Sleep Quality and Insomnia Severity among Italian University Students: A Latent Profile Analysis

Latent profile of the insomnia severity index: A longitudinal study

STUDY OBJECTIVES

To identify the distinct classification of insomnia symptoms and to explore their association with sleep problems and depression.

METHODS

Latent profile analysis was used to examine patterns of insomnia symptoms in two samples. Discovery and replication samples comprised 1043 (Mean age at baseline = 18.95 ± 0.93 years, 62.2% females) and 729 (Mean age at baseline = 18.71 ± 1.02 years, 66.4% females) college students, respectively. Participants completed measures of sleep problems (insomnia symptoms, sleep quality, susceptibility to insomnia, perceived consequences of insomnia, dream recall frequency, and percentage of recurring nightmares) and other psychological variables (rumination and depression). Binary logistic regression was used to analyze the effects of different types of insomnia symptoms at baseline on sleep problems and depression two years later.

RESULTS

Four classes of insomnia symptoms were identified, and classified as "non-insomnia" (class 1, 45.7%), "mild subjective symptoms but severe subjective feelings" (class 2, 23.9%), "severe subjective symptoms but mild subjective feelings" (class 3, 22.0%), and "high insomnia risk" (class 4, 8.4%), respectively. Compared with the group classified as non-insomnia group, other classifications significantly predicted insomnia two years later, only class 4 significantly predicted depression, and class 3 significantly predicted susceptibility to insomnia, after adjusting gender, insomnia, depression, and susceptibility to insomnia at baseline.

CONCLUSIONS

The findings highlighted the importance of identifying the patterns of insomnia symptoms, and the need for tailored intervention to improve sleep problems. Additionally, when screening for insomnia symptoms, simplified screening using Insomnia Severity Index (ISI) dimensions or items should be considered.

Effectiveness of Mindfulness-Based Stress Reduction on Mental Health and Psychological Quality of Life among University Students: A GRADE-Assessed Systematic Review

Background

Psychological distress is a progressive health problem that has been linked to decreased quality of life among university students. This meta-analysis reviews existing randomized controlled trials (RCTs) that have examined the effects of mindfulness-based stress reduction (MBSR) on the relief of psychosomatic stress-related outcomes and quality of life among university students.

Methods

The PubMed, EMBASE, Web of Science, PsycINFO (formerly PsychLit), Ovid MEDLINE, ERIC, Scopus, Google Scholar, ProQuest, and Cochrane Library databases were searched in November 2023 to identify the RCTs for analysis. Data on pathology (anxiety, depression, and perceived stress), physical capacity (sleep quality and physical health), and well-being (mindfulness, self-kindness, social function, and subjective well-being) were analyzed.

Results

Of the 276 articles retrieved, 29 met the inclusion criteria. Compared with control therapies, the pooled results suggested that MBSR had significant effects, reducing anxiety (SMD = -0.29; 95% CI: -0.49 to -0.09), depression (SMD = -0.32; 95% CI: -0.62 to -0.02), and perceived stress (SMD = -0.41; 95% CI: -0.60 to -0.29) and improving mindfulness (SMD = 0.34; 95% CI: 0.08 to 0.59), self-kindness (SMD = 0.57; 95% CI: 0.30 to 1.12), and physical health (SMD = -0.59; 95% CI: -1.14 to -0.04). No significant differences were observed in sleep quality (SMD = -0.20; 95% CI: -0.06 to 0.20), social function (SMD = -0.71; 95% CI: -2.40 to 0.97), or subjective well-being (SMD = 0.07; 95% CI: -0.18 to 0.32). The quality of the evidence regarding sleep quality and physical health outcomes was low.

Conclusions

MBSR therapy appears to be potentially useful in relieving functional emotional disorders. However, additional evidence-based large-sample trials are required to definitively determine the forms of mindfulness-based therapy that may be effective in this context and ensure that the benefits obtained are ongoing. Future studies should investigate more personalized approaches involving interventions that are tailored to various barriers and students' clinical characteristics. To optimize the effects of such interventions, they should be developed and evaluated using various designs such as the multiphase optimization strategy, which allows for the identification and tailoring of the most valuable intervention components.

Latent profile analysis for health-related quality of life, sleep quality, morning and evening type, and internet addiction among medical students

Health-related quality of life, sleep quality, morning and evening types, and internet addiction are of significant importance to the development of medical students, yet they have rarely been studied. Taking this into consideration, the study aimed to confirm latent profiles in health-related quality of life, sleep quality, morning and evening types, and internet addiction in medical students and investigate the characteristics of participants in each profile to provide suggestions for students' health. This was an observational cross-sectional study including 1221 medical student subjects at China Medical University in 2019. Multiple correspondence analysis was the initial step to verify the correspondence, dispersion, and approximation of variable categories. Latent profile analysis was used to identify the multiple correspondences between the levels of variables. Three profiles were found, including: (1) The Low sleep quality profile was characterized by the lowest sleep quality among the three existing profiles. (2) The High health-related quality of life and Low internet addiction profile was characterized by the highest level of health-related quality of life but the lowest level of internet addiction. (3) The Low health-related quality of life and High internet addiction profile was characterized by the highest standardized values of internet addiction but the lowest standardized values of health-related quality of life. This study had important implications for improving student health and supported the medical universities and hospitals in implementing targeted policies based on distinctive student characteristics.

A latent profile analysis of sleep disturbance in relation to mental health among college students in China

Aims

This study aimed to examine the subtype classification characteristics of sleep disturbance (SD) in college students and their associations with sample characteristic factors and mental health outcomes.

Methods

The sample comprised 4,302 college students (Mean age = 19.92 ± 1.42 years, 58.6% females). The Youth Self-Rating Insomnia Scale, Beck Depression Inventory, 8-item Positive Subscale of the Community Assessment of Psychic Experiences, and 10-item Connor-Davidson Resilience Scale were used to assess adolescents' sleep disturbance, depressive symptoms, psychotic-like experiences (PLEs), and resilience. Latent profile analysis, logistic regression, and liner regression analysis were used to analyze the data.

Results

Three subtypes of SD in college students were identified: the high SD profile (10.6%), the mild SD profile (37.5%), and the no SD profile (51.9%). Compared with college students in the "no SD" profile, risk factors for "high SD" include being male and poor parental marital status. Sophomores were found to predict the "high SD" profile or "mild SD" profile relative to the "no SD" profile. College students in the "mild SD" profile or "high SD" profile were more likely to have a higher level of depressive symptoms and PLEs, while a lower level of resilience.

Conclusion

The findings highlighted that target intervention is urgently needed for male college students, sophomores, and those with poor parental marital status in the "mild SD" profile or "high SD" profile.

Is Satisfaction with Online Learning Related to Depression, Anxiety, and Insomnia Symptoms? A Cross-Sectional Study on Medical Undergraduates in Romania

The objective of this study was to investigate online learning satisfaction in a sample of university students and its relationship with depression, anxiety, insomnia, and the average number of hours spent online. A total of 463 medical students were recruited for an online survey conducted from February to March 2022 with the main objective of estimating online learning satisfaction, while secondary outcomes involved assessing the relationship between online learning and depression, anxiety, insomnia, and the average number of hours spent online. A total of 285 participants were female (71.4%) and the mean age was 20.2 years. The results revealed that depression, anxiety, and insomnia are negatively correlated with overall satisfaction with e-learning. The more time students spent online, the greater the overall satisfaction. There are significant differences regarding student perceptions of interactivity in online learning satisfaction outcomes (p < 0.05, η2 partial Eta Squared-0.284). The opportunity to learn via chat-box presented differences in overall satisfaction while pleasant aspects of online learning, such as “no travel” and “economy”, were related to satisfaction. The students revealed that the higher the psychopathology scores, the less satisfied they were with online learning, while a higher number of hours spent online contributed positively to satisfaction.

Association of Sleep Patterns with Type 2 Diabetes Mellitus: A Cross-Sectional Study Based on Latent Class Analysis

Sleep duration, sleep quality and circadian rhythm disruption indicated by sleep chronotype are associated with type 2 diabetes. Sleep involves multiple dimensions that are closely interrelated. However, the sleep patterns of the population, and whether these sleep patterns are significantly associated with type 2 diabetes, are unknown when considering more sleep dimensions. Our objective was to explore the latent classes of sleep patterns in the population and identify sleep patterns associated with type 2 diabetes. Latent class analysis was used to explore the best latent classes of sleep patterns based on eleven sleep dimensions of the study population. Logistic regression was used to identify sleep patterns associated with type 2 diabetes. A total of 1200 participants were included in the study. There were three classes of sleep patterns in the study population: "circadian disruption with daytime dysfunction" (class 1), "poor sleep status with daytime sleepiness" (class 2), and "favorable sleep status" (class 3). After controlling for all confounding factors, people in class 2 have significantly higher prevalence of type 2 diabetes than those in class 3 (OR: 2.24, 95% CI 1.26-4.00). Sleep problems have aggregated characteristics. People with sleep patterns involving more or worse sleep problems have higher significantly prevalence of T2DM.