scholarly journals An update on overweight and obesity in rural Northeast China: from lifestyle risk factors to cardiometabolic comorbidities

2014 ◽  
Vol 14 (1) ◽  
Author(s):  
Xiaofan Guo ◽  
Zhao Li ◽  
Liang Guo ◽  
Liqiang Zheng ◽  
Shasha Yu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Northeast China ◽  
Overweight And Obesity ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk

scholarly journals Lifestyle Risk Factors and the Population Attributable Fractions for Overweight and Obesity in Chinese Students of Zhejiang Province

2021 ◽  
Vol 9 ◽  
Author(s):  
Fang Gu ◽  
Siliang Zhou ◽  
Ke Lou ◽  
Rui Deng ◽  
Xingxiu Li ◽  
...  
Keyword(s):  
Risk Factors ◽  
Chinese Students ◽  
Lifestyle Factors ◽  
Overweight And Obesity ◽  
Zhejiang Province ◽  
Lifestyle Risk Factors ◽  
Population Attributable Fractions ◽  
Attributable Fractions ◽  
Lifestyle Risk ◽  
The Relationship

Objectives: To assess the relationship between modifiable lifestyle factors and risk of overweight/obesity in Chinese students, and to evaluate the predicting prevalence of overweight if the lifestyle risk factors were removed.Methods: A cross-sectional survey was conducted among 40,141 students in grade three and above (8–24yrs) in 2019 in Zhejiang Province, China. Physical examination was performed, and a self-administered questionnaire was used to collect lifestyle information, including dietary behavior, physical activity, TV watching, sleeping, smoking, drinking, and tooth-brushing habits. Logistic regression models were performed to assess the relationship between overweight/obesity and a series of lifestyle factors. Population attributable fractions (PAFs) were used to calculate the predicting prevalence of overweight/obesity if lifestyle risk factors were removed.Results: The prevalence of overweight/obesity of participants was 25.5% (male 32.3%, female 18.1%). Overweight/obesity were associated with adverse lifestyle factors, such as watch TV ≥1 h/day (OR = 1.14, 95% CI: 1.11–1.22), insufficient sleep (OR = 1.14, 95% CI: 1.11–1.22), and irregular toothbrushing habits (OR = 1.19, 95% CI: 1.01–1.39). Based on the calculated PAFs, the predicted prevalence of overweight/obesity would decline moderately if lifestyle factors were modified, with the magnitudes of decrease vary by sex, age and residence. Generally, a larger reduction was estimated if the sleeping time was increased and TV time was reduced, with the prevalence of overweight/obesity decreased by 1.1% (95% CI: 0.7, 1.5%) and 0.9% (95% CI: 0.6, 1.2%), respectively.Conclusions: Predicted prevalence of overweight/ obesity in Chinese students may decrease if modifiable lifestyle risk factors were removed. The attributable risk for obesity of lifestyle behaviors varied in age, sex and residence groups. The findings of this study may provide insights for planning and optimizing future obesity intervention endeavors.

scholarly journals Obesity and lifestyle risk factors among an adult population in Legal Amazon, Mato Grosso, Brazil

2016 ◽  
Vol 18 (1) ◽  
pp. 26-36 ◽  
Author(s):  
Rita De Cássia Spanhol ◽  
Carlos Kusano Bucalen Ferrari
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Body Mass Index ◽  
Waist Circumference ◽  
Body Mass ◽  
Soft Drinks ◽  
Overweight And Obesity ◽  
Activity Level ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk

<p>To evaluate the frequency of obesity and lifestyle risk factors in the population of Barra do Garças, Legal Amazon.</p><p><strong>Methodology </strong>A randomized, transversal study with 305 adults of both genders was performed. Weight, height, body mass index (BMI), waist circumference, food dietary habits, frequency of smoking and alcoholic consumption, practice of physical activity, and the physical activity level were evaluated.  </p><p><strong>Results </strong>Smoking and alcoholic consumption was higher among men compared to women. Almost 60 % of men engaged in leisure-time physical activities, whereas only 42.4 % of women practiced physical activity. Women were engaged in mostly sedentary behavior activities, such as watching television and using the computer use than men. Dietary daily intake of fruits and vegetables were higher among women compared to men (65.9 % and 51.5 %, respectively). Men were more prone to drink soft drinks than women. However, 39.4 % of men and only 3.4 % of women drank soft drinks three or more days <em>per</em> week. The prevalence of overweight and obesity according to body mass index (BMI) was higher in this study. 34.4 % of men and 33.7 % of women were overweight and 15.10 % and 17.50% of men and women, respectively, were classifyied as obese. Elevated values of waist circumference were found in 35.35 % of men and 70.73 % of women.</p><p><strong>Conclusión</strong> Women were more sedentary than men and had higher prevalence of abnormal waist circumference values.</p>

scholarly journals Lifestyle factors and lifestyle diseases among rural population of Bengaluru rural district

2017 ◽  
Vol 4 (5) ◽  
pp. 1558
Author(s):  
Deepadarshan H. ◽  
Shweta D. Hiremath
Keyword(s):  
Risk Factors ◽  
Rural Population ◽  
Lifestyle Factors ◽  
Overweight And Obesity ◽  
Research Centre ◽  
Rapid Urbanization ◽  
Training Centre ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk ◽  
Lifestyle Diseases

Background: Rapid urbanization and industrialization is leading to increased lifestyle risk factors and thus lifestyle diseases. Lifestyle diseases are causing more number of deaths and disability worldwide in recent years. Recent studies have shown a higher risk of lifestyle diseases among rural population. Hence this study was conducted to assess the lifestyle factors and lifestyle diseases and to know the prevalence of lifestyle diseases among rural population. Study design and setting: Cross-sectional study in Rural Health Training Centre, Sapthagiri Institute of Medical Sciences and Research Centre, Bengaluru.Methods: For a sample size of 108, systematic sampling was done and a questionnaire was administered. Data collected regarding lifestyle risk factors and diseases and analyzed using SPSS v 20. Results: 66 out of 108 participants (61.1%) had one or more lifestyle risk factors. Prevalence of lifestyle diseases was 37.03%. Hypertension was the most common disease with 27 (25%) cases followed by diabetes mellitus (16.7%) and asthma/COPD (7.5%). There was significant association between lifestyle factors like Tobacco and cigarette use, junk foods, overweight and obesity with lifestyle diseases. Conclusions: There is a need for population based program at primary level on lifestyle modification in the prevention of lifestyle diseases. 

scholarly journals Prevalence of Lifestyle Risk Factors in Myotonic Dystrophy Type 1

2013 ◽  
Vol 40 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Cynthia Gagnon ◽  
Maud-Christine Chouinard ◽  
Luc Laberge ◽  
Diane Brisson ◽  
Daniel Gaudet ◽  
...  
Keyword(s):  
Risk Factors ◽  
Myotonic Dystrophy ◽  
Overweight And Obesity ◽  
Myotonic Dystrophy Type 1 ◽  
Myotonic Dystrophy Type ◽  
Unhealthy Lifestyle ◽  
Lifestyle Habits ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk

Abstract:Background:The prevalence of unhealthy lifestyle habits such as smoking has seldom been described in neuromuscular disorders, including myotonic dystrophy type 1 (DM1). However, it is essential to document the unhealthy lifestyle habits as they can exacerbate existing impairments and disabilities. The objectives are: 1) To determine the prevalence of risk factors among individuals with DM1; 2) To compare the prevalence among classic and mild phenotypes.Method:A survey was done on a sample of two-hundred (200) patients with DM1 as part of a larger study. Lifestyle risk factors included being overweight or obese, tobacco smoking, illicit drug use, excessive alcohol consumption and physical inactivity. A registered nurse administered the validated public health survey. Categorization of risk factors were based on national standards and compared with provincial and regional prevalences.Results:50% of DM1 patients were overweight or obese, 23.6% were regular smokers, and 76% were physically inactive. Except for overweight and obesity, significant differences were observed between patients with classic and mild phenotypes for all the other lifestyle risk factors: those with the classic phenotype being more often regular smokers, consuming more often illicit drugs and being less physically active.Conclusions:The results of this study will provide guidance for the development of better adapted and focussed health promotion interventions in the future.

Novel Machine-Learned Approach for COVID-19 Resource Allocation: A Tool for Evaluating Community Susceptibility (Preprint)

2020 ◽  
Author(s):  
Neil Kale
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Insurance Coverage ◽  
Premature Mortality ◽  
Health Insurance Coverage ◽  
Heart Attacks ◽  
Lifestyle Risk Factors ◽  
The People ◽  
Potential Risk Factors ◽  
Lifestyle Risk

BACKGROUND Despite worldwide efforts to develop an effective COVID vaccine, it is quite evident that initial supplies will be limited. Therefore, it is important to develop methods that will ensure that the COVID vaccine is allocated to the people who are at major risk until there is a sufficient global supply. OBJECTIVE The purpose of this study was to develop a machine-learning tool that could be applied to assess the risk in Massachusetts towns based on community-wide social, medical, and lifestyle risk factors. METHODS I compiled Massachusetts town data for 29 potential risk factors, such as the prevalence of preexisting comorbid conditions like COPD and social factors such as racial composition, and implemented logistic regression to predict the amount of COVID cases in each town. RESULTS Of the 29 factors, 14 were found to be significant (p < 0.1) indicators: poverty, food insecurity, lack of high school education, lack of health insurance coverage, premature mortality, population, population density, recent population growth, Asian percentage, high-occupancy housing, and preexisting prevalence of cancer, COPD, overweightness, and heart attacks. The machine-learning approach is 80% accurate in the state of Massachusetts and finds the 9 highest risk communities: Lynn, Brockton, Revere, Randolph, Lowell, New Bedford, Everett, Waltham, and Fitchburg. The 5 most at-risk counties are Suffolk, Middlesex, Bristol, Norfolk, and Plymouth. CONCLUSIONS With appropriate data, the tool could evaluate risk in other communities, or even enumerate individual patient susceptibility. A ranking of communities by risk may help policymakers ensure equitable allocation of limited doses of the COVID vaccine.

scholarly journals The Prevalence of Insulin Resistance and the Associated Risk Factors in a Sample of 14–18-Year-Old Slovak Adolescents

2021 ◽  
Vol 18 (3) ◽  
pp. 909
Author(s):  
Jana Jurkovičová ◽  
Katarína Hirošová ◽  
Diana Vondrová ◽  
Martin Samohýl ◽  
Zuzana Štefániková ◽  
...  
Keyword(s):  
Physical Activity ◽  
Risk Factors ◽  
Insulin Resistance ◽  
Venous Blood ◽  
Significant Risk ◽  
Lifestyle Changes ◽  
Skewed Distribution ◽  
Model Assessment ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk

The prevalence of cardiometabolic risk factors has increased in Slovakian adolescents as a result of serious lifestyle changes. This cross-sectional study aimed to assess the prevalence of insulin resistance (IR) and the associations with cardiometabolic and selected lifestyle risk factors in a sample of Slovak adolescents. In total, 2629 adolescents (45.8% males) aged between 14 and 18 years were examined in the study. Anthropometric parameters, blood pressure (BP), and resting heart rate were measured; fasting venous blood samples were analyzed; and homeostasis model assessment (HOMA)-insulin resistance (IR) was calculated. For statistical data processing, the methods of descriptive and analytical statistics for normal and skewed distribution of variables were used. The mean HOMA-IR was 2.45 ± 1.91, without a significant sex differences. IR (cut-off point for HOMA-IR = 3.16) was detected in 18.6% of adolescents (19.8% males, 17.6% females). IR was strongly associated with overweight/obesity (especially central) and with almost all monitored cardiometabolic factors, except for total cholesterol (TC) and systolic BP in females. The multivariate model selected variables such as low level of physical fitness, insufficient physical activity, breakfast skipping, a small number of daily meals, frequent consumption of sweetened beverages, and low educational level of fathers as significant risk factors of IR in adolescents. Recognizing the main lifestyle risk factors and early IR identification is important in terms of the performance of preventive strategies. Weight reduction, regular physical activity, and healthy eating habits can improve insulin sensitivity and decrease the incidence of metabolic syndrome, type 2 diabetes, and cardiovascular disease (CVD).

scholarly journals Smoking, drinking, diet and physical activity—modifiable lifestyle risk factors and their associations with age to first chronic disease

2019 ◽  
Vol 49 (1) ◽  
pp. 113-130 ◽  
Author(s):  
Ryan Ng ◽  
Rinku Sutradhar ◽  
Zhan Yao ◽  
Walter P Wodchis ◽  
Laura C Rosella
Keyword(s):  
Risk Factors ◽  
Chronic Disease ◽  
Chronic Diseases ◽  
Lifestyle Factors ◽  
Competing Risk ◽  
Chronic Obstructive ◽  
Common Disease ◽  
Heavy Smoking ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk

AbstractBackgroundThis study examined the incidence of a person’s first diagnosis of a selected chronic disease, and the relationships between modifiable lifestyle risk factors and age to first of six chronic diseases.MethodsOntario respondents from 2001 to 2010 of the Canadian Community Health Survey were followed up with administrative data until 2014 for congestive heart failure, chronic obstructive respiratory disease, diabetes, lung cancer, myocardial infarction and stroke. By sex, the cumulative incidence function of age to first chronic disease was calculated for the six chronic diseases individually and compositely. The associations between modifiable lifestyle risk factors (alcohol, body mass index, smoking, diet, physical inactivity) and age to first chronic disease were estimated using cause-specific Cox proportional hazards models and Fine-Gray competing risk models.ResultsDiabetes was the most common disease. By age 70.5 years (2015 world life expectancy), 50.9% of females and 58.1% of males had at least one disease and few had a death free of the selected diseases (3.4% females; 5.4% males). Of the lifestyle factors, heavy smoking had the strongest association with the risk of experiencing at least one chronic disease (cause-specific hazard ratio = 3.86; 95% confidence interval = 3.46, 4.31). The lifestyle factors were modelled for each disease separately, and the associations varied by chronic disease and sex.ConclusionsWe found that most individuals will have at least one of the six chronic diseases before dying. This study provides a novel approach using competing risk methods to examine the incidence of chronic diseases relative to the life course and how their incidences are associated with lifestyle behaviours.

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