ORCID Profile
0000-0002-6679-9535
Current Organisations
University of South Australia
,
Flinders University
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Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 08-2003
Publisher: American Medical Association (AMA)
Date: 10-01-2001
Publisher: Springer Science and Business Media LLC
Date: 04-09-2004
DOI: 10.1007/S00421-003-0932-1
Abstract: Participation in at least 30 min of moderate intensity activity on most days is assumed to confer health benefits. This study accordingly determined whether the more vigorous household and garden tasks (sweeping, window cleaning, vacuuming and lawn mowing) are performed by middle-aged men at a moderate intensity of 3-6 metabolic equivalents (METs) in the laboratory and at home. Measured energy expenditure during self-perceived moderate-paced walking was used as a marker of exercise intensity. Energy expenditure was also predicted via indirect methods. Thirty-six males [ X (SD): 40.0 (3.3) years 179.5 (6.9) cm 83.4 (14.0) kg] were measured for resting metabolic rate (RMR) and oxygen consumption ( VO(2)) during the five activities using the Douglas bag method. Heart rate, respiratory frequency, CSA (Computer Science Applications) movement counts, Borg scale ratings of perceived exertion and Quetelet's index were also recorded as potential predictors of exercise intensity. Except for vacuuming in the laboratory, which was not significantly different from 3.0 METs ( P=0.98), the MET means in the laboratory and home were all significantly greater than 3.0 ( P</=0.006). The sweeping and vacuuming MET means were significantly higher ( P<0.001) at home than in the laboratory, whereas the converse applied for window cleaning and lawn mowing. Measured RMR was significantly lower ( P<0.001) than the 1-MET constant. Estimating METs by fitting random intercept regression models to the data resulted in standard deviations for the "leave-one-out" prediction errors (predicted-measured) of 0.4 and 0.5 METs for the laboratory and home equations, respectively. While the means indicate that all the activities were performed at a moderate intensity, there was great inter-in idual variability in energy expenditure. The laboratory and home-based equations predicted with correct classification rates of 89% and 88%, respectively, whether energy expenditure was /=3.0 METs.
Publisher: Informa UK Limited
Date: 22-05-2020
Publisher: Springer Science and Business Media LLC
Date: 31-03-2004
Publisher: Springer Science and Business Media LLC
Date: 24-07-2003
Publisher: Springer Science and Business Media LLC
Date: 31-03-2004
Publisher: No publisher found
Date: 2002
Abstract: This study: (a) generated regression equations for predicting the resting metabolic rate (RMR) of 30-60-y-old Australian males from age, height, mass and fat-free mass (FFM) and (b) cross-validated RMR prediction equations which are currently used in Australia against our measured and predicted values. A power analysis demonstrated that 41 subjects would enable the detection of (alpha=0.05, power=0.80) statistically and physiologically significant differences of 8% between predicted/measured RMRs in this study and those predicted from the equations of other investigators. Forty-one males ([X]+/-s.d.:, 44.8+/-8.6 y 83.50+/-11.32 kg 179.1+/-5.0 cm) were recruited for this study. The following variables were measured: skinfold thicknesses RMR using open circuit indirect calorimetry and FFM via a four-compartment (fat mass, total body water, bone mineral mass and residual) body composition model. A multiple regression equation using mass, height and age as predictors correlated 0.745 with RMR and the s.e.e. was 509 kJ/day. Inclusion of FFM as a predictor increased both the correlation and the precision of prediction, but there was no difference between FFM via the four-compartment model (r=0.816, s.e.e.=429 kJ/day) and that predicted from skinfold thicknesses (r=0.805, s.e.e.=441 kJ/day). Cross-validation analyses emphasised that equations need to be generated from a large database for the prediction of the RMR of 30-60-y-old Australian males.
Publisher: Springer Science and Business Media LLC
Date: 03-2001
Abstract: The aims of this study were: (a) to generate regression equations for predicting the resting metabolic rate (RMR) of 18 to 30-y-old Australian males from age, height, mass and fat-free mass (FFM) and (b) cross-validate RMR prediction equations, which are frequently used in Australia, against our measured and predicted values. A power analysis demonstrated that 38 subjects would enable us to detect (alpha = 0.05, power = 0.80) statistically and physiologically significant differences of 8% between our predicted/measured RMRs and those predicted from the equations of other investigators. Thirty-eight males (chi +/- s.d.: 24.3+/-3.3y 85.04+/-13.82 kg 180.6+/-8.3 cm) were recruited from advertisements placed in a university newsletter and on community centre noticeboards. The following measurements were conducted: skinfold thicknesses, RMR using open circuit indirect calorimetry and FFM via a four-compartment (fat mass, total body water, bone mineral mass and residual) body composition model. A multiple regression equation using the easily measured predictors of mass, height and age correlated 0.841 with RMR and the SEE was 521 kJ/day. Inclusion of FFM as a predictor increased both the R and the precision of prediction, but there was virtually no difference between FFM via the four-compartment model (R = 0.893, SEE = 433 kJ/day) and that predicted from skinfold thicknesses (R = 0.886, SEE = 440 kJ/day). The regression equations of Harris & Benedict (1919) and Schofield (1985) all overestimated the mean RMR of our subjects by 518 - 600 kJ/day (P < 0.001) and these errors were relatively constant across the range of measured RMR. The equations of Hayter & Henry (1994) and Piers et al (1997) only produced physiologically significant errors at the lower end of our range of measurement. Equations need to be generated from a large database for the prediction of the RMR of 18 to 30-y-old Australian males and FFM estimated from the regression of the sum of skinfold thicknesses on FFM via the four compartment body composition model needs to be further explored as an expedient RMR predictor.
Publisher: American Physiological Society
Date: 02-2003
DOI: 10.1152/JAPPLPHYSIOL.00436.2002
Abstract: This study compared body composition by dual-energy X-ray absorptiometry (DEXA Lunar DPX-L) with that via a four-compartment (4C water, bone mineral mass, fat, and residual) model. Relative body fat was determined for 152 healthy adults [30.0 ± 11.1 (SD) yr 75.10 ± 14.88 kg 176.3 ± 8.7 cm] aged from 18 to 59 yr. The 4C approach [20.7% body fat (%BF)] resulted in a significantly ( P 0.001) higher mean %BF compared with DEXA (18.9% BF), with intrain idual variations ranging from −2.6 to 7.3% BF. Linear regression and a Bland and Altman plot demonstrated the tendency for DEXA to progressively underestimate the %BF of leaner in iduals compared with the criterion 4C model (4C %BF = 0.862 × DEXA %BF + 4.417 r 2 = 0.952, standard error of estimate = 1.6% BF). This bias was not attributable to variations in fat-free mass hydration but may have been due to beam-hardening errors that resulted from differences in anterior-posterior tissue thickness.
Publisher: Springer Science and Business Media LLC
Date: 04-2001
Abstract: To determine anthropometric and body composition changes in female bodybuilders during preparation for competition. There was an attempt to match subjects in the control and experimental groups for height and percentage body fat (%BF) for the initial test of this longitudinal study. Five competitive bodybuilders (-X +/- s.d.: 35.3 +/- 5.7 y 167.3 +/- 3.7 cm 66.38 +/- 6.30 kg 18.3 +/- 3.5 %BF) and five athletic females (-X +/- s.d.: 30.9 +/- 13.0 y 166.9 +/- 3.9 cm 55.94 +/- 3.59 kg 19.1 +/- 3.3 %BF) were recruited from advertisements in a bodybuilding newsletter and placed on sports centre noticeboards. The following measurements were conducted 12 weeks, 6 weeks and 3-5 d before the bodybuilders' competitions: anthropometric profile, body density by underwater weighing, total body water via deuterium dilution and bone mineral mass from a dual-energy X-ray absorptiometry scan. A combination of the last three measurements enabled the %BF to the determined by a four compartment model. A significant (P < or = 0.001) 5.80 kg body mass loss by the bodybuilders as they prepared for competition was primarily due to a reduction in fat mass (FM -4.42 kg 76.2%) as opposed to fat-free mass (FFM -1.38 kg 23.8%). The decreases in body mass and FM over the final 6 weeks were greater than those over the first 6 weeks. Their %BF decreased (P < 0.001) from 18.3 to 12.7, whereas the values for the control group remained essentially unchanged at 19.1-19.6 %BF. These body composition changes by the bodybuilders were accompanied by a significant decline (P < 0.001) of 25.5 mm (76.3-50.8 mm) in the sum of eight skinfold thicknesses (triceps + subscapular + biceps + iliac crest + supraspinale + abdominal + front thigh + medial calf). Although the bodybuilders presented with low %BFs at the start of the experiment, they still significantly decreased their body mass during the 12 week preparation for competition and most of this loss was due to a reduction in FM as opposed to FFM.
Publisher: Springer Science and Business Media LLC
Date: 08-1993
DOI: 10.1007/BF00376665
Publisher: MDPI AG
Date: 25-01-2023
Abstract: The growth of sport science technology is enabling more sporting teams to implement athlete monitoring practices related to performance testing and load monitoring. Despite the increased emphasis on youth athlete development, the lack of longitudinal athlete monitoring literature in youth athletes is concerning, especially for indoor sports such as basketball. The aim of this study was to evaluate the effectiveness of six different athlete monitoring methods over 10 weeks of youth basketball training. Fourteen state-level youth basketball players (5 males and 9 females 15.1 ± 1.0 years) completed this study during their pre-competition phase prior to their national basketball tournament. Daily wellness and activity surveys were completed using the OwnUrGoal mobile application, along with heart rate (HR) and inertial measurement unit (IMU) recordings at each state training session, and weekly performance testing (3x countermovement jumps [CMJs], and 3x isometric mid-thigh pulls [IMTPs]). All of the athlete monitoring methods demonstrated the coaching staff’s training intent to maintain performance and avoid spikes in workload. Monitoring IMU data combined with PlayerLoad™ data analysis demonstrated more effectiveness for monitoring accumulated load (AL) compared to HR analysis. All six methods of athlete monitoring detected similar trends for all sessions despite small-trivial correlations between each method (Pearson’s correlation: −0.24 r 0.28). The use of subjective monitoring questionnaire applications, such as OwnUrGoal, is recommended for youth sporting clubs, given its practicability and low-cost. Regular athlete education from coaches and support staff regarding the use of these questionnaires is required to gain the best data.
No related grants have been discovered for Grant van der Ploeg.