, and a thermoneutral testing environment, etc.), combined with the often cost-prohibitive equipment needed, means that the use of IC is not feasible for many nutrition professionals working with patients and/or athletes. Thus, prediction equations are routinely used to estimate RMR in a variety of
Reid J. Reale, Timothy J. Roberts, Khalil A. Lee, Justina L. Bonsignore and Melissa L. Anderson
Christopher M. Saliba, Allison L. Clouthier, Scott C.E. Brandon, Michael J. Rainbow and Kevin J. Deluzio
predict peak medial contact force 13 , 14 and the contact force throughout the gait cycle 15 , 16 from the knee adduction and flexion moments. Although these models could provide instantaneous predictions of contact force from real-time kinematics and kinetics, 14 they vary considerably across subjects
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
Berit Steenbock, Marvin N. Wright, Norman Wirsik and Mirko Brandes
relationships between accelerometer output and EE differ in preschoolers compared with older children, prediction equations require development and validation in this specific age group ( Butte et al., 2014 ). Considerable progress has been made in predicting EE for adults and older children ( Jimmy, Seiler
Job Fransen, Stephen Bush, Stephen Woodcock, Andrew Novak, Dieter Deprez, Adam D.G. Baxter-Jones, Roel Vaeyens and Matthieu Lenoir
prediction of APHV. This equation predicts the years from APHV and terms this BA a “maturity offset” (years from APHV) using measures of stature, body mass, leg length, sitting height, and CA to predict a maturity offset. Using this predicted BA and the CA at time of measurement, the APHV can be estimated
Alexander H.K. Montoye, John Vusich, John Mitrzyk and Matt Wiersma
strong likelihood that CPAMs use step counts to derive kcals, which gives an additional layer for error in kcal prediction. However, CPAMs are often used and show promise for promoting weight loss ( Hartman et al., 2016 ; Lyons, Lewis, Mayrsohn, & Rowland, 2014 ); it may be that increasing the accuracy
Michael Joch, Mathias Hegele, Heiko Maurer, Hermann Müller and Lisa K. Maurer
Motor learning can be monitored by observing the development of neural correlates of error processing. Among these neural correlates, the error- and feedback-related negativity (Ne/ERN and FRN) represent error processing mechanisms. While the Ne/ERN is more related to error prediction, the FRN is found after an error is manifested. The questions the current study strives to answer are: What information is needed by the system to make error predictions and how is this represented by the Ne/ERN and FRN in a complex motor task? We reduced the information and increased the difficulty level for the prediction in a semivirtual throwing task and found no Ne/ERN but a large FRN when the action result was finally observed (hitting or missing a target). We assume that uncertainty for error prediction was too high (either due to insufficient information or due to lacking prerequisites for prediction), such that error processing had to be mainly based on feedback. The finding is in line with the reinforcement theory of learning, after which Ne/ERN and FRN should behave complementary.
This review focuses on three different processes: action priming, action prediction, and outcome evaluation. Together, these processes form a foundation for social perception early in life. Priming and prediction is argued to be separable processes with different degrees of plasticity, based in part on unique neural structures. These two future-oriented processes are assumed to operate in a sequential manner. A third set of processes, outcome evaluations, follows the completion of observed events and compare the actual events with the assumptions postulated by the preceding future-oriented processes. Together, these processes are argued to provide good grounds for learning via internal models that detect error signals that arise from the potential mismatch between priming and prediction and actual events as they unfold in the external world and use this information to update the accuracy of future-oriented processes.
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.
Debra J. Rose, C. Jessie Jones and Nicole Lucchese
The purpose of this study was to determine whether performance on the 8-ft up-and-go test (UG) could discriminate between older adult fallers (n = 71) and nonfallers (n = 63) and whether it would be as sensitive and specific a predictor of falls as the timed up-and-go test (TUG). Performance on the UG was significantly different between the recurrent faller and nonfaller groups (p < .01), as was performance on the TUG (p < .001). Older adults who required 8.5 s or longer to complete the UG were classified as fallers, with an overall prediction rate of 82%. The specificity of the test was 86% and the sensitivity was 78%. Conversely, the overall prediction rate for older adults who completed the TUG in 10 s or longer was 80%. The specificity of the TUG was 86% and the sensitivity was 71%.