智能手机妊娠应用程序：功能、科学指导、商业化和用户感知的系统分析。

Smartphone pregnancy apps: systematic analysis of features, scientific guidance, commercialization, and user perception.

机构信息

Department Artificial Intelligence in Biomedical Engineering, Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.

Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander- Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.

出版信息

BMC Pregnancy Childbirth. 2024 Nov 25;24(1):782. doi: 10.1186/s12884-024-06959-1.

DOI:10.1186/s12884-024-06959-1

PMID:39587534

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11587608/

Abstract

BACKGROUND

Over 50% of pregnant women use pregnancy applications (apps). Some app s lack credibility, information accuracy, and evidence-based clinical advice, containing potentially harmful functionality. Previous studies have only conducted a limited analysis of pregnancy app functionalities, expert involvement/evidence-based content, used commercialization techniques, and user perception.

METHODS

We used the keyword "pregnancy" to scrape (automatically extract) apps and app information from Apple App Store and Google Play. Unique functionalities were derived from app descriptions and user reviews. App descriptions were screened for evidence-based content and expert involvement, and apps were subsequently analyzed in detail. Apps were opened and searched for used commercialization techniques, such as advertisements or affiliate marketing. Automated text analysis (natural language processing) was used on app reviews to assess users' perception of evidence-based content/expert involvement and commercialization techniques.

RESULTS

In total, 495 apps were scraped. 226 remained after applying exclusion criteria. Out of these, 36 represented 97%/88% of the total market share (Apple App Store/Google Play), and were thus considered for review. Overall, 49 distinct functionalities were identified, out of which 6 were previously unreported. Functionalities for fetal kick movement counting were found. All apps are commercial. Only 15 apps mention the involvement of medical experts. 10.3% of two-stars user reviews include commercial topics, and 0.6% of one-/two-/three-/five stars user reviews include references to scientific content accuracy.

CONCLUSION

Problematic features and inadequate advice continue to be present in pregnancy apps. App developers should adopt an evidence-based development approach and avoid implementing as many features as possible, potentially at the expense of their quality or over-complication ("feature creep"). Financial incentives, such as grant programs, could support adequate content quality. Caregivers play a key role in pregnant individuals' decision-making, should be aware of potential dangers, and could guide them to trustworthy apps.

摘要

背景

超过 50%的孕妇使用怀孕应用程序（apps）。有些应用程序缺乏可信度、信息准确性和基于证据的临床建议，包含潜在有害的功能。以前的研究仅对怀孕应用程序功能、专家参与/基于证据的内容、使用商业化技术和用户感知进行了有限的分析。

方法

我们使用关键字“pregnancy”从 Apple App Store 和 Google Play 中抓取（自动提取）应用程序和应用程序信息。独特的功能源自应用程序描述和用户评论。我们筛选了应用程序描述，以寻找基于证据的内容和专家参与情况，然后对应用程序进行了详细分析。我们打开应用程序并搜索了使用的商业化技术，例如广告或联盟营销。我们对应用评论进行了自动文本分析（自然语言处理），以评估用户对基于证据的内容/专家参与和商业化技术的看法。

结果

总共抓取了 495 个应用程序。应用排除标准后，剩余 226 个。其中 36 个代表了苹果应用商店/谷歌应用商店的 97%/88%的总市场份额，因此被考虑进行审查。总体而言，确定了 49 个不同的功能，其中 6 个是以前未报告的。我们发现了胎儿踢动计数的功能。所有应用程序都是商业化的。只有 15 个应用程序提到了医学专家的参与。两星用户评论中 10.3%包含商业主题，一星/两星/三星/五星用户评论中 0.6%包含对科学内容准确性的引用。

结论

怀孕应用程序中仍然存在有问题的功能和不充分的建议。应用程序开发人员应采用基于证据的开发方法，避免尽可能多地实现功能，这可能会影响其质量或过度复杂化（“功能蔓延”）。资助计划等财政激励措施可以支持足够的内容质量。医疗保健提供者在孕妇的决策中起着关键作用，应该意识到潜在的危险，并指导他们使用值得信赖的应用程序。