Investigating the effect of daily sleep on memory capacity in college students

Assessment of sleepiness role in working memory and whole-body reaction time

BACKGROUND

Sleep provides physical and mental strength, and natural sleep is essential for cell growth, strengthening, stabilizing, and accelerating the improvement of memory function.

OBJECTIVE

The current investigation aimed to explore working memory influenced by sleepiness and related to whole-body reaction time, in order to identify some facets of the dynamics of this memory. To the best of our knowledge, this triple has not yet been explored in the literature.

METHODS

This study cross-sectional, descriptive-analytical was performed on a sample total of 45 volunteer undergraduate academic students were recruited by convenience sampling, including 35 females and 10 males with a mean age of 21.08 ± 1.10 years of old. Data were collected via a demographic checklist, Epworth Sleepiness Scale (ESS) questionnaire, Digital Maze test (for working memory), and visual/auditory whole-body reaction time measurement.

RESULTS

The working memory of each subject was divided into three types:1) thoughtful and precise, 2) Cautious and Conservative, and 3) messy and inaccurate. The triple of working memory, reaction time, and sleep versus sleepiness were all significantly related (P = 0.017-0.05).

CONCLUSION

The authors concluded that there might be some established infrastructure for adult working memory, while there might be a floating operator of working memory as well; influenced by various parameters, this study was influenced by sleep adequacy and physical readiness.

Deep learning approaches for sleep disorder prediction in an asthma cohort

OBJECTIVE

Sleep is a natural activity of humans that affects physical and mental health; therefore, sleep disturbance may lead to fatigue and lower productivity. This study examined 1 million samples included in the Taiwan National Health Insurance Research Database (NHIRD) in order to predict sleep disorder in an asthma cohort from 2002-2010.

METHODS

The disease histories of the asthma patients were transferred to sequences and matrices for the prediction of sleep disorder by applying machine learning (ML) algorithms, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF), and deep learning (DL) models, including Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and Convolution Neural Network (CNN).

RESULTS

Among 14,818 new asthma subjects in 2002, there were 4469 sleep disorder subjects from 2002 to 2010. The KNN, SVM, and RF algorithms were demonstrated to be successful sleep disorder prediction models, with accuracies of 0.798, 0.793, and 0.813, respectively (AUC: 0.737, 0.690, and 0.719, respectively). The results of the DL models showed the accuracies of the RNN, LSTM, GRU, and CNN to be 0.744, 0.815, 0.782, and 0.951, respectively (AUC: 0.658, 0.750, 0.732, and 0.934, respectively).

CONCLUSIONS

The results showed that the CNN model had the best performance for sleep disorder prediction in the asthma cohort.

Sleep Disturbances Rate among Medical and Allied Health Professions Students in Iran: Implications from a Systematic Review and Meta-Analysis of the Literature

Medicine and healthcare professions are prestigious and valued careers and, at the same time, demanding, challenging, and arduous jobs. Medical and allied health professions students, experiencing a stressful academic and clinical workload, may suffer from sleep disturbances. In Iran, several studies have been conducted to explore the prevalence rate among medical and healthcare professions students. The aim of this systematic review and meta-analysis was to quantitatively and rigorously summarize the existing scholarly literature, providing the decision- and policy-makers and educators with an updated, evidence-based synthesis. Only studies utilizing a reliable psychometric instrument, such as the Pittsburgh sleep quality index (PSQI), were included, in order to have comparable measurements and estimates. Seventeen investigations were retained in the present systematic review and meta-analysis, totaling a sample of 3586 students. Studies were conducted between 2008 and 2018 and reported an overall rate of sleep disturbances of 58% (95% confidence interval or CI 45-70). No evidence of publication bias could be found, but formal analyses on determinants of sleep disturbances could not be run due to the dearth of information that could be extracted from studies. Poor sleep is highly prevalent among Iranian medical and healthcare professions students. Based on the limitations of the present study, high-quality investigations are urgently needed to better capture the determinants of poor sleep quality among medical and healthcare professions students, given the importance and the implications of such a topic.