Dysfunctional beliefs, sleep hygiene and sleep quality in university students

An Artificial Intelligence Platform to Stratify the Risk of Experiencing Sleep Disturbance in University Students After Analyzing Psychological Health, Lifestyle, and Sports: A Multicenter Externally Validated Study

Background

Sleep problems are prevalent among university students, yet there is a lack of effective models to assess the risk of sleep disturbance. Artificial intelligence (AI) provides an opportunity to develop a platform for evaluating the risk. This study aims to develop and validate an AI platform to stratify the risk of experiencing sleep disturbance for university students.

Methods

A total of 2243 university students were included, with 1882 students from five universities comprising the model derivation group and 361 students from two additional universities forming the external validation group. Six machine learning techniques, including extreme gradient boosting machine (eXGBM), decision tree (DT), k-nearest neighbor (KNN), random forest (RF), neural network (NN), and support vector machine (SVM), were employed to train models using the same set of features. The models' prediction performance was assessed based on discrimination and calibration, and feature importance was determined using Shapley Additive exPlanations (SHAP) analysis.

Results

The prevalence of sleep disturbance was 44.69% in the model derivation group and 49.58% in the external validation group. Among the developed models, eXGBM exhibited superior performance, surpassing other models in metrics such as area under the curve (0.779, 95% CI: 0.728-0.830), accuracy (0.710), precision (0.737), F1 score (0.692), Brier score (0.193), and log loss (0.569). Calibration and decision curve analyses demonstrated favorable calibration ability and clinical net benefits, respectively. SHAP analysis identified five key features: stress score, severity of depression, vegetable consumption, age, and sedentary time. The AI platform was made available online at https://sleepdisturbancestudents-xakgzwectsw85cagdgkax9.streamlit.app/, enabling users to calculate individualized risk of sleep disturbance.

Conclusion

Sleep disturbance is prevalent among university students. This study presents an AI model capable of identifying students at high risk for sleep disturbance. The AI platform offers a valuable resource to guide interventions and improve sleep outcomes for university students.

Sleep behaviors predicted sleep disturbances among Chinese health science students: a cross-sectional study

PURPOSE

Healthy sleep is essential for individuals' physiological and psychological health. Health science students experience a high prevalence of sleep disturbances which may be due to maladaptive behaviors. This study aimed to examine the associations of sleep behaviors including sleep hygiene and bedtime procrastination with the associations of sleep disturbances (e.g., poor sleep quality, insomnia, and short sleep).

METHODS

This cross-sectional study included health science students from a medical university in Shanghai, China. Sleep disturbances included poor sleep quality, insomnia, and short sleep. They were measured by the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), and one question "How many hours of sleep did you usually get during the past week?", respectively. Sleep behaviors included sleep hygiene and bedtime procrastination measured by the Sleep Hygiene Index (SHI) and Bedtime Procrastination Scale (BPS), respectively. Logistic regression was performed while controlling for potential confounders.

RESULTS

A total of 464 health science students participated. Poorer overall sleep hygiene and more bedtime procrastination were independently associated with higher odds of poor sleep quality (OR=1.065, 95% CI 1.028-1.103; OR=1.040, 95% CI 1.006-1.075, respectively) and insomnia (OR=1.059, 95% CI 1.018-1.101; OR=1.093, 95% CI 1.049-1.139, respectively). More bedtime procrastination was associated with higher odds of short sleep (OR=1.148, 95% CI 1.093-1.206). Commonly reported specific sleep behaviors, such as "Going to bed later than intended", "Doing other things than sleep at bedtime", and "Easily stopping what I am doing at bedtime", were also related to higher odds of sleep disturbances.

CONCLUSIONS

Sleep hygiene and bedtime procrastination were strong predictors of sleep disturbances. Tailored interventions targeting specific sleep behaviors are warranted to clarify their effect on sleep disturbances.

A systematic review and meta-analysis of the association between young adults' sleep habits and substance use, with a focus on self-medication behaviours

Young adults (18-30 years) are vulnerable to sleep-wake disturbances and substance use, which are bi-directionally associated. The present work aims to organise the literature that deals with the association between sleep and substance use in young adults, also considering self-medication behaviours. We adopted a framework that accounts for the multidimensionality of sleep and the effect of different substances. We considered sleep disturbances (insomnia symptoms, sleep quality), sleep health dimensions (duration, satisfaction, efficiency, timing, daytime alertness), circadian characteristics (chronotype). Substances were alcohol, caffeine, nicotine, cannabis, others. We included 46 studies. The use of caffeine and nicotine was associated with higher odds of sleep disturbances. No significant effect was detected for sleep duration. In narrative findings, daytime dysfunction was associated with alcohol and caffeine use, and poor sleep satisfaction with nicotine use. Few evidence were available for the other sleep health dimensions. Evening chronotype was associated with alcohol, caffeine, and nicotine use. Few studies focused on cannabis or self-medication. Longitudinal results were inconclusive. We found a distinct pattern of associations between different substances and different sleep outcomes. Further investigation considering the multidimensionality of sleep would create a better understanding of the complex relationship between substance use and sleep health in young adults.

Insomnia, Daytime Sleepiness, and Quality of Life among 20,139 College Students in 60 Countries around the World-A 2016-2021 Study

Background: Sleep disorders are a widespread phenomenon, and the number of individuals suffering from them is increasing every year, especially among young adults. Currently, the literature lacks studies that cover both countries with different levels of development and a period before the announcement of the ongoing COVID-19 pandemic. Therefore, this study aims to globally assess the prevalence of insomnia and daytime sleepiness among students and assess their quality of life. Methods: For this purpose, our own questionnaire was distributed online via Facebook.com. In addition to the questions that assessed socioeconomic status, the survey included psychometric tools, such as the Athens insomnia scale (AIS), the Epworth sleepiness scale (ESS), and the Manchester short assessment of the quality of life (MANSA). The survey distribution period covered 31 January 2016 to 30 April 2021. Results: The survey involved 20,139 students from 60 countries around the world. The vast majority of the students were women (78.2%) and also those residing in countries with very high levels of development and/or high GDP (gross domestic product) per capita at 90.4% and 87.9%, respectively. More than half (50.6%) of the respondents (10,187) took the survey before the COVID-19 pandemic was announced. In the group analyzed, 11,597 (57.6%) students obtained a score indicative of insomnia and 5442 (27.0%) a score indicative of daytime sleepiness. Women, low-income residents, and nonmedical students were significantly more likely to have scores indicating the presence of insomnia. Individuals experiencing both sleepiness (B = −3.142; p < 0.001) and daytime sleepiness (B = −1.331; p < 0.001) rated their quality of life significantly lower. Conclusions: Insomnia and excessive daytime sleepiness are common conditions among students worldwide and are closely related. The COVID-19 pandemic significantly altered students’ diurnal rhythms, which contributed to an increase in insomnia. Students in countries with a high GDP per capita index are significantly less likely to develop insomnia compared to the residents of countries with a low GDP per capita index. Sleep disorders definitely reduce the quality of life of students.

Üniversite Öğrencilerinde Vücut Kompozisyonu ve Antropometrik Ölçümlerin Uyku Süresi ve Uyku Kalitesiyle İlişkisi

Amaç: Bu çalışmada üniversite öğrencilerinde vücut kompozisyonu ve antropometrik ölçümlerin uyku süresi ve uyku kalitesiyle ilişkisinin incelenmesi amaçlanmıştır.Yöntem: Kesitsel tipteki çalışma bir fakültede öğrenim gören, araştırmaya katılmayı kabul eden ve dahil edilme kriterlerini karşılayan Bandırma Onyedi Eylül Üniversitesi Sağlık Bilimleri Fakültesinin 310 öğrencisiyle yürütülmüştür. Araştırmanın verileri Tanıtıcı Bilgi Formu ve Pittsburg Uyku Kalitesi İndeksi’ni içeren anket formu kullanılarak yüz yüze görüşme yöntemiyle toplanmıştır. Öğrencilerin vücut kompozisyonlarının belirlenmesinde Biyoelektrik İmpedans Analizi (BİA) ile yağsız vücut kütlesi, vücut yağ ve kas kütlesi incelenmiş, Beden Kütle İndeksi (BKİ), bel çevresi, Bel Kalça Oranı (BKO), Bel Boy Oranı (BBO) ve triseps, supskapular, biseps, suprailiak Deri Kıvrım Kalınlıkları (DKK) değerlendirilmiştir. Verilerin analizinde sayı, yüzde, ortalama, standart sapma, Student’s t testi ve Pearson korelasyon analizi kullanılmıştır.Bulgular: Öğrencilerin uyku süresi ortalama 7,10±1,66 saattir, %74,8’i kötü uyku kalitesine sahiptir. Her iki cinsiyette yağsız vücut kütlesi (kg) ve vücut kas kütlesi (kg) ile uyku kalitesi skoru arasında istatistiksel olarak anlamlı, negatif yönlü zayıf ilişki, erkek öğrencilerde ise suprailiak DKK ve biseps DKK ile uyku kalitesi arasında istatistiksel olarak anlamlı, pozitif yönlü zayıf ilişki saptanmıştır (p&lt;0,05).Sonuç: Üniversite öğrencilerinin uyku süreleri önerilen düzeydedir, buna karşın öğrencilerin dörtte üçü kötü uyku kalitesine sahiptir. Kız ve erkek öğrencilerde yağsız vücut kütlesi (kg) ve vücut kas kütlesi (kg) azaldıkça uyku kalitesi kötüleşmekte, erkek öğrencilerde ise suprailiak DKK ve biseps DKK arttıkça uyku kalitesi kötüleşmektedir.  

Associations of Sleep Insufficiency and Chronotype with Inflammatory Cytokines in College Students

PURPOSE

Sleep insufficiency and circadian rhythm disturbances in college students have become prominent. Current findings show that sleep insufficiency is closely related to inflammation. Studies on the correlation between chronotype and inflammatory factors are still lacking. Therefore, this research intended to examine the relationships between sleep duration, chronotype and inflammatory cytokines in young adults, and to estimate the correlation between chronotype and inflammatory cytokines stratified by sleep duration.

PATIENTS AND METHODS

We conducted a cross-sectional study in April and May 2019. Participants were recruited from two colleges located in central China. The Pittsburgh Sleep Quality Index (PSQI) and the Morning and Evening Questionnaire-5 (MEQ-5) were administered to assess sleep duration and chronotype. Sleep duration less than 7 hours was defined as insufficient sleep. Fasting venous blood was collected to measure plasma levels of inflammatory markers including IL-1β, IL-6, TNF-α and IL-10.

RESULTS

A total of 723 participants were included in this study, with a mean age of 18.68 years (standard deviation=0.99). After adjusting for confounding factors, the results of generalized linear model showed that sleep insufficiency was positively correlated with IL-1β, TNF-α and IL-10; and evening-types (E-types) were positively associated with the levels of IL-1β and IL-6 (p<0.05). Compared to the control group (sleep sufficiency and M-types), there were positive interaction effects of sleep insufficiency and neutral-types (N-types) on the levels of IL-1β, IL-6, TNF-α and IL-10 (p<0.05). The hierarchical regression model showed that N-types and E-types were positively correlated to the levels of IL-1β, IL-6, TNF-α and IL-10 among college students with sleep insufficiency (p<0.05).

CONCLUSION

The levels of inflammatory markers were higher among college students with sleep insufficiency and E-types. N-types and E-types were positively correlated with IL-1β, IL-6, TNF-α and IL-10 among college students with sleep insufficiency.