Preconception and Diabetes Information (PADI) App for Women with Pregestational Diabetes: a Feasibility and Acceptability Study.

IF 5.9 Q1 Computer Science
Journal of Healthcare Informatics Research Pub Date : 2021-08-26 eCollection Date: 2021-12-01 DOI:10.1007/s41666-021-00104-9
Chidiebere H Nwolise, Nicola Carey, Jill Shawe
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引用次数: 4

Abstract

Diabetes mellitus increases the risk of adverse maternal and fetal outcomes. Preconception care is vital to minimise complications; however, preconception care service provision is hindered by inadequate knowledge, resources and care fragmentation. Mobile health technology, particularly smartphone apps, could improve preconception care and pregnancy outcomes for women with diabetes. The aim of this study is to co-create a preconception and diabetes information app with healthcare professionals and women with diabetes and explore the feasibility, acceptability and preliminary effects of the app. A mixed-methods study design employing questionnaires and semi-structured interviews was used to assess preliminary outcome estimates (preconception care knowledge, attitudes and behaviours), and user acceptability. Data analysis included thematic analysis, descriptive statistics and non-parametric tests. Improvements were recorded in knowledge and attitudes to preconception care and patient activation measure following the 3-month app usage. Participants found the app acceptable (satisfaction rating was 72%), useful and informative. The app's usability and usefulness facilitated usage while manual data input and competing priorities were barriers which participants felt could be overcome via personalisation, automation and use of daily reminders. This is the first study to explore the acceptability and feasibility of a preconception and diabetes information app for women with diabetes. Triangulated data suggest that the app has potential to improve preconception care knowledge, attitudes and behaviours. However, in order for women with DM to realise the full potential of the app intervention, particularly improved maternal and fetal outcomes, further development and evaluation is required.

妊娠前糖尿病妇女的孕前和糖尿病信息(PADI)应用程序的可行性和可接受性研究
糖尿病会增加母体和胎儿不良结局的风险。孕前护理对于尽量减少并发症至关重要;然而,由于知识、资源和护理碎片化,先入为主的护理服务提供受到阻碍。移动健康技术,尤其是智能手机应用程序,可以改善糖尿病女性的孕前护理和妊娠结局。本研究的目的是与医疗保健专业人员和糖尿病女性共同创建一个先入为主和糖尿病信息应用程序,并探索该应用程序的可行性、可接受性和初步效果。采用问卷调查和半结构化访谈的混合方法研究设计,评估初步结果估计(先入为主的护理知识、态度和行为)和用户可接受性。数据分析包括专题分析、描述性统计和非参数检验。在使用应用程序3个月后,对先入为主的护理和患者激活措施的知识和态度有所改善。参与者发现该应用程序可接受(满意度为72%),有用且信息丰富。该应用程序的可用性和有用性促进了使用,而手动数据输入和相互竞争的优先级是参与者认为可以通过个性化、自动化和使用日常提醒来克服的障碍。这是第一项探索糖尿病女性使用先入为主和糖尿病信息应用程序的可接受性和可行性的研究。三角数据表明,该应用程序有潜力改善先入为主的护理知识、态度和行为。然而,为了让患有糖尿病的女性充分发挥应用程序干预的潜力,特别是改善孕产妇和胎儿的预后,还需要进一步的开发和评估。
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来源期刊
Journal of Healthcare Informatics Research
Journal of Healthcare Informatics Research Computer Science-Computer Science Applications
CiteScore
13.60
自引率
1.70%
发文量
12
期刊介绍: Journal of Healthcare Informatics Research serves as a publication venue for the innovative technical contributions highlighting analytics, systems, and human factors research in healthcare informatics.Journal of Healthcare Informatics Research is concerned with the application of computer science principles, information science principles, information technology, and communication technology to address problems in healthcare, and everyday wellness. Journal of Healthcare Informatics Research highlights the most cutting-edge technical contributions in computing-oriented healthcare informatics.  The journal covers three major tracks: (1) analytics—focuses on data analytics, knowledge discovery, predictive modeling; (2) systems—focuses on building healthcare informatics systems (e.g., architecture, framework, design, engineering, and application); (3) human factors—focuses on understanding users or context, interface design, health behavior, and user studies of healthcare informatics applications.   Topics include but are not limited to: ·         healthcare software architecture, framework, design, and engineering;·         electronic health records·         medical data mining·         predictive modeling·         medical information retrieval·         medical natural language processing·         healthcare information systems·         smart health and connected health·         social media analytics·         mobile healthcare·         medical signal processing·         human factors in healthcare·         usability studies in healthcare·         user-interface design for medical devices and healthcare software·         health service delivery·         health games·         security and privacy in healthcare·         medical recommender system·         healthcare workflow management·         disease profiling and personalized treatment·         visualization of medical data·         intelligent medical devices and sensors·         RFID solutions for healthcare·         healthcare decision analytics and support systems·         epidemiological surveillance systems and intervention modeling·         consumer and clinician health information needs, seeking, sharing, and use·         semantic Web, linked data, and ontology·         collaboration technologies for healthcare·         assistive and adaptive ubiquitous computing technologies·         statistics and quality of medical data·         healthcare delivery in developing countries·         health systems modeling and simulation·         computer-aided diagnosis
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