Modeling and Predicting Real World Behavior Using Mobile Sensor Data on Patients With Major Depressive Disorder: Protocol for a Randomized Controlled Study
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 1,004
- 主要终点
- Change in depression symptom severity
研究概览
简要总结
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.
详细描述
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)
研究设计
- 研究类型
- 干预性
- 分配方式
- 随机
- 干预模型
- 平行分组
- 主要目的
- 支持治疗
- 盲法
- 双盲 (受试者、结局评估者)
入排标准
- 年龄范围
- 18 Years 至 65 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- 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
排除标准
- 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
研究组 & 干预措施
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
干预措施: Behavioral-data driven support (Other)
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
干预措施: Behavioral-data driven support (Other)
Control
No intervention
结局指标
主要结局
Change in depression symptom severity
时间窗: 6 months
Change in the 9-item Patient Health Questionnaire (PHQ-9) score from baseline
次要结局
- Change in patient activation(6 months)
研究者
Gourab De
Data Scientist
Ginger.io
标识符
- NCT 编号
- NCT02499094
- 其他研究编号
- GIO-001
日期
- 首次提交
- (11年前)
- 首次发布
- (11年前)
- 主要完成日期
- (11年前)
- 最近核实
- (11年前)
- 最近更新
- (11年前)
监管与共享
- FDA 监管药物
- 否
- FDA 监管器械
- 否
- 是否有结果
- 否