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A Multinomial Logistic Regression Examination of TV Time and Two Different Measures of Obesity in U.S. Adults

Peter D. Hart
American Journal of Public Health Research. 2018, 6(5), 222-226. DOI: 10.12691/ajphr-6-5-3
Received August 05, 2018; Revised September 10, 2018; Accepted September 18, 2018

Abstract

Background: Physical inactivity and sedentary behavior are known factors related to the growing obesity rates in US adults. However, most population-based physical activity research primarily use a single measure of obesity. Therefore, the purpose of this study was to examine the relationship between television (TV) time and two different measures of obesity in US adults. Methods: This study used data from adults 20+ years of age participating in the 2015-2016 National Health and Nutrition Examination Survey (NHANES). Using body mass index (BMI), participants were categorized as obese if their values were 30 kg/m2 or greater. Using waist circumference (WC), participants were categorized as obese if their values were greater than 88 (females) or 102 cm (males). TV time was assessed from a survey question and adults were categorized into one of four different groups. Multinomial logistic regression was used to model the relationship between TV time and three different obese status categories. Results: In fully adjusted models, odds of being BMI obese (OR=1.98; 95% CI: 1.32-2.98) and WC obese (OR=2.76; 95% CI: 1.88-4.05) were significantly greater in adults with 5+ hours of TV time as compared to those with < 1 hour. In fully adjusted multinomial models, odds of being BMI or WC obese (OR=2.18; 95% CI: 1.43-3.34) and BMI and WC obese (OR=2.80; 95% CI: 1.68-4.65) were significantly greater in adults with 5+ hours of TV time as compared to those with < 1 hour. Conclusion: Results from this study indicate that TV time is clearly related to both overall and abdominal obesity in US adults. Furthermore, this relationship remains in light of MVPA and appears stronger for adults with both types of obesity.

1. Introduction

Obesity is a growing concern both in the US and world-wide and is receiving much attention from national public health organizations 1, 2, 3, 4. Obesity is defined as an abnormal accumulation of more than twenty-five percent (males) or thirty-five percent (females) body fat 5. Additionally, abdominal obesity is considered a major concern due to its links with increased visceral fat and in consequence its links with lipotoxicity 6. Despite these definitions of obesity, most epidemiological and population-based research use body mass index (BMI) as a proxy for obesity 7.

Physical activity is a known correlate of obesity in US adults 8, 9, 10, 11. Moreover, health education efforts to combat obesity have focused on physical activity and energy balance intervention 12, 13, 14. Screen time (combined use of technology devices with screens) is a measure of sedentary behavior that is also related to obesity in adults 15, 16, 17. Given this background, few studies have inspected the relationship between specific forms of screen time and obesity measured using multiple assessments. Therefore, the purpose of this study was to examine the relationship between television (TV) time and two different measures of obesity in US adults.

2. Methods

2.1. Study Design

Data for this research came from the 2015-2016 National Health and Nutrition Examination Survey (NHANES) 18. The sample consisted of participants who were 20+ years of age and had complete physical activity and body composition data.

2.2. Variables Utilized

The main independent variable was TV time. The two main dependent variables were obesity status measured by body mass index (BMI) and obesity status measured by waist circumference (WC). Covariates were moderate-to-vigorous physical activity (MVPA), age, sex, race, income.

2.3. Assessment of TV Time and MVPA

TV time was assessed from a survey question asking participants how many hours per day they sat and watched TV or videos during the past 30 days 19. For this study, four discrete TV time groups were formed: (1) < 1 hour, (2) 1-3 hours, (3) 4 hours, and (4) 5+ hours.

A physical activity control variable was computed from constructed variables of minutes of moderate physical activity (MPA) per week and minutes of vigorous physical activity (VPA) per week 19. VPA was assessed from the responses to two questions. The first question asked respondents how many days they participated in vigorous intensity sports, fitness, or recreational activities. The second question asked respondents how much time they spend doing vigorous-intensity activity on a typical day. Multiplying days with minutes yielded VPA measured per week. The same two questions were asked regarding moderate-intensity activities to assess MPA per week. These two physical activity variables were then used to compute minutes of MVPA per week.

2.4. Assessment of Obesity

Using BMI, participants were categorized as obese if their values were 30 kg/m2 or greater. Using WC, participants were categorized as obese if their values were greater than 88 (females) or 102 cm (males) 20. For the multinomial analyses, a three-level obesity outcome variable was constructed; consisting of 1) non-obese, 2) BMI or WC obese, and 3) BMI and WC obese. Measurements for both BMI (height and weight) and WC were collected by trained NHANES health professionals during a medical examination.

2.5. Statistical Analyses

Prevalence estimates, 95% confidence intervals (95% CI), and Rao-Scott chi-square tests of independence were used to describe BMI categories (Non-Obese, BMI or WC Obese, BMI and WC Obese) across demographic characteristics. The general linear model was used to form analysis of covariance models (ANCOVA) to test for adjusted mean BMI and WC differences across TV time groups. Logistic regression was used to calculate the adjusted odds ratios (ORs) and 95% CIs of being obese as opposed to being non-obese for each TV time group, while adjusting for age, race, gender, income, and MVPA. Multinomial logistic regression models were run modeling the probability of being BMI or WC obese only and being BMI and WC obese simultaneously, with non-obese as the reference. All analyses were performed using the complex samples module of SPSS version 24 21, 22, 23. All p-values are reported as 2-sided and statistical significance was defined as p-values < 0.05.

3. Results

Table 1 contains prevalence estimates for the three obese status groups. A total of N=5,107 adult participants were included with 39.3.6% (95% CI: 34.6-44.1) classified as non-obese, 22.5% (95% CI: 20.0-25.2) as BMI or WC obese, and 38.2% (95% CI: 34.9-41.6) as BMI and WC obese (p < .001). All other demographic variables were significantly related to obese status (ps < .05).

Table 2 contains results from two sets of logistic regression analyses. In the fully adjusted BMI model, odds of being BMI obese were significantly greater for 4 hours (OR=1.92; 95% CI: 1.28-2.88) and 5+ hours (OR=1.98; 95% CI: 1.32-2.98) of TV time, as compared to < 1 hour. In the fully adjusted WC model, odds of being WC obese were significantly greater for 1-3 hours (OR=1.38; 95% CI: 1.03-1.84), 4 hours (OR=2.46; 95% CI: 1.48-4.09) and 5+ hours (OR=2.76; 95% CI: 1.88-4.05) of TV time, as compared to < 1 hour. Both sets of models showed a significant linear trend in probability of being obese and TV time (ps < .05).

Table 3 contains results from the multinomial logistic regression analyses. In fully adjusted models, adults in the highest TV time group were more likely (OR=2.18; 95% CI: 1.43-3.34) to be BMI or WC obese (vs. non-obese) than those in the lowest TV time group. Furthermore, adults in the highest TV time group were more likely (OR=2.80; 95% CI: 1.68-4.65) to be BMI and WC obese (vs. non-obese) than those in the lowest TV time group. Similar to the previous analysis, both sets of models showed a significant linear trend in probability of being obese and TV time (ps < .05).

Figure 1 displays age-adjusted and sex-specific mean BMI across TV time groups. Both male and female adults had significantly different mean BMI across TV time groups (ps < .05). Additionally, both male and female adjusted mean BMI showed a significant linear trend (ps < .001) across increasing TV time groups. Figure 2 displays age-adjusted and sex-specific mean WV across TV time groups. Similarly, both male and female adults had significantly different mean WC across TV time groups (ps < .05). Additionally, both male and female adjusted mean WC showed a significant linear trend (ps < .001) across increasing TV time groups.

4. Discussion

The purpose of this study was to examine the relationship between TV time and two different measures of obesity in US adults. As expected, both BMI and WC models showed an increasing odds of being obese as TV time increased. Similar findings relating sedentary behavior with obesity have been shown using both subjective and objective measures of sedentary behavior in adults 24, 25, 26, 27. A novel finding from this study, however, was the increasing greater odds of being both BMI and WC obese with ascending TV times. Specifically, adults with 5+ hours of TV time were approximately three times more likely to be BMI and WC obese, as compared to adults with < 1 hour per day. A similar 2009 study examined the association between screen time and BMI-defined and WC-defined obesity in separate analyses 28. Results showed a positive relationship between screen time and both measures of obesity. However, this study did not examine the relationship between screen time and both measures of obesity simultaneously.

This study should be interpreted along with its limitations. One limitation is its cross-sectional properties which limits these findings to correlational generalizations as opposed to cause-and-effect inferences. However, the test of linear trend in this study, which also tests for dose-response effects, is a valuable attribute that aids in cross-sectional generalizations. Another limitation of this study was the use of self-reporting of TV time. Therefore, estimating TV time in this study may include un-accounted for measurement error.

5. Conclusions

Results from this study indicate that TV time, a measure of sedentary behavior, is related to obesity in US adults. Furthermore, the evidence supports a dose-response relationship between amounts of TV time and likelihood of being obese. This relationship remains in light of MVPA and appears stronger for adults with both overall and abdominal obesity. Health promotion programs should target the reduction of TV time as a possible means for reducing obesity in adults.

Acknowledgements

No financial assistance was used to assist with this project.

References

[1]  Abarca-Gómez, L., Abdeen, Z.A., Hamid, Z.A., Abu-Rmeileh, N.M., Acosta-Cazares, B., Acuin, C., Adams, R.J., Aekplakorn, W., Afsana, K., Aguilar-Salinas, C.A. and Agyemang, C. (2017). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128· 9 million children, adolescents, and adults. The Lancet, 390(10113), 2627-2642.
In article      View Article
 
[2]  US Department of Health and Human Services, Office of Disease Prevention and Health Promotion. Healthy people 2020.
In article      
 
[3]  Ogden, C. L., Fakhouri, T. H., Carroll, M. D., Hales, C. M., Fryar, C. D., Li, X., & Freedman, D. S. (2017). Prevalence of obesity among adults, by household income and education-United States, 2011-2014. MMWR. Morbidity and mortality weekly report, 66(50), 1369.
In article      View Article  PubMed
 
[4]  Hales, C. M., Carroll, M. D., Fryar, C. D., & Ogden, C. L. (2017). Prevalence of obesity among adults and youth: United States, 2015-2016. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics.
In article      
 
[5]  Wilmore, J. H. (1994). Exercise, obesity, and weight control. President's Council on Physical Fitness and Sports.
In article      
 
[6]  Sørensen, T. I., Virtue, S., & Vidal-Puig, A. (2010). Obesity as a clinical and public health problem: is there a need for a new definition based on lipotoxicity effects? Biochimica et Biophysica Acta (BBA)-Molecular and Cell Biology of Lipids, 1801(3), 400-404.
In article      View Article  PubMed
 
[7]  Hruby, A., & Hu, F. B. (2015). The epidemiology of obesity: a big picture. Pharmacoeconomics, 33(7), 673-689.
In article      View Article  PubMed
 
[8]  Hart, P. D. (2018). Using structural equation modeling to examine the effects of sex and physical activity on the metabolic syndrome and health-related quality of life relationship. Exercise Medicine. 2: 3.
In article      View Article
 
[9]  Hart, P. D., Benavidez, G. A., & Erickson, J. (2017). Meeting recommended levels of physical activity in relation to preventive health behavior and health status among adults. Journal of Preventive Medicine and Public Health. 2017; 50(1):10-17.
In article      View Article  PubMed
 
[10]  Hart, P. D. (2017). Contribution of physical activity to the Life's Simple 7 metric in older rural adults. American Journal of Cardiovascular Disease Research. 5(1): 1-4.
In article      
 
[11]  Hart, P. D. (2017). Self-reported physical inactivity and waist circumference independently predict all-cause mortality in U.S. adults. American Journal of Public Health Research. 5(6): 184-189.
In article      View Article
 
[12]  Stankevitz, K., Dement, J., Schoenfisch, A., Joyner, J., Clancy, S. M., Stroo, M., & Østbye, T. (2017). Perceived Barriers to Healthy Eating and Physical Activity Among Participants in a Workplace Obesity Intervention. Journal of occupational and environmental medicine, 59(8), 746-751.
In article      View Article  PubMed
 
[13]  Meng, Y., Manore, M., Schuna, J., Patton-Lopez, M., Branscum, A., & Wong, S. (2018). Promoting Healthy Diet, Physical Activity, and Life-Skills in High School Athletes: Results from the WAVE Ripples for Change Childhood Obesity Prevention Two-Year Intervention. Nutrients, 10(7), 947.
In article      View Article  PubMed
 
[14]  Koo, H. C., Poh, B. K., & Ruzita, A. T. (2016). Intervention on whole grain with healthy balanced diet to manage childhood obesity (GReat-Child™ trial): study protocol for a quasi-experimental trial. SpringerPlus, 5(1), 840.
In article      View Article  PubMed
 
[15]  Shin, J. (2018). Joint Association of Screen Time and Physical Activity with Obesity: Findings from the Korea Media Panel Study. Osong public health and research perspectives, 9(4), 207.
In article      View Article  PubMed
 
[16]  Duncan, M. J., Vandelanotte, C., Caperchione, C., Hanley, C., & Mummery, W. K. (2012). Temporal trends in and relationships between screen time, physical activity, overweight and obesity. BMC Public Health, 12(1), 1060.
In article      View Article  PubMed
 
[17]  Banks, E., Jorm, L., Rogers, K., Clements, M., & Bauman, A. (2011). Screen-time, obesity, ageing and disability: findings from 91 266 participants in the 45 and Up Study. Public health nutrition, 14(1), 34-43.
In article      View Article  PubMed
 
[18]  Centers for Disease Control and Prevention. National Center for Health Statistics. National Health and Nutrition Examination Survey: Plan and Operations, 1999-2010: https://wwwn.cdc.gov/nchs/nhanes/analyticguidelines.aspx.
In article      View Article
 
[19]  Centers for Disease Control and Prevention National Center for Health Statistics. NHANES 2015-2016 Physical Activity And Physical Fitness – PAQ; 2013.
In article      
 
[20]  Centers for Disease Control and Prevention National Center for Health Statistics. NHANES 2015-2016 Anthropometry Procedures Manual; 2016.
In article      
 
[21]  Stokes ME, Davis CS, Koch GG. Categorical data analysis using SAS. Cary: SAS institute; 2012, p. 189-258.
In article      
 
[22]  IBM Corporation. IBM SPSS complex samples: correctly compute complex samples statistics [cited 2017 Jan 5]. Available from: http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=YTD03116USEN&appname=skmwww.
In article      View Article
 
[23]  Tabachnick BG, Fidell LS. Using multivariate statistics. 5th ed. Boston: Allyn & Bacon; 2007, p. 439-484.
In article      
 
[24]  Judice, P. B., Silva, A. M., & Sardinha, L. B. (2015). Sedentary bout durations are associated with abdominal obesity in older adults. The journal of nutrition, health & aging, 19(8), 798-804.
In article      View Article  PubMed
 
[25]  Maher, C. A., Mire, E., Harrington, D. M., Staiano, A. E., & Katzmarzyk, P. T. (2013). The independent and combined associations of physical activity and sedentary behavior with obesity in adults: NHANES 2003‐06. Obesity, 21(12), E730-E737.
In article      View Article  PubMed
 
[26]  Nurwanti, E., Uddin, M., Chang, J.S., Hadi, H., Syed-Abdul, S., Su, E., Nursetyo, A., Masud, J. and Bai, C.H. (2018). Roles of sedentary behaviors and unhealthy foods in increasing the obesity risk in adult men and women: A cross-sectional national study. Nutrients, 10(6), 704.
In article      View Article  PubMed
 
[27]  Mun, J., Kim, Y., Farnsworth, J. L., Suh, S., & Kang, M. (2018). Association between objectively measured sedentary behavior and a criterion measure of obesity among adults. American Journal of Human Biology, 30(2), e23080.
In article      View Article  PubMed
 
[28]  Stamatakis, E., Hirani, V., & Rennie, K. (2008). Moderate-to-vigorous physical activity and sedentary behaviours in relation to body mass index-defined and waist circumference-defined obesity. British Journal of Nutrition, 101(5), 765-773.
In article      View Article  PubMed
 

Published with license by Science and Education Publishing, Copyright © 2018 Peter D. Hart

Creative CommonsThis work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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Normal Style
Peter D. Hart. A Multinomial Logistic Regression Examination of TV Time and Two Different Measures of Obesity in U.S. Adults. American Journal of Public Health Research. Vol. 6, No. 5, 2018, pp 222-226. http://pubs.sciepub.com/ajphr/6/5/3
MLA Style
Hart, Peter D.. "A Multinomial Logistic Regression Examination of TV Time and Two Different Measures of Obesity in U.S. Adults." American Journal of Public Health Research 6.5 (2018): 222-226.
APA Style
Hart, P. D. (2018). A Multinomial Logistic Regression Examination of TV Time and Two Different Measures of Obesity in U.S. Adults. American Journal of Public Health Research, 6(5), 222-226.
Chicago Style
Hart, Peter D.. "A Multinomial Logistic Regression Examination of TV Time and Two Different Measures of Obesity in U.S. Adults." American Journal of Public Health Research 6, no. 5 (2018): 222-226.
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  • Table 1. Prevalence of BMI- and WC-determined obese classification by demographic characteristic, US adults 20+ years of age 2015-16
  • Table 2. Odds of being BMI and WC obese relative to the least TV time group, US adults 20+ years of age 2015-16
[1]  Abarca-Gómez, L., Abdeen, Z.A., Hamid, Z.A., Abu-Rmeileh, N.M., Acosta-Cazares, B., Acuin, C., Adams, R.J., Aekplakorn, W., Afsana, K., Aguilar-Salinas, C.A. and Agyemang, C. (2017). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128· 9 million children, adolescents, and adults. The Lancet, 390(10113), 2627-2642.
In article      View Article
 
[2]  US Department of Health and Human Services, Office of Disease Prevention and Health Promotion. Healthy people 2020.
In article      
 
[3]  Ogden, C. L., Fakhouri, T. H., Carroll, M. D., Hales, C. M., Fryar, C. D., Li, X., & Freedman, D. S. (2017). Prevalence of obesity among adults, by household income and education-United States, 2011-2014. MMWR. Morbidity and mortality weekly report, 66(50), 1369.
In article      View Article  PubMed
 
[4]  Hales, C. M., Carroll, M. D., Fryar, C. D., & Ogden, C. L. (2017). Prevalence of obesity among adults and youth: United States, 2015-2016. US Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics.
In article      
 
[5]  Wilmore, J. H. (1994). Exercise, obesity, and weight control. President's Council on Physical Fitness and Sports.
In article      
 
[6]  Sørensen, T. I., Virtue, S., & Vidal-Puig, A. (2010). Obesity as a clinical and public health problem: is there a need for a new definition based on lipotoxicity effects? Biochimica et Biophysica Acta (BBA)-Molecular and Cell Biology of Lipids, 1801(3), 400-404.
In article      View Article  PubMed
 
[7]  Hruby, A., & Hu, F. B. (2015). The epidemiology of obesity: a big picture. Pharmacoeconomics, 33(7), 673-689.
In article      View Article  PubMed
 
[8]  Hart, P. D. (2018). Using structural equation modeling to examine the effects of sex and physical activity on the metabolic syndrome and health-related quality of life relationship. Exercise Medicine. 2: 3.
In article      View Article
 
[9]  Hart, P. D., Benavidez, G. A., & Erickson, J. (2017). Meeting recommended levels of physical activity in relation to preventive health behavior and health status among adults. Journal of Preventive Medicine and Public Health. 2017; 50(1):10-17.
In article      View Article  PubMed
 
[10]  Hart, P. D. (2017). Contribution of physical activity to the Life's Simple 7 metric in older rural adults. American Journal of Cardiovascular Disease Research. 5(1): 1-4.
In article      
 
[11]  Hart, P. D. (2017). Self-reported physical inactivity and waist circumference independently predict all-cause mortality in U.S. adults. American Journal of Public Health Research. 5(6): 184-189.
In article      View Article
 
[12]  Stankevitz, K., Dement, J., Schoenfisch, A., Joyner, J., Clancy, S. M., Stroo, M., & Østbye, T. (2017). Perceived Barriers to Healthy Eating and Physical Activity Among Participants in a Workplace Obesity Intervention. Journal of occupational and environmental medicine, 59(8), 746-751.
In article      View Article  PubMed
 
[13]  Meng, Y., Manore, M., Schuna, J., Patton-Lopez, M., Branscum, A., & Wong, S. (2018). Promoting Healthy Diet, Physical Activity, and Life-Skills in High School Athletes: Results from the WAVE Ripples for Change Childhood Obesity Prevention Two-Year Intervention. Nutrients, 10(7), 947.
In article      View Article  PubMed
 
[14]  Koo, H. C., Poh, B. K., & Ruzita, A. T. (2016). Intervention on whole grain with healthy balanced diet to manage childhood obesity (GReat-Child™ trial): study protocol for a quasi-experimental trial. SpringerPlus, 5(1), 840.
In article      View Article  PubMed
 
[15]  Shin, J. (2018). Joint Association of Screen Time and Physical Activity with Obesity: Findings from the Korea Media Panel Study. Osong public health and research perspectives, 9(4), 207.
In article      View Article  PubMed
 
[16]  Duncan, M. J., Vandelanotte, C., Caperchione, C., Hanley, C., & Mummery, W. K. (2012). Temporal trends in and relationships between screen time, physical activity, overweight and obesity. BMC Public Health, 12(1), 1060.
In article      View Article  PubMed
 
[17]  Banks, E., Jorm, L., Rogers, K., Clements, M., & Bauman, A. (2011). Screen-time, obesity, ageing and disability: findings from 91 266 participants in the 45 and Up Study. Public health nutrition, 14(1), 34-43.
In article      View Article  PubMed
 
[18]  Centers for Disease Control and Prevention. National Center for Health Statistics. National Health and Nutrition Examination Survey: Plan and Operations, 1999-2010: https://wwwn.cdc.gov/nchs/nhanes/analyticguidelines.aspx.
In article      View Article
 
[19]  Centers for Disease Control and Prevention National Center for Health Statistics. NHANES 2015-2016 Physical Activity And Physical Fitness – PAQ; 2013.
In article      
 
[20]  Centers for Disease Control and Prevention National Center for Health Statistics. NHANES 2015-2016 Anthropometry Procedures Manual; 2016.
In article      
 
[21]  Stokes ME, Davis CS, Koch GG. Categorical data analysis using SAS. Cary: SAS institute; 2012, p. 189-258.
In article      
 
[22]  IBM Corporation. IBM SPSS complex samples: correctly compute complex samples statistics [cited 2017 Jan 5]. Available from: http://www-01.ibm.com/common/ssi/cgi-bin/ssialias?htmlfid=YTD03116USEN&appname=skmwww.
In article      View Article
 
[23]  Tabachnick BG, Fidell LS. Using multivariate statistics. 5th ed. Boston: Allyn & Bacon; 2007, p. 439-484.
In article      
 
[24]  Judice, P. B., Silva, A. M., & Sardinha, L. B. (2015). Sedentary bout durations are associated with abdominal obesity in older adults. The journal of nutrition, health & aging, 19(8), 798-804.
In article      View Article  PubMed
 
[25]  Maher, C. A., Mire, E., Harrington, D. M., Staiano, A. E., & Katzmarzyk, P. T. (2013). The independent and combined associations of physical activity and sedentary behavior with obesity in adults: NHANES 2003‐06. Obesity, 21(12), E730-E737.
In article      View Article  PubMed
 
[26]  Nurwanti, E., Uddin, M., Chang, J.S., Hadi, H., Syed-Abdul, S., Su, E., Nursetyo, A., Masud, J. and Bai, C.H. (2018). Roles of sedentary behaviors and unhealthy foods in increasing the obesity risk in adult men and women: A cross-sectional national study. Nutrients, 10(6), 704.
In article      View Article  PubMed
 
[27]  Mun, J., Kim, Y., Farnsworth, J. L., Suh, S., & Kang, M. (2018). Association between objectively measured sedentary behavior and a criterion measure of obesity among adults. American Journal of Human Biology, 30(2), e23080.
In article      View Article  PubMed
 
[28]  Stamatakis, E., Hirani, V., & Rennie, K. (2008). Moderate-to-vigorous physical activity and sedentary behaviours in relation to body mass index-defined and waist circumference-defined obesity. British Journal of Nutrition, 101(5), 765-773.
In article      View Article  PubMed