Background: Examining the extent to which physical activity (PA) and body measure (BM) variables correlate is useful for the promotion of a healthy lifestyle. Therefore, the purpose of this study was to examine the associations between PA and BM variables in a representative sample of US adults. Methods: Participants 20+ years of age from the 2017-2020 (pre-pandemic, 3.2 year) National Health and Nutrition Examination Survey (NHANES) were used. PA variables included work (VWPA, MWPA), recreation (VRPA, MRPA), transportation (TPA), sedentary time (SED), moderate-to-vigorous PA (MVPA), met PA guidelines status (METPA), and physical inactivity status (PIA). BM variables included body mass index (BMI), waist circumference (WC), arm circumference (AC), BMI category (BMICAT), obese status (OBESE), and overweight status (OVWT). Spearman correlations were computed both for bivariate association and controlling for age, race, and income. Multiple logistic regression was used to examine the adjusted relationship between PA and BM categorical variables. All analyses were performed separately by sex. Results: Multivariate BM correlations were strongest for TPA and OVWT (males) and MVPA and WC (females). Adjusted models showed the odds of being obese were greatest for those reporting low (versus high) amounts of VRPA in both males (OR = 1.84, 95% CI: 1.28 - 2.67) and females (OR = 2.47, 95% CI: 1.83 - 3.34). Additionally, odds of meeting PA guidelines were greatest for those with low (versus high) WC in males (OR = 1.51, 95% CI: 1.2 3- 1.84) and low (versus high) BMI in females (OR = 2.06, 95% CI: 1.73 - 2.46). Conclusion: Results from this study indicate that PA and BM variables are related in U.S. adults prior to the COVID pandemic. Furthermore, low WC for males and low BMI for females were the stronger adjusted categorical predictors of meeting PA guidelines.
Physical activity (PA) and body measures (BMs) are both health-related concepts predictive of medical outcomes such as chronic disease 1, 2, 3, 4, premature death 5, 6, 7, functional status 8, 9, and health-related quality of life 10, 11. PA is more commonly promoted and researched in adults as a recreational or leisure activity with campaigns and intervention strategies focusing on steps, minutes, or distance as markers for goal attainments 12. However, PA benefits can also be obtained during work 13, via transportation 14, 15, and/or simply avoiding sedentary behavior 16. Similarly, health-related research has placed much emphasis on using body mass index (BMI) as a measure of body composition for predicting outcomes 17, 18. However, waist circumference (WC), albeit not as easy to assess as BMI, has increased in popularity in recent years and shown to be a valuable predictor of health outcomes 19, 20.
In sum, both health predicting concepts of PA and BMs can be assessed using several different measured variables. Additionally, the interrelationships between these variables can be important to health promotion efforts. That is, intervention efforts directed at improving leisure time PA in adults may consequently help benefit body composition 21. Nevertheless, data establishing the interrelationships between these different variables in a represented sample of Unites States (US) adults are scant. Furthermore, representative data relating these concepts using multivariate adjustments of confounders are currently nonexistent. Therefore, the purpose of this study was to examine the associations between PA and BM variables in a representative sample of US adults. Specifically, the aim was fourfold: 1) examine the bivariate associations between PA and BM variables, 2) examine the multivariate associations between PA and BM variables after controlling for demographic covariates, 3) examine the strength that each study variable has as a correlate with the set of cross-concept variables (e.g., average correlation between BMI and all PA variables), and 4) examine the relationship between PA and BM categorical variables.
Data for this study came from the 2017 to March Pre-pandemic 2020 National Health and Nutrition Examination Survey (NHANES) 22. Since the coronavirus of 2019 interrupted the 2019-2020 NHANES cycle, the 2019-2020 data were not considered valid for use alone. Thus, the incomplete 2019-2020 NHANES cycle was combined with the 2017-2018 NHANES cycle to create a nationally representative pre-pandemic cycle, spanning a 3.2-year period. Just as in previous cycles, the current NHANES is a continual survey designed to assess health behavior, health status, and nutrition of noninstitutionalized civilian residents of the US. The survey collects data from individuals using personal interviews, standardized physical examinations, and laboratory tests. This research used data specifically from personal interviews (demographic and PA components) and physical examinations (BMs component). The sample in the current study consisted of adult participants who were 20+ years of age and had PA and BM data.
2.2. Assessment of Physical Activity (PA) VariablesSeven different continuous PA variables were used in this study and included vigorous work-related PA (VWPA), moderate work-related PA (MWPA), transportation-related PA (TPA), vigorous recreational PA (VRPA), moderate recreational PA (MRPA), sedentary time (SED), and moderate-to-vigorous PA (MVPA). Both work-related PA variables were assessed by asking participants if they engaged in paid or unpaid work, household chores, or yard work for at least 10 minutes continuously. VWPA more specifically asked about vigorous-intensity activities that cause large increases in breathing or heart rate and included examples like carrying or lifting heavy loads, digging or construction work. MWPA specifically asked about moderate-intensity activities that cause small increases in breathing or heart rate and included examples such as brisk walking or carrying light loads. Follow-up questions for work-related PA included the average number of days per week performed and the average duration engaged per day to create final average work-related PA variables with units of minutes per week (min/week).
TPA was assessed by asking participants if they walk or bicycle to and from places (e.g., school, shopping, or work) for at least 10 minutes continuously. Similar follow-up questions were asked to derive a TPA variable with min/week units. Both recreation-related PA variables were assessed from questions that asked participants to exclude work-related and transportation-related PA and include sport, fitness and recreational activities engaged in for at least 10 minutes continuously. VRPA asked about vigorous-intensity activities that cause large increases in breathing or heart rate and included examples like running or basketball. MRPA asked about moderate-intensity activities that cause small increases in breathing or heart rate and included examples like brisk walking, bicycling, swimming, or volleyball. Follow-up questions were asked for both recreational PA variables to compute final variables with min/week units. SED was assessed using a single question asking participants how much time they usually spend sitting on a typical day, including school, home, transportation, and work, and excluding sleep. SED units were in minutes per day (min/day). MVPA was computed from both VRPA and MRPA by adding MRPA plus two times VRPA and used units of min/week.
Additionally, two binary PA variables were created from the above continuous PA variables and included met PA guidelines (METPA) and physical inactivity (PIA). METPA was computed from MVPA and categorized participants as either ‘0’ for < 150 min/week of MVPA or ‘1’ for 150+ min/week of MVPA. Finally, PIA was also computed from MVPA where participants with 0 min/week were categorized as ‘1’ for being physically inactive or otherwise ‘0’ for not being physically inactive.
2.3. Assessment of Body Measure (BM) VariablesThree different continuous BM variables were used in this study and included BMI, WC, and arm circumference (AC). Each BM variable was assessed by trained health professionals using standardized procedures. BMI was assessed by first measuring participant height and weight using a digital stadiometer and digital floor scale, respectively. The final BMI variable was afterwards computed by dividing a participant’s weight (kg) by height (m2). WC measurement site was first marked on the participant’s skin just above the uppermost lateral border of the right ilium and at the midaxillary line. A mirror was used to ensure that a measuring tape remained parallel to the floor. WC measurement was recorded to the nearest 0.1 cm after a normal expiration by the participant. AC measurement site was first marked midway and at the posterior surface of the arm between the tip of the olecranon process and the bony part of the mid-elbow. The measuring tape was set perpendicular to the long axis of the upper arm with the participant standing and arm hanging loose to the side. AC measurement was recorded to the nearest 0.1 cm. Additionally, two binary BM variables and one 4-level categorical variable were created from the BMI variable and included obese status (OBESE), overweight status (OVWT), and BMI category (BMICAT). OBESE was computed by categorizing participants as either ‘1’ for obese (30+ kg/m2) or ‘0’ for not obese (< 30 kg/m2). OVWT was computed by categorizing participants as either ‘1’ for overweight (25+ kg/m2) or ‘0’ for not overweight (< 25 kg/m2). Finally, BMICAT was computed categorizing participants as either 1) underweight (< 18.5 kg/m2), normal weight (18.5 kg/m2 to 24.9 kg/m2), overweight (25.0 kg/m2 to 29.9 kg/m2), or obese (30+ kg/m2).
2.4. Other VariablesFor multivariate statistical adjustment purposes, age, race, and income were used in this study. Age was used as a continuous variable, ranging from 20 years to 80+ years. Race/ethnicity was used as a categorical variable and included White, Black, Hispanic, and Other groupings. Finally, income was used as a continuous variable and computed as a ratio of the family income to poverty, ranging from 0 to 5.
2.5. Statistical AnalysesDescriptive statistics were computed, separately by sex, for all study variables, including means, standard errors (SEs), and 95% confidence intervals (CIs). Test of sex differences were performed for study variables suing both parametric and nonparametric methods. Due to the non-normal nature of most of the variables under study, the Spearman correlation coefficient (rS) was used to examined bivariate and adjusted associations. To compute these correlation statistics so that they were both Spearman coefficients and adjusted and weighted for the complex sampling scheme, variables were first
1) converted to ranks and secondly 2) run through a regression analysis accounting for the appropriate survey weights and stratified cluster design. For the multivariate demographic adjusted coefficients, a preliminary step was added that first outputted residual variables after regressing each dependent variable separately onto age, race, and income. Mean absolute Spearman correlations (rS.Mean) were also computed for each PA variable across all BM variables as well as for each BM variable across all PA variables, to assess an overall collective association strength for each study variable. Similarly, all correlations were computed separately by sex. Finally, multiple logistic regression was used to examine the adjusted relationship between PA and BM categorical variables, first predicting OBESE status and then predicting METPA status. All analyses were performed using the base and survey procedures of SAS version 9.4 23. Correlation graphs were constructed using the R statistical program and the corrplot package by importing the appropriate Spearman correlation matrices 24. All p-values were reported as 2-sided and statistical significance was defined as p-values < 0.05.
Table 1 contains descriptive statistics for study variables in adult males. Most noteworthy is the large amounts of VWPA (Mean = 348.4, 95% CI: 327.0 - 369.7) in comparison to VRPA (Mean = 87.1, 95% CI: 81.8 - 92.5) among males. Similarly, large amounts of MWPA (Mean = 464.1, 95% CI: 442.4 - 485.9) was seen in males as compared to MRPA (Mean = 112.9, 95% CI: 106.2 - 119.6). For BM findings, majority of males were overweight (Mean = 0.77, 95% CI: 0.76 - 0.78) and a considerable proportion were considered obese (Mean = 0.42, 95% CI: 0.40 - 0.43). Table 2 contains parallel statistics for female participants. Also notable is the large amounts of VWPA (Mean = 135.5, 95% CI: 122.8 - 148.2) in comparison to VRPA (Mean = 51.3, 95% CI: 47.6 - 55.0) among females. Similarly, large amounts of MWPA (Mean = 325.2, 95% CI: 307.0 - 343.3) was seen in females as compared to MRPA (Mean = 84.4, 95% CI: 79.6 - 89.2). For BM findings, majority of females were also overweight (Mean = 0.71, 95% CI: 0.69 - 0.72) and a large proportion were considered obese (Mean = 0.42, 95% CI: 0.41 - 0.44). All study variables in tables 1 and 2 showed significant (p < .05) sex differences, less SED (p = .155), BMI (p = .133), BMICAT (p = .102), and OBESE (p = .757).
Table 3 displays the bivariate Spearman correlation coefficients for all study variables for both males and females. The more standout significant (p < .05) PA-by-BM correlations in males was between VRPA and WC (rS = -.209) and MVPA and WC (rS = -.173). Additionally, SED (rS ≥ .108) and TPA (rS ≤ -.081) are significant (p < .05) and consistent bivariate correlates with all BM measures in males. Mean absolute bivariate BM correlations for males were strongest for SED (rS.Mean = .144) and TPA (rS.Mean = .098) and mean absolute bivariate PA correlations strongest for WC (rS.Mean = .124). For females, standout significant (p < .05) bivariate correlations involved WC with VRPA (rS = -.244) and WC with MVPA (rS = -.239). Furthermore, TPA (rS ≤ -.077), VRPA (rS ≤ -.150), MRPA (rS ≤ -.096), SED (rS ≥ .077), MVPA (rS ≤ -.137), METPA (rS ≤ -.153), and PIA (rS ≥ -.110) were significant (p < .05) and consistent bivariate correlates with all BM measures in females. Mean absolute bivariate BM correlations for females were strongest for METPA (rS.Mean = .190) and VRPA (rS.Mean = .186) and mean absolute bivariate PA correlations strongest for WC (rS.Mean = .150). Figure 1 shows a correlation plot containing the same (only significant) bivariate Spearman correlations for both males (bottom) and females (top).
Table 4 displays the multivariate (partial) Spearman correlation coefficients for all study variables for both males and females, adjusted for age, race, and income. Interestingly, the strongest significant (p < .05) PA-by-BM multivariate correlations in males is between TPA and OVWT (rS = -.230) and SED and WC (rS = .166). Like the bivariate analyses, SED (rS ≥ .076) and TPA (rS ≤ -.069) were significant (p < .05) and consistent multivariate correlates with all BM measures in males. Additionally, mean absolute multivariate BM correlations for males were strongest for SED (rS.Mean = .127) and TPA (rS.Mean = .119) and mean absolute multivariate PA correlations strongest for WC (rS.Mean = .102). For females, the largest significant (p < .05) multivariate correlations involved WC with MVPA (rS = -.200) and OBESE with METPA (rS = -.190). Furthermore, VRPA (rS ≤ -.130), MRPA (rS ≤ -.075), SED (rS ≥ .087), MVPA (rS ≤ -.139), METPA (rS ≤ -.142), and PIA (rS ≥ -.101) were significant (p < .05) and consistent multivariate correlates with all BM measures in females. Additionally, mean absolute multivariate BM correlations for females were strongest for METPA (rS.Mean = .177) and MVPA (rS.Mean = .171) and mean absolute multivariate PA correlations strongest for WC (rS.Mean = .122). Figure 2 shows a second correlation plot containing the same (only significant) multivariate Spearman correlations for both males (bottom) and females (top).
Table 5 contains results from logistic regression analyses modeling the relationship between grouped (low vs high) PA variables and OBESE status in both males and females. Most noteworthy, adjusted models showed the odds of being obese (i.e., OBESE = 1) were greatest for those reporting low (versus high) amounts of VRPA in both males (OR = 1.84, 95% CI: 1.28 - 2.67) and females (OR = 2.47, 95% CI: 1.83 - 3.34). Additionally, odds of being obese were also great for those reporting low (versus high) amounts of MVPA in both males (OR = 1.56, 95% CI: 1.26 - 1.92) and females (OR = 2.01, 95% CI: 1.70 - 2.37). Work-related PA was not a significant multivariate predictor of obesity in either male or female adults. Table 6 contains results from a similar set of analyses modeling the relationship between grouped (low vs high) BM variables and METPA status. Results indicate odds of meeting PA guidelines (i.e., METPA = 1) were greatest for those with low (versus high) WC in males (OR = 1.51, 95% CI: 1.23 - 1.84) and low BMI in females (OR = 2.06, 95% CI: 1.73 - 2.46). Additionally, AC was a significant multivariate predictor of METPA status in females (OR = 1.92, 95% CI: 1.52 - 2.41), but not males.
The purpose of this study was to examine the bivariate and multivariate associations between PA and BM variables in a representative sample of US adults. Study findings support both bivariate (zero-order) as well as multivariate (demographics-adjusted) correlations between PA and BM variables in both sexes. Specifically, the first aim was to examine the bivariate associations between PA and BM variables. These results were as predicted in that the stronger correlations were with VRPA and WC in both sexes. The second aim was to examine the multivariate associations between PA and BM variables after controlling for demographic covariates. These results differed in that the stronger correlations were for TPA and OVWT (males) and MVPA and WC (females). Thus, multivariate demographics-adjusted correlations may give a clearer picture of the PA and BM variable associations. The third aim was to examine the strength that each study variable has as a correlate with the set of cross-concept variables (e.g., average correlation between BMI and all PA variables). The multivariate results indicated that SED was a more influential correlate with the set of BM variables for males and METPA for females. Additionally, multivariate results indicated that WC was a more influential correlate with the set of PA variables for both sexes. The fourth and final aim was to examine the relationship between PA and BM categorical variables. The multivariate adjusted models indicated that VRPA was the stronger categorical predictor of obese status in both males and females. Whereas WC was the stronger categorical predictor of meeting PA guidelines in males and BMI the stronger categorical predictor in females. These findings are noteworthy since few studies show sex differences in the BM and PA relationship, when analyzed using different BM variables 25, 26.
A strength of this study was its use of objectively measured BM variables such as BMI (height and weight), WC, and AC, leading to also objective classification of BMICAT, OBESE and OVWT. Another strength of this study was its use of a population-based survey representing pre-pandemic health status in US adults. NHANES data represent the total noninstitutionalized civilian U.S. population residing in the 50 states and District of Columbia. Therefore, results from this study can validly be generalized to all noninstitutionalized adults 20+ years of age residing in the U.S. A limitation in this study however was the use of cross-sectional data, characteristic of NHANES. Cross-sectional data cannot support and should not imply cause-and-effect relationships. That is, results from this study do not advocate the concept that PA can necessarily change BM variables in adults. A randomized controlled trial should be conducted to address such cause-and-effect associations. As an alternative, results from this study should be considered as correlational. Another limitation of this study was the self-report assessment of PA. That is, data from self-reported questionnaires have certain biases over more objective means of measurement. However, the items used to assess the PA variables in this study came from the Global Physical Activity Questionnaire (GPAQ), which has adequate validity and reliability evidence supporting its use in this population 27.
Results from this study indicate that PA and BM variables are related in U.S. adults prior to the COVID pandemic. VRPA and WC had the stronger bivariate correlation in both males and females. However, multivariate demographics-adjusted correlations showed different results. Furthermore, low WC for males and low BMI for females were the stronger adjusted categorical predictors of meeting PA guidelines.
No financial assistance was used to assist with this project.
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Published with license by Science and Education Publishing, Copyright © 2022 Peter D. Hart
This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit
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| [1] | Wei J, Liu X, Xue H, Wang Y, Shi Z. Comparisons of visceral adiposity index, body shape index, body mass index and waist circumference and their associations with diabetes mellitus in adults. Nutrients. 2019 Jul 12; 11(7): 580. | ||
| In article | View Article PubMed | ||
| [2] | Recalde M, Davila-Batista V, Díaz Y, Leitzmann M, Romieu I, Freisling H, Duarte-Salles T. Body mass index and waist circumference in relation to the risk of 26 types of cancer: a prospective cohort study of 3.5 million adults in Spain. BMC medicine. 2021 Dec; 19(1): 1-4. | ||
| In article | View Article PubMed | ||
| [3] | Momin M, Fan F, Li J, Jia J, Zhang L, Zhang Y, Huo Y. Joint effects of body mass index and waist circumference on the incidence of hypertension in a community-based Chinese population. Obesity facts. 2020; 2(2): 245-55. | ||
| In article | View Article PubMed | ||
| [4] | Canoy D, Cairns BJ, Balkwill A, Wright FL, Green J, Reeves G, Beral V, Million Women Study Collaborators. Coronary heart disease incidence in women by waist circumference within categories of body mass index. European journal of preventive cardiology. 2013 Oct 1; 20(5): 759-62. | ||
| In article | View Article PubMed | ||
| [5] | Roswall N, Li Y, Sandin S, Ström P, Adami HO, Weiderpass E. Changes in body mass index and waist circumference and concurrent mortality among Swedish women. Obesity. 2017 Jan; 25(1): 215-22. | ||
| In article | View Article PubMed | ||
| [6] | Lo K, Huang YQ, Shen G, Huang JY, Liu L, Yu YL, Chen CL, Feng YQ. Effects of waist to height ratio, waist circumference, body mass index on the risk of chronic diseases, all-cause, cardiovascular and cancer mortality. Postgraduate Medical Journal. 2021 May 1; 97(1147): 306-11. | ||
| In article | View Article PubMed | ||
| [7] | Kim YH, Kim SM, Han KD, Jung JH, Lee SS, Oh SW, Park HS, Rhee EJ, Lee WY, Yoo SJ. Waist circumference and all-cause mortality independent of body mass index in Korean population from the National Health Insurance Health Checkup 2009-2015. Journal of clinical medicine. 2019 Jan 10; 8(1): 72. | ||
| In article | View Article PubMed | ||
| [8] | Lisko I, Stenholm S, Raitanen J, Hurme M, Hervonen A, Jylhä M, Tiainen K. Association of body mass index and waist circumference with physical functioning: the vitality 90+ study. Journals of Gerontology Series A: Biomedical Sciences and Medical Sciences. 2015 Jul 1; 70(7): 885-91. | ||
| In article | View Article PubMed | ||
| [9] | Kang K, Lee WW, Lee JJ, Park JM, Kwon O, Kim BK. Comparison of body mass index, waist circumference, and waist-height ratio in predicting functional outcome following ischemic stroke. Journal of thrombosis and thrombolysis. 2017 Aug; 44(2): 238-44. | ||
| In article | View Article PubMed | ||
| [10] | Hyun YY, Lee KB, Chung W, Kim YS, Han SH, Oh YK, Chae DW, Park SK, Oh KH, Ahn C. Body Mass Index, waist circumference, and health-related quality of life in adults with chronic kidney disease. Quality of Life Research. 2019 Apr; 28(4): 1075-83. | ||
| In article | View Article PubMed | ||
| [11] | Wang L, Crawford JD, Reppermund S, Trollor J, Campbell L, Baune BT, Sachdev P, Brodaty H, Samaras K, Smith E. Body mass index and waist circumference predict health-related quality of life, but not satisfaction with life, in the elderly. Quality of Life Research. 2018 Oct; 27(10): 2653-65. | ||
| In article | View Article PubMed | ||
| [12] | Piercy KL, Troiano RP, Ballard RM, Carlson SA, Fulton JE, Galuska DA, George SM, Olson RD. The physical activity guidelines for Americans. Jama. 2018 Nov 20; 320(19): 2020-8. | ||
| In article | View Article PubMed | ||
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