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.

摘要 围产期抑郁症，定义为怀孕期间或产后第一年的抑郁发作，是指 这是最常见的分娩并发症，影响全球多达20%的围产期妇女。巨大的差距 帮助患有围产期抑郁症的妇女。移动的保健干预措施，例如 与健康护理工作者（HCW）的交互式SMS文本消息传递已经被提议为资源高效的， 无障碍设施到人的护理。充分实现mHealth在精神卫生方面的公共卫生潜力， 需要战略性地使用自动化和经验性地定义动态适应用户需求的干预措施。 这个K18指导职业提升奖旨在支持流行病学家Keshet Ronen博士， 多学科培训，成为移动健康传播和发展分析专家， 适应性移动健康干预措施。基于Ronen博士在开发和评估 使用短信和社交媒体的移动健康干预措施，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：通过教学课程，个人导师会议，研讨会和会议，Ronen博士 旨在实现以下职业发展目标。(1)熟练掌握和经验，使用自然 语言处理和机器学习方法来分析mHealth通信。(2)熟练掌握 和设计适应性移动健康干预的经验。(3)加深对移动健康干预的理解 为心理健康而设计。(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加Ronen博士先前的培训和经验，并使她能够完成 拟议的研究计划：移动的WACh是一个独特的交互式短信平台，已被证明 改善母婴健康，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，移动的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移动的WACh研究中，向>3000名围产期妇女提供信息，以（1）开发 预测模型可以检测表明抑郁症状加重的客户短信，以及（2）识别HCW 与抑郁症状改善相关的信息特征。(3)目标1-2的模型将 在移动的WACh软件中实施，以开发自适应版本的移动的WACh Neo， 关于短信和指导HCW的短信组成。自适应移动的WACh Neo的初步研究将 在肯尼亚的移动的WACh Neo随机对照试验（R 01 HD 098105）中嵌套进行， 评估其可接受性和对HCW响应客户消息所需时间的初步影响。 总的来说，这些活动将使Ronen博士成为自适应移动健康研究的领导者。 在资源有限的情况下采取干预措施，支持孕产妇心理健康。