Language：English   Publishing type：Research paper (scientific journal)   Publisher：Scientific Reports  

Deep learning methods have gained significant attention in sleep science. This study aimed to assess the performance of a deep learning-based sleep stage classification model constructed using fewer physiological parameters derived from cardiorespiratory and body movement data. Overnight polysomnography (PSG) data from 123 participants (age: 19-82 years) with suspected sleep disorders were analyzed. Multivariate time series data, including heart rate, respiratory rate, cardiorespiratory coupling, and body movement frequency, were input into a bidirectional long short-term memory (biLSTM) network model to train and predict five-class sleep stages. The trained model's performance was evaluated using balanced accuracy, Cohen's κ coefficient, and F1 scores on an epoch-per-epoch basis and compared with the ground truth using the leave-one-out cross-validation scheme. The model achieved an accuracy of 71.2 ± 5.8%, Cohen's κ of 0.425 ± 0.115, and an F1 score of 0.650 ± 0.083 across all sleep stages, and all metrics were negatively correlated with the apnea-hypopnea index, as well as age, but positively correlated with sleep efficiency. Moreover, the model performance varied for each sleep stage, with the highest F1 score observed for N2 and the lowest for N3. Regression and Bland-Altman analyses between sleep parameters of interest derived from deep learning and PSG showed substantial correlations (r = 0.33-0.60) with low bias. The findings demonstrate the efficacy of the biLSTM deep learning model in accurately classifying sleep stages and in estimating sleep parameters for sleep structure analysis using a reduced set of physiological parameters. The current model without using EEG information may expand the application of unobtrusive in-home monitoring to clinically assess the prevalence of sleep disorders outside of a sleep laboratory.

DOI： 10.1038/s41598-023-45020-7

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Language：English   Publishing type：Research paper (scientific journal)  

Maternal and newborn care quality can be measured in three dimensions (Dimensions 1: care provision, 2: care experience, and 3: human and physical resources); however, little is known about which dimensions are associated with newborn and perinatal deaths. We examined the association between care quality and newborn and perinatal deaths in Nepal. This study incorporated secondary data from Nepal Service Provision Assessments (NSPA) 2015 (623 delivery facilities, facility inventory survey; 1,509 women, ANC clients interviews; 1,544 women, ANC observation) and Nepal Demographic and Health Surveys (NDHS) 2016 (5,038 women who reported having given birth in the five years preceding data collection). The outcome variables were newborn and perinatal deaths derived from the NDHS. The exposure variables were district-level maternal and newborn care quality scores calculated from the NSPA data. Covariates were women's sociodemographic, health, and obstetric characteristics. We applied the administrative boundary method to link these two surveys. We conducted binary logistic regression analyses to examine the association between care quality and newborn/perinatal deaths. In Dimension 1, higher mean and maximum quality scores at the district level were associated with a lower number of newborn deaths (mean: odds ratio [OR] = 0.04, 95% confidence interval [CI]: 0.00-0.76; max: OR = 0.09, 95% CI: 0.01-0.58), but not with perinatal deaths. In Dimensions 2 and 3, the quality score was not significantly associated with newborn deaths and perinatal. Enhancing the quality of care provision at its average and highest levels in each district may contribute to the reduction of newborn deaths, but not perinatal death. Health administrators should assess the quality of care at the administrative division level and focus on enhancing both average and maximum care quality of health facilities in each region in the care provision dimension.

DOI： 10.1371/journal.pgph.0002101

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Language：English   Publisher：Springer  

Background: / Sleep problems and irritable temperaments are common among infants with autism spectrum disorder (ASD). The prospective association between such sleep problems and irritable temperaments and ASDs needs to be determined for elucidating the mechanism and exploring the future intervention study. Thus, in this study, we investigated whether sleep quality and temperament in 1-month-old infants are associated with the onset of ASD in 3-year-old children. We also assessed its sex-stratified associations. / Methods: / We conducted a longitudinal study using data from 69,751 mothers and infants from a large-cohort study, the Japan Environment and Children’s Study. We examined the prospective association between infant sleep quality and temperament at 1 month of age and ASD diagnosis by 3 years of age. / Results: / Here we show infants with longer daytime sleep have a higher risk of later ASD than those with shorter daytime sleep (risk ratio [RR]: 1.33, 95% confidence interval [CI]: 1.01–1.75). Infants who experienced intense crying have a higher risk of ASD than those who did not (RR: 1.31, 95% CI: 1.00–1.72). There is a difference in sex in the association between a bad mood and later ASD. In particular, female infants experiencing bad moods have a higher risk of ASD than others (RR: 3.59, 95% CI: 1.91–6.75). / Conclusions: / The study findings provide important information for future intervention to reduce the risk of future ASD.

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Language：Others   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

Abstract

Background

Sleep problems and irritable temperaments are common among infants with autism spectrum disorder (ASD). The prospective association between such sleep problems and irritable temperaments and ASDs needs to be determined for elucidating the mechanism and exploring the future intervention study. Thus, in this study, we investigated whether sleep quality and temperament in 1-month-old infants are associated with the onset of ASD in 3-year-old children. We also assessed its sex-stratified associations.

Methods

We conducted a longitudinal study using data from 69,751 mothers and infants from a large-cohort study, the Japan Environment and Children’s Study. We examined the prospective association between infant sleep quality and temperament at 1 month of age and ASD diagnosis by 3 years of age.

Results

Here we show infants with longer daytime sleep have a higher risk of later ASD than those with shorter daytime sleep (risk ratio [RR]: 1.33, 95% confidence interval [CI]: 1.01–1.75). Infants who experienced intense crying have a higher risk of ASD than those who did not (RR: 1.31, 95% CI: 1.00–1.72). There is a difference in sex in the association between a bad mood and later ASD. In particular, female infants experiencing bad moods have a higher risk of ASD than others (RR: 3.59, 95% CI: 1.91–6.75).

Conclusions

The study findings provide important information for future intervention to reduce the risk of future ASD.

DOI： 10.1038/s43856-023-00314-9

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Other Link： https://www.nature.com/articles/s43856-023-00314-9

Language：English   Publishing type：Research paper (scientific journal)   Publisher：JMIR Publications Inc.  

Background: Ensuring an appropriate continuum of care in maternal, newborn, and child health, as well as providing nutrition care, is challenging in remote areas. To make care accessible for mothers and infants, we developed a telehealth care system called Portable Health Clinic for Maternal, Newborn, and Child Health. / Objective: Our study will examine the telehealth care system’s effectiveness in improving women’s and infants’ care uptake and detecting their health problems. / Methods: A quasi-experimental study will be conducted in rural Bangladesh. Villages will be allocated to the intervention and control areas. Pregnant women (≥16 gestational weeks) will participate together with their infants and will be followed up 1 year after delivery or birth. The intervention will include regular health checkups via the Portable Health Clinic telehealth care system, which is equipped with a series of sensors and an information system that can triage participants’ health levels based on the results of their checkups. Women and infants will receive care 4 times during the antenatal period, thrice during the postnatal period, and twice during the motherhood and childhood periods. The outcomes will be participants’ health checkup coverage, gestational and neonatal complication rates, complementary feeding rates, and health-seeking behaviors. We will use a multilevel logistic regression and a generalized estimating equation to evaluate the intervention’s effectiveness. / Results: Recruitment began in June 2020. As of June 2022, we have consented 295 mothers in the study. Data collection is expected to conclude in June 2024. / Conclusions: Our new trial will show the effectiveness and extent of using a telehealth care system to ensure an appropriate continuum of care in maternal, newborn, and child health (from the antenatal period to the motherhood and childhood periods) and improve women’s and infants’ health status.

DOI： 10.2196/41586

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Language：Others   Publishing type：Research paper (scientific journal)  

DOI： 10.1136/bmjopen-2020-044928

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DOI： 10.1136/bmjopen-2020-042058