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Sleep Duration and Physical Activity Profiles Associated With Self-Reported Stroke in the United States: Application of Bayesian Belief Network Modeling Techniques

Overview of attention for article published in Frontiers in Neurology, July 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

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Title
Sleep Duration and Physical Activity Profiles Associated With Self-Reported Stroke in the United States: Application of Bayesian Belief Network Modeling Techniques
Published in
Frontiers in Neurology, July 2018
DOI 10.3389/fneur.2018.00534
Pubmed ID
Authors

Azizi A. Seixas, Dwayne A. Henclewood, Stephen K. Williams, Ram Jagannathan, Alberto Ramos, Ferdinand Zizi, Girardin Jean-Louis

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 (BBN), 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 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 < 7 h. and long ≥ 9 h, referenced to healthy sleep (7-8 h)], 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 < $35 K. Overall, the model had a precision index of 95.84%. We found that adults who reported 31-60 min of vigorous physical activity six times for the week and average sleep duration (7-8 h) 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 low prevalence of self-reported stroke and that there may be sex differences. Overall, our findings indicate that more than 10 min of moderate or vigorous physical activity, about 5-6 times per week and 7-8 h of sleep is associated with lower self-reported stroke prevalence. Results from the current study could lead to more tailored and personalized behavioral secondary stroke prevention strategies.

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The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 68 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 15%
Researcher 7 10%
Student > Ph. D. Student 7 10%
Student > Doctoral Student 5 7%
Lecturer 3 4%
Other 11 16%
Unknown 25 37%
Readers by discipline Count As %
Medicine and Dentistry 8 12%
Nursing and Health Professions 8 12%
Computer Science 6 9%
Engineering 3 4%
Mathematics 3 4%
Other 10 15%
Unknown 30 44%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 October 2018.
All research outputs
#4,164,034
of 23,096,849 outputs
Outputs from Frontiers in Neurology
#3,423
of 12,012 outputs
Outputs of similar age
#80,316
of 329,152 outputs
Outputs of similar age from Frontiers in Neurology
#65
of 322 outputs
Altmetric has tracked 23,096,849 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 12,012 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has gotten more attention than average, scoring higher than 71% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 329,152 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 322 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.