The purpose of the study was to determine the relationship between sedentary behavior (SB), physical activity (PA), and body fat (total, abdominal) or body size (body-mass index [BMI], waist circumference [WC]) in community-dwelling adults 50 yr old and over. This study included 232 ambulatory adults (50–87 yr, 37.4% ± 9.6% body fat [BF]). Average daily time spent in SB (<100 counts/min) and light (100–759 counts/min), lifestyle-moderate (760–1,951 counts/min), walking-moderate (1,952–5,724cts/min), and vigorous-intensity (≥5,725 counts/min) PA were determined by accelerometer and corrected for wear time. BF was measured with dual-energy X-ray absorptiometry. SB was positively related to measures of BF. Measures of SB, PA, and gender accounted for 55.6% of the variance in total BF, 32.4% of the variance in abdominal fat, and 28.0% of the variance in WC. SB, PA, and age accounted for 27.1% of the variance in BMI. Time spent in SB should be considered when designing obesity interventions for adults 50 yr old and over.
Ann M. Swartz, Sergey Tarima, Nora E. Miller, Teresa L. Hart, Elizabeth K. Grimm, Aubrianne E. Rote and Scott J. Strath
Janne Sallinen, Arto Pakarinen, Mikael Fogelholm, Elina Sillanpää, Markku Alen, Jeff S. Volek, William J. Kraemer and Keijo Häkkinen
This study examined the effects of strength training and diet on serum basal hormone concentrations and muscle mass in aging women. Fifty-one women age 49 to 74 y were divided into two groups: strength training and nutritional counseling (n = 25), and strength training (n = 26). Both groups performed strength training twice a week for 21 wk. Nutritional counseling was given to attain sufficient energy and protein intake and recommended intake of fat and fiber. We found that the cross-sectional area of the quadriceps femoris increased by 9.5 ± 4.1% in the nutritional counseling group versus 6.8 ± 3.5% in the strength training only group after training (P < 0.052). Nutritional counseling evoked dietary changes such as increases in the proportion of energy from protein and the ratio of poly-unsaturated and saturated fatty acids. Strength training increased testosterone and testosterone/sex hormone-binding globulin ratio after the first half of training, but these returned to baseline values at the end of the entire training period. Changes in serum basal hormone concentrations did not differ between the groups. Our results support the conclusion that nutritional counseling can contribute to the increase in the muscle cross-sectional area during prolonged strength training in aging women.
Larry Tucker and Travis Peterson
This study was conducted to determine if cardiorespiratory fitness at baseline, and changes in fitness, influence risk of weight gain (≥3 kg) over 20 months. Another aim was to ascertain if potential confounding factors, including age, education, strength training, energy intake, and weight, influence risk of weight gain.
In a prospective study of 257 women, fitness (VO2max) was assessed using a graded, maximal treadmill test at baseline and follow-up. Energy intake was measured using 7-day, weighed food records. Subjects were divided into quartiles based on fitness. Risk ratios were used to show the risk of weight gain among those who were fit at baseline compared with their counterparts.
Most women gained weight and 23% gained ≥3 kg. Mean VO2max was 35.7 ± 7.2 mL·kg−1·min−1. Women with low-fitness at baseline had 3.18 times (95% CI: 1.46 to 6.93) greater risk, and moderately fit women had 2.24 times (95% CI: 1.04 to 4.82) greater risk of weight gain than women in the high-fitness quartile. Adjusting for potential confounders had little effect on results.
High levels of fitness seem to help protect middle-aged women against weight gain, whereas low and moderate fitness increase risk of weight gain over time.
Xiaoxia Zhang, Xiangli Gu, Tao Zhang, Priscila Caçola and Jing Wang
Purpose: Using 2012 National Health and Nutrition Examination Survey (NHANES) National Youth Fitness Survey data, the authors conducted a cross-sectional secondary analysis to examine the associations of movement behaviors (ie, physical activity [PA] and screen-based sedentary behaviors) and fundamental motor skills (FMS) with fitness (ie, muscular fitness) and fatness (ie, body mass index and waist circumference) in 3- to 5-year-old children. The effect of ethnicity (Hispanic vs non-Hispanic) on these associations was also examined. Methods: A total of 352 children (173 girls; mean age = 4.02 y) from the 2012 NHANES data set were included. Parents reported their child’s PA and screen-based sedentary behaviors. FMS (ie, locomotor and object control) were assessed with the Test of Gross Motor Development, 2nd edition. Other variables used were body mass index, waist circumference, and plank. Results: Hispanic children demonstrated lower levels of PA than non-Hispanic children (P < .05). Children’s FMS emerged as significant predictors of muscular fitness and waist circumference, but not for body mass index in the Hispanic group. In the non-Hispanic group, FMS (ie, object control skills) and PA accounted for significant variances of muscular fitness and waist circumference, respectively. Conclusion: The associations of movement behaviors and FMS with fitness and fatness are different between Hispanic and non-Hispanic young children. Changes in policy or early childhood curriculum may be tailed to promote FMS for an impact on fitness and fatness in both Hispanic and non-Hispanic children.
Kurusart Konharn, Maria Paula Santos and José Carlos Ribeiro
The impact of socioeconomic status (SES) on objective measures of physical activity (PA) in adolescence is poorly understood. The purpose of this cross-sectional study was to evaluate the association between SES and objectively measured PA in Thai adolescents.
PA was objectively measured every 30 seconds for 7 consecutive days using ActiGraph GT1M uniaxial accelerometers in 177 secondary-school adolescents aged 13 to 18 years that were classified into 3 SES groups (low, middle, and high). The associations between SES and adolescents’ PA were examined using 1-way ANOVA with multiple comparisons and Chi-square test.
Adolescents of low-SES accumulated more minutes of PA and less of sedentary behavior than those of high-SES, Additionally, low-SES adolescents tended to meet the daily PA guidelines more than other groups, particularly in girls (P < .01).
This study evidences an inverse relationship between SES and PA levels, and shows the importance of targeting high SES adolescents in intervention programs to enhance health behaviors. Based on these findings, we also suggest that SES must be considered as an important determinant in promoting regular PA and in increasing proportions of adolescents meeting current health-related PA guidelines.
Inas Rashad Kelly, Mary Ann Phillips, Michelle Revels and Dawud Ujamaa
This study analyzed the effect of school practices regarding the provision of physical education (PE) on the physical fitness of children and youth.
Using an untapped sample of approximately 5000 5th and 7th graders from 93 schools in Georgia in 2006, individual-level and merged school-level data on physical education were analyzed. Multivariate regression analyses were conducted to estimate the potential influence of the school environment on measured health outcomes. Controls were included for grade, gender, race/ethnicity, urbanicity, and county of residence.
Variables measuring 8 school-level practices pertaining to physical education were found to have significant effects on cardiovascular fitness as measured by the FitnessGram, with signs in the expected direction. These variables, combined with demographic variables, explained 29.73% of the variation in the Progressive Aerobic Cardiovascular Endurance Run but only 4.53% of the variation in the body mass index.
School-level variables pertaining to PE practices were collectively strong predictors of physical fitness, particularly cardiovascular fitness. Schools that adopt these policies will likely encourage favorable physical activity habits that may last into adulthood. Future research should examine the causal relationships among physical education practices, physical activity, and health outcomes.
Rodrigo Antunes Lima, Karin A. Pfeiffer, Niels Christian Møller, Lars Bo Andersen and Anna Bugge
Background: To analyze the longitudinal association between academic performance and moderate to vigorous physical activity (MVPA), vigorous physical activity (VPA), and sedentary (SED) in a 3-year longitudinal study. A secondary aim was to determine whether MVPA and VPA were indirectly related with academic performance via waist circumference (WC). Methods: Physical activity (PA) and SED were measured by accelerometers. Academic performance was assessed by national tests in Danish and Math. Structural equation modeling was performed to evaluate whether MVPA, VPA, and SED were associated with academic performance and the potential PA–academic performance indirect relationship via WC. Results: MVPA and VPA were associated with academic performance, mediated via WC (β = 0.036; 95% confidence interval [CI], 0.002 to 0.070 and β = 0.096; 95% CI, 0.027 to 0.164, respectively). SED was directly associated with academic performance (β = 0.124; 95% CI, 0.030 to 0.217, MVPA model and β = 0.132; 95% CI, 0.044 to 0.221, VPA model). WC was negatively associated with academic performance. Conclusions: Both PA and SED time were positively associated with academic performance. Based on this, PA should be encouraged in children and youth not only to promote physical health but also to promote academic performance. Future studies should distinguish between school-related SED and other SED activities and their relationship with academic performance.
Kristen L. MacKenzie-Shalders, Neil A. King, Nuala M. Byrne and Gary J. Slater
Increasing the frequency of protein consumption is recommended to stimulate muscle hypertrophy with resistance exercise. This study manipulated dietary protein distribution to assess the effect on gains in lean mass during a rugby preseason. Twenty-four developing elite rugby athletes (age 20.1 ± 1.4 years, mass 101.6 ± 12.0 kg; M ± SD) were instructed to consume high biological value (HBV) protein at their main meals and immediately after resistance exercise while limiting protein intake between meals. To manipulate protein intake frequency, the athletes consumed 3 HBV liquid protein supplements (22 g protein) either with main meals (bolus condition) or between meals (frequent condition) for 6 weeks in a 2 × 2 crossover design. Dietary intake and change in lean mass values were compared between conditions by analysis of covariance and correlational analysis. The dietary manipulation successfully altered the protein distribution score (average number of eating occasions containing > 20 g of protein) to 4.0 ± 0.8 and 5.9 ± 0.7 (p < .01) for the bolus and frequent conditions, respectively. There was no difference in gains in lean mass between the bolus (1.4 ± 1.5 kg) and frequent (1.5 ± 1.4 kg) conditions (p = .91). There was no clear effect of increasing protein distribution from approximately 4–6 eating occasions on changes in lean mass during a rugby preseason. However, other dietary factors may have augmented adaptation.
Tom J. Hazell, T. Dylan Olver, Craig D. Hamilton and Peter W. R. Lemon
Six weeks (3 times/wk) of sprint-interval training (SIT) or continuous endurance training (CET) promote body-fat losses despite a substantially lower training volume with SIT. In an attempt to explain these findings, the authors quantified VO2 during and after (24 h) sprint-interval exercise (SIE; 2 min exercise) vs. continuous endurance exercise (CEE; 30 min exercise). VO2 was measured in male students (n = 8) 8 times over 24 hr under 3 treatments (SIE, CEE, and control [CTRL, no exercise]). Diet was controlled. VO2 was 150% greater (p < .01) during CEE vs. SIE (87.6 ± 13.1 vs. 35.1 ± 4.4 L O2; M ± SD). The observed small difference between average exercise heart rates with CEE (157 ± 10 beats/min) and SIE (149 ± 6 beats/min) approached significance (p = .06), as did the difference in peak heart rates during CEE (166 ± 10 beats/min) and SIE (173 ± 6 beats/min; p = .14). Total O2 consumed over 8 hr with CEE (263.3 ± 30.2 L) was greater (p < .01) than both SIE (224.2 ± 15.3 L; p < .001) and CTRL (163.5 ± 16.1 L; p < .001). Total O2 with SIE was also increased over CTRL (p < .001). At 24 hr, both exercise treatments were increased (p < .001) vs. CTRL (CEE = 500.2 ± 49.2; SIE = 498.0 ± 29.4; CTRL = 400.2 ± 44.6), but there was no difference between CEE and SIE (p = .99). Despite large differences in exercise VO2, the protracted effects of SIE result in a similar total VO2 over 24 hr vs. CEE, indicating that the significant body-fat losses observed previously with SIT are partially due to increases in metabolism postexercise.
Federico Y. Fontana, Alessandro Colosio, Gabriela F. De Roia, Giorgio Da Lozzo and Silvia Pogliaghi
Anthropometric evaluation of athletes is necessary to optimize talent identification and player development.
To provide a specific anthropometric reference database of senior male rugby players competing at different levels in the southern European region.
In 362 professional players (25 ± 4 y; 138 Italian national team, 97 first-division, and 127 second-division national championships) the authors measured mass, stature, and percentage body fat (plicometry). Mean, SD, and coefficient of variation were calculated for forwards and backs and for positional subgroups. Binomial logistic regression and receiver-operating-characteristic curve were performed to assess which variables best predicted level assignment (international vs national level).
For all competitive levels forwards were significantly heavier and taller and had a larger percentage body fat and fat-free mass than backs. The lower the competitive level, the higher the within-role variability observed; furthermore, players in a specific positional subgroup were lighter, shorter, and fatter and had less fat-free mass. Fat-free mass is the variable that best predicts the likelihood of being classified as an international or national player (cutoff value 79.54 kg).
The data confirm the specificity in the physical requirements of rugby in individual playing positions at all competitive levels and document significant differences among elite and 1st- and 2nd-division players in the same positional role. These differences may reflect the variable technical abilities, selection, training practices, and requirements of the game among these categories.