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 最常见的分娩并发症，影响全球多达 20% 的围产期妇女，差距巨大。 存在于支持患有围产期抑郁症的妇女，例如移动健康（mHealth）干预措施。 与医护人员 (HCW) 进行交互式 SMS 短信已被提议为资源高效、 实现移动医疗在心理健康方面的全部公共卫生潜力。 需要战略性地使用自动化和根据经验定义动态适应用户需求的干预措施。 该 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，成为移动医疗传播分析和发展的专家 适应性移动医疗干预措施以 Ronen 博士在开发和评估方面的专业知识为基础。 使用短信和社交媒体进行移动医疗干预，并得到经验丰富的导师团队的支持， 提出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 (1) 获得使用自然的能力和经验 (2) 提高熟练程度 （三）加深对移动健康干预的认识 （4）提升国际研究实施和临床试验设计能力。 拟议的培训计划将增强 Ronen 博士之前的培训和经验，并使她能够完成 拟议的研究计划：Mobile 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 Mobile WACh Neo 的随机对照试验我们建议利用超过 100,000 条短信的数据集。 在之前和正在进行的 Mobile WACh 研究中向超过 3000 名围产期妇女发送消息，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) 识别医护人员 与抑郁症状改善相关的消息特征 (3) 目标 1-2 中的模型将。 在 Mobile WACh 软件中实施，以开发 Mobile WACh Neo 的自适应版本，该版本标记 关于短信编写的消息和指导医护人员将开展自适应移动 WACh Neo 试点研究。 嵌套在肯尼亚的 Mobile WACh Neo 随机对照试验 (R01HD098105) 中进行，以 评估其可接受性以及对医护人员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 博士成为适应性移动医疗研究的领导者 在资源有限的环境中支持孕产妇心理健康的干预措施。