Cardiac parasympathetic reactivation following exercise : implications for training prescription

The objective of exercise training is to initiate desirable physiological adaptations that ultimately enhance physical work capacity. Optimal training prescription requires an individualized approach, with an appropriate balance of training stimulus and recovery and optimal periodization. Recovery from exercise involves integrated physiological responses. The cardiovascular system plays a fundamental role in facilitating many of these responses, including thermoregulation and delivery/removal of nutrients and waste products. As a marker of cardiovascular recovery, cardiac parasympathetic reactivation following a training session is highly individualized. It appears to parallel the acute/intermediate recovery of the thermoregulatory and vascular systems, as described by the supercompensation theory. The physiological mechanisms underlying cardiac parasympathetic reactivation are not completely understood. However, changes in cardiac autonomic activity may provide a proxy measure of the changes in autonomic input into organs and (by default) the blood flow requirements to restore homeostasis. Metaboreflex stimulation (e.g. muscle and blood acidosis) is likely a key determinant of parasympathetic reactivation in the short term (0–90 min post-exercise), whereas baroreflex stimulation (e.g. exercise-induced changes in plasma volume) probably mediates parasympathetic reactivation in the intermediate term (1–48 h post-exercise). Cardiac parasympathetic reactivation does not appear to coincide with the recovery of all physiological systems (e.g. energy stores or the neuromuscular system). However, this may reflect the limited data currently available on parasympathetic reactivation following strength/resistance-based exercise of variable intensity. In this review, we quantitatively analyse post-exercise cardiac parasympathetic reactivation in athletes and healthy individuals following aerobic exercise, with respect to exercise intensity and duration, and fitness/training status. Our results demonstrate that the time required for complete cardiac autonomic recovery after a single aerobic-based training session is up to 24 h following low-intensity exercise, 24–48 h following threshold-intensity exercise and at least 48 h following high-intensity exercise. Based on limited data, exercise duration is unlikely to be the greatest determinant of cardiac parasympathetic reactivation. Cardiac autonomic recovery occurs more rapidly in individuals with greater aerobic fitness. Our data lend support to the concept that in conjunction with daily training logs, data on cardiac parasympathetic activity are useful for individualizing training programmes. In the final sections of this review, we provide recommendations for structuring training microcycles with reference to cardiac parasympathetic recovery kinetics. Ultimately, coaches should structure training programmes tailored to the unique recovery kinetics of each individual.

Temporal aspects of the VO2 response at the power output associated with VO2peak in well trained cyclists: Implications for interval training prescription

The power output achieved at peak oxygen consumption (VO2 peak) and the time this power can be maintained (i.e., Tmax) have been used in prescribing high-intensity interval training. In this context, the present study examined temporal aspects of the VO2 response to exercise at the cycling power that output well trained cyclists achieve their VO2 peak (i.e., Pmax). Following a progressive exercise test to determine VO2 peak, 43 well trained male cyclists (M age = 25 years, SD = 6; M mass = 75 kg SD = 7; M VO2 peak = 64.8 ml(.)kg(1.)min(-1), SD = 5.2) performed two Tmax tests 1 week apart.1. Values expressed for each participant are means and standard deviations of these two tests. Participants achieved a mean VO2 peak during the Tmax test after 176 s (SD = 40; = 74% of Tmax, SD = 12) and maintained it for 66 s (SD = 39; M = 26% of Tmax, SD = 12). Additionally they obtained mean 95 % of VO2 peak after 147 s (SD = 31; M = 62 % of Tmax, SD = 8) and maintained it for 95 s (SD = 38; M = 38 % of Tmax, SD = 8). These results suggest that 60-70% of Tmax is an appropriate exercise duration for a population of well trained cyclists to attain VO2 peak during exercise at Pmax. However due to intraparticipant variability in the temporal aspects of the VO2 response to exercise at Pmax, future research is needed to examine whether individual high-intensity interval training programs for well trained endurance athletes might best be prescribed according to an athlete's individual VO2 response to exercise at Pmax.

The design and implementation of a novel method for quantifying training loads in elite rowing: the T2minute method

Introduction: A systematic approach to managing the training of elite athletes is supported by accurate training load measurement. However, quantifying the training of elite Australian rowers is complex due to unique challenges: 1) the multi-centre, multi-state structure of the national program; 2) the variety of training undertaken, incorporating rowing-specific and non-specific modalities, with continuous and interval efforts that span the full intensity spectrum; and 3) the limitations of existing quantification methods for capturing total training loads undertaken from varied training. These challenges highlighted a need to create a consistent, location-independent framework for prescribing training in elite rowing, with a capacity to account for varied training. Methods: An in-house proprietary measure (the T2minute method) was developed at the National Rowing Centre of Excellence (NRCE), as a collaborative project between sport scientists and national squad coaches. The design phase was informed by assessments of the existing training measures, and built upon standardised intensity zones established at the Australian Institute of Sport. A common measurement unit was chosen: one T2minute equates to one minute of on-water single scull rowing at T2 intensity (∼60–72% VO2max). Each intensity zone was assigned a weighting factor according to the curvilinear relationship between power output, intensity, and blood lactate response. Each training mode was assigned a weighting factor based on whether coaches perceived it to be “harder” or “easier” than onwater rowing. With coaches’ feedback, the method was refined over a period of five months. The T2minute method was implemented as the core framework for prescribing training for elite Australian rowers throughout the 2009–2012 Olympic cycle. Results: The implementation of the T2minute method successfully established consistency with training prescription and monitoring practices within the NRCE high performance program. The national roll out this method has influenced rowing training methodology at elite and sub-elite levels in Australia. Since implementation, the method has undergone scientific validation. Further research is underway, utilising the method to explore complex relationships between rowers’ training and performance outcomes. Conclusion: The T2minute method is a novel approach that allows rowing coaches and sport scientists to utilise one consistent system to quantify load from varied training. Its implementation represents a considerable achievement in establishing a common framework for managing the training process within a complex organisational structure. This collaborative approach used to develop the T2minute method provides unique insight into the important considerations and practical challenges of applying training science to enhance elite sport performance.

Game and training load differences in elite junior Australian football

Game demands and training practices within team sports such as Australian football (AF) have changed considerably over recent decades, including the requirement of coaching staff to effectively control, manipulate and monitor training and competition loads. The purpose of this investigation was to assess the differences in external and internal physical load measures between game and training in elite junior AF. Twenty five male, adolescent players (mean ±SD: age 17.6 ± 0.5 y) recruited from three elite under 18 AF clubs participated. Global positioning system (GPS), heart rate (HR) and rating of perceived exertion (RPE) data were obtained from 32 game files during four games, and 84 training files during 19 training sessions. Matched-pairs statistics along with Cohen's d effect size and percent difference were used to compare game and training events. Players were exposed to a higher physical load in the game environment, for both external (GPS) and internal (HR, Session-RPE) load parameters, compared to in-season training. Session time (d = 1.23; percent difference = 31.4% (95% confidence intervals = 17.4 - 45.4)), total distance (3.5; 63.5% (17.4 - 45.4)), distance per minute (1.93; 33.0% (25.8 - 40.1)), high speed distance (2.24; 77.3% (60.3 - 94.2)), number of sprints (0.94; 43.6% (18.9 - 68.6)), mean HR (1.83; 14.3% (10.5 - 18.1)), minutes spent above 80% of predicted HRmax (2.65; 103.7% (89.9 - 117.6)) and Session-RPE (1.22; 48.1% (22.1 - 74.1)) were all higher in competition compared to training. While training should not be expected to fully replicate competition, the observed differences suggest that monitoring of physical load in both environments is warranted to allow comparisons and evaluate whether training objectives are being met. Key pointsPhysical loads, including intensity, are typically lower in training compared to competition in junior elite Australian football.Monitoring of player loads in team sports should include both internal and external measures.Selected training drills should look to replicate game intensities, however training is unlikely to match the overall physical demands of competition.

The response of the lactate minimum test to a 12-week swimming training

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Comparison of the Lactate Pro and Analox GM7 blood lactate analysers

van Someren KA, Howatson G, Nunan D, Thatcher R, Shave R., Comparison of the Lactate Pro and Analox GM7 blood lactate analysers, Int J Sports Med. 2005 Oct;26(8):657-61. RAE2008

Comparaison des réponses physiologiques lors d’un exercice incrémental maximal sur vélo immergé et sur terrain sec : aspects biomécaniques, cardiopulmonaires et hémodynamiques.

L’exercice en immersion dans l'eau peut générer des réponses hémodynamiques et cardiorespiratoires différentes à celles de l’exercice sur terraine sec. Cependant, aucune étude n’a comparé ces réponses sur vélo aquatique (VA) à celles sur vélo sur terrain sec (VS) à une même puissance mécanique externe (Pext). À cet égard, le premier travail de cette thèse visait, d’abord, à trouver les équivalences de Pext lors du pédalage sur VA en immersion à la poitrine par rapport au VS au laboratoire, en considérant que cela restait non déterminé à ce jour. Une équation de mécanique des fluides fut utilisée pour calculer la force déployée pour le système de pédalage (pales, leviers, pédales) et des jambes à chaque tour de pédale. Ensuite, cette force totale a été multipliée par la vitesse de pédalage pour estimer la Pext sur VA. Ayant trouvé les équivalences de Pext sur VA et VS, nous nous sommes fixés comme objectif dans la deuxième étude de comparer les réponses hémodynamiques et cardiorespiratoires lors d'un exercice maximal progressif sur VS par rapport au VA à une même Pext. Les résultats ont montré que le VO2 (p<0.0001) et la différence artério-veineuse (C(a-v)O2) (p<0.0001) étaient diminués lors de l’exercice sur VA comparativement à celui sur VS. Parmi les variables hémodynamiques, le volume d’éjection systolique (VES) (p˂0.05) et le débit cardiaque (Qc) (p˂0.05) étaient plus élevés sur VA. En plus, on nota une diminution significative de la fréquence cardiaque (FC) (p˂0.05). Étant donné qu’à une même Pext les réponses physiologiques sont différentes sur VA par rapport à celles sur VS, nous avons effectué une troisième étude pour établir la relation entre les différentes expressions de l'intensité relative de l'exercice (% du VO2max,% de la FCmax,% du VO2 de réserve (% de VO2R) et % de la FC réserve (% FCR)). Les résultats ont démontré que la relation % FCR vs % VO2R était la plus corrélée (régression linéaire) et la plus proche de la ligne d’identité. Ces résultats pourraient aider à mieux prescrire et contrôler l’intensité de l'exercice sur VA pour des sujets sains. Finalement, une dernière étude comparant la réactivation parasympathique après un exercice maximal incrémental effectué sur VA et VS en immersion au niveau de la poitrine a montré que la réactivation parasympathique à court terme était plus prédominante sur VA (i,e. t, delta 10 à delta 60 et T30, p<0.05). Cela suggérait, qu’après un exercice maximal sur VA, la réactivation parasympathique à court terme était accélérée par rapport à celle après l'effort maximal sur VS chez de jeunes sujets sains. En conclusion, nous proposons une méthode de calcul de la puissance mécanique externe sur VA en fonction de la cadence de pédalage. Nous avons démontré que pendant l’exercice sur VA les réponses hémodynamiques et cardiorespiratoires sont différentes de celles sur VS à une même Pext et nous proposons des équations pour le calcul du VO2 dans l’eau ainsi qu’une méthode pour la prescription et le contrôle de l’exercice sur VA. Finalement, la réactivation parasympathique à court terme s’est trouvée accélérée après un effort maximal incrémental sur VA comparativement à celle sur VS.

Quantification of physical load in elite junior Australian football players

Results from this thesis provide insights into the physical loads experienced by the elite junior Australian footballer. The information presented can assist in the facilitation of best practice advice for player management and training prescription through the use of training diaries and GPS TMA and HR device technologies.

Respostas agudas pós-exercício dos níveis de lactato sanguíneo e creatinofosfoquinase de atletas adolescentes

O crescente interesse de crianças e adolescentes por esportes competitivos induz a uma maior preocupação em prescrever treinamentos adequados a essa população específica. O conhecimento do impacto da intensidade de treinamento físico competitivo sobre a saúde de adolescentes são ainda incipientes na literatura científica. Este estudo objetivou investigar as respostas agudas do lactato sanguíneo (Lac) e da creatinofosfoquinase (CPK) após uma sessão de treinamento físico em atletas jovens treinados em diferentes modalidades esportivas. Participaram 43 adolescentes do sexo masculino entre 9 e 17 anos, distribuídos em três grupos: nadadores, tenistas, jogadores de futebol de salão. Os protocolos para cada modalidade seguiram o planejamento normal de uma sessão específica. A dieta no dia anterior à coleta foi padronizada e as 24 horas que precediam a avaliação foram dedicadas ao repouso dos atletas. Foram coletados 5ml de sangue da veia antecubital imediatamente antes da realização da sessão de treinamento, repetindo coleta idêntica, imediatamente após a sessão. Foi obtida a idade óssea para a avaliação da maturação esquelética pelo método de Greulich & Pyle. Análise de variância Kruskal-Wallis e teste U-de Mann-Whitney foram utilizados para comparações entre os grupos. Valores de p < 0,05 foram considerados significantes. em todas as modalidades esportivas estudadas os valores prévios do Lac e da CPK foram inferiores aos valores pós-sessão. Com relação aos três grupos de faixas etárias, tanto para o Lac como para a CPK, os valores na pré-sessão foram inferiores aos obtidos na pós-sessão, assinalando que para o grupo de 9 aos 11 anos, tanto na situação pré como na pós, os valores foram inferiores aos observados nos grupos de idade superior. Os resultados indicaram que a magnitude de aumento da CPK e do Lac foram similares aos valores encontrados na literatura e demonstraram um aumento em função da faixa etária, indicando ser maturação biológica dependente.

Correlações entre os valores de lactato mínimo e potência anaeróbia com a potência crítica e a capacidade de trabalho anaeróbio obtidas por método invasivo e não invasivos

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Aptidão aeróbia e amplitude dos domínios de intensidade de exercício no ciclismo

INTRODUÇÃO: A determinação dos domínios de intensidade de exercício tem importantes implicações na prescrição do treino aeróbio e na elaboração de delineamentos experimentais. OBJETIVO: Analisar os efeitos do nível de aptidão aeróbia sobre a amplitude dos domínios de intensidade de exercício durante o ciclismo. MÉTODOS: Doze ciclistas (CIC), 11 corredores (COR) e oito indivíduos não treinados (NT) foram submetidos aos seguintes protocolos em diferentes dias: 1) teste progressivo para determinação do limiar de lactato (LL), consumo máximo de oxigênio (VO2máx) e sua respectiva intensidade (IVO2máx); 2) três testes de carga constante até a exaustão a 95, 100 e 110% IVO2máx para a determinação da potência crítica (PC); 3) testes até a exaustão para determinar a intensidade superior do domínio severo (Isup). As amplitudes dos domínios (moderado < LL; LL > pesado < PC; PC > severo < Isup) foram expressas como percentual da Isup (VO2). RESULTADOS: A amplitude do domínio moderado foi similar entre CIC (52 ± 8%) e COR (47 ± 4%) e significantemente maior no CIC em relação ao NT (41 ± 7%). O domínio pesado foi significantemente menor no CIC (17 ± 6%) em relação ao COR (27 ± 6%) e NT (27 ± 9%). Em relação ao domínio severo não foram encontradas diferenças significantes entre os CIC (31 ± 7%), COR (26 ± 5%) e NT (31 ± 7%). CONCLUSÃO: O domínio pesado de exercício é mais sensível a mudanças determinadas pelo nível de aptidão aeróbia, existindo a necessidade de que se atenda ao princípio da especificidade do movimento, quando se pretende obter um elevado grau de adaptação fisiológica.

Methodological approach to the first and second lactate threshold in incremental cardiopulmonary exercise testing

Determination of an 'anaerobic threshold' plays an important role in the appreciation of an incremental cardiopulmonary exercise test and describes prominent changes of blood lactate accumulation with increasing workload. Two lactate thresholds are discerned during cardiopulmonary exercise testing and used for physical fitness estimation or training prescription. A multitude of different terms are, however, found in the literature describing the two thresholds. Furthermore, the term 'anaerobic threshold' is synonymously used for both, the 'first' and the 'second' lactate threshold, bearing a great potential of confusion. The aim of this review is therefore to order terms, present threshold concepts, and describe methods for lactate threshold determination using a three-phase model with reference to the historical and physiological background to facilitate the practical application of the term 'anaerobic threshold'.

1RM prediction: a data mining case study

In the last few years there has been a heightened interest in data treatment and analysis with the aim of discovering hidden knowledge and eliciting relationships and patterns within this data. Data mining techniques (also known as Knowledge Discovery in Databases) have been applied over a wide range of fields such as marketing, investment, fraud detection, manufacturing, telecommunications and health. In this study, well-known data mining techniques such as artificial neural networks (ANN), genetic programming (GP), forward selection linear regression (LR) and k-means clustering techniques, are proposed to the health and sports community in order to aid with resistance training prescription. Appropriate resistance training prescription is effective for developing fitness, health and for enhancing general quality of life. Resistance exercise intensity is commonly prescribed as a percent of the one repetition maximum. 1RM, dynamic muscular strength, one repetition maximum or one execution maximum, is operationally defined as the heaviest load that can be moved over a specific range of motion, one time and with correct performance. The safety of the 1RM assessment has been questioned as such an enormous effort may lead to muscular injury. Prediction equations could help to tackle the problem of predicting the 1RM from submaximal loads, in order to avoid or at least, reduce the associated risks. We built different models from data on 30 men who performed up to 5 sets to exhaustion at different percentages of the 1RM in the bench press action, until reaching their actual 1RM. Also, a comparison of different existing prediction equations is carried out. The LR model seems to outperform the ANN and GP models for the 1RM prediction in the range between 1 and 10 repetitions. At 75% of the 1RM some subjects (n = 5) could perform 13 repetitions with proper technique in the bench press action, whilst other subjects (n = 20) performed statistically significant (p < 0:05) more repetitions at 70% than at 75% of their actual 1RM in the bench press action. Rate of perceived exertion (RPE) seems not to be a good predictor for 1RM when all the sets are performed until exhaustion, as no significant differences (p < 0:05) were found in the RPE at 75%, 80% and 90% of the 1RM. Also, years of experience and weekly hours of strength training are better correlated to 1RM (p < 0:05) than body weight. O'Connor et al. 1RM prediction equation seems to arise from the data gathered and seems to be the most accurate 1RM prediction equation from those proposed in literature and used in this study. Epley's 1RM prediction equation is reproduced by means of data simulation from 1RM literature equations. Finally, future lines of research are proposed related to the problem of the 1RM prediction by means of genetic algorithms, neural networks and clustering techniques. RESUMEN En los últimos años ha habido un creciente interés en el tratamiento y análisis de datos con el propósito de descubrir relaciones, patrones y conocimiento oculto en los mismos. Las técnicas de data mining (también llamadas de \Descubrimiento de conocimiento en bases de datos\) se han aplicado consistentemente a lo gran de un gran espectro de áreas como el marketing, inversiones, detección de fraude, producción industrial, telecomunicaciones y salud. En este estudio, técnicas bien conocidas de data mining como las redes neuronales artificiales (ANN), programación genética (GP), regresión lineal con selección hacia adelante (LR) y la técnica de clustering k-means, se proponen a la comunidad del deporte y la salud con el objetivo de ayudar con la prescripción del entrenamiento de fuerza. Una apropiada prescripción de entrenamiento de fuerza es efectiva no solo para mejorar el estado de forma general, sino para mejorar la salud e incrementar la calidad de vida. La intensidad en un ejercicio de fuerza se prescribe generalmente como un porcentaje de la repetición máxima. 1RM, fuerza muscular dinámica, una repetición máxima o una ejecución máxima, se define operacionalmente como la carga máxima que puede ser movida en un rango de movimiento específico, una vez y con una técnica correcta. La seguridad de las pruebas de 1RM ha sido cuestionada debido a que el gran esfuerzo requerido para llevarlas a cabo puede derivar en serias lesiones musculares. Las ecuaciones predictivas pueden ayudar a atajar el problema de la predicción de la 1RM con cargas sub-máximas y son empleadas con el propósito de eliminar o al menos, reducir los riesgos asociados. En este estudio, se construyeron distintos modelos a partir de los datos recogidos de 30 hombres que realizaron hasta 5 series al fallo en el ejercicio press de banca a distintos porcentajes de la 1RM, hasta llegar a su 1RM real. También se muestra una comparación de algunas de las distintas ecuaciones de predicción propuestas con anterioridad. El modelo LR parece superar a los modelos ANN y GP para la predicción de la 1RM entre 1 y 10 repeticiones. Al 75% de la 1RM algunos sujetos (n = 5) pudieron realizar 13 repeticiones con una técnica apropiada en el ejercicio press de banca, mientras que otros (n = 20) realizaron significativamente (p < 0:05) más repeticiones al 70% que al 75% de su 1RM en el press de banca. El ínndice de esfuerzo percibido (RPE) parece no ser un buen predictor del 1RM cuando todas las series se realizan al fallo, puesto que no existen diferencias signifiativas (p < 0:05) en el RPE al 75%, 80% y el 90% de la 1RM. Además, los años de experiencia y las horas semanales dedicadas al entrenamiento de fuerza están más correlacionadas con la 1RM (p < 0:05) que el peso corporal. La ecuación de O'Connor et al. parece surgir de los datos recogidos y parece ser la ecuación de predicción de 1RM más precisa de aquellas propuestas en la literatura y empleadas en este estudio. La ecuación de predicción de la 1RM de Epley es reproducida mediante simulación de datos a partir de algunas ecuaciones de predicción de la 1RM propuestas con anterioridad. Finalmente, se proponen futuras líneas de investigación relacionadas con el problema de la predicción de la 1RM mediante algoritmos genéticos, redes neuronales y técnicas de clustering.

Resistance training for obese, type 2 diabetic adults: A review of the evidence

In both developed and developing countries, increased prevalence of obesity has been strongly associated with increased incidence of type 2 diabetes mellitus (T2DM) in the adult population. Previous research has emphasized the importance of physical activity in the prevention and management of obesity and T2DM, and generic exercise guidelines originally developed for the wider population have been adapted for these specific populations. However, the guidelines traditionally focus on aerobic training without due consideration to other exercise modalities. Recent reviews on resistance training in the T2DM population have not compared this modality with others including aerobic training, or considered the implications of resistance training for individuals suffering from both obesity and T2DM. In short, the optimal mix of exercise modalities in the prescription of exercise has not been identified for it benefits to the metabolic, body composition and muscular health markers common in obesity and T2DM. Similarly, the underlying physical, social and psychological barriers to adopting and maintaining exercise, with the potential to undermine the efficacy of exercise interventions, have not been addressed in earlier reviews. Because it is well established that aerobic exercise has profound effects on obesity and T2DM risk, the purpose of this review was to address the importance of resistance training to obese adults with T2DM.

Exploiting system fluctuations. Differential training in physical prevention and rehabilitation programs for health and exercise

Background: Traditional causal modeling of health interventions tends to be linear in nature and lacks multidisciplinarity. Consequently, strategies for exercise prescription in health maintenance are typically group based and focused on the role of a common optimal health status template toward which all individuals should aspire. ----- ----- Materials and methods: In this paper, we discuss inherent weaknesses of traditional methods and introduce an approach exercise training based on neurobiological system variability. The significance of neurobiological system variability in differential learning and training was highlighted.----- ----- Results: Our theoretical analysis revealed differential training as a method by which neurobiological system variability could be harnessed to facilitate health benefits of exercise training. It was observed that this approach emphasizes the importance of using individualized programs in rehabilitation and exercise, rather than group-based strategies to exercise prescription.----- ----- Conclusion: Research is needed on potential benefits of differential training as an approach to physical rehabilitation and exercise prescription that could counteract psychological and physical effects of disease and illness in subelite populations. For example, enhancing the complexity and variability of movement patterns in exercise prescription programs might alleviate effects of depression in nonathletic populations and physical effects of repetitive strain injuries experienced by athletes in elite and developing sport programs.