高风险妊娠管理的数字健康技术:使用Digilego框架的三个案例研究
Sahiti Myneni1, Alexandra Zingg1, Tavleen Singh1
1Department of Clinical and Health Informatics at McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, United States.
JAMIA open
|March 8, 2024
概括
这项研究开发了数字健康工具MyPregnancyChart和MomMind,以改善高风险妊娠管理和产周抑郁症支持. 最初的结果显示了用户的积极接受和改善的PPD识别.
科学领域:
- 数字健康数字健康
- 孕产妇健康 孕产妇健康
- 社交计算社会计算
- 数据科学数据科学数据科学
背景情况:
- 高风险妊娠 (HRP) 疾病如妊娠糖尿病 (GDM),高血压 (HTN) 和产周抑郁症 (PPD) 显著影响母亲和新生儿的健康.
- 有效管理HRP需要强大的患者参与,但目前的商业数字解决方案提供有限的支持.
- 需要先进的数字工具来增强患者参与和HRP的自我管理.
研究的目的:
- 开发一个数字健康产品组合,利用社会计算,数据科学和数字健康进步来管理HRP.
- 解决包括GDM,HTN和PPD在内的条件的非最佳数字支持的差距.
- 创建工具,促进患者参与,提高高风险怀孕的健康结果.
主要方法:
- 在三项研究中利用了Digilego数字健康发展框架.
- 进行了大规模的社交媒体分析 (n=55,301个帖子),以确定人口层面的需求.
- 开发了一个数字存储库来策划HRP信息和一个用于PPD预防的平台,采用定性编码,机器学习和理论映射.
- 对孕妇进行了GDM/HTN信息管理 (n=10) 和PPD预防 (n=30) 的初步验收测试.
主要成果:
- 开发了两个数字健康解决方案:MyPregnancyChart和MomMind.
- 可扩展的社会计算模型准确地确定了HRP管理的心理社会行为驱动因素.
- 最初的评估表明,潜在的最终用户对这两种工具都有积极的接受.
- 在PPD识别和寻找PPD信息的知识方面,MomMind显示了统计学上显著的改善 (P < .05).
结论:
- Digilego框架提供了一种综合性方法,以了解妇女的需求,并优化数字参与疾病管理.
- 开发的数字解决方案显示出增强高风险条件的自我管理的前景.
- 未来的工作应该集中在实施和测试上,以改善产前信息利用和健康结果.
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