Effective Communication Supported by an App for Pregnant Women: Quantitative Longitudinal Study.

IF 2.6 Q2 HEALTH CARE SCIENCES & SERVICES JMIR Human Factors Pub Date : 2024-04-26 DOI:10.2196/48218
Lukas Kötting, Vinayak Anand-Kumar, F. Keller, N. Henschel, Sonia Lippke
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Abstract

BACKGROUND In the medical field of obstetrics, communication plays a crucial role, and pregnant women, in particular, can benefit from interventions improving their self-reported communication behavior. Effective communication behavior can be understood as the correct transmission of information without misunderstanding, confusion, or losses. Although effective communication can be trained by patient education, there is limited research testing this systematically with an app-based digital intervention. Thus, little is known about the success of such a digital intervention in the form of a web-app, potential behavioral barriers for engagement, as well as the processes by which such a web-app might improve self-reported communication behavior. OBJECTIVE This study fills this research gap by applying a web-app aiming at improving pregnant women's communication behavior in clinical care. The goals of this study were to (1) uncover the potential risk factors for early dropout from the web-app and (2) investigate the social-cognitive factors that predict self-reported communication behavior after having used the web-app. METHODS In this study, 1187 pregnant women were recruited. They all started to use a theory-based web-app focusing on intention, planning, self-efficacy, and outcome expectancy to improve communication behavior. Mechanisms of behavior change as a result of exposure to the web-app were explored using stepwise regression and path analysis. Moreover, determinants of dropout were tested using logistic regression. RESULTS We found that dropout was associated with younger age (P=.014). Mechanisms of behavior change were consistent with the predictions of the health action process approach. The stepwise regression analysis revealed that action planning was the best predictor for successful behavioral change over the course of the app-based digital intervention (β=.331; P<.001). The path analyses proved that self-efficacy beliefs affected the intention to communicate effectively, which in turn, elicited action planning and thereby improved communication behavior (β=.017; comparative fit index=0.994; Tucker-Lewis index=0.971; root mean square error of approximation=0.055). CONCLUSIONS Our findings can guide the development and improvement of apps addressing communication behavior in the following ways in obstetric care. First, such tools would enable action planning to improve communication behavior, as action planning is the key predictor of behavior change. Second, younger women need more attention to keep them from dropping out. However, future research should build upon the gained insights by conducting similar internet interventions in related fields of clinical care. The focus should be on processes of behavior change and strategies to minimize dropout rates, as well as replicating the findings with patient safety measures. TRIAL REGISTRATION ClinicalTrials.gov identifier: NCT03855735; https://classic.clinicaltrials.gov/ct2/show/NCT03855735.
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孕妇应用程序支持的有效沟通:定量纵向研究。
背景 在产科医学领域,沟通起着至关重要的作用,尤其是孕妇,可以从改善其自我报告的沟通行为的干预措施中受益。有效的沟通行为可以理解为正确传递信息,而不产生误解、混淆或损失。虽然有效沟通可以通过患者教育进行培训,但通过基于应用程序的数字干预对其进行系统测试的研究却很有限。因此,人们对以下方面知之甚少:网络应用形式的数字干预是否成功、参与的潜在行为障碍以及这种网络应用可能改善自我报告的沟通行为的过程。本研究的目标是:(1) 发现早期退出网络应用的潜在风险因素;(2) 调查预测使用网络应用后自我报告沟通行为的社会认知因素。她们都开始使用以理论为基础的网络应用程序,该程序侧重于改善沟通行为的意向、计划、自我效能感和结果预期。通过逐步回归和路径分析,探讨了接触网络应用后行为改变的机制。此外,还使用逻辑回归法对辍学的决定因素进行了测试。行为改变的机制与健康行动过程方法的预测一致。逐步回归分析表明,在基于应用程序的数字干预过程中,行动规划是预测成功行为改变的最佳指标(β=.331;P<.001)。路径分析证明,自我效能信念影响了有效沟通的意愿,这反过来又激发了行动规划,从而改善了沟通行为(β=.017;比较拟合指数=0.994;塔克-刘易斯指数=0.971;近似均方根误差=0.055)。首先,此类工具将有助于制定改善沟通行为的行动规划,因为行动规划是预测行为改变的关键因素。其次,年轻女性需要更多关注,以防止她们辍学。然而,未来的研究应在已获得的见解基础上,在临床护理的相关领域开展类似的互联网干预。重点应放在行为改变的过程和将辍学率降至最低的策略上,并通过患者安全措施复制研究结果:NCT03855735;https://classic.clinicaltrials.gov/ct2/show/NCT03855735。
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
JMIR Human Factors
JMIR Human Factors Medicine-Health Informatics
CiteScore
3.40
自引率
3.70%
发文量
123
审稿时长
12 weeks
期刊最新文献
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