Optimizing Maternal Health: Assessing Sleep-Based Strategy Enhancement of Diet, Physical Activity and Self-Regulation Interventions in Perinatal Health - Cardiovascular disease (CVD) is the leading cause of death in women. Evidence shows that perinatal risk factors, including excess gestational weight gain and postpartum weight retention, shape short- and long-term CVD risk trajectories. Thus, the perinatal period, the period from pregnancy through the postpartum year, is a critical time to engage people in interventions designed to promote healthy gestational weight gain and postpartum weight loss. Standard weight-management interventions focus on diet and physical activity but are largely ineffective during and after pregnancy. Sleep health, commonly disrupted during and after pregnancy, may represent a target for screening and intervention to promote perinatal health. Still, additional data on sleep health across the perinatal period are needed. Our long-term goal is to inform evidence-based recommendations for clinical preventive services in perinatal populations. This mixed-methods study uses existing trial data to estimate the effects of sleep-based strategies to improve perinatal weight and health. Our research team has a unique opportunity to address this aim by leveraging data from the Health Behaviors in Transition (HABIT) study (n = 312), a sequential multiple assignment (SMART) RCT of two physical activity, diet, and self-regulation interventions (R01 HL132578, PI: Levine). HABIT’s primary aim is to determine if maternal weight and health are improved at one year postpartum with interventions delivered during pregnancy, postpartum, or both. HABIT does not screen for or intervene on sleep duration, but it collects self- reported and actigraphy-assessed sleep data in early pregnancy, before delivery, and postpartum. Thus, we can use trial emulation (i.e., analyzing observation data to mimic a clinical trial) to estimate the effect the HABIT trial would have had if it also intervened on sleep duration. Our mixed-method approach will use qualitative semi-structured interviews with a purposive sample of pregnant (n=24) and postpartum (n=24) people to inform our causal modeling assumptions, which will inform our emulation protocol, bias analyses, and sensitivity analysis in Aim 2 (Aim 1). Specifically, we will ask participants to discuss factors that impact their sleep and health and get their perspective on the feasibility of sleep extension. Next, we will use trial emulation to estimate the effects of a version of the HABIT interventions that also address sleep duration (Aim 2). Lastly, we will use advanced statistical methods to identify early-pregnancy sleep health indicators to identify who benefits from HABIT's current and emulated versions (Aim 3). Successful completion of these aims will generate data directly relevant to the NHLBI’s Strategic Goal of developing and optimizing novel diagnostic and therapeutic strategies to prevent, treat, and cure heart, lung, blood, and sleep diseases. Our work will maximize the use of existing data to determine the role of sleep-based strategies for improving perinatal CVD risk-reducing interventions. We will provide timely data to inform future intervention design and decision-making in the interim.
