Eye-masks and earplugs compared to headband in nulliparas on increasing spontaneous vaginal delivery: a randomized trial.

BACKGROUND: A majority of pregnant women experience sleep disruption during pregnancy, especially in the third trimester. Lack of sleep is associated with preterm birth, prolonged labor and higher cesarean section rate. Six or less hours of night sleep in the last month of pregnancy is associated with a higher rate of caesarean births. Eye-masks and earplugs compared to headband improve night sleep by 30 or more minutes. We sought to evaluate eye-mask and earplugs compared to sham/placebo headbands on spontaneous vaginal delivery. METHODS: This randomized trial was conducted from December 2019-June 2020. 234 nulliparas, 34-36 weeks' gestation with self-reported night sleep < 6 h were randomized to eye-mask and earplugs or sham/placebo headband (both characterized as sleep aids) to be worn each night to delivery. After two weeks, interim outcome data of the average night sleep duration and the trial sleep related questionnaire was answered through the telephone. RESULTS: Spontaneous vaginal delivery rates were 60/117(51.3%) vs. 52/117(44.4%) RR 1.15 95% CI 0.88-1.51 P = 0.30 for eye-mask and earplugs or headband respectively. At 2-weeks into the intervention period, the eye-mask and earplugs arm reported longer night sleep duration 7.0 ± 1.2 vs. 6.6 ± 1.5 h P = 0.04, expressed increased satisfaction with the allocated aid 7[6.0-8.0] vs. 6[5.0-7.5] P < 0.001, agreed they slept better 87/117(74.4%) vs. 48/117(41.0%) RR 1.81 95% CI 1.42-2.30 NNTb 4 (2.2-4.7) P < 0.001 and higher compliance median[interquartile range] 5[3-7] vs. 4[ 2-5] times per week of sleep aid use P = 0.002. CONCLUSION: Eye-mask and earplugs use at home in late third trimester do not increase the spontaneous vaginal delivery rate even though self-reported night sleep duration, sleep quality, satisfaction and compliance with allocated sleep aid were significantly better than for sham/placebo headband. Trial registration This trial was registered with ISRCTN on June 11, 2019 with trial identification number: ISRCTN99834087 .

Study protocol for the St James's Hospital, Tallaght University Hospital, Trinity College Dublin Allied Researchers' (STTAR) Bioresource for COVID-19.

Background: The current coronavirus disease 2019 (COVID-19) pandemic began in Ireland with the first confirmed positive case in March 2020. In the early stages of the pandemic clinicians and researchers in two affiliated Dublin hospitals identified the need for a COVID-19 biobanking initiative to support and enhance research into the disease. Through large scale analysis of clinical, regional, and genetic characteristics of COVID-19 patients, biobanks have helped identify, and so protect, at risk patient groups The STTAR Bioresource has been created to collect and store data and linked biological samples from patients with SARS-CoV-2 infection and healthy and disease controls. Aim: The primary objective of this study is to build a biobank, to understand the clinical characteristics and natural history of COVID-19 infection with the long-term goal of research into improved disease understanding, diagnostic tests and treatments. Methods: This is a prospective dual-site cohort study across two tertiary acute university teaching hospitals. Patients are recruited from inpatient wards or outpatient clinics. Patients with confirmed COVID-19 infection as well as healthy and specific disease control groups are recruited.  Biological samples are collected and a case report form detailing demographic and medical background is entered into the bespoke secure online Dendrite database. Impact: The results of this study will be used to inform national and international strategy on health service provision and disease management related to COVID-19. In common with other biobanks, study end points  evolve over time as new research questions emerge. They currently include patient survival, occurrence of severe complications of the disease or its therapy, occurrence of persistent symptoms following recovery from the acute illness and vaccine responses.

Harnessing artificial intelligence in cardiac rehabilitation, a systematic review.

Aim: This systematic review aims to evaluate the current body of research surrounding the efficacy of artificial intelligence (AI) in cardiac rehabilitation. Presently, AI can be incorporated into personal devices such as smart watches and smartphones, in diagnostic and home monitoring devices, as well as in certain inpatient care settings. Materials & methods: The PRISMA guidelines were followed in this review. Inclusion and exclusion criteria were set using the Population, Intervention, Comparison and Outcomes (PICO) tool. Results: Eight studies meeting the inclusion criteria were found. Conclusion: Incorporation of AI into healthcare, cardiac rehabilitation delivery, and monitoring holds great potential for early detection of cardiac events, allowing for home-based monitoring, and improved clinician decision making.

Lay abstract Artificial intelligence (AI) involves the use of technologies capable of making decisions based on data provided. AI can be used in healthcare to provide actionable data for a clinician by analyzing patterns in patient data to predict outcomes and guide treatment. Cardiovascular disease is the leading cause of death worldwide. Cardiac rehabilitation is a therapy proven to reduce mortality and morbidity from cardiovascular disease. This study outlines three cases of AI based healthcare tools in cardiac rehabilitation. This includes the provision of personalized, home-based cardiac rehabilitation, the early detection of cardiac events through smart watch monitoring and by providing clinician decision making support in cardiac failure rehabilitation.