Rachel Manber

Background: Postpartum sleep is a unique construct, and existing sleep patient-reported outcome measures (PROMs) do not adequately address some of its fundamental domains. We aimed to develop postpartum-specific extension items to the PROMIS Sleep Disturbance (SD) and Sleep Related Impairment (SRI) item banks. Methodology: The postpartum-specific SD and SRI items were developed to add to the existing PROMIS SD and SRI item banks. Postpartum-specific PROMIS items were generated based on findings from literature characterizing postpartum sleep, including systematic reviews and qualitative interviews with patients and experts from diverse specialties. The candidate items were subsequently iteratively adapted and selected through a modified Delphi process involving a panel of 21 stakeholders including experts in obstetrics, sleep, psychiatry, patients and partner representatives. Items achieving consensus with ≥ 70% stakeholder agreement were subsequently piloted and refined through recorded cognitive debriefing interviews with postpartum individuals until no further suggestions were generated in 5 consecutive interviews. Results: Nine new postpartum SD items, and four new SRI items were developed to extend the existing PROMIS SD (27 items) and SRI item banks (16 items). The new items covered six concepts (psychological factors, infant-related sleep disruption, pain, social support, feeding of infant, and sleep interference with self-care, maternal-neonatal bonding, and energy), and ten underlying dimensions (worry, post-traumatic stress symptoms, maternal-neonatal bonding, activities of daily living, pain, neonatal awakenings, maternal awakenings, latency, social support, infant feeding) of postpartum sleep. Conclusions: We developed postpartum-specific extensions to the existing PROMIS SD and SRI item banks using the National Institute of Health PROMIS framework. Next steps include evaluation of their psychometric properties, clinical validation, and studies to establish clinically important differences in scores for clinical application.

Background: Insomnia during pregnancy is a modifiable risk factor for depression. Research in nonpregnant populations and preliminary findings in a pregnant population suggest that targeting insomnia with digital cognitive behavioral therapy for insomnia (CBT-I) may prevent depression. Objective: This study aims to evaluate the efficacy of digital CBT-I for the prevention of depression through 12 months post partum compared to that of a credible, clinically relevant comparator intervention, sleep hygiene education (SHE). This project will also provide valuable information about how and for whom CBT-I prevents perinatal depression. Methods: The Perinatal Research for Improving Sleep and Mental Health study is a fully remote, 2-arm parallel-group randomized controlled trial designed to test the superiority of digital CBT-I versus SHE for the prevention of perinatal depression. We aimed to randomize a target sample of at least 443 pregnant participants with insomnia disorder to digital CBT-I or SHE at a 1:1 ratio. Participants randomized to digital CBT-I received SleepioRx (Big Health, Inc), which uses sleep consolidation, stimulus control, cognitive therapy, relaxation techniques, and SHE in 6 interactive modules (5‐15 min each). Participants randomized to SHE received information about sleep hygiene, the impact of sleep on performance, healthy sleep habits, lifestyle influences on sleep, and creating a sleep-friendly bedroom in 6 weekly digital handouts. Data are collected at baseline, 5 weeks after randomization, 10 weeks after randomization, 36 weeks’ gestation, and 3, 6, 9, and 12 months post partum. The primary outcome is depression incidence, measured using the Structured Clinical Interview for DSM-5 ( Diagnostic and Statistical Manual of Mental Disorders [Fifth Edition]) Disorders, Research Version. Other outcome measures include the Edinburgh Postnatal Depression Scale, Patient Health Questionnaire-9, Columbia Suicide Severity Rating Scale, Generalized Anxiety Disorder Scale, and a birth outcomes questionnaire. Mediator measures include the Insomnia Severity Index, Consensus Sleep Diary, Structured Clinical Interview for Sleep Disorders, Perseverative Thinking Questionnaire, Dysfunctional Beliefs about Sleep Scale, Pre-sleep Arousal Scale, and Difficulties in Emotion Regulation-Short Form. Results: Data collection began in November 2022 and is projected to end in December 2026. Conclusions: If study hypotheses are supported, then this project will provide a new, scalable approach for preventing perinatal depression.

Comprehensive assessment of 24-hour movement profiles can reveal patterns of physical activity, sedentary behavior, and sleep across pregnancy. To fully ca

BACKGROUND Insomnia during pregnancy is a modifiable risk factor for depression. Research in non-pregnant populations and preliminary findings in a pregnant population suggest that targeting insomnia with digital cognitive behavioral therapy for insomnia (CBT-I) may prevent depression. OBJECTIVE This study aims to evaluate the efficacy of digital CBT-I for the prevention of depression through 12 months postpartum compared with a credible, clinically relevant comparator intervention, sleep hygiene education (SHE). This project will also provide valuable information about how and for whom CBT-I prevents perinatal depression. METHODS The Perinatal Research for Improving Sleep and Mental health (PRISM) Study is a fully remote, two-arm parallel-group randomized controlled trial designed to test the superiority of digital CBT-I versus SHE for the prevention of perinatal depression. We aimed to randomize a target sample of at least 443 pregnant participants with insomnia disorder to digital CBT-I or SHE at a 1:1 ratio. Participants randomized to digital CBT-I received SleepioRx (Big Health, Inc), which uses sleep consolidation, stimulus control, cognitive therapy, relaxation techniques, and sleep hygiene education in six interactive modules (5-15 minutes each). Participants randomized to SHE received information about sleep hygiene, the impact of sleep on performance, healthy sleep habits, lifestyle influences on sleep, and creating a sleep-friendly bedroom in six weekly digital handouts. Data are collected at baseline, 5 weeks after randomization, 10 weeks after randomization, 36 weeks’ gestation, and 3, 6, 9, and 12 months postpartum. The primary outcome is depression incidence, measured using the Structured Clinical Interview for DSM-5 Disorders. Other outcome measures include the Edinburgh Postnatal Depression Scale, Patient Health Questionnaire-9, Columbia Suicide Severity Rating Scale, Generalized Anxiety Disorder Scale, and a birth outcomes questionnaire. Mediator measures include the Insomnia Severity Index, Consensus Sleep Diary, Structured Clinical Interview for DSM-5 Sleep Disorders, Perseverative Thinking Questionnaire, Dysfunctional Beliefs about Sleep Scale, Pre-sleep Arousal Scale, and Difficulties in Emotion Regulation-Short Form. RESULTS Data collection began in November 2022 and is projected to end December 2026. CONCLUSIONS If study hypotheses are supported, then this project will provide a new, scalable approach for preventing perinatal depression. CLINICALTRIAL ClinicalTrials.gov NCT05596318; https://clinicaltrials.gov/study/NCT05596318

Study Objectives: Sleep as a construct in the postpartum period and its perceived importance to postpartum patients remain largely under explored. The aim of this concept elicitation study was to develop a conceptual framework for postpartum sleep based on the key themes (domains and subdomains) identified. Secondary aims were to determine the frequency of discussion of individual domains/subdomains among participants and provide exemplar patient quotes for the most frequently discussed subdomains. Methods: This study received Institutional Review Board approval from Stanford Lucile Packard Children’s Hospital and the University of Arkansas for Medical Sciences. Semi-structured interviews were conducted with patients, partners and multidisciplinary experts until thematic saturation was achieved. All interviews were audio recorded and professionally transcribed and de-identified. Thematic codes (domains and subdomains of postpartum sleep) were derived from review of interview transcripts. Iterative thematic analysis of transcripts with constant comparison across cases was conducted systematically by ≥2 analysts. All transcripts were coded in Nvivo software and qualitatively analyzed to report frequency of domain/subdomain discussion and to identify exemplar patient quotes for individual subdomains. Results: Interviews were conducted with 42 patients, 13 multidisciplinary experts, and 6 partners over a total of 20 h. Median interval between delivery and interview for the recruited postpartum women was 8 weeks (interquartile range 6–10 weeks, range 3–52 weeks). Analysis of all 61 participant interviews resulted in derivation of 10 domains (psychological, pharmacological, non-pharmacological, sleep interference, medical factors, feeding of neonate, sleep disruption, social factors, societal and cultural factors, and infant related factors) and 85 subdomains related to the construct of postpartum sleep. The three most frequently discussed domains were sleep disruption, non-pharmacological interventions to improve sleep, and medical factors related to sleep. The top 10 most frequently discussed subdomains were breast feeding/feeding, maternal awakenings, social support, childbirth experience, infant sleep routine, day time sleep, infant care (bottles, milk, diapers), sleep arrangements, chronotype, and nighttime sleep. Conclusions: This study provides a conceptual framework based on 10 domains and 85 subdomains, which can be used to comprehensively describe and study the complex construct of postpartum sleep. These findings can be used to counsel patients regarding postpartum sleep experiences, facilitate patient discussion in the postnatal period when assessing postpartum sleep experiences, guide development of new measures, and assess content validity of existing sleep measures. Statement of Significance Despite the recognized impact of sleep on maternal and infant health outcomes, postpartum sleep experience remains poorly conceptualized in clinical research and practice. This study is the first to develop a comprehensive, patient-informed conceptual framework capturing the multidimensional nature of postpartum sleep through 10 domains and 85 subdomains. By integrating insights from postpartum patients, their partners, and multidisciplinary experts, the framework provides a robust foundation for the development of new postpartum sleep measures and evaluation of sleep-related interventions, patient education, and clinical assessments. These findings offer critical direction for future research and practice aimed at improving postpartum sleep health and, in turn, maternal well-being.

Frequent sleep stage transitions and abnormal sleep stage distribution are features of sleep disorders, including insomnia, sleep apnea, and narcolepsy, and have been associated with altered affective processing, including mood disorders. Research on the role of sleep stage transitions is nascent, and mixed operationalizations abound. In this comparative methods study, we overview the ways that prior research has operationalized sleep stage transitions, propose guidelines for four types of metrics, and compare the relevance of each for different analytic purposes. We then discuss three definitional and methodological “hard problems” for research on sleep stage transitions: bias due to scoring discrepancies, low temporal resolution, and opposing definitions of transitions. We discuss the pros and cons of several solutions that use machine learning (ML) scoring algorithms, with examples derived from the Stanford Sleep and Affect polysomnography dataset scored with validated ML algorithms (Stanford STAGES, U-Sleep, and YASA), and conclude with a call to return to descriptive physiological studies to shift the current framing of sleep stage transitions away from categorical state changes. This intends to lay the foundation for further insight into the role of sleep stage transitions in affective function and in clinical dysfunction. Statement of Significance This comparative methods study examines four common approaches to operationalizing sleep stage transitions. In doing so, it lays out the difficult definitional and methodological challenges that face researchers interested in transitions among sleep stages, both in the context of affective functioning and in the context of clinical sleep disorders more broadly. By taking a novel comparative approach to describing transitions via applying machine learning sleep staging algorithms using both 30- and 6-second epochs, this paper helps clarify potential routes and roadblocks to greater understanding of the role of sleep stage transitions in explaining affective states.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Physical activity (PA) and sleep during pregnancy may mitigate the increased risk for adverse pregnancy outcomes posed by gestational diabetes and high gestational weight gain in pregnant individuals with pre-pregnancy overweight and obesity. Recent studies have conceptualized PA, sedentary behavior, and sleep as part of a 24-hour movement framework; however, there is a gap in knowledge about the relationship between 24-hour movement and pregnancy outcomes. Objective: This paper describes the study protocol for the 24MOVE study, a prospective cohort study that examines associations between 24-hour movement profiles across pregnancy and maternal glucose tolerance, gestational weight gain, and infant birthweight. Methods: Participants (n=306) were recruited from a large, integrated health care delivery system at 10 weeks gestation. In early (10-12 weeks), mid (20-22 weeks), and late (33-35 weeks) pregnancy, all eligible individuals with a pre-pregnancy BMI of ≥25 kg/m2 completed online surveys collecting information on sociodemographic characteristics and pregnancy symptoms and behaviors, including sleep quality. Participants concurrently wore a research-grade accelerometer for 24 hours per day for 7 consecutive days to capture movement, sedentary behavior, and sleep data. Data from accelerometry will be processed to create 24-hour movement profiles. Pregnancy outcomes will be ascertained from the electronic health record. We will use compositional data analysis methods, modeling associations of reallocations of time from one component behavior to another at various timepoints with outcomes. Results: Over an 18-month recruitment period, 2,035 individuals were invited to participate and of those, 306 eligible participants enrolled in this study. The enrolled cohort had a median age of 33 years and BMI of 28.8 kg/m². Most participants had private insurance (87%) and were multiparous (76%). Follow-up data collection is ongoing. Conclusions: The 24MOVE Study is designed to address gaps in our knowledge of the impact of 24-hour movement during pregnancy on maternal glucose metabolism, gestational weight gain, and other risk factors for childhood obesity, such as high birthweight. Data from this study will also serve as a rich resource for future investigations of 24-hour movement profiles and behavior substitutions and other perinatal mental and physical health outcomes.

BACKGROUND Physical activity (PA) and sleep during pregnancy may mitigate the increased risk for adverse pregnancy outcomes posed by gestational diabetes and high gestational weight gain in pregnant individuals with pre-pregnancy overweight and obesity. Recent studies have conceptualized PA, sedentary behavior, and sleep as part of a 24-hour movement framework; however, there is a gap in knowledge about the relationship between 24-hour movement and pregnancy outcomes. OBJECTIVE This paper describes the study protocol for the 24MOVE study, a prospective cohort study that examines associations between 24-hour movement profiles across pregnancy and maternal glucose tolerance, gestational weight gain, and infant birthweight. METHODS Participants (n=306) were recruited from a large, integrated health care delivery system at 10 weeks gestation. In early (10-12 weeks), mid (20-22 weeks), and late (33-35 weeks) pregnancy, all eligible individuals with a pre-pregnancy BMI of ≥25 kg/m2 completed online surveys collecting information on sociodemographic characteristics and pregnancy symptoms and behaviors, including sleep quality. Participants concurrently wore a research-grade accelerometer for 24 hours per day for 7 consecutive days to capture movement, sedentary behavior, and sleep data. Data from accelerometry will be processed to create 24-hour movement profiles. Pregnancy outcomes will be ascertained from the electronic health record. We will use compositional data analysis methods, modeling associations of reallocations of time from one component behavior to another at various timepoints with outcomes. RESULTS Over an 18-month recruitment period, 2,035 individuals were invited to participate and of those, 306 eligible participants enrolled in this study. The enrolled cohort had a median age of 33 years and BMI of 28.8 kg/m². Most participants had private insurance (87%) and were multiparous (76%). Follow-up data collection is ongoing. CONCLUSIONS The 24MOVE Study is designed to address gaps in our knowledge of the impact of 24-hour movement during pregnancy on maternal glucose metabolism, gestational weight gain, and other risk factors for childhood obesity, such as high birthweight. Data from this study will also serve as a rich resource for future investigations of 24-hour movement profiles and behavior substitutions and other perinatal mental and physical health outcomes.