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.
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- Author: Michael I. Lambert x
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Lindsay T. Starling and Michael I. Lambert
Jill Borresen and Michael I. Lambert
Purpose:
To establish the relationship between a subjective (session rating of perceived exertion [RPE]) and 2 objective (training impulse [TRIMP]) and summated-heart-rate-zone (SHRZ) methods of quantifying training load and explain characteristics of the variance not accounted for in these relationships.
Methods:
Thirty-three participants trained ad libitum for 2 wk, and their heart rate (HR) and RPE were recorded to calculate training load. Subjects were divided into groups based on whether the regression equations over- (OVER), under- (UNDER), or accurately predicted (ACCURATE) the relationship between objective and subjective methods.
Results:
A correlation of r = .76 (95% CI: .56 to .88) occurred between TRIMP and session-RPE training load. OVER spent a greater percentage of training time in zone 4 of SHRZ (ie, 80% to 90% HRmax) than UNDER (46% ± 8% vs 25% ± 10% [mean ± SD], P = .008). UNDER spent a greater percentage of training time in zone 1 of SHRZ (ie, 50% to 60% HRmax) than OVER (15% ± 8% vs 3% ± 3%, P = .005) and ACCURATE (5% ± 3%, P = .020) and more time in zone 2 of SHRZ (ie, 60% to 70%HRmax) than OVER (17% ± 6% vs 7% ± 6%, P = .039). A correlation of r = .84 (.70 to .92) occurred between SHRZ and session-RPE training load. OVER spent proportionally more time in Zone 4 than UNDER (45% ± 8% vs 25% ± 10%, P = .018). UNDER had a lower training HR than ACCURATE (132 ± 10 vs 148 ± 12 beats/min, P = .048) and spent more time in zone 1 than OVER (15% ± 8% vs 4% ± 3%, P = .013) and ACCURATE (5% ± 3%, P = .015).
Conclusions:
The session-RPE method provides reasonably accurate assessments of training load compared with HR-based methods, but they deviate in accuracy when proportionally more time is spent training at low or high intensity.
Benoit Capostagno, Michael I. Lambert, and Robert P. Lamberts
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.
Benoit Capostagno, Michael I. Lambert, and Robert P. Lamberts
Purpose:
To determine whether a submaximal cycling test could be used to monitor and prescribe high-intensity interval training (HIT).
Methods:
Two groups of male cyclists completed 4 HIT sessions over a 2-wk period. The structured-training group (SG; n = 8, VO2max = 58.4 ± 4.2 mL · min−1 · kg−1) followed a predetermined training program while the flexible-training group (FG; n = 7, VO2max = 53.9 ± 5.0 mL · min−1 · kg−1) had the timing of their HIT sessions prescribed based on the data of the Lamberts and Lambert Submaximal Cycle Test (LSCT).
Results:
Effect-size calculations showed large differences in the improvements in 40-km time-trial performance after the HIT training between SG (8 ± 45 s) and FG (48 ± 42 s). Heart-rate recovery, monitored during the study, tended to increase in FG and remain unchanged in SG.
Conclusions:
The results of the current study suggest that the LSCT may be a useful tool for coaches to monitor and prescribe HIT.
Ross Tucker, Michael I. Lambert, and Timothy D. Noakes
Purpose:
To analyze pacing strategies employed during men's world-record performances for 800-m, 5000-m, and 10,000-m races.
Methods:
In the 800-m event, lap times were analyzed for 26 world-record performances from 1912 to 1997. In the 5000-m and 10,000-m events, times for each kilometer were analyzed for 32 (1922 to 2004) and 34 (1921 to 2004) world records.
Results:
The second lap in the 800-m event was significantly slower than the first lap (52.0 ± 1.7 vs 54.4 ± 4.9 seconds, P < .00005). In only 2 world records was the second lap faster than the first lap. In the 5000-m and 10,000-m events, the first and final kilometers were significantly faster than the middle kilometer intervals, resulting in an overall even pace with an end spurt at the end.
Conclusion:
The optimal pacing strategy during world-record performances differs for the 800-m event compared with the 5000-m and 10,000-m events. In the 800-m event, greater running speeds are achieved in the first lap, and the ability to increase running speed on the second lap is limited. In the 5000-m and 10,000-m events, an end spurt occurs because of the maintenance of a reserve during the middle part of the race. In all events, pacing strategy is regulated in a complex system that balances the demand for optimal performance with the requirement to defend homeostasis during exercise.
Lara R. Keytel, Michael I. Lambert, Judith Johnson, Timothy D. Noakes, and Estelle V. Lambert
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 (V̇O2max), 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, V̇O2max 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.
Justin Durandt, Jason C. Tee, Sebastian K. Prim, and Michael I. Lambert
Purpose:
The 5-m repeat-sprint test (5-m RST) measures resistance to fatigue after repeated bouts of short-duration, high-intensity activity. This study determined the components of fitness associated with performance in 5-m RSTs.
Methods:
Speed (10-m and 40-m sprints), strength (bench press), agility, strength endurance (pull-ups and push-ups), and aerobic power (20-m shuttle-run test) were measured in male provincial- or national-level rugby (n = 110), hockey (n = 59), and soccer (n = 55) players.
Results:
Subjects with either high (HI) or low (LO) resistance to fatigue in the 5-m RST differed in body mass (76.9 ± 11.6 kg vs 102.1 ± 18.9 kg, HI vs LO, respectively, P < .001), agility (14.55 ± 0.41 seconds vs 15.56 ± 0.30 seconds, P < .001), bench press (86 ± 20 kg vs 114 ± 33 kg, P = .03), pull-ups (13 ± 4 vs 8 ± 5, P = .02), push-ups (56 ± 12 vs 39 ± 13, P = .002), and 20-m shuttle-run test (20-m SRT; 133 ± 11 vs 87 ± 12 shuttles, P < .001). Body mass, strength, and aerobic power were the best predictors of 5-m RST performance: 5-m RST = –1.274(mass) + 0.756(1RM bench press) + 2.053(number of 20-m SRT shuttles) + 549.409 (R 2 = .66).
Conclusions:
Performance in the 5-m RST is predicted best by a combination of factors including body mass, strength, and aerobic ability, rather than by any single component of fitness.
Michael I. Lambert, Janet A. Hefer, Robert P. Millar, and Peter W. Macfarlane
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 BovrilR 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.
