Digital health technologies for high-risk pregnancy management: three case studies using Digilego framework.

IF 2.5 Q2 HEALTH CARE SCIENCES & SERVICES
JAMIA Open Pub Date : 2024-03-07 eCollection Date: 2024-04-01 DOI:10.1093/jamiaopen/ooae022
Sahiti Myneni, Alexandra Zingg, Tavleen Singh, Angela Ross, Amy Franklin, Deevakar Rogith, Jerrie Refuerzo
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引用次数: 0

Abstract

Objective: High-risk pregnancy (HRP) conditions such as gestational diabetes mellitus (GDM), hypertension (HTN), and peripartum depression (PPD) affect maternal and neonatal health. Patient engagement is critical for effective HRP management (HRPM). While digital technologies and analytics hold promise, emerging research indicates limited and suboptimal support offered by the highly prevalent pregnancy digital solutions within the commercial marketplace. In this article, we describe our efforts to develop a portfolio of digital products leveraging advances in social computing, data science, and digital health.

Methods: We describe three studies that leverage core methods from Digilego digital health development framework to (1) conduct large-scale social media analysis (n = 55 301 posts) to understand population-level patterns in women's needs, (2) architect a digital repository to enable women curate HRP related information, and (3) develop a digital platform to support PPD prevention. We applied a combination of qualitative coding, machine learning, theory-mapping, and programmatic implementation of theory-linked digital features. Further, we conducted preliminary testing of the resulting products for acceptance with sample of pregnant women for GDM/HTN information management (n = 10) and PPD prevention (n = 30).

Results: Scalable social computing models using deep learning classifiers with reasonable accuracy have allowed us to capture and examine psychosociobehavioral drivers associated with HRPM. Our work resulted in two digital health solutions, MyPregnancyChart and MomMind are developed. Initial evaluation of both tools indicates positive acceptance from potential end users. Further evaluation with MomMind revealed statistically significant improvements (P < .05) in PPD recognition and knowledge on how to seek PPD information.

Discussion: Digilego framework provides an integrative methodological lens to gain micro-macro perspective on women's needs, theory integration, engagement optimization, as well as subsequent feature and content engineering, which can be organized into core and specialized digital pathways for women engagement in disease management.

Conclusion: Future works should focus on implementation and testing of digital solutions that facilitate women to capture, aggregate, preserve, and utilize, otherwise siloed, prenatal information artifacts for enhanced self-management of their high-risk conditions, ultimately leading to improved health outcomes.

用于高危妊娠管理的数字医疗技术:利用 Digilego 框架进行的三项案例研究。
目的:妊娠糖尿病(GDM)、高血压(HTN)和围产期抑郁症(PPD)等高危妊娠(HRP)病症会影响孕产妇和新生儿的健康。患者参与对于有效的 HRP 管理 (HRPM) 至关重要。虽然数字技术和分析技术前景广阔,但新出现的研究表明,商业市场上高度流行的孕期数字解决方案所提供的支持有限且不尽如人意。在本文中,我们将介绍利用社交计算、数据科学和数字健康领域的进步开发数字产品组合的工作:我们介绍了利用 Digilego 数字健康开发框架的核心方法进行的三项研究:(1)进行大规模社交媒体分析(n = 55 301 个帖子),以了解妇女需求的人口级模式;(2)构建数字资源库,使妇女能够整理与 HRP 相关的信息;(3)开发数字平台,支持 PPD 预防。我们综合运用了定性编码、机器学习、理论映射以及与理论相关的数字功能的程序实施。此外,我们还针对 GDM/HTN 信息管理(10 人)和 PPD 预防(30 人)的孕妇样本,对最终产品的接受程度进行了初步测试:使用深度学习分类器的可扩展社交计算模型具有合理的准确性,这使我们能够捕捉并检查与HRPM相关的社会心理行为驱动因素。我们的工作成果是开发了两个数字健康解决方案:MyPregnancyChart 和 MomMind。对这两个工具的初步评估表明,潜在的最终用户对它们的接受度很高。对 MomMind 的进一步评估表明,该工具在统计学上有显著改善(P 讨论):Digilego 框架提供了一个综合的方法论视角,可从微观和宏观角度了解妇女的需求、理论整合、参与优化以及后续的功能和内容工程,并可将其组织成核心和专门的数字途径,促进妇女参与疾病管理:今后的工作应侧重于实施和测试数字解决方案,以帮助妇女捕捉、汇总、保存和利用原本孤立的产前信息,加强对高风险疾病的自我管理,最终改善健康状况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JAMIA Open
JAMIA Open Medicine-Health Informatics
CiteScore
4.10
自引率
4.80%
发文量
102
审稿时长
16 weeks
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