Green Eric P, Lai Yihuan, Pearson Nicholas, Rajasekharan Sathyanath, Rauws Michiel, Joerin Angela, Kwobah Edith, Musyimi Christine, Jones Rachel M, Bhat Chaya, Mulinge Antonia, Puffer Eve S
Duke Global Health Institute, Durham, NC, United States.
Jacaranda Health, San Francisco, CA, United States.
JMIR Form Res. 2020 Oct 5;4(10):e17895. doi: 10.2196/17895.
Depression during pregnancy and in the postpartum period is associated with poor outcomes for women and their children. Although effective interventions exist for common mental disorders that occur during pregnancy and the postpartum period, most cases in low- and middle-income countries go untreated because of a lack of trained professionals. Task-sharing models such as the Thinking Healthy Program have shown potential in feasibility and efficacy trials as a strategy for expanding access to treatment in low-resource settings; however, there are significant barriers to scale-up. We address this gap by adapting Thinking Healthy for automated delivery via a mobile phone. This new intervention, Healthy Moms, uses an existing artificial intelligence system called Tess (Zuri in Kenya) to drive conversations with users.
This prepilot study aims to gather preliminary data on the Healthy Moms perinatal depression intervention to learn how to build and test a more robust service.
We conducted a single-case experimental design with pregnant women and new mothers recruited from public hospitals outside of Nairobi, Kenya. We invited these women to complete a brief, automated screening delivered via text messages to determine their eligibility. Enrolled participants were randomized to a 1- or 2-week baseline period and then invited to begin using Zuri. We prompted participants to rate their mood via SMS text messaging every 3 days during the baseline and intervention periods, and we used these preliminary repeated measures data to fit a linear mixed-effects model of response to treatment. We also reviewed system logs and conducted in-depth interviews with participants to study engagement with the intervention, feasibility, and acceptability.
We invited 647 women to learn more about Zuri: 86 completed our automated SMS screening and 41 enrolled in the study. Most of the enrolled women submitted at least 3 mood ratings (31/41, 76%) and sent at least 1 message to Zuri (27/41, 66%). A third of the sample engaged beyond registration (14/41, 34%). On average, women who engaged post registration started 3.4 (SD 3.2) Healthy Moms sessions and completed 3.1 (SD 2.9) of the sessions they started. Most interviewees who tried Zuri reported having a positive attitude toward the service and expressed trust in Zuri. They also attributed positive life changes to the intervention. We estimated that using this alpha version of Zuri may have led to a 7% improvement in mood.
Zuri is feasible to deliver via SMS and was acceptable to this sample of pregnant women and new mothers. The results of this prepilot study will serve as a baseline for future studies in terms of recruitment, data collection, and outcomes.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/11800.
孕期及产后抑郁与母婴不良结局相关。尽管针对孕期及产后常见精神障碍有有效的干预措施，但在低收入和中等收入国家，由于缺乏专业培训人员，大多数病例未得到治疗。诸如“健康思考计划”等任务分担模式在可行性和有效性试验中显示出作为在资源匮乏地区扩大治疗可及性策略的潜力；然而，扩大规模存在重大障碍。我们通过将“健康思考计划”改编为通过手机自动提供服务来填补这一空白。这项新的干预措施“健康妈妈”使用一个名为苔丝（在肯尼亚为祖里）的现有人工智能系统与用户进行对话。
这项预试验研究旨在收集关于“健康妈妈”围产期抑郁干预措施的初步数据，以了解如何构建和测试更完善的服务。
我们对从肯尼亚内罗毕以外的公立医院招募的孕妇和新妈妈进行了单病例实验设计。我们邀请这些女性完成通过短信自动发送的简短筛查，以确定她们是否符合条件。入选的参与者被随机分配到1周或2周的基线期，然后被邀请开始使用祖里。在基线期和干预期，我们每3天通过短信提示参与者对自己的情绪进行评分，并使用这些初步的重复测量数据来拟合治疗反应的线性混合效应模型。我们还查看了系统日志，并对参与者进行了深入访谈，以研究对干预措施的参与度、可行性和可接受性。
我们邀请了647名女性了解更多关于祖里的信息：86人完成了自动短信筛查，41人入选研究。大多数入选女性至少提交了3次情绪评分（31/41，76%），并至少给祖里发送了1条信息（27/41，66%）。三分之一的样本在注册后仍有参与（14/41，34%）。平均而言，注册后参与的女性开始了3.4次（标准差3.2）“健康妈妈”课程，并完成了她们开始的3.1次（标准差2.9）课程。大多数试用祖里的受访者对该服务持积极态度，并表示信任祖里。她们还将积极的生活变化归因于该干预措施。我们估计使用祖里的这个alpha版本可能使情绪改善了7%。
通过短信提供祖里是可行的，并且被该样本中的孕妇和新妈妈所接受。这项预试验研究的结果将作为未来研究在招募、数据收集和结果方面的基线。
国际注册报告识别码（IRRID）：RR2 - 10.2196/11800。
