Sleep Duration and Chronic Disease Among Older Native Hawaiians or Other Pacific Islanders and Asians: Analysis of the Behavioral Risk Factor Surveillance System

Rainer, Michelle A; Palmer, Paula H.; Xie, Bin

doi:10.1007/s40615-022-01409-0

Cited by 3 publications

(3 citation statements)

References 60 publications

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“…The association between long sleep duration and stroke mortality was also significant among subjects with limited physical function and poorer health status [ 68 ]. The vulnerability for stroke in long sleepers may differ by ethnicity and gender, highlighting the need for ethnicity-specific targeted education and management [ 52 , 108 , 109 ].…”

Section: Long Sleep Duration and Stroke—mediating Factorsmentioning

confidence: 99%

Long Sleep Duration and Stroke—Highly Linked, Poorly Understood

Cai

Atanasov

2023

Neurology International

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Stroke is one of the leading causes of disability and mortality. Both short and long sleep durations are associated with adverse health outcomes. Cross-sectional studies have shown an increased prevalence of stroke in long sleepers. Long sleep duration increases stroke incidence and mortality in prospective epidemiological studies. Accumulating evidence suggests that the magnitude of the association between sleep and stroke appears to be stronger for longer sleep than shorter sleep, yielding a J-shaped curve. Potential links between long sleep duration and stroke include increased incidence of diabetes and atrial fibrillation, elevated levels of inflammation, arterial stiffness, and blood pressure variability. Long sleep duration is a strong marker and a plausible risk factor for stroke and should be considered in future scoring for risk stratification and stroke prevention.

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Section: Long Sleep Duration and Stroke—mediating Factorsmentioning

confidence: 99%

Long Sleep Duration and Stroke—Highly Linked, Poorly Understood

Cai

Atanasov

2023

Neurology International

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“…It is a common disease associated with marked impairment in function and quality of life, psychiatric and physical morbidity, and accidents [ 2 ]. Insomnia is an important risk factor for diabetes, heart disease, obesity and depression [ 3 , 4 ]. In addition, insomnia leads to low work efficiency, increased stress, and decreased quality of life [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

A flexible and dissolving traditional Chinese medicine microneedle patch for sleep-aid intervention

He,

Fang,

et al. 2024

Heliyon

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“…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]. Additionally, poor quality of sleep has been associated with decreased productivity at work and at school, increased stress, and decreased quality of life [16][17][18][19].…”

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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Sleep Duration and Chronic Disease Among Older Native Hawaiians or Other Pacific Islanders and Asians: Analysis of the Behavioral Risk Factor Surveillance System

Cited by 3 publications

References 60 publications

Long Sleep Duration and Stroke—Highly Linked, Poorly Understood

Long Sleep Duration and Stroke—Highly Linked, Poorly Understood

A flexible and dissolving traditional Chinese medicine microneedle patch for sleep-aid intervention

Use of machine learning to identify risk factors for insomnia

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