The Influence Of Body Composition On Health Monitoring Behavior

Document Type

Conference Presentation

Department

Health, Exercise, and Sport Sciences Department

Conference Title

American College of Sports Medicine - Medicine & Science in Sports & Exercise conference

Organization

American College of Sports Medicine

Location

Virtual

Date of Presentation

8-1-2021

Journal Publication

Medicine & Science in Sports & Exercise

ISSN

0195-9131

DOI

10.1249/01.mss.0000763016.45441.90

Volume

53

Issue

8S

First Page

328

Last Page

329

Abstract

Maintenance of an optimal body composition is an important component of physical functioning and longevity. Comprehensive anthropometric analyses provide patients and clients with objective assessments of their current fitness. It is important to understand the potential consequences of this information on an individual's motivation to continue health monitoring. PURPOSE: To explore which factors of body composition analysis influence future testing behavior. METHODS: We tested 209 men and 219 women from two exercise facilities (a commercial gym and a CrossFit facility) using the InBody 770 bioelectrical impedance analyzer. We documented age, height, weight, BMI, lean body mass, skeletal muscle mass, lean leg mass, arm circumference, body fat mass, trunk fat mass, and body fat percentage. All subjects were eligible for repeated testing on a voluntary basis. We used negative binomial regression to evaluate the effect of anthropometric variables on the number of repeat tests. RESULTS: Subjects were 35.5 ± 10.3 years old, weighed 187.5 ± 50.2 lb, had a BMI of 29.7 ± 6.5 kg/m2, 130.0 ± 31.1 lb lean body mass, 73.4 ± 18.7 lb skeletal muscle mass, 37.6 ± 9.0 lb lean leg mass, 14.4 ± 3.8in arm circumference, 57.6 ± 33.6 lb body fat mass, 29.8 ± 14.0 lb trunk fat mass, and 29.6 ± 11.0% body fat. Subjects were screened 2.9 ± 3.6 times (range: 1 to 33). Holding constant sex (p = 0.004) and duration following the initial test date (p = 0.003), the only anthropometric factor that emerged as a significant predictor of serial testing was skeletal muscle mass (p = 0.001). Each additional pound corresponded to a 1.7% increase in the number of follow-up tests (95% CI of IRR: 1.007 to 1.027). In this model, females were screened 69.3% more times (95% CI of IRR: 1.181 to 2.426). Higher bodyweight (p = 0.089), body fat mass (p = 0.105) and body fat percentage (p = 0.139) exhibited non-significant patterns of reduction in the number of subsequent tests; they were not included in the model. No other predictor was related to testing behavior (p > 0.250). CONCLUSIONS: Understanding which components of body composition analysis affect motivation to continue testing provides health practitioners insight into which populations may benefit from additional encouragement. Our results indicate males with lower muscle mass are more susceptible to attrition.

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