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Costantini G, Epskamp S, Borsboom D, Perugini M, Mõttus R, Waldorp LJ, Cramer AO. State of the aRt personality research: A tutorial on network analysis of personality data in R. Journal of Research in Personality. 2015 Feb 1; 54: 13-29.

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Article

Partial Relationships between Health and Fitness Measures in Adults: A Network Analysis

1Health Promotion Research, Havre, MT 59501

2Kinesmetrics Lab, Tallahassee, FL 32311


American Journal of Public Health Research. 2022, Vol. 10 No. 4, 147-153
DOI: 10.12691/ajphr-10-4-3
Copyright © 2022 Science and Education Publishing

Cite this paper:
Peter D. Hart. Partial Relationships between Health and Fitness Measures in Adults: A Network Analysis. American Journal of Public Health Research. 2022; 10(4):147-153. doi: 10.12691/ajphr-10-4-3.

Correspondence to: Peter  D. Hart, Health Promotion Research, Havre, MT 59501. Email: pdhart@outlook.com

Abstract

Background: Evidence supports the associations between many health and fitness measures in the exercise sciences. However, less is known about how these indicators relate to each other after controlling for their shared variance. Furthermore, understanding the relative importance of health and fitness measures may help prioritize education and promotion efforts. The aim of this study was to examine the strength and direction of partial relationships between health and fitness measures in a sample of adults. Methods: Data from the 2013-2014 National Health and Nutrition Examination Survey (NHANES) were used and included 3,927 adults 20 to 59 years of age. Six different health and fitness variables were utilized and included grip strength (GRIP, kg), percent body fat (PBF, %), body mass index (BMI, kg/m2), waist circumference (WC, cm), moderate-to-vigorous physical activity (MVPA, min/week), and perceived general health (HLTH, 1=poor to 5 = excellent). GRIP, PBF, BMI, and WC were assessed objectively by trained professionals using handgrip dynamometer, DEXA, scale with stadiometer, and tape measure, respectively. HLTH was assessed by a single question asking participants to rate their general health and MVPA was assessed by a series of survey questions regarding recreational activity. Two network analyses were conducted: 1) unadjusted and 2) adjusted for sex, age, race, and income. All analyses were performed using SAS and R software (bootnet and qgraph). Results: All bivariate Spearman correlation coefficients (rS) were significant (p < .05) ranging from -.14 to -.58 for negative correlations and .07 to .93 for positive correlations. Unadjusted network analysis indicated a strong positive partial relationship between BMI and WC (rS = .84) and a strong negative partial relationship between GRIP and PBF (rS = -.73) with no single central measure. Adjusted network analysis indicated similar partial relationships, however, PBF became a central indicator among the health and fitness measures. Conclusion: The findings in this study show that body composition variables such as BMI, WC, and PBF remain associated with each other in a complex health and fitness network. Furthermore, after additionally controlling for demographic variables, PBF may be a standout predictor of health and fitness in adults.

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