This study used regression analysis to examine the relationship between various socioeconomic factors and obesity rates in the United States. The regression results showed that lack of physical activity, increased availability of fast food restaurants, and lack of physician availability were the strongest influences on higher obesity rates. Fast food consumption had the strongest correlation to obesity based on the p-values and t-statistics. Lack of sleep, alcohol consumption, and antidepressant use did not significantly correlate to obesity trends. The final regression prediction estimated that the percentage of the American population that is overweight or obese will continue to increase by 0.3-1% per year until reaching 67.93% in 2018.
1ANNOTATED BIBLIOGRAPHY FOR SEDENTARY LIFESTYLESTHESE ARE.docxhyacinthshackley2629
1
ANNOTATED BIBLIOGRAPHY FOR SEDENTARY LIFESTYLES
THESE ARE THE INSTRUCTORS REMARKS AFTER GRADING AND GIVING ME A ZERO/100. PLEASE CORRECT THIS DOCUMENT FOR ME. THANKS.
I HAVE ALSO ATTACHED A Turnitin Report in pdf format.
Hi, Jude. Your Turnitin report showed that 74% of your draft matches sources that were not cited properly. Please review the plagiarism tutorial in the syllabus, and review the APA materials on how to cite sources. Paraphrase your sources whenever possible; this shows you understand the material and can restate it in your own words. This also enables you to claim ownership of the language while still giving credit for the ideas. When you use source material verbatim, make sure to place it in quotation marks. Avoid copying and pasting large chunks of text. Even if you include proper citations, your essay will lack originality. Please review the attached Turnitin report so you can see which sections need attention. I will review your draft and update your score once you've rewritten it in your own words and cited sources properly. Please note the late policy in the syllabus. Let me know if you have any questions. Thanks.
Annotated Bibliography for Sedentary Lifestyles
Jude Kum
DeVry University
Sedentary lifestyle is predominant in our everyday life be it in workplace, school, social or homes and the fact is we have got accustomed to sitting down and doing many things forgetting the impact this is causing to our health. People fail to realize how valuable exercise is in their life and especially in improving their health and well-being. Sitting down on the computer with all focused attention and forgetting that we need to get up and even eat cause problems to many people.
Guedes, N.G., Lopes, M.V., Leite de Araujo, T. Moreira, R.P. and Martins, L.C. G. (2010). Predictive Factors of the Nursing Diagnosis Sedentary Lifestyle in People with High Blood Pressure. Public Health Nursing. Vol. 28 No. 2, p. 193-200. Wiley Periodicals, Inc.
The research question for the study conducted by Guesdes, et al (2010) is based on the following: 1.what is the result of the defining characteristics and related factors of sedentary lifestyle diagnosis in patients with high blood pressure? 2. What are the predictive value and possible predictors of the nursing diagnosis sedentary lifestyle in patients with high blood pressure? The study looked at the validation of diagnostic groupings of the population being studied including aspects of their clinical situations. The study looked at diagnosis resulting from insufficient physical activity, intolerance of activity, fatigue, impaired physical mobility, self-care deficit.
My assessment: Using this article, I will bring out the important indicators and useful predictors for identification of sedentary lifestyle; demonstrated the benefits of physical fitness, verbalized preferences for activities that are to accomplish real training or exercises. I will point out appr.
STROBE (Strengthening The Reporting of OBservational Studies in Ep.docxsusanschei
STROBE (Strengthening The Reporting of OBservational Studies in Epidemiology) Checklist
Direction: The following is a checklist of items that should be included in reports of observational studies. Use this checklist to evaluate the article by Olotu et al. Give an explanation of whether or not a particular criterion is missing in the article and the page number where a criterion is reported in the article. Do NOT write your name anywhere on the document.
Section and Item
Recommendation
Present?
Explanation
Reported on article
Page #
TITLE AND ABSTRACT
Indicated the study’s design with a commonly used term in the title or the abstract?
☐yes
☐ no
☐n/a
Provided in the abstract an informative and balanced summary of what was done and what was found?
☐yes
☐ no
☐n/a
INTRODUCTION
Background/rationale
Explained the scientific background and rationale for the investigation being reported?
☐yes
☐ no
☐n/a
Objectives
Stated specific objectives, including any pre-specified hypotheses?
☐yes
☐ no
☐n/a
METHODS
Study design
Presented key elements of study design early in the paper?
☐yes
☐ no
☐n/a
ORIGINAL RESEARCH ARTICLE
Use of Statins and the Risk of Incident Diabetes: A Retrospective
Cohort Study
Busuyi S. Olotu1,2,3 • Marvin D. Shepherd2 • Suzanne Novak2,3 • Kenneth A. Lawson2 •
James P. Wilson2 • Kristin M. Richards2 • Rafia S. Rasu1
� Springer International Publishing Switzerland 2016
Abstract
Introduction Even though several landmark statin trials
have demonstrated the beneficial effects of statin therapy
in both primary and secondary prevention of cardiovas-
cular disease, several studies have suggested that statins
are associated with a moderate increase in risk of new-
onset diabetes. These observations prompted the US
FDA to revise statin labels to include a warning of an
increased risk of incident diabetes mellitus as a result of
increases in glycosylated hemoglobin (HbA1c) and fast-
ing plasma glucose. However, few studies have used US-
based data to investigate this statin-associated increased
risk of diabetes.
Objective The primary objective of our study was to
examine whether the use of statins increases the risk of
incident diabetes mellitus using data from the Thomson
Reuters MarketScan
�
Commercial Claims and Encounters
Database.
Method This study was a retrospective cohort analysis
utilizing data for the period 2003–2004. The study popu-
lation included new statin users aged 20–63 years at index
who did not have a history of diabetes.
Results The proportion (3.4 %) of statin users
(N = 53,212) who had incident diabetes was higher
than the proportion (1.2 %) of non-statin users
(N = 53,212) who had incident diabetes. Compared
with no statin use and controlling for demographic and
clinical covariates, statin use was significantly associ-
ated with increased risk of incident diabetes (hazard
ratio 2.01; 99 % confidence interval 1.74–2.33;
p \ 0.0001). In addition, risk of diabetes was highest
amo.
February 14, 2020
On February 14, 2020, Harvard Medical School Center for Bioethics and the Program on Regulation, Therapeutics, and Law (PORTAL) at Brigham and Women's Hospital, in collaboration with the Petrie-Flom Center hosted the monthly health policy consortium on sugar-sweetened beverage excise taxes.
In recent years, some cities have tried to impose soda taxes and other new policies to reduce the obesity epidemic in the US—particularly among children—and its critical impact on society and the health care system. How effective are these policies? What is blocking their uptake? What alternatives should we consider?
For more information visit our website at: https://petrieflom.law.harvard.edu/events/details/soda-taxes-and-other-policy-responses-to-the-american-obesity-epidemic
PREVALENCE OF DEPRESSION AND ITS ASSOCIATED FACTORS IN ADULTS WITH DIABETES M...pijans
The prevalence of Diabetes Mellitus has been rising steadily owing to several factors such as sedentary
lifestyle, obesity and an aging population. The prevalence of diabetes is predicted to double globally from
171 million in 2000 to 366 million in 2030 with a maximum increase in India with up to 79.4 million
individuals in India. Depression occurs frequently with diabetes but there are not many studies in India to
estimate its prevalence and associated factors. This study was done with the aim of estimating the
prevalence of depression among diabetes patients using the validated Patient Health Questionnaire-9 and
also its associated factors.
1
Running head: OBESITY
3
Running head: OBESITY
Obesity
Lauren Urquiza
Chamberlain University
NR503 Population Health, Epidemiology, & Statistical Principles
January 2018
Obesity
Obesity is a chronic medical condition and a significant health concern in the United States that is increasing worldwide. More than one third of the adults in the U.S. are obese. It is a leading cause of preventable illness and death (Centers for Disease Control and Prevention [CDC], 2016). This global epidemic is a leading concern for adults and for children who are predisposed to becoming obese as adults. This paper will discuss the significance of obesity in Florida, provide a background of the disease, review current surveillance and reporting methods, conduct a descriptive epidemiological analysis, discuss diagnosis and screening for prevention tools, develop an evidence based plan along with measureable outcomes to address obesity as an advanced practice nurse, and conclude with an overview of the main points presented.
Background and Significance
According to the CDC (2016), obesity is defined as “weight that is higher than what is considered as a healthy weight for a given height.” It involves excessive weight gain and accumulation of fat. In order to determine obesity, Body Mass Index or BMI is used to indirectly calculate a person’s body fat and health risk based on weight in relation to height. A BMI of 25.0 or above is considered overweight and 30.0 or greater is considered obese. Athletes with a greater amount of muscle mass may have a higher BMI even though they do not have excess body fat. Waist circumference is also used as a tool to diagnose obesity.
There are many causes that contribute to obesity, including behavioral, genetic, hormonal, environmental, and social factors. Increase in caloric intake, unhealthy eating habits, decrease in physical activity, certain medications, age, lack of sleep, quitting smoking, pregnancy, and certain medical disorders can contribute to weight gain (Mayo Clinic, 2018). Driving cars has replaced walking and riding bikes, technology has replaced engaging in physical activity, and easy access to cheaper foods has replaced nutritional importance. Most people are aware when weight is gained. Obvious signs and symptoms are tighter clothes, excess fat, and increased weight on a scale. Being overweight or obese increases the risk for many health diseases. Obesity may cause low endurance, breathing issues, excessive sweating, and joint discomfort. It can also lead to diabetes, gastroesophageal reflux disease, coronary heart disease, hypertension, high cholesterol, stroke, depression, and even certain types of cancer such as bowel, breast, and prostate cancer (Mayo Clinic, 2018).
Below is a map that highlights the obesity prevalence across the U.S. in 2016 according to the CDC. There is no significant difference in overall prevalence between men and women. The prevalence of women with a BMI > 35 ...
Dietary Lifestyle, Way of Life Practices and Corpulence: Towards Present Day Science by Alok Raghav, Aditi, Sneha Gupta, Pratibha Singh, Aman Nikhil, Saba Noor and Jamal Ahmad in Examines in Physical Medicine & Rehabilitation
1Running head OBESITY 4Running head OBESITY.docxvickeryr87
1
Running head: OBESITY
4
Running head: OBESITY
Obesity
NR503 Population Health, Epidemiology, & Statistical Principles
January 2018
Obesity
Obesity is a chronic medical condition and a significant health concern in the United States that is increasing worldwide. More than one third of the adults in the U.S. are obese. It is a leading cause of preventable illness and death (Centers for Disease Control and Prevention [CDC], 2016). This global epidemic is a leading concern for adults and for children who are predisposed to becoming obese as adults. This paper will discuss the significance of obesity in Florida, provide a background of the disease, review current surveillance and reporting methods, conduct a descriptive epidemiological analysis, discuss diagnosis and screening for prevention tools, develop an evidence based plan along with measureable outcomes to address obesity as an advanced practice nurse, and conclude with an overview of the main points presented.
Background and Significance
According to the CDC (2016), obesity is defined as “weight that is higher than what is considered as a healthy weight for a given height.” It involves excessive weight gain and accumulation of fat. In order to determine obesity, Body Mass Index or BMI is used to indirectly calculate a person’s body fat and health risk based on weight in relation to height. A BMI of 25.0 or above is considered overweight and 30.0 or greater is considered obese. Athletes with a greater amount of muscle mass may have a higher BMI even though they do not have excess body fat. Waist circumference is also used as a tool to diagnose obesity.
There are many causes that contribute to obesity, including behavioral, genetic, hormonal, environmental, and social factors. Increase in caloric intake, unhealthy eating habits, decrease in physical activity, certain medications, age, lack of sleep, quitting smoking, pregnancy, and certain medical disorders can contribute to weight gain (Mayo Clinic, 2018). Driving cars has replaced walking and riding bikes, technology has replaced engaging in physical activity, and easy access to cheaper foods has replaced nutritional importance. Most people are aware when weight is gained. Obvious signs and symptoms are tighter clothes, excess fat, and increased weight on a scale. Being overweight or obese increases the risk for many health diseases. Obesity may cause low endurance, breathing issues, excessive sweating, and joint discomfort. It can also lead to diabetes, gastroesophageal reflux disease, coronary heart disease, hypertension, high cholesterol, stroke, depression, and even certain types of cancer such as bowel, breast, and prostate cancer (Mayo Clinic, 2018).
Below is a map that highlights the obesity prevalence across the U.S. in 2016 according to the CDC. There is no significant difference in overall prevalence between men and women. The prevalence of women with a BMI > 35 is 18.3% compared to 12.5% of men. The.
Healthy Food Accessibility and Obesity: Case Study of Pennsylvania, USArsmahabir
Abstract-Obesity is a continuing challenge for any town, city or country faced with this problem. Being obese increases your risk of physical disorders such as high blood pressure (BP), high blood cholesterol, diabetes, coronary heart disease, stroke, cancer and poor reproductive health. Higher obesity rates also leads to increased economic burden on society. In order to better understand and control obesity rates the in uence of various factors on its prevalence should be investigated. We used Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models to analyze spatial relationships using a combination of socio-economic and physical factor for counties in Pennsylvania (PA), USA for 2010. Our ndings suggest that the rate of obesity is impacted by local spatial variation and its prevalence positively correlated with diabetes, physical inactivity and the distance that a person must travel to get to a healthy food store. Additionally, GWR (AICc = 261.59; r-squared = 0.45) was found to signi cantly improve model tting over OLS (AICc = 299.87; r-squared = 0.34). These results indicate that additional factors, including social, cultural and behavioral, are needed to better explain the distribution of obesity rates across PA.
511vol. 14 • no. 5 American Journal of Lifestyle Medicine.docxstandfordabbot
511
vol. 14 • no. 5 American Journal of Lifestyle Medicine
AnAlytic
Abstract: There is overwhelming
evidence in the scientific and medical
literature that physical inactivity is a
major public health problem with a
wide array of harmful effects. Over 50%
of health status can be attributed to
unhealthy behaviors with smoking, diet,
and physical inactivity as the main
contributors. Exercise has been used
in both the treatment and prevention
of a variety of chronic conditions such
as heart disease, pulmonary disease,
diabetes, and obesity. While the
negative effects of physical inactivity
are widely known, there is a gap
between what physicians tell their
patients and exercise compliance.
Exercise is Medicine was established
in 2007 by the American College of
Sports Medicine to inform and educate
physicians and other health care
providers about exercise as well as
bridge the widening gap between health
care and health fitness. Physicians have
many competing demands at the point
of care, which often translates into
limited time spent counseling patients.
The consistent message from all health
care providers to their patients should
be to start or to continue a regular
exercise program. Exercise is Medicine
is a solution that enables physicians to
support their patients in implementing
exercise as part of their disease
prevention and treatment strategies.
Keywords: inactivity; exercise; vitals;
behaviors; referral
Physical inactivity underlies many
of the chronic conditions that
affect people worldwide, has an
astonishing array of harmful health
effects, and is associated with escalating
health care costs. For example, 7 cancers
have been linked to a physically inactive
lifestyle.1 Depression affects 17 million
Americans2 and has been directly linked
to insufficient physical activity.3
Alzheimer’s disease and related
dementias are increasing at a frightening
rate. By 2025, the number of people
aged 65 years and older with Alzheimer’s
disease is expected to reach 7.1 million
people. In the United States alone, more
than 30 million adults are estimated to
have diabetes,4 95% of whom have type
2 diabetes (T2DM). Considering that a
new case of diabetes is diagnosed every
21 seconds, it is no surprise that diabetes
is the most expensive disease in America,
coming in at a price tag of $327 billion
annually.5 Underlying the vast majority of
T2DM are unhealthy lifestyle behaviors
(poor nutrition and insufficient physical
activity leading to overweight and
obesity). In addition to T2DM, an
unhealthy lifestyle (including tobacco
use, excessive alcohol intake, poor sleep,
and stress) underlies prevalent and costly
chronic diseases (eg, heart disease and
cancer) leading to premature morbidity
and mortality.
While other determinants of health
(genetics, environment, and medical
care) influence health outcomes, by far
the most important factor contributing to
health outcomes is in.
Statistical analysis of risk factors associated withanamjavaid13
Gallstones are crystal like collections that formed by merging of normal and abnormal gallbladder content. Usually there are two types of gallstones exist i.e. cholesterol stones & pigment stones. The current paper focuses on symptoms of the disease, major cause for the disease and on the treatments that majority of patients preferred. For this purpose, sample of size 170 data from different hospitals in Multan is collected by using convenience sampling. Main demographic factors involved in this study are Gender, Age group, marital status for patients of GSD. Frequency distribution has been formed for these different demographic and social factors and a bar chart is constructed for differentiating between gender as gender is also an important factor in GSD. For weight factor, paired t test is applied to see the difference between before and after weight after having treatment. Findings show that 67 percent people prefer govt. hospitals because of the people suffering from this disease were from backward areas or villages & their income not meet to pay the private hospitals expense.
Statistical analysis of risk factors associated withanamjavaid13
Gallstones are crystal like collections that formed by merging of normal and abnormal gallbladder content. Usually there are two types of gallstones exist i.e. cholesterol stones & pigment stones. The current paper focuses on symptoms of the disease, major cause for the disease and on the treatments that majority of patients preferred. For this purpose, sample of size 170 data from different hospitals in Multan is collected by using convenience sampling. Main demographic factors involved in this study are Gender, Age group, marital status for patients of GSD. Frequency distribution has been formed for these different demographic and social factors and a bar chart is constructed for differentiating between gender as gender is also an important factor in GSD. For weight factor, paired t test is applied to see the difference between before and after weight after having treatment. Findings show that 67 percent people prefer govt. hospitals because of the people suffering from this disease were from backward areas or villages & their income not meet to pay the private hospitals expense.
Cancer is not all about what we inherit-- it's also about what we eat, how much we move and even how we stay connected. This is good news! This talk reviews the evidence for how we can reduce our risk of cancer through simple lifestyle changes.
1. Janica Jain, Cindy Villamil, Sylvain Jaume, Data Science Graduate Program, Department of Computer Science, Saint Peter’s University, Jersey City, NJ
ABSTRACT
This study looks at various socio-economic factors that have been identified by other researchers as important influences on weight imbalance in the American population: physical activity rates, sugar consumption, availability of physician care,
growth of fast food restaurants, lack of sleep, prescription drugs, and alcohol consumption. Regression analysis is used to determine which among the independent variables are related to the dependent variable (overweight and obesity rates)
and to explore the forms of these relationships. Currently, the obesity epidemic is known to be one of America’s most severe and fastest growing health complications. The growing obesity rates in the country lead to numerous other significant
health problems and contribute to intensified rates of more than 50 severe diseases. These circumstances furthermore create major pressure on America’s health care system. However, it is not exactly clear as to why the prevalence of obesity
has increased so dramatically over the last 30 years.
INTRODUCTION
Obesity is defined as having a body mass index (BMI) of 30 or more, which equals weight in kilograms
divided by height in meters squared (Centers for Disease Control and Prevention, 2008). The sudden
increase in overweight individuals and the epidemic of obesity in America is currently a major public
health fear. Obesity furthermore causes abundant health problems which include but are not limited to:
diabetes, hypertension, high cholesterol, cardiovascular diseases, asthma, arthritis, and even forms of
cancer. In this study we wish to understand why obesity is higher in the United States than in any other
developed country.
METHODOLOGY
Regression analysis was used for the investigation of relationship between the variables and the dependent variable, obesity. We
sought to ascertain the casual effect of the variables upon obesity. We also assessed the “statistical significance” of 95% to the
estimated relationships, which was the degree of confidence that the true relationship is close to the estimated relationship. We might
resolve that the null hypothesis is implausible if the t-statistic associated with our regression estimate lies so far out in one tail of its t-
distribution that such a value, or one even larger in absolute value, would arise less than, say, 5 percent of the time if the null
hypothesis is correct. We will also take into account the P-value, which will tell us how confident we can be that each individual
variable has some correlation with the dependent variable.
RESULTS
CONCLUSION
The only factor for which we did not see an influence on overweight and obesity rates was the delivery of sugar for consumption in different industries in the US. However,
when focusing on the delivery of sugar for consumption through sugary beverages, we saw a strong influence. We also discovered that lack of sleep, lack of physical
activity and low physician availability to citizens were the strongest strong influences on heightened overweight and obesity rates, while also confirming that alcohol
consumption and fast food restaurant growth would be strong influences as well. The factor that seemed to play a minor role in the increase of overweight and obesity
rates was the consumption of prescribed antidepressants. However, this was compared to the population as a whole, and we think it would be interesting to see the
change in overweight and obesity rates in people who consume them only. Obesity is becoming an epidemic in the United States and any knowledge that can be gathered
to prevent it from affecting more people is beneficial.
REFERENCES
• The Organization for Economic Cooperation and Development
• Baum, C., & Yi-Chou, S. (2011). The Socio-Economic Causes of Obesity (Paper
No.17423). New York, NY: The National Bureau of Economic Research.
• Cutler, D., Glaesar, E., & Shapiro, J. (2003). Why Have Americans Become More Obese?
Journal of Economic Perspectives, LXXI(3), 93-118
• Knutson, K., & Cauter, E. (2008). Associations between Sleep Loss and Increased Risk of
Obesity and Diabetes. Chicago, Illinois, USA. University of Chicago, Department of Health
Studies.
Regression Analysis of Socio-Economic Factors Leading to Increased
Body Weight and Obesity in America
Forecasting returns the predicted value of obesity for the
specific value, x, of the independent variable (represented in the
data by factors) by using a best fit linear regression. Final
results show a continuous trend in obesity of about <0.3-1 % per
year until 2018. The final prediction for 2018 shows a
percentage of 67.93% of the American population as overweight
or obese.
Regression Statistics
Lack of
sleep
Fast Food
Restaurants
Lack of
Physical
Activity
Antidepressant
Consumption
Sugar
Availability
Sugar-
Sweetened
Beverages
Alcohol
Consumption
Physician
Availability
Multiple R 0.9414 0.97907 0.97846 0.61714 0.61714 0.89140 0.956410 0.97854
R2 0.8861 0.95858 0.95737 0.38086 0.38086 0.79460 0.91472 0.95753
Adjusted R2 0.8767 0.95540 0.95382 0.32926 0.32926 0.77880 0.90816 0.95427
Standard Error 0.8210 0.55765 0.50231 1.91440 1.91440 1.24183 0.80017 0.56466
Observations
(years) 14 14 14 14 14 14 14 14
P-Value
5.1740E
-07
2.27E-10 1.38E-09 0.018714 0.170848 8.15E-06 2.53E-08 2.67E-10
T-Statistic 9.6641 17.34555 16.41706 2.7169 1.44966 7.09161 11.8085 17.12091
SUMMARY OUTPUT
Regression Statistics
Multiple R 0.9897196
R Square 0.9795449
Adjusted R
Square 0.9590898
Standard Error 0.5340544
Observations 15
ANOVA
df SS MS F Significance F
Regression 7 95.60750098 13.6582 47.8876 2.19E-05
Residual 7 1.996499017 0.28521
Total 14 97.604
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Obesity 33.807146 48.63021776 0.69519 0.50935 -81.185046 148.79934
Alcohol -2.649E-05 0.00012236 -0.21647 0.83479 -0.0003158 0.0002628
Sleep -0.0394214 0.277870747 -0.14187 0.89118 -0.6964813 0.6176385
Physicians 0.1301715 0.201527667 0.64592 0.53892 -0.3463658 0.6067087
Fast Food 0.3645159 0.297074354 1.22702 0.25948 -0.3379533 1.0669851
No Exercise 2.8406219 4.037426425 0.70357 0.50442 -6.7063745 12.387618
Anti-Depress -4.468E-06 4.84873E-05 -0.09216 0.92915 -0.0001191 0.0001102
Beverages 4.688E-06 8.27112E-06 0.56685 0.58852 -1.487E-05 2.425E-05
After conducting a multi-regression analysis of all the factors comparatively to
the obesity trends, the results matched extremely similarly to the individual line
regression tests. As a result of the linear regression tests, three of the factors
proved to be the strongest factors of obesity trends in the United States were:
fast food chain availability, low exercise rate, and physician availability. Also in
a similar sense once again the T-Stat and P-value for these three factors were
closest to our cut-off numbers when the multi-regression analysis was done.
Fast food consumption stood out as the strongest factor in both p and t values.
Sleep, alcohol, and the anti-depressant medication were not significant factors
in obesity trends, which is similar to our linear regression tests.