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Alan J. McCubbin, Bethanie A. Allanson, Joanne N. Caldwell Odgers, Michelle M. Cort, Ricardo J.S. Costa, Gregory R. Cox, Siobhan T. Crawshay, Ben Desbrow, Eliza G. Freney, Stephanie K. Gaskell, David Hughes, Chris Irwin, Ollie Jay, Benita J. Lalor, Megan L.R. Ross, Gregory Shaw, Julien D. Périard and Louise M. Burke

It is the position of Sports Dietitians Australia (SDA) that exercise in hot and/or humid environments, or with significant clothing and/or equipment that prevents body heat loss (i.e., exertional heat stress), provides significant challenges to an athlete’s nutritional status, health, and performance. Exertional heat stress, especially when prolonged, can perturb thermoregulatory, cardiovascular, and gastrointestinal systems. Heat acclimation or acclimatization provides beneficial adaptations and should be undertaken where possible. Athletes should aim to begin exercise euhydrated. Furthermore, preexercise hyperhydration may be desirable in some scenarios and can be achieved through acute sodium or glycerol loading protocols. The assessment of fluid balance during exercise, together with gastrointestinal tolerance to fluid intake, and the appropriateness of thirst responses provide valuable information to inform fluid replacement strategies that should be integrated with event fuel requirements. Such strategies should also consider fluid availability and opportunities to drink, to prevent significant under- or overconsumption during exercise. Postexercise beverage choices can be influenced by the required timeframe for return to euhydration and co-ingestion of meals and snacks. Ingested beverage temperature can influence core temperature, with cold/icy beverages of potential use before and during exertional heat stress, while use of menthol can alter thermal sensation. Practical challenges in supporting athletes in teams and traveling for competition require careful planning. Finally, specific athletic population groups have unique nutritional needs in the context of exertional heat stress (i.e., youth, endurance/ultra-endurance athletes, and para-sport athletes), and specific adjustments to nutrition strategies should be made for these population groups.

Open access

Nicole C.A. Strock, Kristen J. Koltun, Emily A. Southmayd, Nancy I. Williams and Mary Jane De Souza

Energy deficiency in exercising women can lead to physiological consequences. No gold standard exists to accurately estimate energy deficiency, but measured-to-predicted resting metabolic rate (RMR) ratio has been used to categorize women as energy deficient. The purpose of the study was to (a) evaluate the accuracy of RMR prediction methods, (b) determine the relationships with physiological consequences of energy deficiency, and (c) evaluate ratio thresholds in a cross-sectional comparison of ovulatory, amenorrheic, or subclinical menstrual disturbances in exercising women (n = 217). Dual-energy X-ray absorptiometry (DXA) and indirect calorimetry provided data on anthropometrics and energy expenditure. Harris–Benedict, DXA, and Cunningham (1980 and 1991) equations were used to estimate RMR and RMR ratio. Group differences were assessed (analysis of variance and Kruskal–Wallis tests); logistic regression and Spearman correlations related ratios with consequences of energy deficiency (i.e., low total triiodothyronine; TT3). Sensitivity and specificity calculations evaluated ratio thresholds. Amenorrheic women had lower RMR (p < .05), DXA ratio (p < .01), Cunningham1980 (p < .05) and Cunningham1991 (p < .05) ratio, and TT3 (p < .01) compared with the ovulatory group. Each prediction equation overestimated measured RMR (p < .001), but predicted (p < .001) and positively correlated with TT3 (r = .329–.453). A 0.90 ratio threshold yielded highest sensitivity for Cunningham1980 (0.90) and Harris–Benedict (0.87) methods, but a higher ratio threshold was best for DXA (0.94) and Cunningham1991 (0.92) methods to yield a sensitivity of 0.80. In conclusion, each ratio predicted and correlated with TT3, supporting the use of RMR ratio as an alternative assessment of energetic status in exercising women. However, a 0.90 ratio cutoff is not universal across RMR estimation methods.

Open access

James A. Betts, Javier T. Gonzalez, Louise M. Burke, Graeme L. Close, Ina Garthe, Lewis J. James, Asker E. Jeukendrup, James P. Morton, David C. Nieman, Peter Peeling, Stuart M. Phillips, Trent Stellingwerff, Luc J.C. van Loon, Clyde Williams, Kathleen Woolf, Ron Maughan and Greg Atkinson

Open access

Melanna F. Cox, Greg J. Petrucci Jr., Robert T. Marcotte, Brittany R. Masteller, John Staudenmayer, Patty S. Freedson and John R. Sirard

Purpose: Develop a direct observation (DO) system to serve as a criterion measure for the calibration of models applied to free-living (FL) accelerometer data. Methods: Ten participants (19.4 ± 0.8 years) were video-recorded during four, one-hour FL sessions in different settings: 1) school, 2) home, 3) community, and 4) physical activity. For each setting, 10-minute clips from three randomly selected sessions were extracted and coded by one expert coder and up to 20 trained coders using the Observer XT software (Noldus, Wageningen, the Netherlands). The coder defines each whole-body movement which was further described with three modifiers: 1) locomotion, 2) activity type, and 3) MET value (used to categorize intensity level). Percent agreement was calculated for intra- and inter-rater reliability. For intra-rater reliability, the criterion coder coded all 12 clips twice, separated by at least one week between coding sessions. For inter-rater reliability, coded clips by trained coders were compared to the expert coder. Intraclass correlations (ICCs) were calculated to assess the agreement of intensity category for intra- and inter-rater comparisons described above. Results: For intra-rater reliability, mean percent agreement ranged from 91.9 ± 3.9% to 100.0 ± 0.0% across all variables in all settings. For inter-rater reliability, mean percent agreement ranged from 88.2 ± 3.5% to 100.0 ± 0.0% across all variables in all settings. ICCs for intensity category ranged from 0.74–1.00 and 0.81–1.00 for intra- and inter-rater comparisons, respectively. Conclusion: The DO system is reliable and feasible to serve as a criterion measure of FL physical activity in young adults to calibrate accelerometers, subsequently improving interpretation of surveillance and intervention research.

Open access

Sid Mitchell, E. Michael Loovis and Stephen A. Butterfield

Analyzing data in the exercise sciences can be challenging when trying to account for physical changes brought about by maturation (e.g., growth in height, weight, heart/lung capacity, muscle-to-fat ratio). In this paper, we present an argument for using hierarchical linear modeling (HLM) as an approach to analyzing physical performance data. Using an applied example from Butterfield, Lehnhard, Lee, and Coladarci, we will show why HLM is an appropriate analysis technique and provide other examples of where HLM will be beneficial.

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Brigid M. Lynch, Suzanne C. Dixon-Suen, Andrea Ramirez Varela, Yi Yang, Dallas R. English, Ding Ding, Paul A. Gardiner and Terry Boyle

Background: It is not always clear whether physical activity is causally related to health outcomes, or whether the associations are induced through confounding or other biases. Randomized controlled trials of physical activity are not feasible when outcomes of interest are rare or develop over many years. Thus, we need methods to improve causal inference in observational physical activity studies. Methods: We outline a range of approaches that can improve causal inference in observational physical activity research, and also discuss the impact of measurement error on results and methods to minimize this. Results: Key concepts and methods described include directed acyclic graphs, quantitative bias analysis, Mendelian randomization, and potential outcomes approaches which include propensity scores, g methods, and causal mediation. Conclusions: We provide a brief overview of some contemporary epidemiological methods that are beginning to be used in physical activity research. Adoption of these methods will help build a stronger body of evidence for the health benefits of physical activity.

Open access

Juana Willumsen and Fiona Bull

Background: Physical inactivity is a leading risk factor for global mortality and a contributor to the increase in overweight and obesity. The Commission on Ending Childhood Obesity identified the need for guidance on physical activity, particularly for early childhood (<5 y), a period of rapid physical and cognitive development. Methods: The World Health Organization (WHO) has developed the first global guidelines on physical activity, sedentary, and sleep behaviors, building upon high-quality systematic reviews. The WHO guideline process is a rigorous, systematic, and transparent method for the development of recommendations, using the Grading of Recommendations Assessment, Development and Evaluation Evidence to Decision framework. It takes into consideration the strength of the evidence as well as values and preferences, benefits and harms, equity and human rights. Results: The authors summarize the first global guidelines on time spent in physical activity, sedentary behavior (including screen time and time spent restrained), and sleep patterns in infants (birth to 1 y of age), toddlers (1–2.9 y of age), and preschoolers (3–4.9 y of age). Conclusions: WHO is actively disseminating and supporting implementation of these guidelines by national adoption and adaptation, through links with early childhood development and the Global Action Plan on Physical Activity 2018–2030.

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Stephanie A. Hooker, Laura B. Oswald, Kathryn J. Reid and Kelly G. Baron

Background: Little is known about how daily fluctuations in health behaviors relate to chronic disease risk. The goal of this study was to examine whether variability in physical activity, caloric intake, and sleep is related to body composition (body mass index and body fat percentage). Methods: Healthy adults (N = 103; 64% female) were monitored for 7 days to assess physical activity (SenseWear Armband), caloric intake (daily food diaries), and sleep duration and timing (Actiwatch Spectrum). Data were analyzed using correlations (between- and within-subjects correlations) and regression. Results: The results demonstrated that variabilities in physical activity, caloric intake, and sleep were unrelated. Caloric intake and sleep variability were unrelated to body composition. At greater levels of physical activity variability, any level of physical activity was protective for body composition. Conclusions: These results suggest that among healthy adults, variabilities in health behaviors may be independent of each other, and physical activity variability may be more strongly related to body composition among those who are less active.