Leveraging interactive SMS messaging to monitor and support maternal mental health in Kenya

摘要 围产期抑郁，定义为怀孕期间或产后第一年的抑郁发作，是 分娩最常见的并发症，全球高达20%的围产期妇女受到影响。巨大的差距 存在于支持经历围产期抑郁的妇女。移动医疗(MHealth)干预措施，例如 已经提出了与医护人员(HCW)的交互式SMS文本消息收发是资源高效的， 面对面护理的无障碍辅助设备。充分实现移动健康对心理健康的公共健康潜力将 需要战略性地使用自动化和干预措施的经验定义，以动态适应用户需求。 这个K18导师职业提升奖旨在支持Keshet Ronen博士，一位患有 多学科培训，成为分析移动健康传播和发展的专家 适应性移动健康干预。基于Ronen博士在开发和评估方面的成熟专业知识 使用短信和社交媒体的移动健康干预，并得到经验丰富的导师团队的支持， 提出了以下研究和培训目标。 培训计划：通过授课课程、个人导师会议、研讨会和会议，罗宁博士 寻求实现以下职业提升目标。(1)通过自然获取熟练程度和经验 语言处理和机器学习方法来分析mHealth通信。(2)掌握熟练程度 以及设计适应性移动健康干预的经验。(3)深化对移动健康干预的认识 为心理健康而设计。(4)提高国际研究实施和临床试验设计技能。 拟议的培训计划将增强罗宁博士以前的培训和经验，并使她能够完成 建议的研究计划：Mobile Wach是一个独特的交互式短信平台，已经展示 为了改善母婴健康及其对围产期抑郁症的影响，目前正在进行一项 肯尼亚的随机对照试验，Mobile Wach Neo。我们建议利用&>100,000条短信的数据集 在之前和正在进行的Mobile Wach研究中与3000名围产期妇女进行的信息：(1)开发一种 预测模型，可以检测指示抑郁症状升高的客户短信，以及(2)识别HCW 与抑郁症状改善相关的消息特征。(3)AIMS 1-2的模型将 在Mobile Wach软件中实现，以开发自适应版本的Mobile Wach Neo，该标志 关于短信和指导医务工作者撰写短信。自适应移动Wach Neo Will的初步研究 将在肯尼亚Mobile Wach Neo随机对照试验(R01HD098105)内进行，以 评估其可接受性和对卫生保健工作者回复客户消息所需时间的初步影响。 总的来说，这些活动将使罗宁博士成为适应性移动健康研究的领导者 在资源有限的情况下支持产妇心理健康的干预措施。

ABSTRACT Perinatal depression, defined as a depressive episode during pregnancy or in the first year postpartum, is the most common complication of childbirth, affecting up to 20% of peripartum women globally. An enormous gap exists in supporting women experiencing perinatal depression. Mobile health (mHealth) interventions such as interactive SMS text messaging with healthcare workers (HCWs) have been proposed as resource-efficient, accessible adjuncts to in-person care. Realizing the full public health potential of mHealth for mental health will require strategic use of automation and empiric definition of interventions that dynamically adapt to user needs. This K18 mentored career enhancement award aims to support Dr. Keshet Ronen, an epidemiologist with multidisciplinary training, to become an expert in analysis of mHealth communication and development of adaptive mHealth interventions. Building on Dr. Ronen’s established expertise in development and evaluation of mHealth interventions using SMS and social media, and supported by a team of experienced mentors, the following research and training aims are proposed. Training plan: Through didactic coursework, individual mentor meetings, seminars, and conferences, Dr. Ronen seeks to accomplish the following Career Enhancement Goals. (1) Gain proficiency and experience using natural language processing and machine learning methods to analyze mHealth communication. (2) Gain proficiency and experience in design of an adaptive mHealth intervention. (3) Deepen understanding of mHealth intervention design for mental health. (4) Enhance skills in international study implementation and clinical trial design. The proposed training plan will augment Dr. Ronen’s prior training and experience and allow her to complete the proposed Research plan: Mobile WACh is a unique interactive SMS messaging platform that has been shown to improve maternal-child health and whose impact on perinatal depression is currently being evaluated in a randomized controlled trial in Kenya, Mobile WACh Neo. We propose to leverage a dataset of >100,000 SMS messages with >3000 peripartum women in previous and ongoing Mobile WACh studies to (1) develop a predictive model that can detect client SMS indicating elevated depression symptoms, and (2) identify HCW message characteristics associated with improvements in depression symptoms. (3) Models from Aims 1-2 will be implemented in the Mobile WACh software to develop an adaptive version of Mobile WACh Neo that flags concerning messages and guides HCWs on SMS composition. A pilot study of Adaptive Mobile WACh Neo will be conducted, nested within the Mobile WACh Neo randomized controlled trial in Kenya (R01HD098105), to evaluate its acceptability and preliminary impact on time taken for HCWs to respond to client messages. Collectively, these activities will enable Dr. Ronen to become a leader in the study of adaptive mHealth interventions to support maternal mental health in resource-limited settings.