Prediction on the Recurrence of Manic and Depressive Episodes in Bipolar Disorder
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 100
- 试验地点
- 1
- 主要终点
- Development and verification of mood episode prediction algorithm
研究概览
简要总结
Mood disorders (including bipolar disorder and major depressive disorder) are chronic mental disorders with high recurrent rate. The more the number of recurrence is, the worse long-term prognosis is. This study aims to establish a prediction model of recurrence of manic and depressive episodes in mood disorders, with a hope to detect recurrence relapse as early as possible for timely clinical intervention. We will adopt wearable smart watch to collect heart rate, sleep pattern, activity level, as well as emotional status for one year long in 100 patients with bipolar disorder, and annotated their mood status (i.e., manic episode, depressive episode, and euthymic state). We expect to establish prediction models to predict the recurrence of mood episodes.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Prospective
入排标准
- 年龄范围
- 20 Years 至 60 Years（Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •DSM-5 Bipolar disorder or depressive disorder
- •20~60 years old
- •Willing to carry smartwatch and smartphone most of the time
排除标准
- •Comorbid with substance use disorder
- •Unable to use smartwatch and smartphone
结局指标
主要结局
Development and verification of mood episode prediction algorithm
时间窗: 1 year
Collected data will apply to learning algorithm, random forest, which constructs a multitude of decision trees at training time and outputting a class that is the mode of the classes of the individual trees. Performance of the trained prediction model was evaluated by assessing the model's accuracy, sensitivity, specificity, and the area under the curve. In a machine learning evaluation process, a part of data is used for model training, and the other portion is used for model testing.
次要结局
未报告次要终点