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

利用交互式短信监测和支持肯尼亚孕产妇心理健康

• 批准号: 9976950
• 负责人: Keshet Ronen
• 金额: $ 12.81万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9976950
• 关键词: Affect; Automation; Award; Caring; Characteristics; Childbirth; Client; Clinic; Clinic Visits; Clinical Data; Clinical Trials; Clinical Trials Design; Communication; Complication; Computer Models; Computer software; Contraceptive Usage; Data; Data Analytics; Data Set; Development; Diagnostic; Emotional; Enrollment; Epidemiologist; Equation; Evaluation; Exclusive Breastfeeding; Frequencies; Goals; Health Communication; Health Personnel; Health Status; Health Status Indicators; Healthcare; Human; Hybrid Computers; Individual; Infant; International; Intervention; K-18 conjugate; Kenya; Language; Machine Learning; Maternal and Child Health; Mental Depression; Mental Health; Mentors; Methods; Minor; Modeling; Monitor; Natural Language Processing; Parents; Participant; Patients; Perinatal; Persons; Pilot Projects; Postpartum Period; Pregnancy; Pregnancy Complications; Psyche structure; Public Health; Randomized Controlled Trials; Research; Research Personnel; Research Training; Resources; Specificity; Supervision; System; Testing; Text; Text Messaging; Time; Training; Universities; Variant; Washington; Woman; adaptive intervention; adverse outcome; base; career; depressive symptoms; design; digital; experience; high risk; implementation trial; improved; mHealth; machine learning method; meetings; multidisciplinary; open source; peripartum depression; predictive modeling; preservation; response; service intervention; skills; social media; symposium; therapy design; tool

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.

抽象的 围产期抑郁症，定义为怀孕期间或产后第一年的抑郁发作，是 分娩的最常见并发症，全球多达20％的围产社妇女影响。巨大的差距 存在于支持经历围产期抑郁症的妇女。移动健康（MHealth）干预措施，例如 与医疗保健工人（HCW）的交互式SMS文本消息已被提议为资源有效， 可访问的面对面护理的辅助手段。意识到MHealth在心理健康方面的全部公共卫生潜力将 需要对动态适应用户需求的干预措施的自动化和经验定义进行战略使用。 这项K18指导职业增强奖旨在支持Keshet Ronen博士 多学科培训，成为分析MHealth沟通和发展的实验 自适应MHealth干预措施。基于Ronen博士在开发和评估方面既定的专业知识 使用SMS和社交媒体进行的MHealth干预措施，并在经验丰富的导师团队的支持下， 提出了以下研究和培训目标。 培训计划：通过教学课程，个人会议，半手和会议，Ronen博士 试图实现以下职业增强目标。 （1）利用自然的熟练和经验 语言处理和机器学习方法分析MHealth通信。 （2）提高能力 和设计自适应MHealth干预的经验。 （3）加深对MHealth干预的理解 心理健康设计。 （4）提高国际研究实施和临床试验设计的技能。 拟议的培训计划将增强罗恩博士的先前培训和经验，并允许她完成 拟议的研究计划：移动WACH是一个独特的交互式SMS消息平台，已显示 为了改善母子健康，并且目前正在评估其对围产期抑郁症的影响 肯尼亚的随机对照试验，移动WACH NEO。我们建议利用> 100,000 SMS的数据集 在以前和正在进行的移动WACH研究中，有> 3000名围产妇女的消息到（1）开发 可以检测客户SMS的预测模型，表明抑郁症状升高，并且（2）识别HCW 与抑郁症状改善有关的消息特征。 （3）AIMS 1-2的模型将 可以在移动WACH软件中实现，以开发自适应版本的移动WACH NEO 关于消息和指导SMS组成的HCW。自适应移动WACH NEO的试点研究将 在肯尼亚的移动WACH NEO随机对照试验中嵌套（R01HD098105）进行进行 评估其可接受性和对HCW响应客户消息所花费的时间的初步影响。 总的来说，这些活动将使Ronen博士成为适应性MHealth研究的领导者 在资源有限的环境中支持孕产妇心理健康的干预措施。