Editor’s Presentation Benefit of healthy lifestyle on cardiovascular risk factor control: Focus on body weight, exercise and sleep quality

Piepoli, Massimo F

doi:10.1177/2047487319861847

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…Additionally, depression has bene linked to lower quality sleep and increased day time exhaustion [31,34,46,54,55]. There is also strong literature evidence for the link between weight and sleep disorders [4]. There is epidemiological evidence for the relationship between increased age and increased sleep disorders, older age has been associated with increased sleep latency, decreased time spent in rapid eye movement (REM) sleep and stage-3 sleep, and increased frequency of waking up during the night PLOS ONE [56][57][58][59][60][61][62].…”

Section: Plos Onementioning

confidence: 99%

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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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Section: Plos Onementioning

confidence: 99%

“…Sleep is critical to a person's physical and mental health [1][2][3][4][5][6]. However, the prevalence of diagnosed sleep disorders among American patients has significantly increased over the past decade [1,5,[7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%