prediction models developed for count-based accelerometer data are inherently limited in their applicability to other accelerometer brands because counts are brand-specific and often proprietary ( John & Freedson, 2012 ). The use of raw data has the potential to improve the application of models across
Alexander H.K. Montoye, Kimberly A. Clevenger, Kelly A. Mackintosh, Melitta A. McNarry and Karin A. Pfeiffer
Ryan D. Burns, James C. Hannon, Timothy A. Brusseau, Patricia A. Eisenman, Pedro F. Saint-Maurice, Greg J. Welk and Matthew T. Mahar
Cardiorespiratory endurance is a component of health-related fitness. FITNESSGRAM recommends the Progressive Aerobic Cardiovascular Endurance Run (PACER) or One mile Run/Walk (1MRW) to assess cardiorespiratory endurance by estimating VO2 Peak. No research has cross-validated prediction models from both PACER and 1MRW, including the New PACER Model and PACER-Mile Equivalent (PACER-MEQ) using current standards. The purpose of this study was to cross-validate prediction models from PACER and 1MRW against measured VO2 Peak in adolescents. Cardiorespiratory endurance data were collected on 90 adolescents aged 13–16 years (Mean = 14.7 ± 1.3 years; 32 girls, 52 boys) who completed the PACER and 1MRW in addition to a laboratory maximal treadmill test to measure VO2 Peak. Multiple correlations among various models with measured VO2 Peak were considered moderately strong (R = .74–0.78), and prediction error (RMSE) ranged from 5.95 ml·kg-1, min-1 to 8.27 ml·kg-1.min-1. Criterion-referenced agreement into FITNESSGRAM’s Healthy Fitness Zones was considered fair-to-good among models (Kappa = 0.31–0.62; Agreement = 75.5–89.9%; F = 0.08–0.65). In conclusion, prediction models demonstrated moderately strong linear relationships with measured VO2 Peak, fair prediction error, and fair-to-good criterion referenced agreement with measured VO2 Peak into FITNESSGRAM’s Healthy Fitness Zones.
Patricia W. Bauer, James M. Pivarnik, Willa C. Fornetti, Jennifer J. Jallo and Lawrence Nassar
The purpose of this investigation was to evaluate three bioelectrical impedance analysis (BIA) prediction models for fat-free mass (FFM) using the U.S. National Women’s Gymnastics team (N = 48; age = 15.8 ± 1.8 years). One model had been developed recently using dual-energy x-ray absorptiometry (DEXA) as the criterion measure, whereas the other two used hydrodensitometry. In this investigation, FFM predictions were compared with measures obtained via DEXA. FFM measured by DEXA averaged 40.5 ± 7.4 kg (± SD), whereas values generated using the three BIA models were within 0.8 kg of this actual measure. Validity coefficients for all models were high (Rxy = .95-98). FFM prediction error was lowest with the model using DEXA as the criterion measure (1.3 kg) compared with the other two (1.9 and 2.4 kg). All BIA models underpredicted FFM in the heaviest girls, and the Lohman and Van Loan et al. models overpredicted FFM in the lightest girls. Whereas prediction error was significantly correlated to the girls’ bone mineral density in all BIA models, this relationship was strongest in the two that were developed using hydrodensitometry.
Erik Sesbreno, Gary Slater, Margo Mountjoy and Stuart D.R. Galloway
) develop an anthropometric prediction model for FFM and SMM in elite Canadian athletes. Methods Recruitment A total of 65 athletes, 17 years of age or older, were recruited through e-mails sent to sport science staff across the Canadian Olympic and Paralympic Sport Institute Network and National Sport
Zhen-Bo Cao, Nobuyuki Miyatake, Tomoko Aoyama, Mitsuru Higuchi and Izumi Tabata
The purpose was to develop new maximal oxygen uptake (VO2max) prediction models using a perceptually regulated 3-minute walk test.
VO2max was measured with a maximal incremental cycle test in 283 Japanese adults. A 3-minute walk test was conducted at a self-regulated intensity corresponding to ratings of perceived exertion (RPE) 13.
A 3-minute walk distance (3MWD) was significantly related to VO2max (r = .60, P < .001). Three prediction models were developed by multiple regression to estimate VO2max using data on gender, age, 3MWD, and either BMI [BMI model, multiple correlation coefficients (R) = .78, standard error of estimate (SEE) = 5.26 ml⋅kg-1⋅min-1], waist circumference (WC model, R = .80, SEE = 5.04 ml⋅kg-1⋅min-1), or body fat percentage (%Fat model, R = .84, SEE = 4.57 ml⋅kg-1⋅min-1), suggesting that the %Fat model is the best model [VO2max = 37.501 + 0.463 × Gender (0 = women, 1 = men) – 0.195 × Age – 0.589 × %Fat + 0.053 × 3MWD]. Cross-validation by using the predicted residual sum of squares (PRESS) procedures demonstrated a high level of cross-validity of all prediction models.
The new VO2max prediction models are reasonably applicable to estimating VO2max in Japanese adults and represent a quick, low-risk, and convenient means for estimating VO2max in the field.
Hans Luttikholt, Lars R. McNaughton, Adrian W. Midgley and David J. Bentley
There is currently no model that predicts peak power output (PPO) thereby allowing comparison between different incremental exercise test (EXT) protocols. In this study we have used the critical power profile to develop a mathematical model for predicting PPO from the results of different EXTs.
The purpose of this study was to examine the level of agreement between actual PPO values and those predicted from the new model.
Eleven male athletes (age 25 ± 5 years, VO2max 62 ± 8 mL · kg–1 · min–1) completed 3 laboratory tests on a cycle ergometer. Each test comprised an EXT consisting of 1-minute workload increments of 30 W (EXT30/1) and 3-minute (EXT25/3) and 5-minute workload increments (EXT25/5) of 25 W. The PPO determined from each test was used to predict the PPO from the remaining 2 EXTs.
The differences between actual and predicted PPO values were statistically insignificant (P > .05). The random error components of the limits of agreement of ≤30 W also indicated acceptable levels of agreement between actual and predicted PPO values.
Further data collection is necessary to confirm whether the model is able to predict PPO over a wide range of EXT protocols in athletes of different aerobic and anaerobic capacities.
Gerda Jimmy, Roland Seiler and Urs Maeder
Accelerometry has been established as an objective method that can be used to assess physical activity behavior in large groups. The purpose of the current study was to provide a validated equation to translate accelerometer counts of the triaxial GT3X into energy expenditure in young children.
Thirty-two children aged 5–9 years performed locomotor and play activities that are typical for their age group. Children wore a GT3X accelerometer and their energy expenditure was measured with indirect calorimetry. Twenty-one children were randomly selected to serve as development group. A cubic 2-regression model involving separate equations for locomotor and play activities was developed on the basis of model fit. It was then validated using data of the remaining children and compared with a linear 2-regression model and a linear 1-regression model.
All 3 regression models produced strong correlations between predicted and measured MET values. Agreement was acceptable for the cubic model and good for both linear regression approaches.
The current linear 1-regression model provides valid estimates of energy expenditure for ActiGraph GT3X data for 5- to 9-year-old children and shows equal or better predictive validity than a cubic or a linear 2-regression model.
Kari Roethlisberger, Vista Beasley, Jeffrey Martin, Brigid Byrd, Krista Munroe-Chandler and Irene Muir
are presented in Tables 2 and 3 . The prediction model for sport commitment was statistically significant, F (7, 122) = 9.56, p < .001, and accounted for 35.4% of the variance. Sport commitment was significantly predicted by athletic experience (β = .22, p < .008,) athletic identity (β = .46
Alan Chorley, Richard P. Bott, Simon Marwood and Kevin L. Lamb
equation, the prediction model incorporates an element of W ′ reconstitution while work is being performed above CP, contradicting the findings that W ′ is only reconstituted when power output falls below CP. 12 Consequently, the prediction model overestimated W ′ at each point, where the limit of
Michiel Punt, Sjoerd M. Bruijn, Ingrid G. van de Port, Ilona J.M. de Rooij, Harriet Wittink and Jaap H. van Dieën
daily-life characteristics were estimated, see Rispens et al. 22 Predicting Fall Risk Fall risk was predicted based on steady-state gait characteristics and daily-life gait characteristics using our previous established fall prediction models. 9 The steady-state gait characteristics based on model