Sleep of mothers, fathers, and infants: a longitudinal study from pregnancy through 12 months

Fathers' sleep in the first 24 months postpartum: A systematic review and meta-analysis of global data

Maternal sleep is significantly disrupted in the postpartum period, but changes in paternal sleep are less established. Here, we systematically review and meta-analyze available data on paternal sleep in the first 24months post birth, including self-report and objectively measured sleep outcomes. Scopus, PsycINFO, and PubMed were searched for original research articles published until end August 2024. We included studies reporting on quantitative summaries of sleep outcomes and data were pooled using random-effects models primarily. We included 47 studies from 17 countries (N=9684) with most data coming from fathers in North America (K=26), and reporting on a diverse range of sleep outcomes. Most data were available for sleep duration (398.29 minutes; 95% CIs 381.43-415.88), night awakenings (1.14; 95% CIs 1.12-1.16), and wake after sleep onset (36.57 minutes; 95% CIs 20.83-64.20). There was high heterogeneity across these three measures (I2 values >95%). While there were a small number of studies using the Pittsburgh Sleep Quality Index, our pooled estimate suggested poor sleep in fathers (5.93, 95% CIs 4.75-7.41, I2=91%). Overall, we found some evidence for sleep in few fathers being below the recommended levels, but the extent of any paternal deficit depended on the sleep measure. The US-centric dataset limits our understanding of fathers' sleep experiences postnatally, particularly considering the large relative differences between paternity leave access in the United States vs. other countries.

Intrafamilial associations of sleep multitrajectory groups between ages of 3 and 60 months in the SEPAGES cohort

OBJECTIVES

We investigated intrafamilial sleep evolution by identifying children's sleep multitrajectory groups between 3- and 60-month of age and their association with parental sleep multitrajectory groups.

METHODS

We included 180 children from the SEPAGES cohort (Grenoble, France) whose parents belonged to previously identified sleep multitrajectory groups, through group-based multitrajectory modeling, between 3 and 36months postpartum, using nighttime (NSD) and weekend daytime (DSD) sleep durations and subjective sleep loss, comprising "No," "Subjective," and "Global" sleep problems groups. Child sleep information (NSD, DSD, subjective sleep loss, night waking, and sleep onset difficulties) was collected by parental questionnaires at 3-, 12-, 36-, and 60-month. We identified sleep multitrajectory groups using group-based multitrajectory modeling in children and examined their associations with parental sleep multitrajectory groups using multinomial logistic regressions.

RESULTS

We identified three sleep multitrajectory groups in children: the "No/few" group (29.4%) had moderate NSD, long DSD, low subjective sleep loss/night waking/sleep onset difficulties prevalence, the "Moderate" group (60.0%) had long NSD and moderate DSD, and medium subjective sleep loss/night waking/sleep onset difficulties prevalence, and the "Global" group (10.6%) had the shortest NSD and DSD, and the highest subjective sleep loss/night waking/sleep onset difficulties prevalence. After adjusting for covariates, mothers in the "Global" group were more likely to have children in the same group, and mothers in "Subjective" and "Global" groups were less likely to have children in the "Moderate" group than in the "No/few" group. No association was identified with paternal or couple sleep multitrajectory groups.

CONCLUSIONS

The observed associations between parent-child sleep multitrajectory groups suggest greater maternal sensitivity to or involvement in the child's sleep than the fathers. Early preventive sleep actions could improve sleep in children and mothers.

Sleepless on the road: Are mothers of infants with insomnia at risk for impaired driving?

Infant sleep problems have been associated with a myriad of adverse child and parent outcomes, yet whether these problems may pose a risk for parents on the road has received little research attention. This study sought to test whether mothers of infants with insomnia are at an elevated risk for vehicular crashes, by comparing their objectively measured driving performance with that of mothers of well-sleeping infants and with that of women without children. Fifty-four women from these three groups completed a simulated driving task. Outcome measures included standard deviation of lateral position, number of lane crossings, standard deviation of speed, average speed and maximum speed. Women additionally reported on their driving behaviour using the Driving Behaviour Questionnaire, and on sleep, sleepiness and insomnia symptoms using 7-day sleep diaries and questionnaires. Mothers of infants with insomnia demonstrated greater lane deviation (Wald = 9.53, p = 0.009), higher maximum speed (Wald = 6.10, p = 0.04) and poorer self-rated driving behaviour (Wald = 7.44, p = 0.02) compared with control groups. Analyses also indicated that driving performance in mothers of infants with insomnia tended to be poorer relative to control groups with the progression of time on task. While further research is needed to assess the scope of these effects, our findings suggest that parents, healthcare providers and policymakers should be aware of the potential consequences of infant sleep problems on road safety, and collaborate to establish strategies to mitigate these risks.

Identifying signs and symptoms of urinary tract infection from emergency department clinical notes using large language models

BACKGROUND

Natural language processing (NLP) tools including recently developed large language models (LLMs) have myriad potential applications in medical care and research, including the efficient labeling and classification of unstructured text such as electronic health record (EHR) notes. This opens the door to large-scale projects that rely on variables that are not typically recorded in a structured form, such as patient signs and symptoms.

OBJECTIVES

This study is designed to acquaint the emergency medicine research community with the foundational elements of NLP, highlighting essential terminology, annotation methodologies, and the intricacies involved in training and evaluating NLP models. Symptom characterization is critical to urinary tract infection (UTI) diagnosis, but identification of symptoms from the EHR has historically been challenging, limiting large-scale research, public health surveillance, and EHR-based clinical decision support. We therefore developed and compared two NLP models to identify UTI symptoms from unstructured emergency department (ED) notes.

METHODS

The study population consisted of patients aged ≥ 18 who presented to an ED in a northeastern U.S. health system between June 2013 and August 2021 and had a urinalysis performed. We annotated a random subset of 1250 ED clinician notes from these visits for a list of 17 UTI symptoms. We then developed two task-specific LLMs to perform the task of named entity recognition: a convolutional neural network-based model (SpaCy) and a transformer-based model designed to process longer documents (Clinical Longformer). Models were trained on 1000 notes and tested on a holdout set of 250 notes. We compared model performance (precision, recall, F1 measure) at identifying the presence or absence of UTI symptoms at the note level.

RESULTS

A total of 8135 entities were identified in 1250 notes; 83.6% of notes included at least one entity. Overall F1 measure for note-level symptom identification weighted by entity frequency was 0.84 for the SpaCy model and 0.88 for the Longformer model. F1 measure for identifying presence or absence of any UTI symptom in a clinical note was 0.96 (232/250 correctly classified) for the SpaCy model and 0.98 (240/250 correctly classified) for the Longformer model.

CONCLUSIONS

The study demonstrated the utility of LLMs and transformer-based models in particular for extracting UTI symptoms from unstructured ED clinical notes; models were highly accurate for detecting the presence or absence of any UTI symptom on the note level, with variable performance for individual symptoms.

The relationship between sleep- and circadian rhythm-related parameters with dietary practices and food intake of sedentary adults: a cross-sectional study

We aimed to explore the link between sleep-related parameters and dietary practices. This cross-sectional exploratory study includes sedentary individuals between 20 and 59 years of age. We applied exigent inclusion and exclusion criteria, such as weight stability and without humor- or sleep-related diseases. Also, shift workers were not included. We evaluated sleep quality (by Pittsburg Sleep Quality Index; PSQI), sleepiness (by Epworth Sleepiness Scale), chronotype (by Morningness Eveningness Questionnaire; MEQ), and social jetlag from sleep dairy. Moreover, Food Practices Measurement Scale was used to assess dietary practices. Food intake estimates (i. e., energy, eating window, and late-night dinner eating) were derived from two 24-h food recalls (R24h). For analysis, dietary practices and energy intake from R24h were considered dependent variables, while PSQI, ESS, MEQ, STJ, EW, and LNDE were considered independent variables. Our sample comprises 42 adults (21 women and 21 men; 35.4 (12.5) y; 25.6 (5.21) kg/m2 BMI; 26.5 (7.97) % body fat). We verified that persons with poor sleep quality showed lower dietary practice scores (MD - 6.68; p = 0.021). Besides, in regression analysis, chronotype (β = 0.266; p = 0.039) was positively associated with dietary practices, and eating window was positively associated with energy intake (β = 267 kcal; p = 0.023). In contrast to our hypothesis, other sleep- and circadian-related variables were not associated with dietary practices or energy intake. In summary, we conclude that morning chronotype appears to be related to better dietary practices from the Food Guide for the Brazilian Population guide and that higher eating window was positively associated with energy intake.