Real World Interstitial Glucose Profiles of a Large Cohort of Physically Active Men and Women.

The use of continuous glucose monitors (CGMs) in individuals living without diabetes is increasing. The purpose of this study was to profile various CGM metrics around nutritional intake, sleep and exercise in a large cohort of physically active men and women living without any known metabolic disease diagnosis to better understand the normative glycemic response to these common stimuli. A total of 12,504 physically active adults (age 40 ± 11 years, BMI 23.8 ± 3.6 kg/m2; 23% self-identified as women) wore a real-time CGM (Abbott Libre Sense Sport Glucose Biosensor, Abbott, USA) and used a smartphone application (Supersapiens Inc., Atlanta, GA, USA) to log meals, sleep and exercise activities. A total of >1 M exercise events and 274,344 meal events were analyzed. A majority of participants (85%) presented an overall (24 h) average glucose profile between 90 and 110 mg/dL, with the highest glucose levels associated with meals and exercise and the lowest glucose levels associated with sleep. Men had higher mean 24 h glucose levels than women (24 h-men: 100 ± 11 mg/dL, women: 96 ± 10 mg/dL). During exercise, the % time above >140 mg/dL was 10.3 ± 16.7%, while the % time <70 mg/dL was 11.9 ± 11.6%, with the remaining % within the so-called glycemic tight target range (70-140 mg/dL). Average glycemia was also lower for females during exercise and sleep events (p < 0.001). Overall, we see small differences in glucose trends during activity and sleep in females as compared to males and higher levels of both TAR and TBR when these active individuals are undertaking or competing in endurance exercise training and/or competitive events.

Association between pre-exercise food ingestion timing and reactive hypoglycemia: Insights from a large database of continuous glucose monitoring data.

Using a large database of continuous glucose monitoring (CGM) data, this study aimed to gain insights into the association between pre-exercise food ingestion timing and reactive hypoglycemia. A group of 6,761 users self-reported 48,799 pre-exercise food ingestion events and logged minute-by-minute CGM, which was used to detect reactive hypoglycemia (<70 mg/dL) in the first 30 min of exercise. A linear and a non-linear binomial logistic regression model was used to investigate the association between food ingestion timing and the probability of experiencing reactive hypoglycemia. An analysis of variance was conducted to compare the predictive ability of the models. On average, reactive hypoglycemia was detected in 8.34 ± 3.04% of the total events, with <15% of individuals experiencing hypoglycemia in >20% of their events. The majority of the reactive hypoglycemia events were found with pre-exercise food timing between ∼30 and ∼90 min, with a peak at ∼60 min. The superior accuracy (62.05 vs 45.1%) and F-score (0.75 vs 0.59) of the non-linear vs the linear model were statistically superior (P < 0.0001). These results support the notion of an unfavourable 30-to-90 min pre-exercise food ingestion time window which can significantly impact the likelihood of reactive hypoglycemia in some individuals.

Large datasets of self-reported continuous glucose monitoring and food events are used here for the first time to get insights into reactive hypoglycemia, a condition often regarded as negative for endurance performance eventsUsing a binomial non-linear logistic regression model, the association between pre-exercise food ingestion timing and reactive hypoglycemia revealed the presence of an unfavourable window, when reactive hypoglycemia is more likely to occur.Results confirm an individual predisposition to reactive hypoglycemia and, for 8 in 100 individuals, the pre-exercise food ingestion timing can meaningfully impact the likelihood of experiencing reactive hypoglycemia.

Glycaemic patterns of male professional athletes with type 1 diabetes during exercise, recovery and sleep: Retrospective, observational study over an entire competitive season.

AIMS: To analyse glycaemic patterns of professional athletes with type 1 diabetes during a competitive season. MATERIALS AND METHODS: We analysed continuous glucose monitoring data of 12 professional male cyclists with type 1 diabetes during exercise, recovery and sleep on days with competitive exercise (CE) and non-competitive exercise (NCE). We assessed whether differences exist between CE and NCE days and analysed associations between exercise and dysglycaemia. RESULTS: The mean glycated haemoglobin was 50 ± 5 mmol/mol (6.7 ± 0.5%). The athletes cycled on 280.8 ± 28.1 days (entire season 332.6 ± 18.8 days). Overall, time in range (3.9-10 mmol/L) was 70.0 ± 13.7%, time in hypoglycaemia (<3.9 mmol/L) was 6.4 ± 4.7% and time in hyperglycaemia (>10 mmol/L) was 23.6 ± 12.5%. During the nights of NCE days, athletes spent 10.1 ± 7.4% of time in hypoglycaemia, particularly after exercise in the endurance zones. The CE days were characterized by a higher time in hyperglycaemia compared with NCE days (25.2 ± 12.5% vs. 22.2 ± 12.1%, p = .012). This was driven by the CE phase, where time in range dropped to 60.4 ± 13.0% and time in hyperglycaemia was elevated (38.5 ± 12.9%). Mean glucose was higher during CE compared with NCE sessions (9.6 ± 0.9 mmol/L vs. 7.8 ± 1.1 mmol/L, p < .001). The probability of hyperglycaemia during exercise was particularly increased with longer duration, higher intensity and higher variability of exercise. CONCLUSIONS: The analysis of glycaemic patterns of professional endurance athletes revealed that overall glycaemia was generally within targets. For further improvement, athletes, team staff and caregivers may focus on hyperglycaemia during competitions and nocturnal hypoglycaemia after NCE.

An evaluation of the role of the exercise training dose for changes in exercise capacity following a standard cardiac rehabilitation program.

BACKGROUND: To retrospectively characterize and compare the dose of exercise training (ET) within a large cohort of patients demonstrating different levels of improvement in exercise capacity following a cardiac rehabilitation (CR) program. METHODS: A total of 2310 patients who completed a 12-week, center-based, guidelines-informed CR program between January 2018 and December 2019 were included in the analysis. Peak metabolic equivalents (METpeak) were determined pre- and post-CR during which total duration (ET time) and intensity [percent of heart rate peak (%HRpeak)] of supervised ET were also obtained. Training responsiveness was quantified on the basis of changes in METpeak from pre- to post-CR. A cluster analysis was performed to identity clusters demonstrating discrete levels of responsiveness (i.e., negative, low, moderate, high, and very-high). These were compared for several baseline and ET-derived variables which were also included in a multivariable linear regression model. RESULTS: At pre-CR, baseline METpeak was progressively lower with greater training responsiveness (F(4,2305) = 44.2, P < 0.01, η2p = 0.71). Likewise, average training duration (F(4,2305) = 10.7 P < 0.01, η2p = 0.02) and %HRpeak (F(4,2305) = 25.1 P < 0.01, η2p = 0.042) quantified during onsite ET sessions were progressively greater with greater training responsiveness. The multivariable linear regression model confirmed that baseline METpeak, training duration and intensity during ET, BMI, and age (P < 0.001) were significant predictors of METpeak post-CR. CONCLUSIONS: Along with baseline METpeak, delta BMI, and age, the dose of ET (i.e., training duration and intensity) predicts METpeak at the conclusion of CR. A re-evaluation of current approaches for exercise intensity prescription is recommended to extend the benefits of completing CR to all patients.

Nutritional intake when cycling under racing and training conditions in professional male cyclists with type 1 diabetes.

This study sought to detail and compare the in-ride nutritional practices of a group of professional cyclists with type 1 diabetes (T1D) under training and racing conditions. We observed seven male professional road cyclists with T1D (Age: 28 ± 4 years, HbA1c: 6.4 ± 0.4% [46 ± 4 mmol.mol-1], VO2max: 73.9 ± 4.3 ml.kg -1.min-1) during pre-season training and during a Union Cycliste Internationale multi-stage road cycling race (Tour of Slovenia). In-ride nutritional, interstitial glucose, and performance variables were quantified and compared between the two events.    The in-ride energy intake was similar between training and racing conditions     (p = 0.909), with carbohydrates being the major source of fuel in both events during exercise at a rate of 41.9 ± 6.8 g.h-1 and 45.4 ± 15.5 g.h-1 (p = 0.548), respectively. Protein consumption was higher during training (2.6 ± 0.6 g.h-1) than race rides (1.9 ± 0.9 g.h-1; p = 0.051).   A similar amount of time was spent within the euglycaemic range (≥70-≤180 mg.dL-1): training 77.1 ± 32.8% vs racing 73.4 ± 3.9%; p = 0.818. These data provide new information on the in-ride nutritional intake in professional cyclists with T1D during different stages of the competitive season.

Assessment of bike handling during cycling individual time trials with a novel analytical technique adapted from motorcycle racing.

A methodology to study bike handling of cyclists during individual time trials (ITT) is presented. Lateral and longitudinal accelerations were estimated from GPS data of professional cyclists (n = 53) racing in two ITT of different length and technical content. Acceleration points were plotted on a plot (g-g diagram) and they were enclosed in an ellipse. A correlation analysis was conducted between the area of the ellipse and the final ITT ranking. It was hypothesised that a larger area was associated with a better performance. An analytical model for the bike-cyclist system dynamics was used to conduct a parametric analysis on the influence of riding position on the shape of the g-g diagram. A moderate (n = 27, r = -0.40, p = 0.038) and a very large (n = 26, r = -0.83, p < 0.0001) association were found between the area of the enclosing ellipse and the final ranking in the two ITT. Interestingly, this association was larger in the shorter race with higher technical content. The analytical model suggested that maximal decelerations are highly influenced by the cycling position, road slope and speed. This investigation, for the first time, explores a novel methodology that can provide insights into bike handling, a large unexplored area of cycling performance.

Use and Perception of Telemedicine in People with Type 1 Diabetes During the COVID-19 Pandemic: A 1-Year Follow-Up.

Background: In the spring of 2020, our research group circulated a worldwide survey with the aim of gathering information on the use and perception of telemedicine in people living with type 1 diabetes at the start of the COVID-19 pandemic. The data suggested that a large number of respondents had rapidly adopted to telemedicine, as in-person visits were not possible, and that this was perceived positively by many. In this study, we conducted a 1-year follow-up to investigate changes in opinions and experiences to telemedicine over the past year of the pandemic. Methods: An anonymous questionnaire was distributed through social media (Twitter, Facebook, and Instagram) between May 9 and May 15, 2021, using an open-access web-based platform (SurveyMonkey.com). The survey was identical to that used in the original study, covering questions relating to the use and perception of telemedicine, diabetes treatment and control, and medical supplies during the COVID-19 pandemic. The questionnaire was available in English, Spanish, German, French, and Italian. We compared the results from the two surveys descriptively and statistically, results were stratified according to age, gender, and HbA1c. Results: There were 531 survey responses from 40 countries (Europe 54%, North America 36%, South America 2%, and Africa and Asia 2%). A large percentage of respondents (67%) reported meeting with their health care provider remotely since the beginning of the pandemic, a significant increase compared with the 28% in the 2020 survey (P < 0.001). Eighty-three percent of respondents found remote appointments to be somewhat-to-extremely useful, similar to the 86% satisfaction rate in the previous survey (P = 0.061). Remote appointments were most frequently undertaken through telephone (50%) and video call (45%), which are significant changes compared with those in 2020 (72% and 28%, respectively, P < 0.001). Forty-five percent of respondents in 2021 were likely to consider remote appointments instead of in-person appointments in the future-being significantly lower than the 75% in the initial survey (P < 0.001)-whereas 37% indicated they would not. The majority of respondents (84%) reported no issues in their access to diabetes supplies and medication over the past year. Conclusions: This study showed that the use of telemedicine in the form of remote appointments increased during the COVID-19 pandemic in people living with type 1 diabetes, with high levels of satisfaction. However, a remarkable decline took place in the past year in the proportion of patients stating a willingness to continue with remote appointments beyond the pandemic. It seems that a personalized approach is needed since a substantial proportion of respondents in this follow-up still indicated a preference for in-person diabetes care, hence the use of telemedicine should be considered on an individual basis.

Post-exercise recovery for the endurance athlete with type 1 diabetes: a consensus statement.

There has been substantial progress in the knowledge of exercise and type 1 diabetes, with the development of guidelines for optimal glucose management. In addition, an increasing number of people living with type 1 diabetes are pushing their physical limits to compete at the highest level of sport. However, the post-exercise recovery routine, particularly with a focus on sporting performance, has received little attention within the scientific literature, with most of the focus being placed on insulin or nutritional adaptations to manage glycaemia before and during the exercise bout. The post-exercise recovery period presents an opportunity for maximising training adaption and recovery, and the clinical management of glycaemia through the rest of the day and overnight. The absence of clear guidance for the post-exercise period means that people with type 1 diabetes should either develop their own recovery strategies on the basis of individual trial and error, or adhere to guidelines that have been developed for people without diabetes. This Review provides an up-to-date consensus on post-exercise recovery and glucose management for individuals living with type 1 diabetes. We aim to: (1) outline the principles and time course of post-exercise recovery, highlighting the implications and challenges for endurance athletes living with type 1 diabetes; (2) provide an overview of potential strategies for post-exercise recovery that could be used by athletes with type 1 diabetes to optimise recovery and adaptation, alongside improved glycaemic monitoring and management; and (3) highlight the potential for technology to ease the burden of managing glycaemia in the post-exercise recovery period.

Evaluating the Accuracy of Using Fixed Ranges of METs to Categorize Exertional Intensity in a Heterogeneous Group of Healthy Individuals: Implications for Cardiorespiratory Fitness and Health Outcomes.

BACKGROUND: Appropriate quantification of exertional intensity remains elusive. OBJECTIVE: To compare, in a large and heterogeneous cohort of healthy females and males, the commonly used intensity classification system (i.e., light, moderate, vigorous, near-maximal) based on fixed ranges of metabolic equivalents (METs) to an individualized schema based on the exercise intensity domains (i.e., moderate, heavy, severe). METHODS: A heterogenous sample of 565 individuals (females 165; males 400; age range 18-83 years old) were included in the study. Individuals performed a ramp-incremental exercise test from which gas exchange threshold (GET), respiratory compensation point (RCP) and maximum oxygen uptake (VO2max) were determined to build the exercise intensity domain schema (moderate = METs ≤ GET; heavy = METs > GET but ≤ RCP; severe = METs > RCP) for each individual. Pearson's chi-square tests over contingency tables were used to evaluate frequency distribution within intensity domains at each MET value. A multi-level regression model was performed to identify predictors of the amplitude of the exercise intensity domains. RESULTS: A critical discrepancy existed between the confines of the exercise intensity domains and the commonly used fixed MET classification system. Overall, the upper limit of the moderate-intensity domain ranged between 2 and 13 METs and of the heavy-intensity domain between 3 and 18 METs, whereas the severe-intensity domain included METs from 4 onward. CONCLUSIONS: Findings show that the common practice of assigning fixed values of METs to relative categories of intensity risks misclassifications of the physiological stress imposed by exercise and physical activity. These misclassifications can lead to erroneous interpretations of the dose-response relationship of exercise and physical activity.

Use and perception of telemedicine in people with type 1 diabetes during the COVID-19 pandemic-Results of a global survey.

Introduction: The COVID-19 pandemic has forced rapid reconsideration as to the way in which health care is delivered. One potential means to provide care while avoiding unnecessary person-to-person contact is to offer remote services (telemedicine). This study aimed to (1) gather real-time information on the use and perception of telemedicine in people living with type 1 diabetes and (2) assess the challenges, such as restricted access to health care and/or medical supplies. Methods: An anonymous questionnaire was widely distributed between 24 March and 5 May 2020 using an open-access web-based platform. Data were analysed descriptively, and results were stratified according to age, sex and HbA1c. Results: There were 7477 survey responses from individuals in 89 countries. Globally, 30% reported that the pandemic had affected their healthcare access due to cancelled physical appointments with their healthcare providers. Thirty-two per cent reported no fundamental change in their medical follow-up during this period, with 9% stating that no personal contact was established with their doctors over the duration of the study. Twenty-eight per cent received remote care through telephone (72%) or video-calls (28%). Of these, 86% found remote appointments useful and 75% plan to have remote appointments in the future. Glucose control, indicated by HbA1c, was positively associated with positive perception of telemedicine. In males, 45% of respondents with an HbA1c > 9% rated telemedicine not useful compared to those with lower HbA1c, while 20% of females with an HbA1c > 9% rated it not useful (χ2 = 14.2, P = .0016). Conclusion: Remote appointments have largely been perceived as positive in people with type 1 diabetes with the majority (75%) stating that they would consider remote appointments beyond the pandemic. Age and level of education do not appear to influence perception of telemedicine, whereas poor glucose control, particularly in males, seems to negatively affect perception.

Glycemic responses to strenuous training in male professional cyclists with type 1 diabetes: a prospective observational study.

INTRODUCTION: This prospective observational study sought to establish the glycemic, physiological and dietary demands of strenuous exercise training as part of a 9-day performance camp in a professional cycling team with type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: Sixteen male professional cyclists with T1D on multiple daily injections (age: 27±4 years; duration of T1D: 11±5 years; body mass index: 22±2 kg/m2; glycated hemoglobin: 7%±1% (50±6 mmol/mol); maximum rate of oxygen consumption: 73±4 mL/kg/min) performed road cycle sessions (50%-90% of the anaerobic threshold, duration 1-6 hours) over 9 consecutive days. Glycemic (Dexcom G6), nutrition and physiological data were collected throughout. Glycemic data were stratified into predefined glycemic ranges and mapped alongside exercise physiology and nutritional parameters, as well as split into daytime and night-time phases for comparative analysis. Data were assessed by means of analysis of variance and paired t-tests. A p value of ≤0.05 (two-tailed) was statistically significant. RESULTS: Higher levels of antecedent hypoglycemia in the nocturnal hours were associated with greater time spent in next-day hypoglycemia overall (p=0.003) and during exercise (p=0.019). Occurrence of nocturnal hypoglycemia was associated with over three times the risk of next-day hypoglycemia (p<0.001) and a twofold risk of low glucose during cycling (p<0.001). Moreover, there was trend for a greater amount of time spent in mild hypoglycemia during the night compared with daytime hours (p=0.080). CONCLUSION: The higher prevalence of nocturnal hypoglycemia was associated with an increased risk of next-day hypoglycemia, which extended to cycle training sessions. These data highlight the potential need for additional prebed carbohydrates and/or insulin dose reduction strategies around exercise training in professional cyclists with T1D. TRIAL REGISTRATION NUMBER: DRKS00019923.

Evaluation of Factors Related to Glycemic Management in Professional Cyclists With Type 1 Diabetes Over a 7-Day Stage Race.

OBJECTIVE: To investigate factors related to glycemic management among members of a professional cycling team with type 1 diabetes over a 7-day Union Cycliste Internationale World Tour stage race. RESEARCH DESIGN AND METHODS: An observational evaluation of possible factors related to glycemic management and performance in six male professional cyclists with type 1 diabetes (HbA1c 6.4 ± 0.6%) during the 2019 Tour of California. RESULTS: In-ride time spent in euglycemia (3.9-10.0 mmol/L glucose) was 63 ± 11%, with a low percentage of time spent in level 1 (3.0-3.9 mmol/L; 0 ± 1% of time) and level 2 (<3.0 mmol/L; 0 ± 0% of time) hypoglycemia over the 7-day race. Riders spent 25 ± 9% of time in level 1 (10.1-13.9 mmol/L) and 11 ± 9% in level 2 (>13.9 mmol/L) hyperglycemia during races. Bolus insulin use was uncommon during races, despite high carbohydrate intake (76 ± 23 g ⋅ h-1). Overnight, the riders spent progressively more time in hypoglycemia from day 1 (6 ± 12% in level 1 and 0 ± 0% in level 2) to day 7 (12 ± 12% in level 1 and 2 ± 4% in level 2) (χ2[1] > 4.78, P < 0.05). CONCLUSIONS: Professional cyclists with type 1 diabetes have excellent in-race glycemia, but significant hypoglycemia during recovery overnight, throughout a 7-day stage race.

Diabetes Technology and Exercise.

Advances in technologies such as glucose monitors, exercise wearables, closed-loop systems, and various smartphone applications are helping many people with diabetes to be more physically active. These technologies are designed to overcome the challenges associated with exercise duration, mode, relative intensity, and absolute intensity, all of which affect glucose homeostasis in people living with diabetes. At present, optimal use of these technologies depends largely on motivation, competence, and adherence to daily diabetes care requirements. This article discusses recent technologies designed to help patients with diabetes to be more physically active, while also trying to improve glucose control around exercise.

A Critical Evaluation of Current Methods for Exercise Prescription in Women and Men.

Common methods to prescribe exercise intensity are based on fixed percentages of maximum rate of oxygen uptake (V˙O2max), peak work rate (WRpeak), maximal HR (HRmax). However, it is unknown how these methods compare to the current models to partition the exercise intensity spectrum. PURPOSE: Thus, the aim of this study was to compare contemporary gold-standard approaches for exercise prescription based on fixed percentages of maximum values to the well-established, but underutilized, "domain" schema of exercise intensity. METHODS: One hundred individuals participated in the study (women, 46; men, 54). A cardiopulmonary ramp-incremental test was performed to assess V˙O2max, WRpeak, HRmax, and the lactate threshold (LT), and submaximal constant-work rate trials of 30-min duration to determine the maximal lactate steady-state (MLSS). The LT and MLSS were used to partition the intensity spectrum for each individual in three domains of intensity: moderate, heavy, and severe. RESULTS: V˙O2max in women and men was 3.06 ± 0.41 L·min and 4.10 ± 0.56 L·min, respectively. Lactate threshold and MLSS occurred at a greater %V˙O2max and %HRmax in women compared with men (P < 0.05). The large ranges in both sexes at which LT and MLSS occurred on the basis of %V˙O2max (LT, 45%-74%; MLSS, 69%-96%), %WRpeak (LT, 23%-57%; MLSS, 44%-71%), and %HRmax (LT, 60%-90%; MLSS, 75%-97%) elicited large variability in the number of individuals distributed in each domain at the fixed-percentages examined. CONCLUSIONS: Contemporary gold-standard methods for exercise prescription based on fixed-percentages of maximum values conform poorly to exercise intensity domains and thus do not adequately control the metabolic stimulus.

An equation to predict the maximal lactate steady state from ramp-incremental exercise test data in cycling.

OBJECTIVES: The maximal lactate steady state (MLSS) represents the highest exercise intensity at which an elevated blood lactate concentration ([Lac]b) is stabilized above resting values. MLSS quantifies the boundary between the heavy-to-very-heavy intensity domains but its determination is not widely performed due to the number of trials required. DESIGN: This study aimed to: (i) develop a mathematical equation capable of predicting MLSS using variables measured during a single ramp-incremental cycling test and (ii) test the accuracy of the optimized mathematical equation. METHODS: The predictive MLSS equation was determined by stepwise backward regression analysis of twelve independent variables measured in sixty individuals who had previously performed ramp-incremental exercise and in whom MLSS was known (MLSSobs). Next, twenty-nine different individuals were prospectively recruited to test the accuracy of the equation. These participants performed ramp-incremental exercise to exhaustion and two-to-three 30-min constant-power output cycling bouts with [Lac]b sampled at regular intervals for determination of MLSSobs. Predicted MLSS (MLSSpred) and MLSSobs in both phases of the study were compared by paired t-test, major-axis regression and Bland-Altman analysis. RESULTS: The predictor variables of MLSS were: respiratory compensation point (Wkg-1), peak oxygen uptake (V˙O2peak) (mlkg-1min-1) and body mass (kg). MLSSpred was highly correlated with MLSSobs (r=0.93; p<0.01). When this equation was tested on the independent group, MLSSpred was not different from MLSSobs (234±43 vs. 234±44W; SEE 4.8W; r=0.99; p<0.01). CONCLUSIONS: These data support the validity of the predictive MLSS equation. We advocate its use as a time-efficient alternative to traditional MLSS testing in cycling.

Critical power: How different protocols and models affect its determination.

In cycling, critical power (CP) and work above CP (W') can be estimated through linear and nonlinear models. Despite the concept of CP representing the upper boundary of sustainable exercise, overestimations may be made as the models possess inherent limitations and the protocol design is not always appropriate. OBJECTIVES: To measure and compare CP and W' through the exponential (CPexp), 3-parameter hyperbolic (CP3-hyp), 2-parameter hyperbolic (CP2-hyp), linear (CPlinear), and linear 1/time (CP1/time) models, using different combinations of TTE trials of different durations (approximately 1-20min). DESIGN: Repeated measures. METHODS: Thirteen healthy young cyclists (26±3years; 69.0±9.2kg; 174±10cm; 60.4±5.9mLkg-1min-1) performed five TTE trials on separate days. CP and W' were modeled using two, three, four, and/or five trials. All models were compared against a criterion method (CP3-hyp with five trials; confirmed using the leaving-one-out cross-validation analysis) using smallest worthwhile change (SWC) and concordance correlation coefficient (CCC) analyses. RESULTS: CP was considerably overestimated when only trials lasting less than 10min were included, independent of the mathematical model used. Following CCC analysis, a number of alternative methods were able to predict our criterion method with almost a perfect agreement. However, the application of other common approaches resulted in an overestimation of CP and underestimation of W', typically these methods only included TTE trials lasting less than 12min. CONCLUSIONS: Estimations from CP3-hyp were found to be the most accurate, independently of TTE range. Models that include two trials between 12 and 20min provide good agreement with the criterion method (for both CP and W').

Player's success prediction in rugby union: From youth performance to senior level placing.

OBJECTIVES: The study questioned if and to what extent specific anthropometric and functional characteristics measured in youth draft camps, can accurately predict subsequent career progression in rugby union. DESIGN: Original research. METHODS: Anthropometric and functional characteristics of 531 male players (U16) were retrospectively analysed in relation to senior level team representation at age 21-24. Players were classified as International (Int: National team and international clubs) or National (Nat: 1st, 2nd and other divisions and dropout). Multivariate analysis of variance (one-way MANOVA) tested differences between Int and Nat, along a combination of anthropometric (body mass, height, body fat, fat-free mass) and functional variables (SJ, CMJ, t15m, t30m, VO2max). A discriminant function (DF) was determined to predict group assignment based on the linear combination of variables that best discriminate groups. Correct level assignment was expressed as % hit rate. RESULTS: A combination of anthropometric and functional characteristics reflects future level assignment (Int vs. Nat). Players' success can be accurately predicted (hit rate=81% and 77% for Int and Nat respectively) by a DF that combines anthropometric and functional variables as measured at ∼15 years of age, percent body fat and speed being the most influential predictors of group stratification. CONCLUSIONS: Within a group of 15 year-olds with exceptional physical characteristics, future players' success can be predicted using a linear combination of anthropometric and functional variables, among which a lower percent body fat and higher speed over a 15m sprint provide the most important predictors of the highest career success.

Identification of critical intensity from a single lactate measure during a 3-min, submaximal cycle-ergometer test.

We tested the hypothesis that critical intensity in cycling can be determined from a single delta blood lactate in the third minute of a submaximal cycle ergometer trial. Fourteen healthy young men performed four to six constant-power-output trials on a cycle ergometer to the limit of tolerance. Critical intensity was calculated via a linear model and subsequently validated. Lactate was measured at baseline and at 3 min from exercise onset. Delta lactate was the difference between these measures. Based on individual trials, we obtained the delta lactate-% validated critical intensity relationship and thereafter an estimate of critical intensity was computed. Validated and estimated critical intensity were compared by effects sizes, paired-sample t-test and Bland-Altman analysis. Delta lactate was a linear function of the intensity of exercise, expressed as % validated critical intensity (R2 = 0.89). Estimated critical intensity was not different from (d = 0.03, P = 0.98) and highly correlated with (R2 = 0.88) validated critical intensity. The bias between measures was 0.03 W (≠0) with a precision of 7 W. The results suggest that critical intensity in cycling can be accurately and precisely determined from delta lactate during a sub-maximal trial and so provides a practical and valid alternative to direct determination.

Vascular responsiveness determined by near-infrared spectroscopy measures of oxygen saturation.

Vascular impairments at the macro- and microcirculatory levels are associated with increased risk for cardiovascular disease. Flow-mediated dilation (FMD) is currently the most widely used method for non-invasive assessment of vascular endothelial function. Recently, near-infrared spectroscopy (NIRS)-derived measures of tissue oxygen saturation (StO2) have been used to characterize the dynamic response of local tissue perfusion to a brief period of ischaemia. The purpose of the present study was to establish correlations between the reperfusion rate of StO2 and FMD. Ultrasound-derived FMD was quantified after 5 min of distal cuff occlusion of the popliteal artery in 20 healthy young men (26 ± 3 years old). Triplicate measurements of end-diastolic arterial diameter were made every 15 s after cuff release, and FMD response was calculated as the greatest percentage change in diameter from baseline (%FMD). The StO2 was measured using NIRS throughout the duration of each test. Two consecutive FMD tests were performed, separated by 30 min of rest, and were averaged for %FMD and StO2. The %FMD was significantly correlated with the reperfusion slope of StO2 after cuff release (slope 2 StO2; r = 0.63, P = 0.003). In conclusion, the present study established a correlation between slope 2 StO2 and %FMD in healthy young men. These data suggest that NIRS-derived slope 2 StO2 can be used as a measure of vascular endothelial function.