, 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
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Reid J. Reale, Timothy J. Roberts, Khalil A. Lee, Justina L. Bonsignore, and Melissa L. Anderson
Jingzhi Yu, Kristopher Kapphahn, Hyatt Moore, Farish Haydel, Thomas Robinson, and Manisha Desai
method, there is no quantitative metric that identifies this “elbow,” or the point where there is a marked difference in W k . In contrast, we were interested in treating the problem of estimating k as a classification problem. We therefore opted to focus on the prediction strength metric developed
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
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
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
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.
Gustaf Gredebäck
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.
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%.
S. Sofie Lövdal, Ruud J.R. Den Hartigh, and George Azzopardi
witnessed a growth in technologies and machine learning applications, which can be employed to make predictions about future performance, injuries, and thereby improve data-driven guidance in sports. 8 – 10 In the current study, we use a supervised machine learning approach, which relies principally on
Thierry Busso and Luc Thomas
This report aims to discuss the strengths and weaknesses of the application of systems modeling to analyze the effects of training on performance. The simplifications inherent to the modeling approach are outlined to question the relevance of the models to predict athletes’ responses to training. These simplifications include the selection of the variables assigned to the system’s input and output, the specification of model structure, the collection of data to estimate the model parameters, and the use of identified models and parameters to predict responses. Despite the gain in insight to understand the effects of an intensification or reduction of training, the existing models would not be accurate enough to make predictions for a particular athlete in order to monitor his or her training.
