{"title":"基于以用户为中心的方法,开发妊娠糖尿病预防系统(更好的怀孕)及其可接受性:临床可行性研究。","authors":"Beibei Duan, Zheyi Zhou, Mengdi Liu, Zhe Liu, Qianghuizi Zhang, Leyang Liu, Cunhao Ma, Baohua Gou, Weiwei Liu","doi":"10.1177/20552076241266056","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Gestational diabetes mellitus (GDM) can increase the risk of adverse outcomes for both mothers and infants. Preventive interventions can effectively assist pregnant women suffering from GDM. At present, pregnant women are unaware of the importance of preventing GDM, and they possess a low level of self-management ability. Recently, mHealth technology has been used worldwide. Therefore, developing a mobile health app for GDM prevention could potentially help pregnant women reduce the risk of GDM.</p><p><strong>Objective: </strong>To design and develop a mobile application, evaluate its acceptance, and understand the users'using experience and suggestions, thus providing a valid tool to assist pregnant women at risk of GDM in enhancing their self-management ability and preventing GDM.</p><p><strong>Methods: </strong>An evidence-based GDM prevent app (<i>Better pregnancy</i>) was developed using user-centered design methods, following the health belief model, and incorporating GDM risk prediction. A convenient sampling method was employed from June to August 2022 to select 102 pregnant women at risk of GDM for the pilot study. After a week, the app's acceptability was evaluated using an application acceptance questionnaire, and we updated the app based on the feedback from the women. We used SPSS 26.0 for data analysis.</p><p><strong>Results: </strong>The application offers various functionalities, including GDM risk prediction, health management plan, behavior management, health information, personalized guidance and consultation, peer support, family support, and other functions. In total, 102 pregnant women consented to participate in the study, achieving a retention rate of 98%; however, 2% (<i>n</i> = 2) withdrew. The <i>Better pregnancy</i> app's average acceptability score is 4.07 out of 5. Additionally, participants offered several suggestions aimed at enhancing the application.</p><p><strong>Conclusions: </strong>The <i>Better pregnancy</i> app developed in this study can serve as an auxiliary management tool for the prevention of GDM, providing a foundation for subsequent randomized controlled trials.</p>","PeriodicalId":51333,"journal":{"name":"DIGITAL HEALTH","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11311188/pdf/","citationCount":"0","resultStr":"{\"title\":\"Development and acceptability of a gestational diabetes mellitus prevention system (<i>Better pregnancy</i>) based on a user-centered approach: A clinical feasibility study.\",\"authors\":\"Beibei Duan, Zheyi Zhou, Mengdi Liu, Zhe Liu, Qianghuizi Zhang, Leyang Liu, Cunhao Ma, Baohua Gou, Weiwei Liu\",\"doi\":\"10.1177/20552076241266056\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Gestational diabetes mellitus (GDM) can increase the risk of adverse outcomes for both mothers and infants. Preventive interventions can effectively assist pregnant women suffering from GDM. At present, pregnant women are unaware of the importance of preventing GDM, and they possess a low level of self-management ability. Recently, mHealth technology has been used worldwide. Therefore, developing a mobile health app for GDM prevention could potentially help pregnant women reduce the risk of GDM.</p><p><strong>Objective: </strong>To design and develop a mobile application, evaluate its acceptance, and understand the users'using experience and suggestions, thus providing a valid tool to assist pregnant women at risk of GDM in enhancing their self-management ability and preventing GDM.</p><p><strong>Methods: </strong>An evidence-based GDM prevent app (<i>Better pregnancy</i>) was developed using user-centered design methods, following the health belief model, and incorporating GDM risk prediction. A convenient sampling method was employed from June to August 2022 to select 102 pregnant women at risk of GDM for the pilot study. After a week, the app's acceptability was evaluated using an application acceptance questionnaire, and we updated the app based on the feedback from the women. We used SPSS 26.0 for data analysis.</p><p><strong>Results: </strong>The application offers various functionalities, including GDM risk prediction, health management plan, behavior management, health information, personalized guidance and consultation, peer support, family support, and other functions. In total, 102 pregnant women consented to participate in the study, achieving a retention rate of 98%; however, 2% (<i>n</i> = 2) withdrew. The <i>Better pregnancy</i> app's average acceptability score is 4.07 out of 5. Additionally, participants offered several suggestions aimed at enhancing the application.</p><p><strong>Conclusions: </strong>The <i>Better pregnancy</i> app developed in this study can serve as an auxiliary management tool for the prevention of GDM, providing a foundation for subsequent randomized controlled trials.</p>\",\"PeriodicalId\":51333,\"journal\":{\"name\":\"DIGITAL HEALTH\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-08-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11311188/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"DIGITAL HEALTH\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1177/20552076241266056\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"HEALTH CARE SCIENCES & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"DIGITAL HEALTH","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/20552076241266056","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
Development and acceptability of a gestational diabetes mellitus prevention system (Better pregnancy) based on a user-centered approach: A clinical feasibility study.
Background: Gestational diabetes mellitus (GDM) can increase the risk of adverse outcomes for both mothers and infants. Preventive interventions can effectively assist pregnant women suffering from GDM. At present, pregnant women are unaware of the importance of preventing GDM, and they possess a low level of self-management ability. Recently, mHealth technology has been used worldwide. Therefore, developing a mobile health app for GDM prevention could potentially help pregnant women reduce the risk of GDM.
Objective: To design and develop a mobile application, evaluate its acceptance, and understand the users'using experience and suggestions, thus providing a valid tool to assist pregnant women at risk of GDM in enhancing their self-management ability and preventing GDM.
Methods: An evidence-based GDM prevent app (Better pregnancy) was developed using user-centered design methods, following the health belief model, and incorporating GDM risk prediction. A convenient sampling method was employed from June to August 2022 to select 102 pregnant women at risk of GDM for the pilot study. After a week, the app's acceptability was evaluated using an application acceptance questionnaire, and we updated the app based on the feedback from the women. We used SPSS 26.0 for data analysis.
Results: The application offers various functionalities, including GDM risk prediction, health management plan, behavior management, health information, personalized guidance and consultation, peer support, family support, and other functions. In total, 102 pregnant women consented to participate in the study, achieving a retention rate of 98%; however, 2% (n = 2) withdrew. The Better pregnancy app's average acceptability score is 4.07 out of 5. Additionally, participants offered several suggestions aimed at enhancing the application.
Conclusions: The Better pregnancy app developed in this study can serve as an auxiliary management tool for the prevention of GDM, providing a foundation for subsequent randomized controlled trials.