The Combined Effects of Television Viewing and Physical Activity on Cardiometabolic Risk Factors: The Kardiovize Study

The aim of the present study was to evaluate the association between television viewing/physical activity (TVV/PA) interactions and cardiometabolic risk in an adult European population. A total of 2,155 subjects (25-64 years) (45.2% males), a random population-based sample were evaluated in Brno, Czechia. TVV was classified as low (<2hours/day), moderate (2-4), and high (≥4). PA was classified as insufficient, moderate, and high. To assess the independent association of TVV/PA categories with cardiometabolic variables, multiple linear regression was used. After adjustments, significant associations were: high TVV/insufficient PA with body mass index (BMI) (β=2.61,SE=0.63), waist circumference (WC) (β=7.52,SE=1.58), body fat percent (%BF) (β=6.24,SE=1.02), glucose (β=0.25,SE=0.12), triglycerides (β=0.18,SE=0.05), and HDL-C (β=-0.10,SE=0.04); high TVV/moderate PA with BMI (β=1.98,SE=0.45), WC (β=5.43,SE=1.12), %BF (β =5.15,SE=0.72), triglycerides (β =0.08,SE=0.04), total cholesterol (β=0.21,SE=0.10), LDL-c (β=0.19,SE=0.08), and HDL-c (β=-0.07,SE=0.03); and moderate TVV/insufficient PA with WC (β=2.68,SE=1.25), %BF (β=3.80,SE=0.81), LDL-c (β=0.18,SE=0.09), and HDL-c (β=-0.07,SE=0.03). Independent of PA levels, higher TVV was associated with higher amounts of adipose tissue. Higher blood glucose and triglycerides were present in subjects with high TVV and insufficient PA, but not in those with high PA alone. These results affirm the independent cardiometabolic risk of sedentary routines even in subjects with high-levels of PA.

Design and Validation of the Equation for Correction of Body Mass Index

Background and Aims: The Body Mass Index (BMI) is considered one of the most common indexes for examining the health. The purpose of this study was the design and validation of the equations for the accuracy of BMI. Methods: In the current 419 athletic and non-athletic subjects participated in this study. Anthropometrical measurement was performed using International Standard for Anthropometry and Kinanthropometry (ISAK) protocol, and mesomorphic and endomorphic calculated based on the Heath-Carter methods. The Spearman correlation coefficient was used to determine the relationship between BMI and body fat percent, mesomorphic and endomorphic values. Multiple regressions were used to predict BMI. Results: There is a high correlation between BMI with mesomorphic and endomorphic values (P<0.01), but the correlation of BMI with mesomorphic in athletic men was higher than endomorphic (0.77 vs. 0.63). Multiple regression produces some equations for predicting mesomorphic and endomorphic, and bland Altman believes that all equations predict fatness. Conclusion: According to the results, the Equations proposed in this study could appropriately turn BMI to predict body composition so that this equation could determine higher BMI because of the higher muscular mass.

Males With Traumatic Lower Limb Loss Differ in Body Fat Distribution Compared to Those Without Limb Loss

ABSTRACT Objectives The primary objective was to examine body fat composition in males with traumatic lower limb loss and a comparison group without limb loss. A secondary objective was to determine if there are differences in body fat composition by traumatic limb loss level. Research Methods and Procedures Cross-sectional analyses of baseline in-person intake data was completed at a large military medical center. Data were collected from 2011 to 2020, and analysis was conducted in 2020. Participants (n = 89) included males who sustained traumatic lower limb loss (n = 50) and an age-matched comparison group without limb loss (n = 39). Mean age of participants was 36.0 ± 13.2 years. Main outcomes measured included age, height, body mass index, weight, body fat mass and percent, android fat mass and percent, gynoid fat mass and percent, and android/gynoid percent fat ratio. Differences between groups were assessed using t-tests or Mann–Whitney U tests. Differences between limb loss levels were assessed using one-way ANOVA or Wilcoxon signed-rank test. Results Body fat percent (P = .001), gynoid fat percent (P = .010), android fat mass (P = .01), and percent (P = .02) were higher in the group with limb loss. There were no differences in body fat composition between limb loss levels (P &gt; .05). Conclusion Males with traumatic lower limb loss had a higher body fat percent compared to those without limb loss. Given higher body fat composition in individuals with limb loss and the relationship between body fat composition and cardiovascular disease risk, including body composition analysis with clinical screening could identify changes and allow for early intervention.

Ethnic Specific body fat percent prediction equation as surrogate marker of obesity in Ethiopian adults

Background Application of advanced body composition measurement methods is not practical in developing countries context due to cost and unavailability of facilities. This study generated ethnic specific body fat percent prediction equation for Ethiopian adults using appropriate data. Methods A cross-sectional study was carried ifrom February to April 2015 among 704 randomly selected adult employees of Jimma University. Ethnic specific Ethiopian body fat percent (BF%) prediction equation was developed using a multivariable linear regression model with measured BF% as dependent variable and age, sex, and body mass index as predictor variables. Agreement between fat percent measured using air displacement plethysmography and body fat percent estimated using Caucasian prediction equations was determined using Bland Altman plot. Results Comparison of ADP measured and predicted BF% showed that Caucasian prediction equation underestimated body fat percent among Ethiopian adults by 6.78% (P < 0.0001). This finding is consistent across all age groups and ethnicities in both sexes. Bland Altman plot did not show agreement between ADP and Caucasian prediction equation (mean difference = 6.7825) and some of the points are outside 95% confidence interval. The caucasian prediction equation significantly underestimates body fat percent in Ethiopian adults, which is consistent across all ethnic groups in the sample. The study developed Ethnic specific BF% prediction equations for Ethiopian adults. Conclusion The Caucasian prediction equation significantly underestimates body fat percent among Ethiopian adults regardless of ethnicity. Ethiopian ethnic-specific prediction equation can be used as a very simple, cheap, and cost-effective alternative for estimating body fat percent among Ethiopian adults for health care provision in the prevention of obesity and related morbidities and for research purposes.

Investigating the relationship between insulin resistance and adipose tissue in a randomized Tehrani population

Objectives Insulin resistance is the most common metabolic change associated with obesity. The present study aimed to investigate the relationship between insulin resistance and body composition especially adipose tissue in a randomized Tehrani population. Methods This study used data of 2,160 individuals registered in a cross-sectional study on were randomly selected from among subjects who were referred to nutrition counseling clinic in Tehran, from April 2016 to September 2017. Insulin resistance was calculated by homeostasis model assessment formula. The odds ratio (95% CI) was calculated using logistic regression models. Results The mean age of the men was 39 (±10) and women were 41 (±11) (the age ranged from 20 to 50 years). The risk of increased HOMA-IR was 1.03 (95% CI: 1.01–1.04) for an increase in one percent of Body fat, and 1.03 (95% CI: 1.00–1.05) for an increase in one percent of Trunk fat. Moreover, the odds ratio of FBS for an increase in one unit of Body fat percent and Trunk fat percent increased by 1.05 (adjusted odds ratio [95% CI: 1.03, 1.06]) and 1.05 (95% CI: 1.02, 1.08). Also, the risk of increased Fasting Insulin was 1.05 (95% CI: 1.03–1.07) for an increase in one unit of Body fat percent, and 1.05 (95% CI: 1.02–1.08) for an increase in one unit of Trunk fat percent. Conclusions The findings of the present study showed that there was a significant relationship between HOMA-IR, Fasting blood sugar, Fasting Insulin, and 2 h Insulin with percent of Body fat, percent of Trunk fat.

Regular swimming exercise improves metabolic syndrome risk factors: a quasi-experimental study

Background In the past few decades, swimming became one of the most important physical activities within the health system and is considered a practical nonpharmacological approach to managing of type 2 diabetes (T2DM), hyperlipidemia, hypertension (HTN), and obesity. The current study aimed to assess the effect of long-term swimming sessions on glycemic and lipidemic parameters, hemodynamic responses, body fat percent, and body mass index for patients with metabolic risk factors from Palestine. Methods Forty participants from both genders with T2DM and HTN (aged 52.4 ± 5.5 yrs) agreed to participate in this quasi-experimental study and were divided into two groups. The first group included the participants who performed long-term swimming sessions and the second group served as the control. The first group exercised for 2 h, 3 times/week in 29–33 °C swimming pool temperature for 16 weeks. Simultaneously, the control group did not participate in any exercise and advised them to keep on with their everyday lifestyle. All the obtained metabolic syndrome risk factors data were analyzed using a two-way ANOVA analysis of variance (2*2) which was applied to determine the differences according group, time, and interaction. Results The results showed that there were statistically significant differences at p < 0.05 in the variables of Total Cholesterol (TC), High Density of Lipoprotein (HDL), Low Density of Lipoprotein (LDL), Triglycerides (TG), Blood Glucose (BG), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Body Mass Index (BMI), and body fat percent according to group, time, and interaction for the experimental group. Conclusions The findings of the current study suggested that the regular 16 weeks of swimming sessions could be considered nonpharmacological approaches in managing T2DM and HTN.

Regular swimming exercise improves metabolic syndrome risk factors: a quasi-experimental study

Background: In the past few decades, swimming became one of the most important physical activities within the health system and is considered a practical nonpharmacological approach to managing of type 2 diabetes (T2DM), hyperlipidemia, hypertension (HTN), and obesity. The current study aimed to assess the effect of long-term swimming sessions on glycemic and lipidemic parameters, hemodynamic responses, body fat percent, and body mass index for patients with metabolic risk factors from Palestine. Methods: Forty participants from both genders with T2DM and HTN (aged 52.4±5.5 yrs) agreed to participate in this quasi-experimental study and were divided into two groups. The first group included the participants who performed long-term swimming sessions and the second group served as the control. The first group exercised for 2 h, 3 times/week in 29-33◦C swimming pool temperature for 16 weeks. Simultaneously, the control group did not participate in any exercise and advised them to keep on with their everyday lifestyle. All the obtained metabolic syndrome risk factors data were analyzed using a two-way ANOVA analysis of variance (2*2) which was applied to determine the differences according group, time, and interaction.Results: The results showed that there were statistically significant differences at p < 0.05 in the variables of Total Cholesterol (TC), High Density of Lipoprotein (HDL), Low Density of Lipoprotein (LDL), Triglycerides (TG), Blood Glucose (BG), Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Body Mass Index (BMI), and body fat percent according to group, time, and interaction for the experimental group.Conclusions: The findings of the current study suggested that the regular 16 weeks of swimming sessions could be considered nonpharmacological approaches in managing T2DM and HTN.