Search Results

You are looking at 1 - 9 of 9 items for :

  • Author: Michael I. Lambert x
  • Sport and Exercise Science/Kinesiology x
  • Refine by Access: All Content x
Clear All Modify Search
Restricted access

Quantifying Training Load: A Comparison of Subjective and Objective Methods

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.

Restricted access

Monitoring Rugby Players for Fitness and Fatigue: What Do Coaches Want?

Lindsay T. Starling and Michael I. Lambert

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.

Restricted access

Standardized Versus Customized High-Intensity Training: Effects on Cycling Performance

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.

Restricted access

A Systematic Review of Submaximal Cycle Tests to Predict, Monitor, and Optimize Cycling Performance

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.

Restricted access

An Analysis of Pacing Strategies During Men’s World-Record Performances in Track Athletics

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.

Restricted access

Free Living Energy Expenditure in Post Menopausal Women before and after Exercise Training

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.

Restricted access

Failure of Commercial Oral Amino Acid Supplements to Increase Serum Growth Hormone Concentrations in Male Body-Builders

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.

Restricted access

Physical Fitness Components Associated With Performance in a Multiple-Sprint Test

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.

Open access

Status and Trends of Physical Activity Surveillance, Policy, and Research in 164 Countries: Findings From the Global Observatory for Physical Activity—GoPA! 2015 and 2020 Surveys

Andrea Ramírez Varela, Pedro C. Hallal, Juliana Mejía Grueso, Željko Pedišić, Deborah Salvo, Anita Nguyen, Bojana Klepac, Adrian Bauman, Katja Siefken, Erica Hinckson, Adewale L. Oyeyemi, Justin Richards, Elena Daniela Salih Khidir, Shigeru Inoue, Shiho Amagasa, Alejandra Jauregui, Marcelo Cozzensa da Silva, I-Min Lee, Melody Ding, Harold W. Kohl III, Ulf Ekelund, Gregory W. Heath, Kenneth E. Powell, Charlie Foster, Aamir Raoof Memon, Abdoulaye Doumbia, Abdul Roof Rather, Abdur Razzaque, Adama Diouf, Adriano Akira Hino, Albertino Damasceno, Alem Deksisa Abebe, Alex Antonio Florindo, Alice Mannocci, Altyn Aringazina, Andrea Backović Juričan, Andrea Poffet, Andrew Decelis, Angela Carlin, Angelica Enescu, Angélica María Ochoa Avilés, Anna Kontsevaya, Annamaria Somhegyi, Anne Vuillemin, Asmaa El Hamdouchi, Asse Amangoua Théodore, Bojan Masanovic, Brigid M. Lynch, Catalina Medina, Cecilia del Campo, Chalchisa Abdeta, Changa Moreways, Chathuranga Ranasinghe, Christina Howitt, Christine Cameron, Danijel Jurakić, David Martinez-Gomez, Dawn Tladi, Debrework Tesfaye Diro, Deepti Adlakha, Dušan Mitić, Duško Bjelica, Elżbieta Biernat, Enock M. Chisati, Estelle Victoria Lambert, Ester Cerin, Eun-Young Lee, Eva-Maria Riso, Felicia Cañete Villalba, Felix Assah, Franjo Lovrić, Gerardo A. Araya-Vargas, Giuseppe La Torre, Gloria Isabel Niño Cruz, Gul Baltaci, Haleama Al Sabbah, Hanna Nalecz, Hilde Liisa Nashandi, Hyuntae Park, Inés Revuelta-Sánchez, Jackline Jema Nusurupia, Jaime Leppe Zamora, Jaroslava Kopcakova, Javier Brazo-Sayavera, Jean-Michel Oppert, Jinlei Nie, John C. Spence, John Stewart Bradley, Jorge Mota, Josef Mitáš, Junshi Chen, Kamilah S Hylton, Karel Fromel, Karen Milton, Katja Borodulin, Keita Amadou Moustapha, Kevin Martinez-Folgar, Lara Nasreddine, Lars Breum Christiansen, Laurent Malisoux, Leapetswe Malete, Lorelie C. Grepo-Jalao, Luciana Zaranza Monteiro, Lyutha K. Al Subhi, Maja Dakskobler, Majed Alnaji, Margarita Claramunt Garro, Maria Hagströmer, Marie H. Murphy, Matthew  Mclaughlin, Mercedes Rivera-Morales, Mickey Scheinowitz, Mimoza Shkodra, Monika Piątkowska, Moushumi Chaudhury, Naif Ziyad Alrashdi, Nanette Mutrie, Niamh Murphy, Norhayati Haji Ahmad, Nour A. Obeidat, Nubia Yaneth Ruiz Gómez, Nucharapon Liangruenrom, Oscar Díaz Arnesto, Oscar Flores-Flores, Oscar Incarbone, Oyun Chimeddamba, Pascal Bovet, Pedro Magalhães, Pekka Jousilahti, Piyawat Katewongsa, Rafael Alexander Leandro Gómez, Rawan Awni Shihab, Reginald Ocansey, Réka Veress, Richard Marine, Rolando Carrizales-Ramos, Saad Younis Saeed, Said El-Ashker, Samuel Green, Sandra Kasoma, Santiago Beretervide, Se-Sergio Baldew, Selby Nichols, Selina Khoo, Seyed Ali Hosseini, Shifalika Goenka, Shima Gholamalishahi, Soewarta Kosen, Sofie Compernolle, Stefan Paul Enescu, Stevo Popovic, Susan Paudel, Susana Andrade, Sylvia Titze, Tamu Davidson, Theogene Dusingizimana, Thomas E. Dorner, Tracy L. Kolbe-Alexander, Tran Thanh Huong, Vanphanom Sychareun, Vera Jarevska-Simovska, Viliami Kulikefu Puloka, Vincent Onywera, Wanda Wendel-Vos, Yannis Dionyssiotis, and Michael Pratt

Background: Physical activity (PA) surveillance, policy, and research efforts need to be periodically appraised to gain insight into national and global capacities for PA promotion. The aim of this paper was to assess the status and trends in PA surveillance, policy, and research in 164 countries. Methods: We used data from the Global Observatory for Physical Activity (GoPA!) 2015 and 2020 surveys. Comprehensive searches were performed for each country to determine the level of development of their PA surveillance, policy, and research, and the findings were verified by the GoPA! Country Contacts. Trends were analyzed based on the data available for both survey years. Results: The global 5-year progress in all 3 indicators was modest, with most countries either improving or staying at the same level. PA surveillance, policy, and research improved or remained at a high level in 48.1%, 40.6%, and 42.1% of the countries, respectively. PA surveillance, policy, and research scores decreased or remained at a low level in 8.3%, 15.8%, and 28.6% of the countries, respectively. The highest capacity for PA promotion was found in Europe, the lowest in Africa and low- and lower-middle-income countries. Although a large percentage of the world’s population benefit from at least some PA policy, surveillance, and research efforts in their countries, 49.6 million people are without PA surveillance, 629.4 million people are without PA policy, and 108.7 million live in countries without any PA research output. A total of 6.3 billion people or 88.2% of the world’s population live in countries where PA promotion capacity should be significantly improved. Conclusion: Despite PA is essential for health, there are large inequalities between countries and world regions in their capacity to promote PA. Coordinated efforts are needed to reduce the inequalities and improve the global capacity for PA promotion.