Sleep Duration and Physical Activity Profiles Associated With Self-Reported Stroke in the United States: Application of Bayesian Belief Network Modeling Techniques. Seixas, A., A., Henclewood, D., A., Williams, S., K., Jagannathan, R., Ramos, A., Zizi, F., & Jean-Louis, G. Frontiers in Neurology, 9:534, Frontiers, 7, 2018. ![Sleep Duration and Physical Activity Profiles Associated With Self-Reported Stroke in the United States: Application of Bayesian Belief Network Modeling Techniques [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex Introduction: Physical activity (PA) and sleep are associated with cerebrovascular disease and events like stroke. Though the interrelationships between PA, sleep, and other stroke risk factors have been studied, we are unclear about the associations of different types, frequency and duration of PA, sleep behavioral patterns (short, average and long sleep durations), within the context of stroke-related clinical, behavioral, and socio-demographic risk factors. The current study utilized Bayesian Belief Network analysis, a type of machine learning analysis, to develop profiles of physical activity (duration, intensity, and frequency) and sleep duration associated with or no history of stroke, given the influence of multiple stroke predictors and correlates. Such a model allowed us to develop a predictive classification model of stroke which can be used in post-stroke risk stratification and developing targeted stroke rehabilitation care based on an individual’s profile. Method: Analysis was based on the 2004-2013 National Health Interview Survey (n=288,888). Bayesian Belief Network analysis (BBN) was used to model the omnidirectional relationships of sleep duration and physical activity to history of stroke. Demographic, behavioral, health/medical, and psychosocial factors were considered as well as sleep duration [defined as short < 7hrs. and long ≥ 9hrs., referenced to healthy sleep (7-8 hrs.)], and intensity (moderate and vigorous) and frequency (times/week) of physical activity. Results: Of the sample, 48.1% were ≤45 years; 55.7% female; 77.4% were White; 15.9%, Black/African American; and 45.3% reported an annual income< $35K. Overall, the model had a precision index of 95.84%. We found that adults who reported 31-60 minutes of vigorous physical activity six times for the week and average sleep duration (7-8 hrs.) had the lowest stroke prevalence. Of the 36 sleep (short, average, and long sleep) and physical activity profiles we tested, 30 profiles had a self-reported stroke prevalence lower than the US national average of approximately 3.07%. Women, compared to men with the same sleep and physical activity profile, appeared to have higher self-reported stroke prevalence. We also report age differences across three groups 18-45, 46-65, and 66+ . Conclusion: Our findings indicate that several profiles of sleep duration and physical activity are associated with
@article{
title = {Sleep Duration and Physical Activity Profiles Associated With Self-Reported Stroke in the United States: Application of Bayesian Belief Network Modeling Techniques},
type = {article},
year = {2018},
keywords = {Stroke,gender,machine learning,physical activity,sleep duration},
pages = {534},
volume = {9},
websites = {https://www.frontiersin.org/article/10.3389/fneur.2018.00534/full},
month = {7},
publisher = {Frontiers},
day = {19},
id = {09dfc6a1-a77c-3ade-be08-a4dfd1608364},
created = {2018-07-22T20:13:09.567Z},
accessed = {2018-07-22},
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last_modified = {2018-07-22T20:13:09.567Z},
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abstract = {Introduction: Physical activity (PA) and sleep are associated with cerebrovascular disease and events like stroke. Though the interrelationships between PA, sleep, and other stroke risk factors have been studied, we are unclear about the associations of different types, frequency and duration of PA, sleep behavioral patterns (short, average and long sleep durations), within the context of stroke-related clinical, behavioral, and socio-demographic risk factors. The current study utilized Bayesian Belief Network analysis, a type of machine learning analysis, to develop profiles of physical activity (duration, intensity, and frequency) and sleep duration associated with or no history of stroke, given the influence of multiple stroke predictors and correlates. Such a model allowed us to develop a predictive classification model of stroke which can be used in post-stroke risk stratification and developing targeted stroke rehabilitation care based on an individual’s profile. Method: Analysis was based on the 2004-2013 National Health Interview Survey (n=288,888). Bayesian Belief Network analysis (BBN) was used to model the omnidirectional relationships of sleep duration and physical activity to history of stroke. Demographic, behavioral, health/medical, and psychosocial factors were considered as well as sleep duration [defined as short < 7hrs. and long ≥ 9hrs., referenced to healthy sleep (7-8 hrs.)], and intensity (moderate and vigorous) and frequency (times/week) of physical activity. Results: Of the sample, 48.1% were ≤45 years; 55.7% female; 77.4% were White; 15.9%, Black/African American; and 45.3% reported an annual income< $35K. Overall, the model had a precision index of 95.84%. We found that adults who reported 31-60 minutes of vigorous physical activity six times for the week and average sleep duration (7-8 hrs.) had the lowest stroke prevalence. Of the 36 sleep (short, average, and long sleep) and physical activity profiles we tested, 30 profiles had a self-reported stroke prevalence lower than the US national average of approximately 3.07%. Women, compared to men with the same sleep and physical activity profile, appeared to have higher self-reported stroke prevalence. We also report age differences across three groups 18-45, 46-65, and 66+ . Conclusion: Our findings indicate that several profiles of sleep duration and physical activity are associated with},
bibtype = {article},
author = {Seixas, Azizi A. and Henclewood, Dwayne A. and Williams, Stephen K. and Jagannathan, Ram and Ramos, Alberto and Zizi, Ferdinand and Jean-Louis, Girardin},
doi = {10.3389/fneur.2018.00534},
journal = {Frontiers in Neurology}
}
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