An interventional study of Lōvu platform and Standard Care Arm in Perinatal Mental Health, sponsored by University of Utah. Recruiting at 1 site in United States. Open to female participants aged 18 Years to 45 Years, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-08-31.
Sponsored by University of Utah · Not applicable, Interventional, and Supportive care
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.
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.
Exclusion Criteria:
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.
Behavioral: Lōvu platform
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.
Behavioral: Standard Care Arm
Standard prenatal care + Lōvu platform. Participant receives a Bluetooth-enabled scale, BP cuff, and handheld fetal Doppler. Orientation to Lōvu app and device use. Lōvu-generated data (e.g., weight, BP, HR) will be visible to the Lovu team and sent to the UU clinical team weekly Participant-reported outcomes (satisfaction, usability) collected via System Usability Scale (SUS)
Standard prenatal care per UU clinical protocols Mental health screening via PHQ-9, GAD-7, EPDS, and NIDA Quick Screen at standard intervals (these are all validated surveys) Data collected from EHR.
Feasibility Outcomes
Recruitment rate and retention rate at 12 months
Time frame: 12 Months
Mental Health Screening Completion & Clinical Care Metrics
Proportion of participants completing PHQ-9, GAD-7, NIDA. Time to referral and first mental health visit analyzed using non-parametric t-test such as Mann Whitney U. All surveys have their own scales however investigators are only assessing the proportion of participants who complete them, not the scores themself.
Time frame: 12 Months
Care Utilization
Counts of e-messages and phone calls compared between arms using non-parametric t-test such as Mann Whitney U.
Time frame: 12 Months
Satisfaction & Usability
Continuous scale scores compared using t-tests or non-parametric equivalents if distributions are non-normal
Time frame: 12 Months
AI Preparation
Data integration and variable harmonization will be evaluated descriptively. No AI model training will occur during the pilot, but variables will be characterized by frequency, completeness, and time series properties to inform future modeling.
Time frame: 12 Months
Plan to share: No
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University of Utah