Digital Mindfulness Based Cognitive Therapy for Perinatal Depression

基于数字正念的认知疗法治疗围产期抑郁症

• 批准号: 10021734
• 负责人: Arne L. Beck
• 金额: $ 62.79万
• 依托单位: KAISER FOUNDATION RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-23 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10021734
• 关键词: Address; Adherence; Adopted; Adoption; Adult; Antidepressive Agents; Anxiety; Area; Behavioral; California; Clinical; Cognitive; Colorado; Discipline of obstetrics; Effectiveness; Equilibrium; Exposure to; General Population; Health Services Research; Health Technology; Health system; Healthcare; Healthcare Systems; Hybrids; Intervention; Maintenance; Mental Depression; Mental Health; Mental Health Services; Modeling; Moods; Neonatal; Online Systems; Patient-Focused Outcomes; Persons; Phase; Pregnant Women; Prevention; Preventive Intervention; Preventive treatment; Public Health; Randomized; Recording of previous events; Relapse; Research; Residual state; Risk; Risk Factors; Role; Science; Site; Stress; System; Technology; Testing; Woman; Work; World Health; clinically significant; cost effective; depressive symptoms; digital; effective intervention; effectiveness implementation trial; effectiveness testing; efficacy trial; experience; group intervention; improved; incremental cost-effectiveness; infant outcome; mindfulness; mindfulness based cognitive therapy; mindfulness meditation; mobile application; patient population; perinatal outcomes; perinatal period; peripartum depression; population health; practice-based research network; preference; prenatal exposure; primary outcome; programs; recurrent depression; relapse risk; relative effectiveness; response; routine care; scale up; service delivery; tool; treatment as usual; trial comparing

An increasing number of digital mental health technologies are being developed to expand access to mental health treatments and deliver them in a cost-effective manner. Although efficacy trials of these technologies demonstrate improved patient outcomes, especially when combined with coaching support, there is little evidence that such digital tools can be widely implemented and sustained in routine care settings. Perinatal depression is one area of significant public health concern where the role of digital mental health technology is especially relevant. Approximately 30-40% of women with histories of depression experience relapse during the perinatal period, a majority show poor adherence to antidepressants (ADs), the most common prevention treatment, and a majority express a preference for non-pharmacologic treatments. However, effective and easily accessible non-pharmacologic treatments are not widely available. Inadequate treatment for perinatal depression poses unique risks, including potential obstetrical and neonatal complications associated with perinatal depression itself and with fetal exposure to ADs. It is therefore imperative to test the implementation of effective and scalable non-pharmacological treatments to reduce the risk of depression relapse in the perinatal period. Mindfulness Based Cognitive Therapy (MBCT) is a promising preventive intervention for pregnant women with recurrent depression (as well as for adults in general), demonstrating significant reductions in rates of depressive relapse and residual depressive symptoms. MBCT is an eight-session in-person group intervention targeting risk factors for depressive relapse through a combination of mindfulness meditation and cognitive-behavioral strategies. Because of challenges in delivering in-person MBCT (difficulty for health systems to scale up the intervention, barriers to access for pregnant women), we developed a mobile-first digital adaptation of MBCT for pregnant women, Mindful Mood Balance for Moms (MMBFM). The critical next phase of our work is to evaluate the potential of MMBFM as an effective intervention that can be more widely adopted, implemented and sustained across heterogeneous patient populations and health care systems. We propose a large pragmatic hybrid type II effectiveness--implementation trial comparing MMBFM to usual care (UC) among pregnant women at risk for recurrent depression at four MHRN sites: KP Colorado, KP Southern California, HealthPartners, and KP Georgia to address the following aims: AIM 1: Test the effectiveness of MMBFM in reducing depression symptoms, reducing risk of relapse or significant worsening, and improving perinatal outcomes when implemented in real-world health systems. AIM 2: Evaluate the incremental cost effectiveness of MMBFM compared to UC. AIM 3: Evaluate healthcare system's implementation of MMBFM using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) model.

越来越多的数字心理健康技术正在开发，以扩大获得 心理健康治疗，并以具有成本效益的方式提供。尽管这些药物的功效试验 技术证明了改善的患者结果，特别是当与教练支持相结合时， 很少有证据表明这种数字化工具可以在常规护理环境中广泛实施和维持。 围产期抑郁症是一个重要的公共卫生问题，其中数字心理的作用 卫生技术尤其重要。大约30-40%有抑郁症史的女性 在围产期经历复发，大多数人表现出对抗抑郁药（AD）的依从性差， 最常见的预防治疗，大多数人表示偏好非药物治疗。 然而，有效和容易获得的非药物治疗并不普遍。不足 围产期抑郁症的治疗具有独特的风险，包括潜在的产科和新生儿 与围产期抑郁症本身和胎儿暴露于AD相关的并发症。因此 必须测试有效和可扩展的非药物治疗的实施，以减少 围产期抑郁症复发的风险。 基于正念的认知疗法（MBCT）是一种很有前途的预防性干预措施， 复发性抑郁症的女性（以及一般的成年人），表现出显着减少， 抑郁复发率和残余抑郁症状。MBCT是一个八节的面对面小组 针对抑郁症复发的风险因素的干预，通过正念冥想和 认知行为策略。由于在提供面对面MBCT方面的挑战（健康困难）， 系统，以扩大干预，孕妇的障碍），我们开发了一个移动优先， 孕妇MBCT的数字适应，MMBFM（MMBFM）。 我们工作的关键下一阶段是评估MMBFM作为有效干预措施的潜力 可以在不同的患者人群中更广泛地采用、实施和维持， 医疗保健系统。我们提出了一个大的务实的混合型第二类有效性-实施试验 比较MMBFM与常规护理（UC）在4个MHRN的复发性抑郁症风险孕妇中的作用 研究中心：KP科罗拉多、KP南加州、HealthPartners和KP格鲁吉亚，以实现以下目标： 目的1：测试MMBFM在减轻抑郁症状，降低复发风险或 显著恶化，并改善围产期结果时，在现实世界的卫生系统实施。 目的2：评价MMBFM与UC相比的增量成本效益。 目的3：使用RE-AIM（范围，有效性， 采用、实施和维护）模型。