Transforming Maternal Mental Health Care Using Innovative Digital Solutions for the Intermountain West
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 50
- 试验地点
- 1
- 主要终点
- Feasibility Outcomes
研究概览
简要总结
The overall objective of this pilot study is to assess the feasibility of a RCT comparing Lōvu-augmented with usual prenatal care at UU. This would be a critical next step toward the long-term goal to identify technology-based interventions to improve maternal mental health in the Intermountain West. The investigator's central hypothesis is that Lōvu will be both feasible and have high patient and clinician satisfaction, and that Lōvu-generated data will be a promising substrate for AI-based mental health risk stratification.
详细描述
Collectively, perinatal mental health disorders impact 20% of pregnancies in the U.S. and are the leading cause of pregnancy-related mortality, contributing to 23% of deaths. In the U.S., 50-75% of perinatal depression (PND) is undiagnosed and PND is untreated in nearly 85%. This is a result, in part, of the systematic underfunding of investigations into maternal mental health. The critical relevance of this reality for "Women Across the Lifespan" could not be more pressing than in Utah and the Intermountain West, where expanding maternity care deserts limit access to mental health care, and suicide rates are 57% above the national average. The traditional prenatal care paradigm does not address maternal mental health needs. It utilizes relatively wide intervals between prenatal visits during the first three quarters of pregnancy-a time of substantial need for education, psychosocial support, and mental health services-only to culminate in a 'care cliff' postpartum, when the risk of mental health crises and maternal mortality is highest.
Technology has enabled remote visits, but telehealth implementations remain anchored to the traditional prenatal care model and consist of two elements: virtual rather than in-person visits, and EHR-based electronic messaging. This amounts to a "worst of both worlds", in which diluted personal connection and the escalating burden of e-messaging contribute to clinician burnout while the core deficiencies of the traditional paradigm remain. Thus, there is a critical need for novel approaches to prenatal care that more fully deliver on the promise of technology to 1) increase patient access and support, 2) reduce clinician burnout, and 3) improve maternal and newborn outcomes.
Aim 1: Assess the feasibility of a randomized, controlled trial comparing usual vs. Lōvu-augmented prenatal care among N=50 pregnant patients. Feasibility will be defined as the successful recruitment of > 50% of patients approached and 90% participant retention for the 12-month study period.
Aim 2: Assess mental health screening completion, care utilization, and user satisfaction in Lovu-augmented vs usual care. Investogators will assess the following outcomes: for clinical care: participant completion of PHQ-9 (PND), GAD-7 (anxiety), NIDA quick screen (substance use), and time to mental health referral and first visit; for utilization: burden of patient-generated e-messages and phone calls to the UU MFM clinic; satisfaction: telehealth usability questionnaire12 and telemedicine satisfaction questionnaire;13 system usability scale.14
Aim 3: Utilize pilot study data to inform the future development of a novel AI-based mental health early warning and referral system. Investigators will 1) harmonize data from the user app, digital sensors, UU electronic health records (EHR) and 2) develop streamlined data transfer and harmonization workflows.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Supportive Care
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 45 Years(Adult)
- 性别
- Female
- 接受健康志愿者
- 是
入选标准
- •Pregnant people receiving care at University of Utah Clinics
- •Less than 14 weeks gestation at enrollment
排除标准
- •Patients enrolled in other studies utilizing remote monitoring
研究组 & 干预措施
Lōvu-Augmented Arm
The intervention is pragmatic in that the only thing patients are actually asked to do is enroll in Lovu, which is facilitated by the research and clinical personnel after randomization. After enrollment, participants are not specifically instructed to do anything accept engage with Lovu as prompted (e.g. in response to reminders to measure BP and submit perinatal depression screening questionnaires) and to use Lovu as a resource for questions and concerns. The data generated by the participant's engagements with Lovu (vital sign measurements, questions, concerns, screening results) will be summarized and transmitted to the clinical team on a weekly basis ), which the team can make use of in clinical care. An example of a clinical impact would be that home BP monitoring with automated transmission of data to the clinical team can improve early detection of preeclampsia and other pregnancy complications.
干预措施: Lōvu platform (Behavioral)
Standard Care Arm
Mental health screening via PHQ-9, GAD-7, EPDS, and NIDA Quick Screen (these are all validated surveys) at least three times during the pregnancy: early in pregnancy, at least once later in pregnancy, and then at least once in the postpartum period.
干预措施: Standard Care Arm (Behavioral)
结局指标
主要结局
Feasibility Outcomes
时间窗: 12 Months
Recruitment rate and retention rate at 12 months
次要结局
- Care Utilization(12 Months)
- Satisfaction & Usability(12 Months)
- AI Preparation(12 Months)
- Mental Health Screening Completion & Clinical Care Metrics(12 Months)
研究者
Nathan Blue
MD, Associate Professor
University of Utah
