使用自我报告的怀孕应用程序的怀孕症状的患病率和过程症状跟踪器数据
Michael Nissen1, Nuria Barrios Campo2, Madeleine Flaucher2
1Machine Learning and Data Analytics (MaD) Lab, Department Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Carl-Thiersch-Straße 2b, 91052, Erlangen, Bavaria, Germany. michael.nissen@fau.de.
NPJ digital medicine
|October 11, 2023
概括
这项研究从智能手机应用程序中分析了超过150万个怀孕症状,揭示了整个怀孕期间不同的症状模式. 研究结果澄清了以前未知的症状轨迹及其关系.
科学领域:
- 产科和妇科 产科和妇科
- 数字健康数字健康
- 数据科学数据科学数据科学
背景情况:
- 怀孕症状很常见,但它们与结果和典型轨迹的关系尚不清楚.
- 现有的怀孕应用程序收集症状数据,但这些信息在很大程度上未被用于科学研究.
- 了解症状模式可以改善产前护理和患者指导.
研究的目的:
- 分析怀孕症状的发生,过程和相关性,使用来自智能手机应用程序的真实世界数据.
- 开发处理商业应用程序中的杂,自我报告数据的方法.
- 提供每周的症状数据,并澄清此前有争议的症状轨迹.
主要方法:
- 分析了来自183732名智能手机怀孕应用程序用户的1,549,186个跟踪症状.
- 开发方法来处理和分析噪音,现实世界的应用程序数据.
- 每个症状的每周症状报告数据的呈现.
主要成果:
- 在怀孕的第一季度发现了疲劳的高峰.
- 在怀孕15周左右的头痛报告中发现了峰值.
- 在整个怀孕期间,观察到睡眠困难报告的稳步增加.
结论:
- 工业产生的应用数据的二次使用可以产生重要的科学见解.
- 这项研究澄清了以前未知的或有争议的轨迹和常见妊娠症状的关系.
- 学术界和工业界之间的合作对于推动数字健康领域的科学知识有价值.
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