快乐Mums移动应用程序研究协议:使用智能手机应用程序收集预测产前抑郁症的数据
Kristi Priestley1, Riddhi Laijawala1, Katie Hazelgrove1
1Department of Psychological Medicine, King's College London Institute of Psychiatry Psychology and Neuroscience, London, England, UK.
BMJ open
|February 4, 2026
概括
本研究测试了HappyMums移动应用程序,用于监测孕妇的产前抑郁症. 它将应用程序使用数据与机器学习相结合,以预测心理健康轨迹,旨在改善围产期心理健康护理.
科学领域:
- 数字健康数字健康
- 围产期心理健康 围产期心理健康
- 医疗保健中的机器学习
背景情况:
- 移动健康 (mHealth) 显示出对监测心理健康的承诺,但在围产期人群中的有效性是混合的.
- 现有的研究主要集中在产后抑郁症,而产前抑郁症研究中存在差距.
- 对于产前抑郁症的机器学习临床决策支持系统尚未得到充分探索.
研究的目的:
- 测试HappyMums移动应用程序用于监测和预测孕妇产前抑郁症.
- 评估基于机器学习的新型模型的实用性,该模型整合了积极和被动的数字数据,用于风险预测.
- 评估用户参与度和应用程序作为个性化抑郁症识别工具的潜力.
主要方法:
- 在六个欧洲国家招募1000名患有或面临产前抑郁症的孕妇.
- 使用HappyMums应用程序从入学到产后2个月的被动 (智能手机传感器) 和主动 (问卷,活动) 数据收集.
- 将数字数据与传统的心理健康评估相结合,开发心理健康轨迹的预测模型.
主要成果:
- 主要结果:每周使用HappyMums应用程序的用户比例.
- 二次探索结果:机器学习模型的应用可用性评估和预测准确性.
- 新闻:集成主动和被动数据来评估心理健康轨迹.
结论:
- 这项HappyMums研究旨在利用mHealth和机器学习解决产前抑郁症监测方面的差距.
- 调查结果将提供关于围产期心理健康综合数字健康工具可行性和预测能力的见解.
- 这项研究有助于开发针对产前抑郁症的个性化风险预测和识别工具.
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