Body Mass Index Implications Using Data Analysis in the Soccer Sports

AbstractUnemployment shocks from the COVID-19 pandemic have reignited concerns over the long-term effects of job loss on population health. Past research has highlighted the corrosive effects of unemployment on health and health behaviors. This study examines whether the effects of job loss on changes in body mass index (BMI) are moderated by genetic predisposition using data from the U.S. Health and Retirement Study (HRS). To improve detection of gene-by-environment (G × E) interplay, we interacted layoffs from business closures—a plausibly exogenous environmental exposure—with whole-genome polygenic scores (PGSs) that capture genetic contributions to both the population mean (mPGS) and variance (vPGS) of BMI. Results show evidence of genetic moderation using a vPGS (as opposed to an mPGS) and indicate genome-wide summary measures of phenotypic plasticity may further our understanding of how environmental stimuli modify the distribution of complex traits in a population.

Download Full-text

54 Body Mass Index vs Weight-for-Length in Infancy and Cardiometabolic Outcomes at Age 8-10 Years

Paediatrics & Child Health ◽

10.1093/pch/pxab061.043 ◽

2021 ◽

Vol 26 (Supplement_1) ◽

pp. e39-e39

Author(s):

Jean-Baptiste Roberge ◽

Soren Harnois-Leblanc ◽

Vanessa McNealis ◽

Andraea van Hulst ◽

Tracie A Barnett ◽

...

Keyword(s):

Body Mass Index ◽

Body Mass ◽

Predictive Ability ◽

Subject Area ◽

Overweight And Obesity ◽

Growth Monitoring ◽

Linear Regression Models ◽

Eta Squared ◽

Z Scores ◽

Using Data

Abstract Primary Subject area Public Health and Preventive Medicine Background The WHO provides body mass index (BMI) curves for infants 0 to < 2 years old, but how these compare to the recommended method (weight-for-length [WFL]) in predicting later adiposity and cardiometabolic measures is uncertain. Objectives Our project aimed to: 1) confirm that WFL and BMI in infancy are associated with adiposity and cardiometabolic measures at 8-10 years old; and 2) compare the predictive ability of the two methods. We hypothesized that both methods would perform similarly. Design/Methods WFL and BMI Z-scores (zWFL and zBMI) at 6, 12, and 18 months of age were computed using data extracted from health booklets, used among participants in a prospective cohort study investigating the natural history of obesity and cardiovascular risk in youth (n = 464). Outcome measures at 8-10 years included adiposity, lipid profile, blood pressure, and insulin dynamics. The relationships between zWFL, zBMI, and each outcome were estimated using multivariable linear regression models. Outcome prediction at 8-10 years was compared between the two methods, using eta-squared and Lin’s concordance correlation. Results zWFL and zBMI were associated with all measures of adiposity at 8-10 years. Associations with other cardiometabolic measures were less consistent. For both zWFL and zBMI across infancy, eta-squared were highly similar and the Lin’s coefficients were markedly high (> 0.991) for all outcomes. Conclusion zBMI measured in infants appeared to be equivalent to zWFL for predicting adiposity and cardiometabolic measures in childhood. This lends support to the sole use of zBMI for growth monitoring and screening of overweight and obesity from birth to 18 years.

Download Full-text

Associations of movement behaviors and body mass index: comparison between a report-based and monitor-based method using Compositional Data Analysis

International Journal of Obesity ◽

10.1038/s41366-020-0638-z ◽

2020 ◽

Vol 45 (1) ◽

pp. 266-275 ◽

Cited By ~ 1

Author(s):

Youngwon Kim ◽

Ryan D. Burns ◽

Duck-chul Lee ◽

Gregory J. Welk

Keyword(s):

Physical Activity ◽

Body Mass Index ◽

Data Analysis ◽

Body Mass ◽

Compositional Data ◽

Activity Measurement ◽

Compositional Data Analysis ◽

Lifestyle Behaviors ◽

Linear Regression Models ◽

Index Comparison

Abstract Background/objectives Evidence on the associations between lifestyle movement behaviors and obesity has been established without taking into account the time-constrained nature of categorized, time-based lifestyle behaviors. We examined the associations of sleep, sedentary behavior (SED), light-intensity physical activity (LPA), and moderate-to-vigorous PA (MVPA) with body mass index (BMI) using Compositional Data Analysis (CoDA), and compared the associations between a report-based method (24-h Physical Activity Recall; 24PAR) and a monitor-based method (SenseWear Armband; SWA). Subjects/methods Replicate data from a representative sample of 1247 adults from the Physical Activity Measurement Survey (PAMS) were used in the study. Participants completed activity monitoring on two randomly selected days, each of which required wearing a SWA for a full day, and then completing a telephone-administered 24PAR the following day. Relationships among behavioral compositional parts and BMI were analyzed using CoDA via multiple linear regression models with both 24PAR and SWA data. Results Using 24PAR, time spent in sleep (γ = −3.58, p = 0.011), SED (γ = 3.70, p = 0.002), and MVPA (γ = −0.53, p = 0.018) was associated with BMI. Using SWA, time spent in sleep (γ = −5.10, p < 0.001), SED (γ = 8.93, p < 0.001), LPA (γ = −3.12, p < 0.001), and MVPA (γ = −1.43, p < 0.001) was associated with BMI. The SWA models explained more variance in BMI (R2 = 0.28) compared with the 24PAR models (R2 = 0.07). The compositional isotemporal substitution models revealed reductions in BMI when replacing SED by MVPA, LPA (not with 24PAR) or sleep for both 24PAR and SWA, but the effect estimates were larger with SWA. Conclusions Favorable levels of relative time spent in lifestyle movement behaviors were, in general, associated with decreased BMI. The observed associations were stronger using the monitor-based SWA method compared with the report-based 24PAR method.

Download Full-text

What drives the change in Body Mass Index over a 14-year period? A panel data analysis

Canadian Journal of Diabetes ◽

10.1016/s1499-2671(11)52025-8 ◽

2011 ◽

Vol 35 (2) ◽

pp. 143

Author(s):

K. Thavorn ◽

A. Laporte ◽

P.C. Coyte

Keyword(s):

Body Mass Index ◽

Data Analysis ◽

Panel Data ◽

Body Mass ◽

Panel Data Analysis

Download Full-text

Relação entre duas equações do índice de massa corporal em mulheres / Relationship between two equations of the body mass index in women

Saúde e meio ambiente revista interdisciplinar ◽

10.24302/sma.v7i1.1359 ◽

2018 ◽

Vol 7 (1) ◽

pp. 72

Author(s):

William Cordeiro de Souza ◽

André De Camargo Smolarek ◽

Denise Barth Rebesco ◽

Marcos Tadeu Grzelczak ◽

Valderi Abreu de Lima ◽

...

Keyword(s):

Body Mass Index ◽

Data Analysis ◽

Body Mass ◽

Pearson Correlation ◽

The Body ◽

Descriptive Statistics ◽

Pearson Correlation Coefficient ◽

Anthropometric Measures ◽

Significance Level ◽

The Relationship

Objetivo: Relacionar duas equações do método antropométrico do Índice de Massa Corporal (IMC) proposto por Quételet e Trefethen em mulheres. Métodos: A amostra foi constituída por 222 mulheres do município de Canoinhas/SC, com idades entre 18 e 49 anos. Para as medidas antropométricas foi avaliada a massa corporal e a estatura. Por meio dos dados obtidos foi calculado o IMC utilizando às fórmulas: Quételet= (kg/m2) e Trefethen= (1,3*kg/m2,5). Na análise dos dados realizou-se a estatística descritiva, o fator de correlação de Pearson (r) foi realizado para verificar a relação entre os métodos e o teste de Bland-Altman foi utilizado para verificar a concordância entre as equações. Foi adotado um nível de significância de p<0,05. Resultados: Foi encontrada uma relação muito forte e significante (r= 0,98; p= <0,001) com intervalo de confiança (IC 95% = 0,93-0,99) e concordância entre as equações do IMC. Conclusão: A equação do IMC de Trefethen apresentou associação muito forte e significante com a equação IMC de Quételet em mulheres.AbstractObjective: To relate two equations of the anthropometric method of the Body Mass Index (BMI) proposed by Quételet and Trefethen in women. Methods: The sample consisted of 222 women from the municipality of Canoinhas/SC, aged between 18 and 49 years. For the anthropometric measures body mass and height were evaluated. By means of the data obtained, the BMI was calculated using the formulas: Quételet= (kg/m2) and Trefethen= (1,3*kg/m2,5). In the data analysis, descriptive statistics were performed, the Pearson correlation coefficient (r) was performed to verify the relationship between the methods and the Bland-Altman test was used to verify the concordance between the equations. A significance level of p <0.05 was adopted. Results: A very strong and significant relationship was found (r= 0.98, p= <0.001) with a confidence interval (95% CI= 0.93-0.99) and concordance between the BMI equations. Conclusion: The BMI equation of Trefethen showed a very strong and significant association with the BMI equation of Quételet in women.

Download Full-text

Significance of fulfillment of nutrition on body mass index and physical activity

Jurnal SPORTIF Jurnal Penelitian Pembelajaran ◽

10.29407/js_unpgri.v6i1.14138 ◽

2020 ◽

Vol 6 (1) ◽

pp. 118-131

Author(s):

Indah Prasetyawati Tri Purnama Sari ◽

Erwin Setyo Kriswanto ◽

Riky Dwihandaka ◽

Danang Pujo Broto ◽

Abdul Mahfudin Alim

Keyword(s):

Physical Activity ◽

Body Mass Index ◽

Data Analysis ◽

Body Mass ◽

Survey Methods ◽

Pearson Correlation ◽

Sampling Technique ◽

School Students ◽

Nutrition And Physical Activity ◽

Data Collection Technique

Malnutrition troubles body mass index and physical activity. This study aims to analyze the relation of fulfillment of Nutrition on body mass index and physical activity. This research is a correlational study supported by survey methods. Data collection technique employs questionnaires and measurements of body weight and height. The population in this study is elementary school students in Sleman Yogyakarta and a sampling technique employs purposive random sampling. The total samples are 236 students. The research instrument uses 24-hour food recalls to determine fulfillment of Nutrition; tanita weight scales, microtoise to measure body mass index, and GPAQ (Global Physical Activity Questionnaire) to determine physical activity. Furthermore, analysis technique employs Pearson correlation analysis. The data analysis results of fulfillment of Nutrition and body mass index obtain syndication calculations 0.00 <0.05, meaning there is a relationship between the fulfillment of nutrition and body mass index. Additionally, the data analysis results of fulfillment of Nutrition and physical activity obtain significance calculation 0.00 <0.05, which means there is a relation between fulfillment of Nutrition and physical activity. Briefly, there is significant relation between the fulfillment of nutrition and body mass index as well as between the fulfillment of nutrition and physical activity. In another words, the study shows positive link between fulfillment of Nutrition with body mass index and physical activity.

Download Full-text

Body mass index estimates using a categorical body weight variable: A cross-sectional secondary data analysis

International Journal of Nursing Studies ◽

10.1016/j.ijnurstu.2012.08.002 ◽

2012 ◽

Vol 49 (12) ◽

pp. 1552-1557 ◽

Cited By ~ 1

Author(s):

Kihye Han ◽

Carla L. Storr ◽

Alison M. Trinkoff

Keyword(s):

Body Mass Index ◽

Body Weight ◽

Data Analysis ◽

Body Mass ◽

Secondary Data ◽

Secondary Data Analysis ◽

Cross Sectional

Download Full-text

Association between Body Mass Index and Short-Term Clinical Outcomes in Critically Ill Patients with Sepsis: A Real-World Study

BioMed Research International ◽

10.1155/2020/5781913 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Shan Lin ◽

Shanhui Ge ◽

Wanmei He ◽

Mian Zeng

Keyword(s):

Body Mass Index ◽

Clinical Outcomes ◽

Critically Ill ◽

Body Mass ◽

Critically Ill Patients ◽

Medical Information ◽

Cox Regression ◽

Normal Weight ◽

Male Patients ◽

Using Data

Background. Obesity is now recognized as one of the major public health threats, especially for patients with a critical illness. However, studies regarding whether and how body mass index (BMI) affects clinical outcomes in patients with sepsis are still scarce and controversial. The aim of our study was to determine the effect of BMI on critically ill patients with sepsis. Materials and Methods. We performed this study using data from the Medical Information Center for Intensive Care III database. A multivariate Cox regression model was used to assess the independent association of BMI with the primary outcome. Results. A total of 7,967 patients were enrolled in this study. Firstly, we found that the 28-day mortality was reduced by 22% ( HR = 0.78 , 95% CI 0.69–0.88) and 13% ( HR = 0.87 , 95% CI 0.78–0.98) for obese and overweight compared to normal weight, respectively. Subsequently, a U-shaped association of BMI with 28-day mortality was observed in sepsis patients, with the lowest 28-day mortality at the BMI range of 30–40 kg/m2. Finally, significant interactions were observed only for sex ( P = 0.0071 ). Male patients with a BMI of 25-30 kg/m2 ( HR = 0.74 , 95% CI 0.63–0.86) and 30-40 kg/m2 ( HR = 0.63 , 95% CI 0.53–0.76) had a significantly lower risk of 28-day mortality. Conclusions. A U-shaped association of BMI with 28-day mortality in critically ill sepsis patients was found, with the lowest 28-day mortality at a BMI range of 30–40 kg/m2. Notably, male patients were protected by a higher BMI more effectively than female patients as males had a significantly lower mortality risk.

Download Full-text

Impacts of Advertising, Attitudes, Lifestyles, and Health on the Demand for U.S. Pork: A Micro-Level Analysis

Journal of Agricultural and Applied Economics ◽

10.1017/s1074070800002108 ◽

2002 ◽

Vol 34 (1) ◽

pp. 1-15 ◽

Cited By ~ 14

Author(s):

Oral Capps ◽

Jaehong Park

Keyword(s):

Body Mass Index ◽

Body Mass ◽

Smoking Status ◽

Lifestyle Factors ◽

Factors Affecting ◽

Generic Advertising ◽

Micro Level ◽

Using Data ◽

Level Analysis

AbstractUsing data from the 1994-1996 CSFII/DHKS, we identify and assess factors affecting the decision to consume pork and conditional on consuming pork, the decision of the amount of pork intake. Branded and generic advertising of pork play a prominent role in both decisions. Beef advertising, however, does not significantly affect either the probability of consuming pork or the amount of pork intake. Key health, attitudinal and lifestyle factors are smoking status, dietary status, body mass index, the importance of nutrition in buying food, and trimming visible fat from meat. These factors however impact the probability of consuming pork rather than the amount of pork consumed. Region, urbanization, race, age, income, and seasonality also affect pork demand.

Download Full-text