Thomas W. Kaminski
Column-editor : Brent L. Arnold
Norihide Sugisaki, Kai Kobayashi, Hiroyasu Tsuchie and Hiroaki Kanehisa
(IBM Corp, Armonk, NY, USA). The significance level was set at P < .05. Results Table 1 shows mean values of maximal muscle CSA and muscle volume. Significant simple correlation with t 100 was found for 12 muscularity indices (Table 2 ). A stepwise multiple-regression analysis produced the
Jesús Dapena, Craig McDonald and Jane Cappaert
It is difficult to ascertain for an individual high jumper the optimum values of the horizontal velocity and height of the center of mass at the end of the approach ran (VHO and H0, respectively) and of the activeness of the arms during the takeoff phase (AACT), because they depend on each athlete’s ability to resist buckling of the takeoff leg. However, the strongest jumpers will generally be those with the largest vertical elocity values at the end of the takeoff phase (VZ1). Therefore, VZ1 may serve as a rough indicator of a high jumper’s ability to resist buckling. This project derived equations that permit the use of the measured VZ1 value of a high jumper to predict what values should be expected for VHO, H0, and AACT. Comparison of the predicted and actual values of these parameters should help to diagnose the technique deficiencies of individual jumpers.
Richard R. Suminski, Larry T. Wier, Walker Poston, Brian Arenare, Anthony Randles and Andrew S. Jackson
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.
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.
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).
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.
Danny M. Pincivero, Rachael R. Polen and Brittany N. Byrd
material (available online). Single-variable regression analysis was applied to investigate the nonlinear relationship between maximal effort muscle force and arm volume, assuming a second-order polynomial fit, using Microcal OriginPro 2017 (OriginLab, Northampton, MA). All statistical testing for the
Declan Ryan, Jorgen Wullems, Georgina Stebbings, Christopher Morse, Claire Stewart and Gladys Onambele-Pearson
Background: Physical behavior [PB, physical activity (PA), and sedentary behavior (SB)] can adjust cardiovascular mortality risk in older adults. The aim of this study was to predict cardiovascular parameters (CVPs) using 21 parameters of PB. Methods: Participants [n = 93, 73.8 (6.23) y] wore a thigh-mounted accelerometer for 7 days. Phenotype of the carotid, brachial, and popliteal arteries was conducted using ultrasound. Results: Sedentary behavior was associated with one of the 19 CVPs. Standing and light-intensity PA was associated with 3 and 1 CVP, respectively. Our prediction model suggested that an hourly increase in light-intensity PA would be negatively associated with popliteal intima-media thickness [0.09 mm (95% confidence interval, 0.15 to 0.03)]. sMVPA [moderate–vigorous PA (MVPA), accumulated in bouts <10 min] was associated with 1 CVP. 10MVPA (MVPA accumulated in bouts ≥10 min) had no associations. W50% had associations with 3 CVP. SB%, alpha, true mean PA bout, daily sum of PA bout time, and total week 10MVPA each were associated with 2 CVP. Conclusions: Patterns of PB are more robust predictors of CVP than PB (hours per day). The prediction that popliteal intima-media thickness would be negatively associated with increased standing and light-intensity PA engagement suggests that older adults could obtain health benefits without MVPA engagement.
Tomoyuki Matsuo, Glenn S. Fleisig, Naiquan Zheng and James R. Andrews
Elbow varus torque is a primary factor in the risk of elbow injury during pitching. To examine the effects of shoulder abduction and lateral trunk tilt angles on elbow varus torque, we conducted simulation and regression analyses on 33 college baseball pitchers. Motion data were used for computer simulations in which two angles— shoulder abduction and lateral trunk tilt—were systematically altered. Forty-two simulated motions were generated for each pitcher, and the peak elbow varus torque for each simulated motion was calculated. A two-way analysis of variance was performed to analyze the effects of shoulder abduction and trunk tilt on elbow varus torque. Regression analyses of a simple regression model, second-order regression model, and multiple regression model were also performed. Although regression analyses did not show any significant relationship, computer simulation indicated that the peak elbow varus torque was affected by both angles, and the interaction of those angles was also significant. As trunk tilt to the contralateral side increased, the shoulder abduction angle producing the minimum peak elbow varus torque decreased. It is suggested that shoulder abduction and lateral trunk tilt may be only two of several determinants of peak elbow varus torque.
Melinda Asztalos, Greet Cardon, Ilse De Bourdeaudhuij and Katrien De Cocker
Sedentary behavior (including sitting) is negatively associated with physical health, independent from physical activity (PA). Knowledge on the associations with mental health is less elaborated. Therefore this study aims to investigate the relationship between sitting and 5 indices of mental health in adults (psychological distress, depression, anxiety, somatization, and sleeping problems), and between sitting interactions (sitting×gender, sitting×age, sitting×education, and sitting×PA) and these mental health indices.
A cohort of Belgian adults (25–64 years; n = 4344) provided self-reported data on sitting and PA and on 5 mental health indices. Cross-sectional associations were examined using multiple linear regression analyses.
Analyses adjusted for gender, age, education, and PA showed significant positive associations between sitting and the 5 mental health indices (P < .05). All associations were true for both men and women, and for low and high educated individuals, while some were only found in older individuals (somatization, P < .001) and those being insufficiently active (psychological distress, P = .007; depression, P = .002; and anxiety, P = .014).
More sitting seems to be associated with poorer mental health, independently of gender, age, education, and PA. Moderation analyses showed that these associations may differ according to age and PA levels.
Christina Huy, Simone Becker, Uwe Gomolinsky, Thomas Klein and Ansgar Thiel
Few middle-aged and elderly people get enough exercise from sports or leisure-time physical activity. Therefore, the impact of everyday physical activity on health is a matter of interest. The main objective of this study was to establish whether bicycle use in everyday life is positively associated with health. A sample of 982 randomly selected men and 1,020 women age 50–70 were asked in a computer-assisted telephone interview to provide information including a self-assessment of their health and physical activity. Self-assessed health correlates positively with bicycle use in everyday life (OR = 1.257; 95% CI: 1.031–1.532). Likewise, people who regularly cycle for transport are less likely to have medical risk factors (OR = 0.794; 95% CI: 0.652–0.967). This negative correlation is not diminished when sporting activity is controlled for. This indicates that positive effects of physical activity on risk factors can be also achieved solely by integrating more physical activity into routine everyday life.
Andressa Silva, Fernanda V. Narciso, Igor Soalheiro, Fernanda Viegas, Luísa S.N. Freitas, Adriano Lima, Bruno A. Leite, Haroldo C. Aleixo, Rob Duffield and Marco T. de Mello
, sleep variables × amount of injuries, and sleep variables × absence time after injury. Linear regression analysis was conducted to analyze the relationship between the sleep variables (sleep efficiency and WASO) and injury variables (severity, amount of injuries, and absence time after injury). The