Obesity and Overweight Prevalence in Polish 7- to 9-Year-Old Children

2005 ◽  
Vol 13 (6) ◽  
pp. 964-968 ◽  
Author(s):  
Ewa Małecka-Tendera ◽  
Katarzyna Klimek ◽  
Paweł Matusik ◽  
Magdalena Olszanecka-Glinianowicz ◽  
Yves Lehingue ◽  
...  
Keyword(s):  
Obesity And Overweight ◽  
Overweight Prevalence

scholarly journals Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study (Preprint)

2020 ◽  
Author(s):  
Olubusola Oladeji ◽  
Chi Zhang ◽  
Tiam Moradi ◽  
Dharmesh Tarapore ◽  
Andrew C Stokes ◽  
...  
Keyword(s):  
Risk Factors ◽  
Information Seeking ◽  
Behavioral Risk Factors ◽  
Behavioral Risk ◽  
Obesity Prevalence ◽  
African Countries ◽  
Search Terms ◽  
Obesity And Overweight ◽  
Search Data ◽  
Overweight Prevalence

BACKGROUND The prevalence of chronic conditions such as obesity, hypertension, and diabetes is increasing in African countries. Many chronic diseases have been linked to risk factors such as poor diet and physical inactivity. Data for these behavioral risk factors are usually obtained from surveys, which can be delayed by years. Behavioral data from digital sources, including social media and search engines, could be used for timely monitoring of behavioral risk factors. OBJECTIVE The objective of our study was to propose the use of digital data from internet sources for monitoring changes in behavioral risk factors in Africa. METHODS We obtained the adjusted volume of search queries submitted to Google for 108 terms related to diet, exercise, and disease from 2010 to 2016. We also obtained the obesity and overweight prevalence for 52 African countries from the World Health Organization (WHO) for the same period. Machine learning algorithms (ie, random forest, support vector machine, Bayes generalized linear model, gradient boosting, and an ensemble of the individual methods) were used to identify search terms and patterns that correlate with changes in obesity and overweight prevalence across Africa. Out-of-sample predictions were used to assess and validate the model performance. RESULTS The study included 52 African countries. In 2016, the WHO reported an overweight prevalence ranging from 20.9% (95% credible interval [CI] 17.1%-25.0%) to 66.8% (95% CI 62.4%-71.0%) and an obesity prevalence ranging from 4.5% (95% CI 2.9%-6.5%) to 32.5% (95% CI 27.2%-38.1%) in Africa. The highest obesity and overweight prevalence were noted in the northern and southern regions. Google searches for diet-, exercise-, and obesity-related terms explained 97.3% (root-mean-square error [RMSE] 1.15) of the variation in obesity prevalence across all 52 countries. Similarly, the search data explained 96.6% (RMSE 2.26) of the variation in the overweight prevalence. The search terms yoga, exercise, and gym were most correlated with changes in obesity and overweight prevalence in countries with the highest prevalence. CONCLUSIONS Information-seeking patterns for diet- and exercise-related terms could indicate changes in attitudes toward and engagement in risk factors or healthy behaviors. These trends could capture population changes in risk factor prevalence, inform digital and physical interventions, and supplement official data from surveys.

scholarly journals Obesity and Overweight Prevalence among a Mississippi Low-Income Preschool Population: A Five-Year Comparison

ISRN Nursing ◽  
2011 ◽  
Vol 2011 ◽  
pp. 1-7
Author(s):  
Bonnie L. Harbaugh ◽  
Jerome R. Kolbo ◽  
Elaine F. Molaison ◽  
Geoffrey M. Hudson ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Global Health ◽  
Low Income ◽  
Early Onset ◽  
Significant Decline ◽  
Age Group ◽  
Chi Square ◽  
Obesity And Overweight ◽  
Global Health Issue ◽  
Lifetime Exposure ◽  
Overweight Prevalence

Purpose. This study determined 2010 rates of overweight/obesity in a representative sample of low-income preschoolers in Mississippi, USA and compared rates between 2005 () and 2010 (). Significance. Obesity is a significant global health issue because of its well-established negative health consequences. Child obesity is a concern due to risk of early-onset obesity-related illnesses and the longevity of lifetime exposure to those illnesses. Methods. Identical measures were used in 2005 and 2010 with complex-stratified sampling designs. Results. Chi-square tests revealed that overall obesity/overweight rates between 2005 (20.6%/17.9%) and 2010 (20.8%/17.0%) had not changed significantly for the samples as a whole, nor by gender or race. Age group comparisons indicated a significant decline in obesity rates of 3 year olds (20.3% in 2005, reduced to 13.1% in 2010, ). These findings mimic the trend toward stabilization of obesity rates noted in national low-income preschool populations.

scholarly journals Ten-Year obesity and overweight prevalence in Greek children: A systematic review and meta-analysis of 2001-2010 data

HORMONES ◽  
2013 ◽  
Vol 12 (4) ◽  
pp. 537-549 ◽  
Author(s):  
Eleni Kotanidou ◽  
Maria Grammatikopoulou ◽  
Bessie Spiliotis ◽  
Christina Kanaka-Gantenbein ◽  
Maria Tsigga ◽  
...  
Keyword(s):  
Systematic Review ◽  
Meta Analysis ◽  
Obesity And Overweight ◽  
Overweight Prevalence

scholarly journals Dramatic Increases in Obesity and Overweight Prevalence among Asian Subgroups in the United States, 1992–2011

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Gopal K. Singh ◽  
Sue C. Lin
Keyword(s):  
Asian Indians ◽  
Chinese Immigrants ◽  
The United States ◽  
Pacific Islanders ◽  
Native Hawaiians ◽  
Obesity Prevalence ◽  
Foreign Born ◽  
Obesity And Overweight ◽  
The Us ◽  
Overweight Prevalence

We examined trends in adult obesity and overweight prevalence among major Asian/Pacific Islander (API) subgroups and the non-Hispanic whites from 1992 to 2011. Using 1992–2011 National Health Interview Surveys, obesity, overweight, and BMI differentials were analyzed by logistic, linear, and log-linear regression. Between 1992 and 2011, obesity prevalence doubled for the Chinese, the Asian Indians, the Japanese, and the Hawaiians/Pacific Islanders; and tripled for the Filipinos. Obesity prevalence among API adults tripled from 3.7% in 1992 to 13.3% in 2010, and overweight prevalence doubled from 23.2% to 43.1%. Immigrants in each API subgroup had lower prevalence than their US-born counterparts, with immigrants’ obesity and overweight risks increasing with increasing duration of residence. During 2006–2011, obesity prevalence ranged from 3.3% for Chinese immigrants to 22.3% for the US-born Filipinos and 41.1% for the Native Hawaiians/Pacific Islanders. The Asian Indians, the Filipinos, and the Hawaiians/Pacific Islanders had, respectively, 3.1, 3.8, and 10.9 times higher odds of obesity than those of the Chinese adults. Compared with Chinese immigrants, the adjusted odds of obesity were 3.5–4.6 times higher for the US-born Chinese and the foreign-born Filipinos, 9 times higher for the US-born Filipinos and whites, 3.8–5.5 times higher for the US-born and foreign-born Asian Indians, and 21.9 times higher for the Native Hawaiians. Substantial ethnic heterogeneity and rising prevalence underscore the need for increased monitoring of obesity and obesity-related risk factors among API subgroups.

scholarly journals Monitoring Information-Seeking Patterns and Obesity Prevalence in Africa With Internet Search Data: Observational Study

10.2196/24348 ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. e24348
Author(s):  
Olubusola Oladeji ◽  
Chi Zhang ◽  
Tiam Moradi ◽  
Dharmesh Tarapore ◽  
Andrew C Stokes ◽  
...  
Keyword(s):  
Risk Factors ◽  
Information Seeking ◽  
Behavioral Risk Factors ◽  
Behavioral Risk ◽  
Obesity Prevalence ◽  
African Countries ◽  
Search Terms ◽  
Obesity And Overweight ◽  
Search Data ◽  
Overweight Prevalence

Background The prevalence of chronic conditions such as obesity, hypertension, and diabetes is increasing in African countries. Many chronic diseases have been linked to risk factors such as poor diet and physical inactivity. Data for these behavioral risk factors are usually obtained from surveys, which can be delayed by years. Behavioral data from digital sources, including social media and search engines, could be used for timely monitoring of behavioral risk factors. Objective The objective of our study was to propose the use of digital data from internet sources for monitoring changes in behavioral risk factors in Africa. Methods We obtained the adjusted volume of search queries submitted to Google for 108 terms related to diet, exercise, and disease from 2010 to 2016. We also obtained the obesity and overweight prevalence for 52 African countries from the World Health Organization (WHO) for the same period. Machine learning algorithms (ie, random forest, support vector machine, Bayes generalized linear model, gradient boosting, and an ensemble of the individual methods) were used to identify search terms and patterns that correlate with changes in obesity and overweight prevalence across Africa. Out-of-sample predictions were used to assess and validate the model performance. Results The study included 52 African countries. In 2016, the WHO reported an overweight prevalence ranging from 20.9% (95% credible interval [CI] 17.1%-25.0%) to 66.8% (95% CI 62.4%-71.0%) and an obesity prevalence ranging from 4.5% (95% CI 2.9%-6.5%) to 32.5% (95% CI 27.2%-38.1%) in Africa. The highest obesity and overweight prevalence were noted in the northern and southern regions. Google searches for diet-, exercise-, and obesity-related terms explained 97.3% (root-mean-square error [RMSE] 1.15) of the variation in obesity prevalence across all 52 countries. Similarly, the search data explained 96.6% (RMSE 2.26) of the variation in the overweight prevalence. The search terms yoga, exercise, and gym were most correlated with changes in obesity and overweight prevalence in countries with the highest prevalence. Conclusions Information-seeking patterns for diet- and exercise-related terms could indicate changes in attitudes toward and engagement in risk factors or healthy behaviors. These trends could capture population changes in risk factor prevalence, inform digital and physical interventions, and supplement official data from surveys.

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