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Jos J. de Koning and Dionne A. Noordhof

know that it is not that simple. Is the winner really the best athlete? Did the training intervention give a performance benefit, and which performance-determining variable was improved? There is a lot of uncertainty in our day-to-day practice, while the world around us is asking for unambiguous

Open access

Darren J. Burgess

Research describing load-monitoring techniques for team sport is plentiful. Much of this research is conducted retrospectively and typically involves recreational or semielite teams. Load-monitoring research conducted on professional team sports is largely observational. Challenges exist for the practitioner in implementing peer-reviewed research into the applied setting. These challenges include match scheduling, player adherence, manager/coach buy-in, sport traditions, and staff availability. External-load monitoring often attracts questions surrounding technology reliability and validity, while internal-load monitoring makes some assumptions about player adherence, as well as having some uncertainty around the impact these measures have on player performance This commentary outlines examples of load-monitoring research, discusses the issues associated with the application of this research in an elite team-sport setting, and suggests practical adjustments to the existing research where necessary.

Full access

Thomas Gotschi

Background:

Promoting bicycling has great potential to increase overall physical activity; however, significant uncertainty exists with regard to the amount and effectiveness of investment needed for infrastructure. The objective of this study is to assess how costs of Portland’s past and planned investments in bicycling relate to health and other benefits.

Methods:

Costs of investment plans are compared with 2 types of monetized health benefits, health care cost savings and value of statistical life savings. Levels of bicycling are estimated using past trends, future mode share goals, and a traffic demand model.

Results:

By 2040, investments in the range of $138 to $605 million will result in health care cost savings of $388 to $594 million, fuel savings of $143 to $218 million, and savings in value of statistical lives of $7 to $12 billion. The benefit-cost ratios for health care and fuel savings are between 3.8 and 1.2 to 1, and an order of magnitude larger when value of statistical lives is used.

Conclusions:

This first of its kind cost-benefit analysis of investments in bicycling in a US city shows that such efforts are cost-effective, even when only a limited selection of benefits is considered.

Open access

Damien Moore, Tania Pizzari, Jodie McClelland and Adam I. Semciw

Context: Many different rehabilitation exercises have been recommended in the literature to target the gluteus medius (GMed) muscle based mainly on single-electrode, surface electromyography (EMG) measures. With the GMed consisting of 3 structurally and functionally independent segments, there is uncertainty on whether these exercises will target the individual segments effectively. Objective: To measure individual GMed segmental activity during 6 common, lower-limb rehabilitation exercises in healthy young adults, and determine if there are significant differences between the exercises for each segment. Method: With fine-wire EMG electrodes inserted into the anterior, middle, and posterior segments of the GMed muscle, 10 healthy young adults performed 6 common, lower-limb rehabilitation exercises. Main Outcome Measures: Recorded EMG activity was normalized, then reported and compared with median activity for each of the GMed segments across the 6 exercises. Results: For the anterior GMed segment, high activity was recorded for the single-leg squat (48% maximum voluntary isometric contraction [MVIC]), the single-leg bridge (44% MVIC), and the resisted hip abduction–extension exercise (41% MVIC). No exercises recorded high activity for the middle GMed segment, but for the posterior GMed segment very high activity was recorded by the resisted hip abduction–extension exercise (69% MVIC), and high activity was generated by the single-leg squat (48% MVIC) and side-lie hip abduction (43% MVIC). For each of the GMed segments, there were significant differences (P < .05) in the median EMG activity levels between some of the exercises and the side-lie clam with large effect sizes favoring these exercises over the side-lie clam. Conclusions: Open-chain hip abduction and single-limb support exercises appear to be effective options for recruiting the individual GMed segments with selection dependent on individual requirements. However, the side-lie clam does not appear to be effective at recruiting the GMed segments, particularly the anterior and middle segments.

Open access

Jennifer M. Medina McKeon and Patrick O. McKeon

outcomes (95% confidence around the mean difference), there was uncertainty as to whether vestibular rehabilitation would result, on average, in beneficial changes for every athlete who suffered a concussion. In other words, there appeared to be a trend in improvement at the group level, but this was

Open access

International Olympic Committee Expert Group on Dietary Supplements in Athletes

evaluation, body composition analysis, biochemical testing, nutrition-focused clinical examination, and patient health and performance history. Assessment should take account of maturation status, sex, ethnicity and culture. The limitations and uncertainties in all of the methods employed must be recognised

Open access

Jennifer M. Medina McKeon and Patrick O. McKeon

. Uncertainty of results and the ambiguity of the clinical options: Lastly, especially for SOR grades B and C, we need to consider the level of uncertainty of the results and ambiguity of possible clinical options when implementing recommendations. Again, a re-weighting of the recommendation and the possible

Free access

Sergio Jiménez-Rubio, Archit Navandar, Jesús Rivilla-García and Victor Paredes-Hernández

anaerobic conditions (70–90 s per set). 3 min 30 s–4 min 30 s 6 Four sets of 4 repetitive actions (without uncertainty) with change of direction and deceleration (total displacements of 8–14 m, total time < 12 s), followed by shooting at a minigoal 12 m away. Rest of 15 s between sets. 1 min 30 s 7 Three

Open access

David P. Looney, Mark J. Buller, Andrei V. Gribok, Jayme L. Leger, Adam W. Potter, William V. Rumpler, William J. Tharion, Alexander P. Welles, Karl E. Friedl and Reed W. Hoyt

& Bishop, 1995 ) that is comprised of a time update and observation model. The original development and validation study ( Buller et al., 2013 ) contains a detailed description of how each model coefficient was derived. The time update model relates how CT changes minute-to-minute and the uncertainty

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Brian M. Wood, Herman Pontzer, Jacob A. Harris, Audax Z.P. Mabulla, Marc T. Hamilton, Theodore W. Zderic, Bret A. Beheim and David A. Raichlen

this model, we calculate its R 2 value, mean average error (MAE) and mean average percent error (MAPE). In order to best represent model estimation uncertainty, we then adopt a Bayesian approach, and fit a multilevel model (M6) using the R package ‘brms’ ( Bürkner, 2017 ), using uniform priors. In