Varma Deepthi S, Mualem Maya, Goodin Amie, Gurka Kelly K, Wen Tony Soo-Tung, Gurka Matthew J, Roussos-Ross Kay
Department of Epidemiology, College of Public Health and Health Professions, College of Medicine, University of Florida, Gainesville, FL, United States.
Pharmaceutical Outcomes and Policy, Center for Drug Evaluation and Safety Consortium for Medical Marijuana Clinical Outcomes Research, College of Pharmacy, University of Florida, Gainesville, FL, United States.
JMIR Form Res. 2023 Jun 7;7:e44500. doi: 10.2196/44500.
Up to 15% of pregnant and postpartum women commonly experience undiagnosed and untreated mental health conditions, such as depression and anxiety, which may result in serious health complications. Mobile health (mHealth) apps related to mental health have been previously used for early diagnosis and intervention but not among pregnant and postpartum women.
This study aims to assess the acceptability of using mHealth to monitor and assess perinatal and postpartum depression and anxiety.
Focus group discussions with pregnant and postpartum women (n=20) and individual interviews with health care providers (n=8) were conducted to inform the acceptability of mHealth and determine its utility for assessing perinatal and postpartum mood symptoms. Participants were recruited via purposive sampling from obstetric clinics and the surrounding community. A semistructured interview guide was developed by an epidemiologist with qualitative research training in consultation with an obstetrician. The first author conducted all focus group discussions and provider interviews either in person or via Zoom (Zoom Video Communications, Inc) depending on the COVID-19 protocol that was in place during the study period. All interviews were audio recorded with consent; transcribed; and uploaded for coding to ATLAS.ti 8 (ATLAS.ti Scientific Software Development Gmb H), a qualitative data analysis and retrieval software. Data were analyzed using the deductive content analysis method using a set of a priori codes developed based on the interview guide. Methodological rigor and quality were ensured by adopting a systematic approach during the implementation, data collection, data analysis, and reporting of the data.
Almost all women and providers had downloaded and used at least 1 health app. The respondents suggested offering short questions in layperson language that could be understood by women of all educational levels and offering no more than 2 to 3 assessments per day at preferred timings decided by the women themselves. They also suggested that the women themselves receive the alerts first, with other options being family members, spouses, or friends if the women themselves did not respond within 24 to 72 hours. Customization and snooze features were strongly endorsed by women and providers to improve acceptability and utility. Women mentioned competing demands on their time during the postpartum period, fatigue, privacy, and the security of mental health data as concerns. Health care professionals highlighted the long-term sustainability of app-based mood assessment and monitoring as an important challenge.
The findings from this study show that mHealth would be acceptable to pregnant and postpartum women for monitoring mood symptoms. This could inform the development of clinically meaningful and inexpensive tools for facilitating the continuous monitoring of, the early diagnosis of, and an early intervention for mood disorders in this vulnerable population.
高达15%的孕妇和产后女性通常患有未被诊断和治疗的心理健康问题，如抑郁症和焦虑症，这可能导致严重的健康并发症。此前，与心理健康相关的移动健康（mHealth）应用程序已被用于早期诊断和干预，但未应用于孕妇和产后女性。
本研究旨在评估使用移动健康技术监测和评估围产期及产后抑郁症和焦虑症的可接受性。
与孕妇和产后女性（n = 20）进行焦点小组讨论，并与医疗保健提供者（n = 8）进行个人访谈，以了解移动健康技术的可接受性，并确定其在评估围产期和产后情绪症状方面的效用。通过目的抽样从产科诊所和周边社区招募参与者。由一位接受过定性研究培训的流行病学家与一位产科医生协商制定了一份半结构化访谈指南。根据研究期间实施的COVID-19协议，第一作者亲自或通过Zoom（Zoom Video Communications, Inc）进行了所有焦点小组讨论和提供者访谈。所有访谈均在获得同意后进行录音；转录；并上传至ATLAS.ti 8（ATLAS.ti Scientific Software Development GmbH）进行编码，这是一款定性数据分析和检索软件。使用基于访谈指南制定的一组先验代码，采用演绎性内容分析法对数据进行分析。在数据的实施、收集、分析和报告过程中，通过采用系统的方法确保了方法的严谨性和质量。
几乎所有女性和提供者都下载并使用过至少一款健康应用程序。受访者建议用通俗易懂的语言提出简短问题，以便所有教育水平的女性都能理解，并在女性自己确定的首选时间每天提供不超过2至3次评估。他们还建议女性自己首先收到警报，如果女性自己在24至72小时内没有回复，则其他选项可以是家庭成员、配偶或朋友。女性和提供者强烈支持定制化和暂停功能，以提高可接受性和效用。女性提到产后期间时间紧张、疲劳、隐私以及心理健康数据的安全性是她们关心的问题。医疗保健专业人员强调基于应用程序的情绪评估和监测的长期可持续性是一项重要挑战。
本研究结果表明，移动健康技术对于孕妇和产后女性监测情绪症状是可以接受的。这可为开发具有临床意义且成本低廉的工具提供参考，以促进对这一弱势群体情绪障碍的持续监测、早期诊断和早期干预。
