Epidemiology of Sleep Disorders Among Adolescents in Kerman, Iran, in 2019

Shamsadini, Ayeh; Amizadeh, Maryam; Motamed, Soheil; Rajabi, Fatemeh

doi:10.1080/19371918.2022.2089310

Cited by 3 publications

(5 citation statements)

References 29 publications

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“…The findings showed that large proportions of school-going adolescents experienced worry-related sleep problems (51.6%). This prevalence was higher than that reported in other countries, such as China, Iran, Germany, Australia, and the Netherlands (Fatima et al, 2016;Glasbeek et al, 2022;Guo et al, 2014;Lewien et al, 2021;Shamsadini et al, 2022). This indicated that adolescent worry-related sleep problems in Timor-Leste were more serious than those in middle-and high-income countries, thereby underscoring the urgent need for prevention efforts.…”

Section: Discussionmentioning

confidence: 68%

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Demographic, lifestyle, social, and psychological factors associated with worry‐related sleep problems among school‐going adolescents in Timor‐Leste

Cheah,

Kee,

Lim

et al. 2024

Child Adoles Psych Nursing

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ObjectiveAdolescent sleep problems are a worldwide public health issue. The present study examines factors associated with worry‐related sleep problems among school‐going adolescents. The country of interest is Timor‐Leste, a low‐income country, where studies pertaining to adolescent sleep problems are lacking.Design and MeasuresData were analysed from the Global School‐Based Student Health Survey Timor‐Leste (n = 3455). An ordered probit model was used to assess the effects of demographic, lifestyle, social, and psychological factors on different levels of worry‐related sleep problems (i.e., no, mild and severe sleep problems).ResultsSchool‐going adolescents were more likely to face mild or severe worry‐related sleep problems if they were older, passive smokers, alcohol drinkers and moderately active. School‐going adolescents who sometimes or always went hungry were more likely to experience worry‐related sleep problems than those who did not. Involvement in physical fights, being bullied, and loneliness were positively associated with the probability of having modest or severe worry‐related sleep problems.ConclusionAge, exposure to second‐hand smoke, alcohol consumption, physical activity, going hungry, physical fights, being bullied and loneliness are the important determining factors of adolescent worry‐related sleep problems. Policymakers should pay special attention to these factors when formulating intervention measures.

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Section: Discussionmentioning

confidence: 68%

“…Lewien et al (2021) conducted a cohort study in Germany and revealed that 20% of adolescents aged 10-17 years had sleep difficulties. Drawing from a sample of 451 adolescents in Iran, Shamsadini et al (2022) found that 31.9% of adolescents had sleep disturbance.…”

mentioning

confidence: 99%

Demographic, lifestyle, social, and psychological factors associated with worry‐related sleep problems among school‐going adolescents in Timor‐Leste

Cheah,

Kee,

Lim

et al. 2024

Child Adoles Psych Nursing

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“…However, these studies seem insufficient, especially in the young population. A review of previous research indicates that studies on the prevalence of sleep problems in Iran have been previously conducted (27)(28)(29)(30). However, some of these studies are from more than a decade ago (27,29), during which significant social changes have occurred.…”

Section: Introductionmentioning

confidence: 99%

“…Issues such as the COVID-19 pandemic create the need to gather new information. A study on the adolescent population (28) highlighted the need to investigate the prevalence of sleep problems in the young population. Some other studies (29,30) have only examined the severity of insomnia, but we have explored various dimensions of sleep problems in this study.…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting Sleep Problems Among Young People During COVID-19 Pandemic: A Report from Western Iran

Khazaie,

Rostampour,

Rezaie

et al. 2024

J Kermanshah Univ Med Sci

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Background: Sleep disorders are a public health problem, and sleep studies among ethnic groups are few. Therefore, this study aimed to evaluate sleep quality and sleep problems among Iranian people. Methods: This cross-sectional study was conducted on 4748 young people aged 18 to 35 (average: 27.58 years) in western Iran during the COVID-19 pandemic. In addition, 60.4% of the participants were men. The data were collected using the Pittsburgh Sleep Quality Inventory (PSQI), the Insomnia Severity Index (ISI), and the Demographic Information Questionnaire and analyzed with SPSS software version 22. Results: About 44.4% of the participants had unfavorable sleep quality and had sleep problems. Besides, the mean score of sleep problems (measured with PSQI) reported by the participants was 5.68±2.67. The logistic regression analysis showed that the participant’s age, education, marital status, occupation, drug abuse, alcohol consumption, use of mobile phones and the internet, and a history of previous sleep problems accounted for sleep problems during the COVID-19 pandemic. Conclusions: Based on the results, there was a high prevalence of sleep problems among Iranian young people during the COVID-19 pandemic.

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“…However, the prevalence of diagnosed sleep disorders among American patients has significantly increased over the past decade [1,5,[7][8][9][10]. Sleep disorders are a broad categorization of disorders that encompass conditions that lead to difficulty falling asleep, poor sleep quality, early waking, circadian rhythm disorders, parasomnias, sleep-related movement disorders, and sleep-related breathing disorders [11][12][13]. This is particularly important as sleep disorders are a significant risk factor for diabetes, heart disease, obesity, and depression, leading to decreased quality of life and increased healthcare usage [14,15].…”

Section: Introductionmentioning

confidence: 99%

Use of machine learning to identify risk factors for insomnia

Huang

2023

PLoS ONE

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Importance Sleep is critical to a person’s physical and mental health, but there are few studies systematically assessing risk factors for sleep disorders. Objective The objective of this study was to identify risk factors for a sleep disorder through machine-learning and assess this methodology. Design, setting, and participants A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. Methods A physician diagnosis of insomnia was the outcome of this study. Univariate logistic models, with insomnia as the outcome, were used to identify covariates that were associated with insomnia. Covariates that had a p<0.0001 on univariate analysis were included within the final machine-learning model. The machine learning model XGBoost was used due to its prevalence within the literature as well as its increased predictive accuracy in healthcare prediction. Model covariates were ranked according to the cover statistic to identify risk factors for insomnia. Shapely Additive Explanations (SHAP) were utilized to visualize the relationship between these potential risk factors and insomnia. Results Of the 7,929 patients that met the inclusion criteria in this study, 4,055 (51% were female, 3,874 (49%) were male. The mean age was 49.2 (SD = 18.4), with 2,885 (36%) White patients, 2,144 (27%) Black patients, 1,639 (21%) Hispanic patients, and 1,261 (16%) patients of another race. The machine learning model had 64 out of a total of 684 features that were found to be significant on univariate analysis (P<0.0001 used). These were fitted into the XGBoost model and an AUROC = 0.87, Sensitivity = 0.77, Specificity = 0.77 were observed. The top four highest ranked features by cover, a measure of the percentage contribution of the covariate to the overall model prediction, were the Patient Health Questionnaire depression survey (PHQ-9) (Cover = 31.1%), age (Cover = 7.54%), physician recommendation of exercise (Cover = 3.86%), weight (Cover = 2.99%), and waist circumference (Cover = 2.70%). Conclusion Machine learning models can effectively predict risk for a sleep disorder using demographic, laboratory, physical exam, and lifestyle covariates and identify key risk factors.

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Epidemiology of Sleep Disorders Among Adolescents in Kerman, Iran, in 2019

Cited by 3 publications

References 29 publications

Demographic, lifestyle, social, and psychological factors associated with worry‐related sleep problems among school‐going adolescents in Timor‐Leste

Demographic, lifestyle, social, and psychological factors associated with worry‐related sleep problems among school‐going adolescents in Timor‐Leste

Factors Affecting Sleep Problems Among Young People During COVID-19 Pandemic: A Report from Western Iran

Use of machine learning to identify risk factors for insomnia

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