random effects of competition identity, athlete identity, and athlete × season, the logistic-regression model included a multiplicative overdispersion factor instead of the model residual as in the mixed models. The generalized mixed linear models were run with the default option of allowing estimation
Øyvind Skattebo and Thomas Losnegard
Sean Williams, Grant Trewartha, Matthew J. Cross, Simon P.T. Kemp and Keith A. Stokes
Numerous derivative measures can be calculated from the simple session rating of perceived exertion (sRPE), a tool for monitoring training loads (eg, acute:chronic workload and cumulative loads). The challenge from a practitioner’s perspective is to decide which measures to calculate and monitor in athletes for injury-prevention purposes. The aim of the current study was to outline a systematic process of data reduction and variable selection for such training-load measures.
Training loads were collected from 173 professional rugby union players during the 2013–14 English Premiership season, using the sRPE method, with injuries reported via an established surveillance system. Ten derivative measures of sRPE training load were identified from existing literature and subjected to principal-component analysis. A representative measure from each component was selected by identifying the variable that explained the largest amount of variance in injury risk from univariate generalized linear mixed-effects models.
Three principal components were extracted, explaining 57%, 24%, and 9% of the variance. The training-load measures that were highly loaded on component 1 represented measures of the cumulative load placed on players, component 2 was associated with measures of changes in load, and component 3 represented a measure of acute load. Four-week cumulative load, acute:chronic workload, and daily training load were selected as the representative measures for each component.
The process outlined in the current study enables practitioners to monitor the most parsimonious set of variables while still retaining the variation and distinct aspects of “load” in the data.
Trent Stellingwerff, James P. Morton and Louise M. Burke
Over the last decade, in support of training periodization, there has been an emergence around the concept of nutritional periodization. Within athletics (track and field), the science and art of periodization is a cornerstone concept with recent commentaries emphasizing the underappreciated complexity associated with predictable performance on demand. Nevertheless, with varying levels of evidence, sport and event specific sequencing of various training units and sessions (long [macrocycle; months], medium [mesocycle; weeks], and short [microcycle; days and within-day duration]) is a routine approach to training periodization. Indeed, implementation of strategic temporal nutrition interventions (macro, meso, and micro) can support and enhance training prescription and adaptation, as well as acute event specific performance. However, a general framework on how, why, and when nutritional periodization could be implemented has not yet been established. It is beyond the scope of this review to highlight every potential nutritional periodization application. Instead, this review will focus on a generalized framework, with specific examples of macro-, meso-, and microperiodization for the macronutrients of carbohydrates, and, by extension, fat. More specifically, the authors establish the evidence and rationale for situations of acute high carbohydrate availability, as well as the evidence for more chronic manipulation of carbohydrates coupled with training. The topic of periodized nutrition has made considerable gains over the last decade but is ripe for further scientific progress and field application.
Maria Hagströmer, Barbara E. Ainsworth, Lydia Kwak and Heather R. Bowles
The quality of methodological papers assessing physical activity instruments depends upon the rigor of a study’s design.
We present a checklist to assess key criteria for instrument validation studies.
A Medline/PubMed search was performed to identify guidelines for evaluating the methodological quality of instrument validation studies. Based upon the literature, a pilot version of a checklist was developed consisting of 21 items with 3 subscales: 1) quality of the reported data (9 items: assess whether the reported information is sufficient to make an unbiased assessment of the findings); 2) external validity of the results (3 items: assess the extent to which the findings are generalizable); 3) internal validity of the study (9 items: assess the rigor of the study design). The checklist was tested for interrater reliability and feasibility with 6 raters.
Raters viewed the checklist as helpful for reviewing studies. They suggested minor wording changes for 8 items to clarify intent. One item was divided into 2 items for a total of 22 items.
Checklists may be useful to assess the quality of studies designed to validate physical activity instruments. Future research should test checklist internal consistency, test-retest reliability, and criterion validity.
John J. Reilly, Smita Dick, Geraldine McNeill and Mark S. Tremblay
The Active Healthy Kids Scotland Report Card aims to consolidate existing evidence, facilitate international comparisons, encourage more evidence-informed physical activity and health policy, and improve surveillance of physical activity.
Application of the Active Healthy Kids Canada Report Card process and methodology to Scotland, adapted to Scottish circumstances and availability of data.
The Active Healthy Kids Scotland Report Card 2013 consists of indicators of 7 Health Behaviors and Outcomes and 3 Influences on Health Behaviors and Outcomes. Grades of F were assigned to Overall Physical Activity, Sedentary Behavior (recreational screen time), and Obesity Prevalence. A C was assigned to Active Transportation and a D- was assigned to Diet. Two indicators, Active and Outdoor Play and Organized Sport Participation, could not be graded. Among the Influences, Family Influence received a D, while Perceived Safety, Access, and Availability of Spaces for Physical Activity and the National Policy Environment graded more favorably with a B.
The Active Healthy Kids Canada process and methodology was readily generalizable to Scotland. The report card illustrated low habitual physical activity and extremely high levels of screen-based sedentary behavior, and highlighted several opportunities for improved physical activity surveillance and promotion strategies.
Aaron J. Coutts
’s limitations and generalizability should also be addressed and recommendations made for future research, where necessary. This section is critical in assisting readers to comprehend how findings may be applied in practice. These translational statements can also be important for authors, acting as a source of
Miranda Brunett and René Revis Shingles
research done on patient satisfaction and provider cultural competence, the studies examined were either the first of their kind or their results fit with evidence done from other studies. Not all of the results from the studies included in this paper can be generalized to all populations, but the studies
Jos J. de Koning and Dionne A. Noordhof
about the degree of transfer and generalizability of our study findings, we should also report this uncertainty. Our readers can then decide themselves if they are willing to accept this uncertainty or not, when using the research outcomes in sport practice. In summary, high-quality sport physiology
Narayan Subedi, Susan Paudel, Sudip Nepal, Ashmita Karki, Mahendra Magar and Suresh Mehata
size and the study being limited to a small geographic area, it might not give a generalized scenario of the Nepalese context. Nepal lacks data on 5 out of 10 indicators, which clearly highlights the research gap. In the current socio-political context, there is a need for large-scale studies
Jennifer M. Medina McKeon and Patrick O. McKeon
internal and external validity of that study. There is an appreciable balancing act between control (internal validity) and generalizability (external validity). 5 In general, studies with more control are higher on the hierarchy of clinical evidence. For example, cohort studies tend to have more control