Effectiveness of sleep education programs to improve sleep hygiene and/or sleep quality in college students

Author(s):  
Shellene K. Dietrich ◽  
Coleen M. Francis-Jimenez ◽  
Melida Delcina Knibbs ◽  
Ismael L. Umali ◽  
Marie Truglio-Londrigan
2016 ◽  
Vol 3 (1) ◽  
pp. 1168768 ◽  
Author(s):  
Hannah Peach ◽  
Jane F. Gaultney ◽  
David D. Gray ◽  
Peter Walla

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A73-A73
Author(s):  
Allison Nickel ◽  
Candice Lage ◽  
Abbye Porro ◽  
Chenlu Gao ◽  
Dayna Johnson ◽  
...  

Abstract Introduction Female and under-represented minority students (URMs) disproportionately experience sleep disturbances. Such sleep disparities may contribute to health disparities and academic achievement gaps. A potential solution is to improve sleep quality via education-based sleep interventions, but it remains unclear whether such interventions produce equitable sleep outcomes across gender and racial/ethnic groups. Methods We conducted a meta-analysis on sleep education interventions in high school and college students. We requested that authors provide demographic-separated data on how the intervention changed sleep knowledge, sleep quality, and sleep duration. Data were shared from 12 of the studies that met inclusion criteria (N=964; 64.8% female; 27.6% URM). We used random-effects models and computed Hedges’ g for each demographic group for each variable/study separately. We also systematically reviewed the content of each intervention to evaluate diversity, inclusion, and cultural sensitivity metrics. Results Sleep education significantly improved sleep knowledge (g=.82, p<.001) and sleep quality (g=.14, p=.01), but not sleep duration (g=.12, p=.28). Pre-to-post change scores indicated that the sleep education intervention was similarly effective for sleep knowledge across males (g=.80, p=.01) and females (g=.76, p=.002); sleep knowledge also similarly improved in White/Caucasian students (g=.94, p=.002), Asian students (g=.85, p=.08), and URM students (g=1.24, p=.01). Furthermore, sleep quality improved in Asian students (g=.28, p=.03), White/Caucasian students (g=.12, p=.09), and female students (g=.22, p=.008; but not males; g=.11, p=.22). Whereas URM students showed the largest improvement in sleep knowledge (g=1.24), they showed the least improvement in sleep quality (g=.07, p=.58). Systematic review of intervention content showed that 75% of interventions were individually-focused (e.g., interviews, participants selected their own goals), but only one sleep intervention was explicitly designed to be culturally sensitive and no interventions addressed financial, social, or neighborhood-level barriers to poor sleep. Conclusion Sleep education programs increase sleep knowledge in all student groups, but may not equitably improve sleep quality. Future sleep interventions will need to utilize theories of behavioral change, incorporate cultural tailoring, and address system-level financial, social, and other barriers to sleep quality in URM students. Support (if any) National Science Foundation (1920730 and 1943323)


2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1578.2-1578
Author(s):  
N. Gokcen ◽  
A. Komac ◽  
F. Tuncer ◽  
A. Yazici ◽  
A. Cefle

Background:Sleep disturbances have been described in Systemic Sclerosis (SSc). Confounding factors related to sleep quality are also investigated. Although sleep hygiene plays an important role in sleep quality, as far as we know, there are not enough data to show the effect of sleep hygiene on sleep quality of SSc.Objectives:To investigate sleep hygiene, its impact on sleep quality, and its association with demographic-clinical factors in patients with SSc, rheumatoid arthritis (RA), and healthy controls.Methods:The study was designed as cross-sectional. Forty-nine patients with SSc who fulfilled the 2013 ACR/EULAR classification criteria for SSc, 66 patients with RA who fulfilled 1987 revised classification criteria, and 30 healthy controls were included in the study. All participants were female. Demographic and clinical variables were documented. Disease activity index of both SSc and RA was calculated. SSc patients were assessed by questionnaires including Short Form 36 (SF-36), The Health Assessment Questionnaire Disability Index (HAQ-DI), Beck Anxiety and Beck Depression Inventory, Pittsburg Sleep Quality Index (PSQI), Sleep Hygiene Index (SHI). Additionally, RA patients and healthy controls were estimated by HAQ-DI, Beck Anxiety and Beck Depression Inventory, PSQI, and SHI. Logistic regression analysis was used to determine the predictors of sleep quality.Results:Preliminary results of the study were given. The baseline demographics were similar among groups. When comparing groups according to HAQ-DI, Beck Anxiety and Beck Depression Inventory, PSQI, and SHI, we found higher scores in SSc and RA rather than healthy controls (p<0.001, p=0.001, p=0.001, p<0.001, p=0.003; respectively). While depression and sleep hygiene were determined as the risk factors of sleep quality in SSc in univariate analysis, depression (OR=1.380, 95%CI: 1.065−1.784, p=0.015) and sleep hygiene (OR=1.201, 95%CI: 1.003−1.439, p=0.046) were also found in multivariate logistic model. In RA patients, while health status, depression, and anxiety were found as risk factors according to the univariate analysis, depression (OR=1.120, 95%CI: 1.006−1.245, p=0.038) was the only factor according to multivariate logistic model (Table).Conclusion:Although depression is a well-known clinical variable impacting on sleep quality, sleep hygiene should also be kept in mind as a confounding factor.References:[1]Milette K, Hudson M, Körner A, et al. Sleep disturbances in systemic sclerosis: evidence for the role of gastrointestinal symptoms, pain and pruritus. Rheumatology (Oxford). 2013 Sep;52(9):1715-20.[2]Sariyildiz MA, Batmaz I, Budulgan M, et al. Sleep quality in patients with systemic sclerosis: relationship between the clinical variables, depressive symptoms, functional status, and the quality of life. Rheumatol Int. 2013 Aug;33(8):1973-9.TableUnivariate logistic regression analysis of clinical variables to assess predictors of sleep qualitySystemic sclerosisRheumatoid arthritisOR (95% CI)pOR (95% CI)pHAQ-DI1.019 (0.882−1.177)0.8011.089 (1.011−1.173)0.025BDI score1.293 (1.082−1.547)0.0051.129 (1.036−1.230)0.006BAI score1.080 (0.997−1.169)0.0591.122 (1.038−1.214)0.004SHI1.200 (1.060−1.357)0.0041.048 (0.965−1.137)0.264Disease activitya0.707 (0.439−1.138)0.1531.446 (0.839−2.492)0.185aDisease activity was calculated by Valentini disease activity index for SSc and DAS28-CRP for RA.Disclosure of Interests:None declared


2019 ◽  
pp. 135910531986997 ◽  
Author(s):  
Huazhan Yin ◽  
Li Zhang ◽  
Dan Li ◽  
Lu Xiao ◽  
Mei Cheng

This study investigated the neuroanatomical basis of the association between depression/anxiety and sleep quality among 370 college students. The results showed that there was a significant correlation between sleep quality and depression/anxiety. Moreover, mediation results showed that the gray matter volume of the right insula mediated the relationship between depression/anxiety and sleep quality, which suggested that depression/anxiety may affect sleep quality through the right insula volume. These findings confirmed a strong link between sleep quality and depression/anxiety, while highlighting the volumetric variation in the right insula associated with emotional processing, which may play a critical role in improving sleep quality.


Author(s):  
Jessica Murphy ◽  
Christopher Gladney ◽  
Philip Sullivan

Student athletes balance academic, social, and athletic demands, often leading to increased levels of stress and poor sleep. This study explores the relationship between sleep quality, sleep hygiene, and psychological distress in a sample of student athletes. Ninety-four student athletes completed the six-item Kessler Psychological Distress Scale (K6), Sleep Hygiene Practice Scale, and four components from the Pittsburgh Sleep Quality Index. Age, gender, and sport were also collected. The Pittsburgh Sleep Quality Index revealed that 44.7% of student athletes received ≥6.5 hr of sleep each night; 31% of athletes showed signs of severe mental illness according to the K6. Stepwise regression predicted K6 scores with the Pittsburgh Sleep Quality Index and the Sleep Hygiene Practice Scale scores as independent variables. A significant model accounting for 26% of the variation in K6 scores emerged; sleep schedule and sleep disturbances were significant predictors. Athletic staff should highlight the importance of sleep for mental health; suggestions on how to help athletes are provided.


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