Search Results

You are looking at 1 - 10 of 118 items for :

  • "prediction equation" x
Clear All
Restricted access

Alison Keogh, Barry Smyth, Brian Caulfield, Aonghus Lawlor, Jakim Berndsen and Cailbhe Doherty

“Prediction is very difficult, especially about the future.” These words from Danish writer Robert Peterson highlight not only why prediction equations are problematic but also the biggest question facing most athletes of any kind prior to competition: “How will I do?” This is especially pertinent

Restricted access

Sijie Tan, Jianxiong Wang and Shanshan Liu

The purpose of this study was to establish the one-repetition maximum (1RM) prediction equations of a biceps curl, bench press, and squat from the submaximal skeletal muscle strength of 4–10RM or 11–15RM in older adults. The first group of 109 participants aged 60–75 years was recruited to measure their 1RM, 4–10RM, and 11–15RM of the three exercises. The 1RM prediction equations were developed by multiple regression analyses. A second group of participants with similar physical characteristics to the first group was used to evaluate the equations. The actual measured 1RM of the second group correlated significantly to the predicted 1RM obtained from the equations (r values were from .633–.985), and standard error of estimate ranged from 1.08–5.88. Therefore, the equations can be used to predict 1RM from submaximal skeletal muscle strength accurately for older adults.

Restricted access

Gabriel Lozano-Berges, Ángel Matute-Llorente, Alejandro Gómez-Bruton, Alejandro González-Agüero, Germán Vicente-Rodríguez and José A. Casajús

manuscript. The authors reported no potential conflict of interest. The present study has developed an accurate prediction equation to assess %BF in male and female adolescent football players. Although it is true that football coaches could estimate percentage of body fat with previous anthropometric

Restricted access

Stacy N. Scott, Cary M. Springer, Jennifer F. Oody, Michael S. McClanahan, Brittany D. Wiseman, Tyler J. Kybartas and Dawn P. Coe

™ software uses the Mahar et al 2018 equation ( 10 ), which includes only laps and age, and does not take into account sex and BMI when estimating VO 2 peak. Inaccuracies arise in the regression prediction equations from differences in children’s fitness level and weight status. A study by Melo et al ( 16

Restricted access

Samantha Stephens, Tim Takken, Dale W. Esliger, Eleanor Pullenayegum, Joseph Beyene, Mark Tremblay, Jane Schneiderman, Doug Biggar, Pat Longmuir, Brian McCrindle, Audrey Abad, Dan Ignas, Janjaap Van Der Net and Brian Feldman

The purpose of this study was to assess the criterion validity of existing accelerometer-based energy expenditure (EE) prediction equations among children with chronic conditions, and to develop new prediction equations. Children with congenital heart disease (CHD), cystic fibrosis (CF), dermatomyositis (JDM), juvenile arthritis (JA), inherited muscle disease (IMD), and hemophilia (HE) completed 7 tasks while EE was measured using indirect calorimetry with counts determined by accelerometer. Agreement between predicted EE and measured EE was assessed. Disease-specific equations and cut points were developed and cross-validated. In total, 196 subjects participated. One participant dropped out before testing due to time constraints, while 15 CHD, 32 CF, 31 JDM, 31 JA, 30 IMD, 28 HE, and 29 healthy controls completed the study. Agreement between predicted and measured EE varied across disease group and ranged from (ICC) .13–.46. Disease-specific prediction equations exhibited a range of results (ICC .62–.88) (SE 0.45–0.78). In conclusion, poor agreement was demonstrated using current prediction equations in children with chronic conditions. Disease-specific equations and cut points were developed.

Restricted access

Fabio Bertapelli, Stamatis Agiovlasitis, Robert W. Motl, Roberto A. Soares, Marcos M. de Barros-Filho, Wilson D. do Amaral-Junior and Gil Guerra-Junior

-validation sample as previously recommended ( Tabachnick & Fidell, 2019 ), and the remaining 98 cases composed the sample for the development of the prediction equation. Protocol The demographic, clinical, anthropometric, and DXA scans were performed at the School of Medical Sciences of the University of Campinas

Restricted access

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

explained 90% variance in DXA-BF% using the ‘Zemski Polynesian’ formula: BF % = 5.577 + ( 0.170     abdominal ) + ( 0.749     calf ) Discussion The primary finding of this investigation is that the skinfold prediction equations evaluated had a reasonably poor ability to estimate BF% relative to the

Restricted access

Nathan F. Meier, Yang Bai, Chong Wang and Duck-chul Lee

). Widely accepted DXA serves as the standard for muscle mass measurement in sarcopenia, but it is difficult to obtain in research, prevention, and clinical practice. Prediction equations to estimate whole-body skeletal mass were developed using two-lead BIA against magnetic resonance imaging ( Janssen et

Restricted access

Jared M. Tucker, Greg Welk, Sarah M. Nusser, Nicholas K. Beyler and David Dzewaltowski

Background:

This study was designed to develop a prediction algorithm that would allow the Previous Day Physical Activity Recall (PDPAR) to be equated with temporally matched data from an accelerometer.

Methods:

Participants (n = 121) from a large, school-based intervention wore a validated accelerometer and completed the PDPAR for 3 consecutive days. Physical activity estimates were obtained from PDPAR by totaling 30-minute bouts of activity coded as ≥4 METS. A regression equation was developed in a calibration sample (n = 91) to predict accelerometer minutes of moderate to vigorous physical activity (MVPA) from PDPAR bouts. The regression equation was then applied to a separate, holdout sample (n = 30) to evaluate the utility of the prediction algorithm.

Results:

Gender and PDPAR bouts accounted for 36.6% of the variance in accelerometer MVPA. The regression model showed that on average boys obtain 9.0 min of MVPA for each reported PDPAR bout, while girls obtain 4.8 min of MVPA per bout. When applied to the holdout sample, predicted minutes of MVPA from the models showed good agreement with accelerometer minutes (r = .81).

Conclusions:

The prediction equation provides a valid and useful metric to aid in the interpretation of PDPAR results.

Restricted access

R. Randall Clark, Jacqueline M. Kuta and Robert A. Oppliger

Wisconsin has mandated minimal weight (MW) testing for high school wrestlers. In preparation, six MW predictions were cross-validated on 69 Wisconsin wrestlers (age 15.7±1.1 yrs, height 169.2±6.3 cm, weight 63.3±8.1 kg, percent fat 11.2±4.7%, and MW 58.9±6.9 kg). Minimal weight, defined as fat-free body/.93, determined by hydrostatic weighing (HW) and residual volume using 02 dilution, served as the criterion. Analyzed using repeated-measures ANOVA, statistically significant but clinically small (<1.3 kg) differences were shown in four of six predictions. Lohman 1, Lohman2, and Katch equations appear more appropriate with smaller mean differences, smaller total error, and higher correlations.