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James C. Martin, Douglas L. Milliken, John E. Cobb, Kevin L. McFadden and Andrew R. Coggan

This investigation sought to determine if cycling power could be accurately modeled. A mathematical model of cycling power was derived, and values for each model parameter were determined. A bicycle-mounted power measurement system was validated by comparison with a laboratory ergometer. Power was measured during road cycling, and the measured values were compared with the values predicted by the model. The measured values for power were highly correlated (R 2 = .97) with, and were not different than, the modeled values. The standard error between the modeled and measured power (2.7 W) was very small. The model was also used to estimate the effects of changes in several model parameters on cycling velocity. Over the range of parameter values evaluated, velocity varied linearly (R 2 > .99). The results demonstrated that cycling power can be accurately predicted by a mathematical model.

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Judith L. Oslin, Stephen A. Mitchell and Linda L. Griffin

The purpose of this article is to report on the development and validation of the Game Performance Assessment Instrument (GPAI). The GPAI is a multidimensional system designed to measure game performance behaviors that demonstrate tactical understanding, as well as the player’s ability to solve tactical problems by selecting and applying appropriate skills. The GPAI provides analyses of individual game performance components (e.g., decisions made, skill execution, and support) and/or overall performance (e.g., game involvement and game performance). The individual game performance components were developed and evaluated by experts to determine validity and reliability. The GPAI protocol was field tested across three categories of games: invasion (soccer and basketball), net/wall (volleyball), and field/run/score (softball). Validity and reliability were examined through three separate studies using middle school physical education specialists and their sixth-grade classes. Findings suggest that the GPAI provides a valid and reliable method for assessing game performance.

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Andreas Heissel, Anou Pietrek, Michael A. Rapp, Stephan Heinzel and Geoffrey Williams

validated during a weight-loss study ( Williams, Grow, Freedman, Ryan, & Deci, 1996 ). To reduce item redundancy and for economical reasons, researchers have used adjusted versions with fewer items (see Kasser & Ryan, 1999 ; Williams et al., 1999 ; Williams, Freedman, & Deci, 1998 ). As the HCCQ is

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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.

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Katherine E. Robben, David C. Poole and Craig A. Harms

A two-test protocol (incremental/ramp (IWT) + supramaximal constant-load (CWR)) to affirm max and obviate reliance on secondary criteria has only been validated in highly fit children. In girls (n = 15) and boys (n = 12) with a wide range of VO2max (17–47 ml/kg/min), we hypothesized that this procedure would evince a VO2-WR plateau and unambiguous VO2max even in the presence of expiratory flow limitation (EFL). A plateau in the VO2-work rate relationship occurred in 75% of subjects irrespective of EFL There was a range in RER at max exercise for girls (0.97–1.14; mean 1.06 ± 0.04) and boys (0.98−1.09; mean 1.03 ± 0.03) such that 3/15 girls and 2/12 boys did not achieve the criterion RER. Moreover, in girls with RER > 1.0 it would have been possible to achieve this criterion at 78% VO2max. Boys achieved 92% VO2max at RER = 1.0. This was true also for HRmax where 8/15 girls’ and 6/12 boys’ VO2max would have been rejected based on HRmax being < 90% of age-predicted HRmax. In those who achieved the HRmax criterion, it represented a VO2 of 86% (girls) and 87% (boys) VO2max. We conclude that this two-test protocol confirms VO2max in children across a threefold range of VO2max irrespective of EFL and circumvents reliance on secondary criteria.

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J.C. Norling, Jim Sibthorp and Edward Ruddell

Background:

The purpose of this study was to develop the Perceived Restorativeness for Activities Scale (PRAS) based on the conceptual framework of attention-restoration theory (ART). ART suggests that 4 latent constructs (being away, fascination, extent, and compatibility) must be present to enable a switch from voluntary (effortful, directed) attention to involuntary (effortless) attention and facilitate restored attention.

Method:

Data were collected from 238 participants in a variety of university exercise classes. Exploratory factor analysis reduced items to a parsimonious 12-item scale. Confirmatory factor analysis tested the best fit between a 1-dimensional versus a 4-factor solution.

Results:

The Cronbach alpha was .925. The significant analysis (P < .001) suggested that the model with 4 distinct subscales has the best data fit (goodness-of-fit index = .94, standardized root-mean-square residual = .041, incremental-fit index = .98, expected-cross-validation index = .66, comparative-fit index = .98). Composite reliability and variance extracted were calculated for each construct represented by ART: being away, .81, .59; fascination, .79, .63; extent, .89, .78; and compatibility, .68, .42.

Conclusion:

The 12-item, 4-factor solution of the PRAS can help researchers understand the within-individual preconceptions toward the activity experience that can influence cognitive restoration.

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Willemijn M.J. van Rooij, H.J.G. van den Berg-Emons, Herwin L.D. Horemans, Malou H.J. Fanchamps, Fred A. de Laat and Johannes B.J. Bussmann

Berg-Emons, & Bussmann, 2019 ) and people after stroke ( Fanchamps, Horemans, Ribbers, Stam, & Bussmann, 2018 ). However, this validation needs to be extended to more patient populations. For example, people with a lower-limb amputation often have postures that differ from the normal population

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Samuel Mettler, Christof Mannhart and Paolo C. Colombani

Food-guide pyramids help translate nutrient goals into a visual representation of suggested food intake on a population level. No such guidance system has ever been specifically designed for athletes. Therefore, the authors developed a Food Pyramid for Swiss Athletes that illustrates the number of servings per food group needed in relation to the training volume of an athlete. As a first step, an average energy expenditure of 0.1 kcal · kg−1 · min−1 for exercise was defined, which then was translated into servings of different food groups per hour of exercise per day. Variable serving sizes were defined for athletes’ different body-mass categories. The pyramid was validated by designing 168 daily meal plans according to the recommendations of the pyramid for male and female athletes of different body-mass categories and training volumes of up to 4 hr/d. The energy intake of the meal plans met the calculated reference energy requirement by 97% ± 9%. The carbohydrate and protein intakes were linearly graded from 4.6 ± 0.6–8.5 ± 0.8 g · kg−1 · d−1 and 1.6 ± 0.2–1.9 ± 0.2 g · kg−1 · d−1, respectively, for training volumes of 1–4 hr of exercise per day. The average micronutrient intake depended particularly on the dietary energy intake level but was well above the dietary reference intake values for most micronutrients. No tolerable upper intake level was exceeded for any micronutrient. Therefore, this Food Pyramid for Swiss Athletes may be used as a new tool in sports nutrition education (e.g., teaching and counseling).

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

Prediction Equations and Cross-Validation Statistics by Variable Method Prediction equation Adjusted R 2 MAPE SEE ALM Simple model DXA ALM  = 0.0673 + (0.6732 × BIA ALM ) + (2.33507 × sex) + (0.13349 × BMI) .936 0.051 1.276 SBC DXA ALM  = 0.100752 + (0.667613 × BIA ALM ) − (0.004767 × age) + (2.363436 × sex

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Yaohui He, Phillip Ward and Xiaozan Wang

physical educators (SHAPE) America standards for beginning physical education teachers ( SHAPE America, 2017 ), and they are being proposed as standards for Chinese physical education teachers (Z. Yin, personal communication, July 28, 2017). In physical education, CCK and SCK have been validated in a