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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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André O. Werneck, Edilson S. Cyrino, Paul J. Collings, Enio R.V. Ronque, Célia L. Szwarcwald, Luís B. Sardinha and Danilo R. Silva

, overeating, tobacco smoking, and poor sleep hygiene. 6 , 7 Robust evidence has suggested that the negative effects of high overall sitting time can be eliminated by high levels of moderate-intensity physical activity; however, the positive association between TV viewing and mortality remains significant

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Diego Munguia-Izquierdo, Carmen Mayolas-Pi, Carlos Peñarrubia-Lozano, Federico Paris-Garcia, Javier Bueno-Antequera, Miguel Angel Oviedo-Caro and Alejandro Legaz-Arrese

have been associated with improvements in psychosocial health, 6 obesity, 7 fitness, 8 health-related quality of life, 9 sleep, 10 diet, 11 and alcohol and tobacco consumption 12 and may predict improved health and behaviors in adulthood 13 ; therefore, they play a relevant role in the

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Woubeshet Ayenew, Emily C. Gathright, Ellen M. Coffey, Amber Courtney, Jodi Rogness and Andrew M. Busch

current tobacco use were collected at C2P2 intake. Nice Ride provided objective data on number of rides (ie, how many times a bike was checked out from a station) and total duration of rides (ie, total accumulative time between checking out a bike and returning the bike). This objective data were

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Alessandra Madia Mantovani, Manoel Carlos Spiguel de Lima, Luis Alberto Gobbo, Enio Ricardo Vaz Ronque, Marcelo Romanzini, Bruna Camilo Turi-Lynch, Jamile Sanches Codogno and Rômulo Araújo Fernandes

reported no tobacco use. Age distribution and alcohol consumption (higher among men) were different between men and women (Table  1 ). Sports participation was similar between men and women. Table 1 Characteristics of the Sample by Sex (Brazil, N = 225) Variable Categories Male, n = 108, n (%), Female, n

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Ashley Walker, Jody Langdon and Krystina Johnson

Background:

Young adults have the highest participation in physical activity but also have the highest incidence rates of binge drinking, cigarette smoking, and smokeless tobacco use. We examined these factors to determine whether there are relationships among physical activity and health risk behaviors.

Methods:

We conducted correlation and χ2 analyses using the American College Health Association-National College Health Assessment fall 2009 data set (N = 34,208) to examine the relationship among meeting physical-activity guidelines, binge drinking, and tobacco use among survey participants.

Results:

The data suggest a positive relationship between meeting physical-activity guidelines and binge drinking, with the strongest relationship between those reporting binge drinking 4 times in a 2-week period. Meeting physical-activity guidelines was negatively associated with cigarette use but positively associated with all other types of tobacco use.

Conclusion:

Associations between physical activity and binge-drinking episodes indicate a need to address the relationship between heavy drinking and alcohol dependence and physical-activity behavior patterns. Further studies should examine relationships between physical activity and binge drinking in other age groups. Results also suggest the need to examine differing associations between physical activity and types of tobacco use.

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Kerri McCaul, Joseph Baker and John K. Yardley

Adolescence is characterized as a period of change and adaptation typically marked by a decline in physical activity participation and accompanied by an increase in substance use. The purpose of this study was to examine the relationships among the type (team and individual activity) and intensity (high, medium, and low intensity) of physical activity and substance use (tobacco, marijuana, and alcohol use, and binge drinking) in a sample of 738 adolescents. Results indicated differing relationships among study variables depending on the type and intensity of physical activity and the type of substance used For instance, a positive relationship was found for physical activity intensity and alcohol use, but negative relationships were found for physical activity and tobacco and marijuana use. Collectively, the results reveal that the relationships between physical activity type and intensity and substance use are more complex than previously believed.

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Krista G. Austin, Christina E. Carvey, Emily K. Farina and Harris R. Lieberman

Background:

U.S. Army Soldiers must meet body weight and composition standards and consequently may use nutritional supplements (NS) purported to assist in weight modification (WM). Nutritional supplements are dietary supplements (DS) and foods intended to supplement the diet.

Purpose:

This study assessed relationships between NS use, demographic characteristics, health-related behaviors, and WM goals among U.S. Army personnel.

Methods:

Participants (N = 990) self-reported NS use, categorized as energy drinks, sport nutrition products, or DS, and WM goal (lose, gain, or maintain) was ascertained by survey. DS were subcategorized as health, weight-loss, weight-gain, or other DS. Chi-square and logistic regression were used to assess relationships between predictors, NS use, and WM goal. Most respondents (70.3% ± 1.7%) consumed some NS; however, overall NS use was not related to WM goal. Significant relationships were observed between predictors (tobacco use, age, body-mass index, fitness score, general health, and eating habits) and both WM goal and NS use. Respondents attempting to lose or maintain weight were less likely to consume energy drinks and weight-gain DS.

Conclusion:

WM goal is related to multiple health behaviors including tobacco use, physical fitness score, and self-perception of health and eating behavior. NS are consumed in this population regardless of WM goal.