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Purpose: The advantages of monitoring players in a team are well documented. However, barriers associated with lack of resources and time prevent teams from implementing systematic monitoring programs. This study aimed to identify (1) the methods rugby teams use to monitor the training load and associated response to the training load and (2) prerequisites of a monitoring protocol that are scientifically suitable and practically applicable for monitoring fitness and fatigue of rugby players. Methods: Coaches and support staff working with varying levels of rugby union were invited to complete an online questionnaire. Results: Of the 55 respondents, 96% indicated that although they regarded monitoring the training load and training-load response as important, there is no monitoring protocol that is cost-effective, time efficient, and nonaversive to the players. Respondents measured several variables when monitoring and incorporated more subjective than objective measures. Respondents (41%) indicated that they would like a protocol that is time efficient (5–10 min) and provides immediate feedback on players who identify as fatigued (50%). For coaches to have confidence in the information provided by the protocol, it needs to meet basic clinimetric principles of reliability and validity. The technical and biological error in the measurement needs to be known so that meaningful changes in fatigue and fitness can be distinguished from natural variations in the measurements. Conclusions: Prerequisites of an ideal monitoring protocol for rugby players were identified. It follows that a monitoring protocol that fulfills these prerequisites should satisfy both scientific principles and the coach’s demands.

Finding the optimal balance between high training loads and recovery is a constant challenge for cyclists and their coaches. Monitoring improvements in performance and levels of fatigue is recommended to correctly adjust training to ensure optimal adaptation. However, many performance tests require a maximal or exhaustive effort, which reduces their real-world application. The purpose of this review was to investigate the development and use of submaximal cycling tests that can be used to predict and monitor cycling performance and training status. Twelve studies met the inclusion criteria, and 3 separate submaximal cycling tests were identified from within those 12. Submaximal variables including gross mechanical efficiency, oxygen uptake (VO₂), heart rate, lactate, predicted time to exhaustion (pTE), rating of perceived exertion (RPE), power output, and heart-rate recovery (HRR) were the components of the 3 tests. pTE, submaximal power output, RPE, and HRR appear to have the most value for monitoring improvements in performance and indicate a state of fatigue. This literature review shows that several submaximal cycle tests have been developed over the last decade with the aim to predict, monitor, and optimize cycling performance. To be able to conduct a submaximal test on a regular basis, the test needs to be short in duration and as noninvasive as possible. In addition, a test should capture multiple variables and use multivariate analyses to interpret the submaximal outcomes correctly and alter training prescription if needed.

The aim of the study was to determine the effects of 8 weeks of moderate exercise training, on 24-hour free living energy expenditure in previously sedentary post-menopausal women. The experimental group (EX) included 9 women. Ten non-exercising control subjects (CON) were recruited to undergo pre- and post-testing. Estimated total daily energy expenditure (TDEE), total 24-hour heart beats (HB), total energy intake (TEI), resting metabolic rate, maximal oxygen consumption (V̇O_2max), body composition, and submaximal heart rate were measured before and after the exercise intervention. Body composition did not change (body fat % in CON 34.0 ± 4.0% vs. 33.9 ± 3.6% and EX 34.1 ± 4.0% vs. 34.0 ± 3.4%). Mean submaximal heart rate during steady-state exercise in EX was lower after training compared to CON (p < .05); however, V̇O_2max did not significantly (CON 1.96 ± 0.23 vs. 1.99 ± 0.241 LO₂/min and EX 1.86 ± 0.39 vs. 1.94 ± 0.30 LO₂/min). Neither estimated TDEE (CON, 11.6 ± 2.0 vs. 11.4 ± 2.78 MJ; and EX 11.4 ± 3.3 vs. 11.5 ± 2.5 MJ, pre vs. post, respectively), RMR (CON 134.2 ± 9.4 vs. 136.9 ± 15.0 KJ/kgFFM/day, and EX 138.4 ± 6.4 vs. 140.7 ± 14.2 KJ/kgFFM/day, pre vs. post, respectively), TEI (CON 7.9 ± 2.2 vs. 8.2 ± 2.5 MJ, and EX 9.4 ±1.6 vs. 8.3 ± 2.8 MJ), nor HB (CON 110,808 ± 12,574 vs. 107,366 ± 12,864 beats, and EX 110,188 ± 9,219 vs. 114,590 ± 12,750 beats) change over 8 weeks in either group. These data suggest that a moderate exercise program may not impact on TDEE, RMR, TEI, or HB in previously sedentary, older women.

Amino acids are commonly ingested as ergogenic aids in the belief that they enhance protein synthesis and stimulate growth hormone release. The aim of this study was to determine the acute effect that amino acid supplements have on serum growth hormone (GH) concentration. Seven male bodybuilders reported to the laboratory on four occasions after an 8-hr fast and ingested, in random order, either a placebo, a 2.4-g arginine/lysine supplement, a 1.85-g ornithine/tyrosine supplement, or a 20-g Bovril^R drink. Blood was collected before each treatment and again every 30 minutes for 3 hours for the measurement of serum GH concentration. On a separate occasion, subjects had an intravenous infusion of 0.5 fig GH-releasing hormone-kg ' body weight to confirm that GH secretory response was normal. The main finding was that serum GH concentrations were not altered consistently in healthy young males following the ingestion of the amino acid supplements in the quantities recommended by the manufacturers.