from baseline
Full Information
1. Study Identification
Unique Protocol Identification Number
NCT02499094
Brief Title
Modeling and Predicting Real World Behavior Using Mobile Sensor Data on Patients With Major Depressive Disorder
Official Title
Modeling and Predicting Real World Behavior Using Mobile Sensor Data on Patients With Major Depressive Disorder: Protocol for a Randomized Controlled Study
Study Type
Interventional
2. Study Status
Record Verification Date
July 2015
Overall Recruitment Status
Completed
Study Start Date
December 2014 (undefined)
Primary Completion Date
July 2015 (Actual)
Study Completion Date
undefined (undefined)
3. Sponsor/Collaborators
Responsible Party, by Official Title
Principal Investigator
Name of the Sponsor
Ginger.io
4. Oversight
Data Monitoring Committee
Yes
5. Study Description
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.
6. Conditions and Keywords
Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Depressive Disorder
7. Study Design
Primary Purpose
Supportive Care
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Masking
ParticipantOutcomes Assessor
Allocation
Randomized
Enrollment
1004 (Actual)
8. Arms, Groups, and Interventions
Arm Title
Intervention A- Heuristic based
Arm Type
Experimental
Arm Description
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
Arm Title
Intervention B- Machine Learning Based
Arm Type
Experimental
Arm Description
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
Arm Title
Control
Arm Type
No Intervention
Arm Description
No intervention
Intervention Type
Other
Intervention Name(s)
Behavioral-data driven support
Intervention Description
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)
Primary Outcome Measure Information:
Title
Change in depression symptom severity
Description
Change in the 9-item Patient Health Questionnaire (PHQ-9) score from baseline
Time Frame
6 months
Secondary Outcome Measure Information:
Title
Change in patient activation
Description
Change in the 13-item Patient Activation Measure score from baseline
Time Frame
6 months
10. Eligibility
Sex
All
Minimum Age & Unit of Time
18 Years
Maximum Age & Unit of Time
65 Years
Accepts Healthy Volunteers
No
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
12. IPD Sharing Statement
Learn more about this trial
Modeling and Predicting Real World Behavior Using Mobile Sensor Data on Patients With Major Depressive Disorder
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