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Danielle L. Gyemi, Charles Kahelin, Nicole C. George and David M. Andrews

the head (a), neck (b), trunk (c), pelvis (d), fat mass (FM) for the head (e), neck (f), trunk (g), pelvis (h). Lean mass (LM) for the head (i), neck (j), trunk (k), and pelvis (l). Wobbling mass (WM) for the head (m), neck (n), trunk (o), and pelvis (p). Discussion Regression equations

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Jeffrey D. Holmes, David M. Andrews, Jennifer L. Durkin and James J. Dowling

The purpose of this study was to derive and validate regression equations for the prediction of fat mass (FM), lean mass (LM), wobbling mass (WM), and bone mineral content (BMC) of the thigh, leg, and leg + foot segments of living people from easily measured segmental anthropometric measures. The segment masses of 68 university-age participants (26 M, 42 F) were obtained from full-body dual photon x-ray absorptiometry (DXA) scans, and were used as the criterion values against which predicted masses were compared. Comprehensive anthropometric measures (6 lengths, 6 circumferences, 8 breadths, 4 skinfolds) were taken bilaterally for the thigh and leg for each person. Stepwise multiple linear regression was used to derive a prediction equation for each mass type and segment. Prediction equations exhibited high adjusted R 2 values in general (0.673 to 0.925), with higher correlations evident for the LM and WM equations than for FM and BMC. Predicted (equations) and measured (DXA) segment LM and WM were also found to be highly correlated (R 2 = 0.85 to 0.96), and FM and BMC to a lesser extent (R 2 = 0.49 to 0.78). Relative errors between predicted and measured masses ranged between 0.7% and –11.3% for all those in the validation sample (n = 16). These results on university-age men and women are encouraging and suggest that in vivo estimates of the soft tissue masses of the lower extremity can be made fairly accurately from simple segmental anthropometric measures.

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Alexander H.K. Montoye, Kimberly A. Clevenger, Kelly A. Mackintosh, Melitta A. McNarry and Karin A. Pfeiffer

combination thereof) yield higher EE prediction accuracy, and 3) compare the accuracy of these machine learning models to three count-based EE prediction regression equations. Methods In the present study, we describe the development (calibration) of six artificial neural networks (ANNs) and then focus on the

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Adam J. Zemski, Elizabeth M. Broad and Gary J. Slater

advantageous. The aims of this study were to 1) assess the ability of currently available skinfold regression equations to estimate body composition relative to DXA in an elite rugby union population of different ethnic backgrounds; and 2) derive rugby union and ethnicity-sensitive equations for predicting

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Gareth N. Sandford, Simon A. Rogers, Avish P. Sharma, Andrew E. Kilding, Angus Ross and Paul B. Laursen

relationship between average 1500-m “gun-to-tape” race speed (1500 v ) and vVO 2 max collected in the laboratory. A second aim was to produce regression equations enabling the use of a 1500-m race time to predict the vVO 2 max component of the ASR specific to elite middle-distance runners. Methods A total of 8

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Robert K. Jensen, Tina Treitz and Han Sun

The purpose of the study was to use the elliptical cylinder model adapted for infants (Sun & Jensen, 1994) with a cross-sectional sample to select appropriate multiple linear regression equations for predicting masses and nonlinear regression equations for predicting principal moments of inertia (Yeadon & Morlock, 1989). The linear and nonlinear predictions were evaluated with an independent cross-validation sample of infants and a sample where inertias ranged below and above the cross-sectional sample. The cross-validation for masses was compared to a cross-validation of four linear regressions for masses developed by Schneider and Zernicke (1992). It is recommended that the linear regression equations developed in this study be used to predict infant segment masses. It is also recommended that the nonlinear regression equations developed in this study be used to predict the principal moments of inertia of all infant segments, other than head Ix and lower trunk Ix and Iy.

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Jiabei Zhang, Lee deLisle and Shihui Chen

The purpose of this study was to explore historical research trends in adapted physical activity by analyzing abstracts published under special populations by AAHPERD conventions from 1968 to 2004. There were 562 abstracts retrieved that were coded into seven categories: (a) number of authors, (b) data source, (c) sample size, (d) disability type, (e) data analysis, (f) type of study, and (g) focus of study. The coded data are presented as descriptive statistics and linear regression equations. The results of descriptive statistics describe an overall picture of the knowledge accumulation in adapted physical activity. The results of linear regression equations reveal a number of trends over the 37-year period. These trends suggest that adapted physical activity is a growing profession distinguished by several important research directions.

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Kenneth H. Pitetti, Bo Fernhall and Steve Figoni

Two regression equations were developed to predict cardiovascular fitness (CVF) based on the 20-m shuttle run test (20-MST) for nondisabled youth and for youth with mild mental retardation (MR). The purpose of this study was to compare the validity of both regression formulas to predict CVF in nondisabled, healthy youths (ages 8 to 15 yrs; 38 females and 13 males). Participants performed two modified Bruce protocol treadmill (TM) tests and two 20-MSTs on separate days. CVF (V̇O2peak, ml • kg−1 • min−1) was measured during the TM tests and computed for the 20-MST using both regression equations. Results indicate that test-retest correlations for the 20-MST (# of laps; r = 0.89) and TM test (V̇O2peak, ml • kg−1 • min−1; r = 0.86) were high. Predicted V̇O2peak values were moderately significant (nondisabled youth: r = 0.55, p < .01; youth with MR: r = 0.66, p < .01) when compared with TM V̇O2peak. Correlation between the two regression equations was significant (r = 0.78, p < .01).

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Sofiya Alhassan, Kate Lyden, Cheryl Howe, Sarah Kozey Keadle, Ogechi Nwaokelemeh and Patty S. Freedson

This study examined the validity of commonly used regression equations for the Actigraph and Actical accelerometers in predicting energy expenditure (EE) in children and adolescents. Sixty healthy (8–16 yrs) participants completed four treadmill (TM) and five self-paced activities of daily living (ADL). Four Actigraph (AG) and three Actical (AC) regression equations were used to estimate EE. Bias (±95% CI) and root mean squared errors were used to assess the validity of the regression equations compared with indirect calorimetry. For children, the Freedson (AG) model accurately predicted EE for all activities combined and the Treuth (AG) model accurately predicted EE for TM activities. For adolescents, the Freedson model accurately predicted EE for TM activities and the Treuth model accurately predicted EE for all activities and for TM activities. No other equation accurately estimated EE. The percent agreement for the AG and AC equations were better for light and vigorous compared with moderate intensity activities. The Trost (AG) equation most accurately classified all activity intensity categories. Overall, equations yield inconsistent point estimates of EE.

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Mollie G. DeLozier, Bernard Gutin, Jack Wang, Charles E. Basch, Isobel Contento, Steven Shea, Matilde Irigoyen Patricia Zybert, Jill Rips and Richard Pierson

Anthropometric and bioimpedance regression equations were developed for young children using total body water (TBW) as the criterion. Ninety-six boys and girls, 4-8 years of age, served as subjects. Measures included height, weight, five skinfold thicknesses, three circumferences, total body bioimpedance, and separate bioimpedance measures of the arm, trunk, and leg. Height and weight alone accounted for .70 of the variance in TBW. Adding other measures did not significantly increase the R 2. Standard errors of estimate for TBW were similar to those reported for older individuals (1.39-1.44 1) but may be too large relative to the small size of the subjects for the equations to be acceptable.