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Richard R. Suminski, Larry T. Wier, Walker Poston, Brian Arenare, Anthony Randles and Andrew S. Jackson

Background:

Nonexercise models were developed to predict maximal oxygen consumption (VO2max). While these models are accurate, they don’t consider smoking, which negatively impacts measured VO2max. The purpose of this study was to examine the effects of smoking on both measured and predicted VO2max.

Methods:

Indirect calorimetry was used to measure VO2max in 2,749 men and women. Physical activity using the NASA Physical Activity Status Scale (PASS), body mass index (BMI), and smoking (pack-y = packs·day * y of smoking) also were assessed. Pack-y groupings were Never (0 pack-y), Light (1–10), Moderate (11–20), and Heavy (>20). Multiple regression analysis was used to examine the effect of smoking on VO2max predicted by PASS, age, BMI, and gender.

Results:

Measured VO2max was significantly lower in the heavy smoking group compared with the other pack-y groups. The combined effects of PASS, age, BMI, and gender on measured VO2max were significant. With smoking in the model, the estimated effects on measured VO2max from Light, Moderate, and Heavy smoking were –0.83, –0.85, and –2.56 ml·kg−1·min−1, respectively (P < .05).

Conclusions:

Given that 21% of American adults smoke and 12% of them are heavy smokers, it is recommended that smoking be considered when using nonexercise models to predict VO2max.

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Disa J. Smee, Anthony Walker, Ben Rattray, Julie A. Cooke, Ben G. Serpell and Kate L. Pumpa

Given the importance of body composition in maintaining optimal physical and functional capacities, the use of appropriate, field-based assessment tools should be a priority to assist in maintaining the occupational safety of firefighters and the community. For ease, body mass index has often been used to assess these changes. However, it is limited in its accuracy. The purposes of this study were twofold: (a) to compare the validity of different measures of body composition against dual-energy X-ray absorptiometry (DXA) in urban firefighters and (b) to assess these measures in their ability to provide meaningful interpretation of criteria-driven categories of adiposity. A total of 64 male firefighters (age = 44.0 ± 9.5 years) underwent full anthropometric profiling (predictor equations used to determine body fat percentage [BF%]), bioelectrical impedance analysis (BIA), and DXA assessments. Participants’ body mass index was calculated, and BF% and lean mass were determined along with criteria-driven categorizations of adiposity. Anthropometric (skinfolds) predictor equations (e.g., mean bias = −4.4% for BF%) were typically closer to DXA measures, compared with BIA (9.4% for BF%). However, when determining categories of criteria-driven adiposity, BIA (42.9% overweight or obese) provided closer estimates to the DXA-determined distribution (44.6%) than anthropometric-based measures (up to 40%). Body mass index appears an inappropriate measure for accurately determining categories of adiposity with 64.1% classified as overweight or obese. Given the logistical constraints of anthropometric profiling, and the closeness of BIA to DXA in adiposity categories, BIA may be a suitable alternative to DXA for assessing body composition in professional urban firefighters.

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Rachel G. Walker, Joyce Obeid, Thanh Nguyen, Hilde Ploeger, Nicole A. Proudfoot, Cecily Bos, Anthony K. Chan, Linda Pedder, Robert M. Issenman, Katrin Scheinemann, Maggie J. Larché, Karen McAssey and Brian W. Timmons

The objectives of this study were to (i) assess sedentary time and prevalence of screen-based sedentary behaviors of children with a chronic disease and (ii) compare sedentary time and prevalence of screen-based sedentary behaviors to age- and sex-matched healthy controls. Sixty-five children (aged 6-18 years) with a chronic disease participated: survivors of a brain tumor, hemophilia, type 1 diabetes mellitus, juvenile idiopathic arthritis, cystic fibrosis, and Crohn’s disease. Twenty-nine of these participants were compared with age- and sex-matched healthy controls. Sedentary time was measured objectively by an ActiGraph GT1M or GT3x accelerometer worn for 7 consecutive days and defined as less than 100 counts per min. A questionnaire was used to assess screen-based sedentary behaviors. Children with a chronic disease engaged in an average of 10.2 ± 1.4 hr of sedentary time per day, which comprised 76.5 ± 7.1% of average daily monitoring time. There were no differences between children with a chronic disease and controls in sedentary time (adjusted for wear time, p = .06) or in the prevalence of TV watching, and computer or video game usage for varying durations (p = .78, p = .39 and, p = .32 respectively). Children with a chronic disease, though relatively healthy, accumulate high levels of sedentary time, similar to those of their healthy peers.