Eumorfia Kondili

Adolescents often experience insufficient sleep and have unhealthy sleep habits. Our aim was to investigate the sleep patterns of secondary education students in Heraklion, Crete, Greece and their association with school performance and health habits. We conducted a community-based cross-sectional study with 831 students aged 13–19 years who completed an online self-reported questionnaire related to sleep and health habits. The data are mostly numerical or categorical, and an analysis was performed using t-tests, chi-square tests and multiple logistic regression. During weekdays, the students slept for an average of 7 ± 1.1 h, which is significantly lower than the 7.8 ± 1.5 h average on weekends (p < 0.001). Nearly 79% reported difficulty waking up and having insufficient sleep time, while 73.8% felt sleepy at school at least once a week. Having sufficient sleep time ≥ 8 h) was positively correlated with better academic performance (OR: 1.48, CI: 1.06–2.07, p = 0.022) and frequent physical exercise (never/rarely: 13.5%, sometimes: 21.2%, often: 65.3%; p = 0.002). Conversely, there was a negative correlation between adequate sleep and both smoking (OR: 0.29, CI: 0.13–0.63) and alcohol consumption (OR: 0.51, CI: 0.36–0.71, p = 0.001). In conclusion, this study shows that students in Heraklion, Crete frequently experience sleep deprivation, which is associated with compromised academic performance, reduced physical activity and an increased likelihood of engaging in unhealthy behaviors like smoking and alcohol consumption.

During pressure support ventilation (PSV), excessive assist results in weak inspiratory efforts and promotes diaphragm atrophy and delayed weaning. The aim of this study was to develop a classifier using a neural network to identify weak inspiratory efforts during PSV, based on the ventilator waveforms. Recordings of flow, airway, esophageal and gastric pressures from critically ill patients were used to create an annotated dataset, using data from 37 patients at 2–5 different levels of support, computing the inspiratory time and effort for every breath. The complete dataset was randomly split, and data from 22 patients (45,650 breaths) were used to develop the model. Using a One-Dimensional Convolutional Neural Network, a predictive model was developed to characterize the inspiratory effort of each breath as weak or not, using a threshold of 50 cmH2O*s/min. The following results were produced by implementing the model on data from 15 different patients (31,343 breaths). The model predicted weak inspiratory efforts with a sensitivity of 88%, specificity of 72%, positive predictive value of 40%, and negative predictive value of 96%. These results provide a ‘proof-of-concept’ for the ability of such a neural-network based predictive model to facilitate the implementation of personalized assisted ventilation.