scholarly journals Association between Screen Viewing Duration and Sleep Duration, Sleep Quality, and Excessive Daytime Sleepiness among Adolescents in Hong Kong

2014 ◽  
Vol 11 (11) ◽  
pp. 11201-11219 ◽  
Author(s):  
Yim Mak ◽  
Cynthia Wu ◽  
Donna Hui ◽  
Siu Lam ◽  
Hei Tse ◽  
...  
Keyword(s):  
Hong Kong ◽  
Sleep Quality ◽  
Sleep Duration ◽  
Excessive Daytime Sleepiness ◽  
Daytime Sleepiness ◽  
Screen Viewing

scholarly journals Predictors of Excessive Daytime Sleepiness in Medical Students: A Meta-Regression

2019 ◽  
Vol 1 (2) ◽  
pp. 209-219 ◽  
Author(s):  
Haitham Jahrami ◽  
Hajar Alshomili ◽  
Noora Almannai ◽  
Noora Althani ◽  
Adel Aloffi ◽  
...  
Keyword(s):  
Medical Students ◽  
Sleep Quality ◽  
Sleep Duration ◽  
Excessive Daytime Sleepiness ◽  
Daytime Sleepiness ◽  
Regression Models ◽  
Statistical Significance ◽  
Outcome Variable ◽  
Supporting Evidence ◽  
Meta Regression

Excessive daytime sleepiness (EDS) is highly prevalent among medical students and can have serious negative outcomes for both students and their patients. Little is known about the magnitude and predictors of EDS among medical college students. A meta-regression analysis was conducted to achieve these two targets. A systematic search was performed for English-language studies that reported the prevalence of EDS among medical students using the Epworth sleepiness scale (ESS), age, sex, sleep duration and sleep quality as predictive variables. A total of nine observational studies (K = 9, N = 2587) were included in the analyses. Meta-regression analyses were performed using mean age (years), sex (proportion of male subjects), sleep duration (hours/night) and sleep quality index score (continuous scale) as moderators for EDS—with the prevalence of EDS as an outcome variable. An interaction term of sleep duration X sleep quality was created to assess if these two variables simultaneously influenced the outcome variable. Utilizing the ESS, the pooled prevalence of EDS among medical students was 34.6% (95% Confidence Interval (CI) 18.3–50.9%). Meta-regression models of age, sex, sleep duration and sleep quality alone revealed poor predictive capabilities. Meta-regression models of sleep duration–sleep quality interaction revealed results with high statistical significance. The findings from this review contribute supporting evidence for the relationship between sleep duration and sleep quality scores (i.e., sleep duration X sleep quality score) in predicting EDS in medical students.

Excessive daytime sleepiness and sleep quality in medical students and their association with smartphone and internet addiction: A cross-sectional study

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
AnantprakashSiddharthkumar Saraf ◽  
HarshalShriram Sathe ◽  
Manoj Talapalliwar ◽  
Vrushti Patil ◽  
Vinay Kumar ◽  
...  
Keyword(s):  
Medical Students ◽  
Sleep Quality ◽  
Internet Addiction ◽  
Excessive Daytime Sleepiness ◽  
Daytime Sleepiness ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional

Restless leg syndrome in adult patients with β-thalassemia major

2021 ◽  
Vol 74 (7-8) ◽  
pp. 266-272
Author(s):  
Ramazan Erdem ◽  
Nazan Şimşek Erdem ◽  
Erdal Kurtoğlu
Keyword(s):  
Sleep Quality ◽  
Restless Legs Syndrome ◽  
Excessive Daytime Sleepiness ◽  
Daytime Sleepiness ◽  
Thalassemia Major ◽  
Adult Patients ◽  
Poor Sleep ◽  
Poor Sleep Quality ◽  
Restless Legs

To investigate the quality of sleep and the presence of Restless Legs Syndrome (RLS) in the Turkish population with β-thalassemia major (TM). The second aim was to assess the risk factors of RLS in TM adults. The study sample comprised of 121 patients at least 18 years old with TM. The patients’ socio-demographic information, body mass indexes (BMI), current medications, laboratory data were recorded. The patients were asked if they had a history of chronic kidney disease, diabetes mellitus (DM), and polyneuropathy. Restless legs syndrome was diagnosed according to the International Restless Legs Syndrome Study Group criteria. The sleep quality of the patients was assessed using the Pittsburgh Sleep Quality Index (PSQI) scale. The Epworth Sleepiness Scale (ESS) was used to assess excessive daytime sleepiness in the patients. The median age of the patients was 25 years (range 18-52). The mean BMI was 21.49±2.5 (R 14-26.5) for all patients. The prevalence of RLS was 5% in TM adult patients. The TM patients with RLS had no major complications of TM. The median PSQI global score of all patients was 3. Twenty-two (18.1%) patients had poor sleep quality. The reason for poor sleep quality was RLS symptoms in four patients (18%). There was no significant association between PSQI total score and blood parameters of the patients. Twelve (9.9%) patients had ESS scores greater than 10, which indicates excessive daytime sleepiness. The prevalence of RLS in TM patients was similar to that of the general Turkish adult population. These results indicate that RLS may occur in patients with TM, although they had a high level of serum ferritin.

scholarly journals 1092 Attention-deficit/hyperactivity Disorder Symptoms And Sleep Characteristics Within A Seasonal Affective Disorder Spectrum

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A415-A416
Author(s):  
K N Kim ◽  
D L Wescott ◽  
P L Franzen ◽  
B P Hasler ◽  
K A Roecklein
Keyword(s):  
Sleep Quality ◽  
Sleep Duration ◽  
Adhd Symptom ◽  
Symptom Severity ◽  
Daytime Sleepiness ◽  
Total Sleep Time ◽  
Sleep Time ◽  
Sleep Onset Latency ◽  
Adhd Symptoms ◽  
Depression Severity

Abstract Introduction Seasonal affective disorder (SAD) increases risk for attention-deficit/hyperactivity disorder (ADHD), although the mechanism linking SAD and ADHD is unknown. Prior research has identified insomnia and delayed sleep phase in both ADHD and SAD. We hypothesized that sleep duration and timing in SAD would be associated with the severity of ADHD symptoms. Methods Adults with SAD (n = 45) and subsyndromal SAD (S-SAD; n = 18) aged 19-66 years from Pittsburgh, PA., were assessed for ADHD symptoms, self-report sleep quality, depression severity, and daytime sleepiness in the Winter. Participants wore an Actiwatch for 4-14 days, from which we calculated sleep-onset latency, total sleep time, sleep midpoint, and sleep efficiency. We conducted a hierarchical multivariate linear regression to determine if sleep characteristics predict ADHD symptom severity in our sample while controlling for depressive symptoms. Age and gender were added in Step 1, seasonal depression severity in Step 2, actigraphy-based total sleep time, sleep onset latency, midpoint, and efficiency in Step 3, and self-reported sleep quality and daytime sleepiness in Step 4. Results Participants mostly scored in the “likely” or “highly likely” ADHD range (87.30%, n=55), higher than the national prevalence rate (4.4%). When controlling for age, gender, and depression severity, only shorter actigraphy-based total sleep time was associated with higher ADHD symptom severity (β=-0.30, p<0.05). However, when self-reported sleep quality and daytime sleepiness were added as predictors, total sleep time was no longer a statistically-significant predictor of ADHD symptom severity and only daytime sleepiness predicted ADHD symptom severity (β=0.31, p<0.05). Conclusion Our results suggest that individuals with SAD who experience daytime sleepiness and/or possibly shorter actigraphy-based sleep duration experience higher ADHD symptom severity. Treatments like Trans-C or CBT-I to improve daytime sleepiness and sleep duration may be indicated for SAD patients who present with comorbid ADHD symptoms. Support NIMH K.A.R. MH103303

scholarly journals Natural history of excessive daytime sleepiness: a population-based 5-year longitudinal study

SLEEP ◽  
2019 ◽  
Vol 43 (3) ◽  
Author(s):  
I Jaussent ◽  
C M Morin ◽  
H Ivers ◽  
Y Dauvilliers
Keyword(s):  
Sleep Duration ◽  
Excessive Daytime Sleepiness ◽  
Daytime Sleepiness ◽  
Adult Population ◽  
Population Based ◽  
Normal Weight ◽  
Point Reduction ◽  
Nighttime Sleep ◽  
Over Time

Abstract Study Objectives To document the rates of persistent, remitted, and intermittent excessive daytime sleepiness (EDS) in a longitudinal 5-year community study of adults and to assess how changes in risk factors over time can predict improvement of daytime sleepiness (DS). Methods Participants were recruited in 2007–2008 as part of a population-based epidemiological study implemented in Canada. They completed postal assessments at baseline and at each yearly follow-up. An Epworth Sleepiness Scale total score >10 indicated clinically significant EDS; a 4-point reduction between two consecutive evaluations defined DS improvement. Socio-demographic, lifestyle, health characteristics, and sleep-related measures (e.g. insomnia symptoms, sleep duration, sleep medication) were self-reported at each time point. Cox proportional-hazard models were used to predict EDS and DS remissions over 5 years. Results Among the 2167 participants, 33% (n = 714) met criteria for EDS at baseline, of whom 33% had persistent EDS, 44% intermittent EDS, and 23% remitted EDS over the follow-up. Furthermore, 61.4% of 2167 initial participants had stable DS, 27.1% sustained DS improvement and 8.5% transient improvement over the follow-up. The main predictors of EDS remission or DS improvement were normal weight, taking less hypnotics, having hypertension, increased nighttime sleep duration, and decreased insomnia, and depressive symptoms. Conclusions EDS waxes and wanes over time with frequent periods of remission and is influenced by behavioral characteristics and changes in psychological, metabolic, and nighttime sleep patterns. Targeting these predictors in future interventions is crucial to reduce DS in the general adult population.

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