Determining lactate breakpoint and aerobic capacity using non-invasive field tests with minimal equipment is critical for athletes and coaches to assess training progress as well as to set accurate training intensity targets. PURPOSE: To calculate lactate breakpoint (LB) power output versus a predicted LB power and to compare measured oxygen consumption versus predicted maximum oxygen consumption. METHODS: 18 healthy athletes (3 women; age 18-71) completed two consecutive assessments. For the first test, we collected HR and venous blood lactate during 8, 4-minute stages of progressive power output. All blood lactate, HR and power values were then processed using a commercial analysis program to determine power at LB using the log-log exponential Dmax method (LLED). For the second test, we measured O2 consumption and CO2 production during warm-up, 1-minute incremental maximal ramp protocol, 6-minute recovery, and 12-minute HR constrained effort. Predicted VO2 max was calculated by averaging the 2.5 and 5.0 minute mean maximal power outputs achieved during the ramp protocol by 80 watts/liter of O2. Predicted LB power output was calculated via linear regression of power versus HR from the warm-up, constrained effort and ramp. We set 90.5% of the peak 1-minute HR as the intercept for the HR associated with predicted LB power. RESULTS: The predicted LB mean value of 231 watts was not statistically different from the mean LLED LB value of 233 watts (p = .65) with a high correlation between the two methods (R2 = .919). The predicted VO2 max value (3.93 L/min) was highly correlated with the measured VO2 max value (3.95 L/min, R2 = .923) and not statistically different (p = 0.72). CONCLUSION: Power output at lactate breakpoint and maximum oxygen consumption can be accurately predicted using a novel cycling assessment with heart rate and power output.

A well-established predictor of cycling performance is VO2 max, which is highly correlated with Maximal Aerobic Power (MAP). The best predictor of uphill cycling performance is watts per kilogram of body weight (WKG). Previous studies have shown power output is greater when cycling on an incline. However, there is minimal research investigating the performance impacts of time spent training on an incline. PURPOSE: To determine how training with dynamic incline (DI) impacts performance. Our hypothesis is that all participants who train twice a week with a structured plan will improve MAP but participants who train with DI will show greater WKG improvements during a simulated climb test. METHODS: Twenty-two active adults (5 women, mean age 45 + 8 years) were recruited based on access to a standardized at-home indoor trainer with DI. Participants were free to maintain their normal activity levels during the 6-week study. Weeks 1 and 6 consisted of a graded exercise test (GXT) at 0% incline and a 4.8 km simulated climb (SC) at an average 7% incline. Average intervention training time for weeks 2-5 was 1 hour 43 minutes. Participants were divided into TILT and FLAT groups. TILT completed all intervals at an 8% DI (47% of the total training time), while FLAT completed all training at a static incline of 0%. Power targets in both groups were based on GXT results. RESULTS: In agreement with our hypothesis, MAP was greater in both groups (TILT = 3.2%, FLAT = 2.3%) during the GXT after structured training (p < 0.05), but WKG was only greater (2.6%) in TILT during the SC (TILT, p < 0.05). CONCLUSION: Regular structured training can improve standard measures of cycling performance on a 0% incline regardless of training time at different inclines. However, spending significant time training with an incline >7% is necessary to see improvements in simulated climbing.

High variability in blood glucose is associated with earlier onset of disease in healthy adults without diabetes. More specifically, prospective studies demonstrate that higher glucose variability is associated with an increased risk of cardiovascular disease, Alzheimer's disease, frailty, cardiovascular death, and cancer death compared to lower glucose variability. Similarly, other prospective research illustrates that these fluctuations induce endothelial dysfunction and may accelerate the development of atherosclerosis. Unfortunately, there are limited data on glucose concentrations in individuals without diabetes. PURPOSE: To correlate lifestyle variables- exercise, nutrition, sleep, emotions- with glucose variability. METHODS: Thirty-five healthy, active adults (8 women, mean age 47 + 8 years) wore a continuous glucose monitor for two weeks, maintained their typical routines, and recorded the data. They also completed each planned exercise session with a heart rate chest transmitter. The study participants logged these training sessions (total time, intensity zones, perceived exertion) as well as daily meals (time of day, macronutrient grams), sleep (total time, subjective quality), and emotions (stress, motivation, fatigue). RESULTS: Daily glucose variability was significantly correlated (n = 532) with number of cardio sessions per day (ρ = -0.19, p < 0.0001), protein grams within the first meal of the day (ρ = -0.14, p < 0.0001), percent fat per day (ρ = -0.12, p < 0.0001), and subjective fatigue (ρ = 0.14, p < 0.0001). CONCLUSIONS: Our data demonstrate that there are multiple lifestyle factors that can minimize glucose variability and thereby potentially lower future disease risk. With respect to planned exercise, a greater number of independent cardio sessions is more impactful than a singular session for a longer duration. In terms of nutrition, greater protein grams at breakfast and a higher daily fat percentage lessen glucose variability. And finally, reducing fatigue through lifestyle choices may diminish detrimental fluctuations.

Dynamic (DYN) strength training is a critical component of performance; improving endurance and power. But skill level, movement restrictions, and supervision are all relevant considerations for safety before including dynamic strength training into a plan. Multiple studies show isometric strength training (ISO) to be an effective alternative. PURPOSE: To compare endurance (time to exhaustion, TTE) and power (maximal movement distance, MMD) after either ISO or DYN training. We hypothesize that ISO and DYN training will not differ in endurance (TTE) or power (MMD). METHODS: Eight adults (3 women, mean age 40 + 19 years) not actively strength training, completed upper and lower body assessments pre and post pilot intervention. We measured endurance with TTE for a push-up hold and single leg wall sit. Power was measured with MMD for a seated medicine ball push and vertical jump. After the initial assessment, participants were randomly assigned to the ISO or DYN group. The training plan consisted of 3 days/wk for 6 weeks, scaled for each individual using a linear progression based on their initial assessment results. The ISO group performed push up and single leg wall sit static holds while the DYN group performed push up and lunge repetitions using a metronome. Groups were matched for time under tension. RESULTS: In agreement with our hypothesis, endurance was significantly greater for both groups after training. Specifically, TTE was greater during upper (33%) and lower (34%) body post assessments (all values, p < 0.05). The ISO group showed greater gains in cumulative TTE (62% vs. 13%, p < 0.05) and max TTE (36% vs. 27%, p < 0.05) for the single leg wall sit. However, in contrast to our hypothesis, there was no significant difference in power for either the ISO or DYN group. CONCLUSION: ISO training is a practical alternative to DYN and can be used in place of, or in conjunction with, DYN training as a safe and effective way to improve endurance.

The American College of Sports Medicine recognizes the benefits of high intensity interval training (HIIT) for the improvement of health, fitness, and performance. They also support the recent surge in research focused on HIIT to refine our understanding of the relationship between frequency, duration, and intensity. The current literature provides valuable information towards this goal correlated with demographics (age, sex) utilizing various singular intensity assessments (functional threshold, maximal aerobic, anaerobic). However, it is optimal to utilize an integrated protocol with diverse participants to provide more concrete guidelines specific to various training targets. PURPOSE: To evaluate the connection between demographics and intensity to provide accurate training parameters for maximal gains in health, fitness, and performance. We hypothesize that the correlation between peak cycling power over varying durations is significantly different depending on sex and age. METHODS: Seven thousand, one hundred and sixty-two individuals (3581 men, 3581 women matched for age) completed a single session, multidimensional indoor cycling test to assess neuromuscular power (NMP; 2, 7-second sprints), maximal aerobic power (MAP; 5-minute maximal effort), functional threshold power (FTP; 20-minute maximal effort) and anaerobic power (AP; 1-minute maximal effort). RESULTS: In agreement with our hypothesis, there was a statistically significant effect for sex (male, female; p-value < 0.0001) and age category (20-29, 30-39, 40-49, 50-59, 60-69 years; p < 0.0001) as well as a significant interaction between sex and age category (p < 0.0001) for each of the four power intensities (watts/kg). More specifically, power significantly decreased with age and the decline was greatest at the highest intensity (NMP) for both sexes. Also, the decrease in power with advancing age was significantly less in women compared to men apart from NMP. CONCLUSIONS: Our data demonstrates that all peak power durations progressively decline with age and women maintain their aerobic power more than men. Due to these significant interactions, high intensity exercise targets can only be prescribed accurately with multidimensional assessment tests.