Central and Peripheral Oxygen Distribution in Two Different Modes of Interval Training

In high-intensity interval training the interval duration can be adjusted to optimize training results in oxygen uptake, cardiac output, and local oxygen supply. This study aimed to compare these variables in two interval trainings (long intervals HIIT3m: 3 min work, 3 min active rest vs. short intervals HIIT30s: 30 s work, 30 s active rest) at the same overall work rate and training duration. 24 participants accomplished both protocols, (work: 80% power output at VO2peak, relief: 85% power output at gas exchange threshold) in randomized order. Spirometry, impedance cardiography, and near-infrared spectroscopy were used to analyze the physiological stress of the cardiopulmonary system and muscle tissue. Although times above gas exchange threshold were shorter in HIIT3m (HIIT3m 1669.9 ± 310.9 s vs. HIIT30s 1769.5 ± 189.0 s, p = 0.034), both protocols evoked similar average fractional utilization of VO2peak (HIIT3m 65.23 ± 4.68% VO2peak vs. HIIT30s 64.39 ± 6.78% VO2peak, p = 0.261). However, HIIT3m resulted in higher cardiovascular responses during the loaded phases (VO2p < 0.001, cardiac output p < 0.001). Local hemodynamics were not different between both protocols. Average physiological responses were not different in both protocols owning to incomplete rests in HIIT30s and large response amplitudes in HIIT3m. Despite lower acute cardiovascular stress in HIIT30s, short submaximal intervals may also trigger microvascular and metabolic adaptions similar to HIIT3m. Therefore, the adaption of interval duration is an important tool to adjust the goals of interval training to the needs of the athlete or patient.

Relationship of critical speed derived from a 10-minute submaximal treadmill test to 5-km and 10-km running performances

It has been shown that the critical speed (CS) predicted from a perceptually self-regulated 10-min submaximal treadmill test (T10) is reliable and closely matches the CS estimated from conventional methods. To assess the relationship between the T10 and 5-km and 10-km running performances, 36 recreational runners (mean SD: age: 32.2 ± 6.2 years, height: 173.2 ± 7.3 cm, weight: 70.9 ± 8.8 kg, V̇O2max: 53.3 ± 6.1 mL.kg-1.min-1) performed a ramp incremental test and two T10 tests (the first as a familiarization trial). Results showed that the T10 CS (3.9 ± 0.44 m.s-1) was significantly correlated with runners’ last 6 months best performances in 5-km (20.3 ± 2.7 min; r = -0.90) and 10-km (42.7 ± 5.7 min; r = -0.91), the V̇O2max (r = 0.75), the speed associated with the gas exchange threshold (vGET: 3.38 ± 0.36 m.s-1; r = 0.76), the speed associated with the second ventilatory threshold (vVT2: 4.15 ± 0.49 m.s-1; r = 0.84), and the speed associated with the V̇O2max (vV̇O2max: 4.78 ± 0.54 m.s-1; r = 0.87). Moreover, 79% and 83% of the variance in 5-km and 10-km performances could be explained solely by the CS predicted from the T10. Results evidenced the strong relationship and practical performance relevance of the T10 CS test. Novelty: • Critical speed derived from a 10-min submaximal treadmill test (T10) is significantly correlated with 5-km and 10-km running performances • The T10 critical speed test may represent a useful tool for assessing running performance capabilities

Spleen Emptying Does Not Correlate With Faster Oxygen Kinetics During a Step-Transition Supine Cycling

This manuscript quantified spleen volume changes and examined the relationship between those changes and V̇O2 kinetics during supine cycling. Ten volunteers (age=22±3), completed three step-transitions from 20 W to their power output at 90% gas exchange threshold. Ultrasonic measurements of the spleen were performed each minute. The largest spleen volume reduction was 105 mL (p=.001). No associations existed between: i) spleen volumes at rest ii) spleen volume changes (%) and τV̇O2p. Larger resting spleen volume and greater emptying do not correlate with a faster τV̇O2p. Novelty: • Greater splenic contractions do not augment τV̇O2p, irrespective of spleen emptying and subsequent erythrocyte release.

Evaluation of the repeatability and reliability of the cross-training specific Fight Gone Bad workout and its relation to aerobic fitness

Cross-training is a high-intensity functional training (HIFT) with multiple workout modalities. Despite the increasing number of studies in HIFT, there is still no validated test to measure its specific performance. It would also be advisable to determine whether selected cross-training workouts can implement a stimulus corresponding to maximize aerobic work. For these reasons, the purpose of our study was to evaluate the repeatability and reliability of Fight Gone Bad (FGB) workout and to assess its relationship with aerobic fitness. Twenty-one cross-training participants (9 females) finished the study protocol which included three two-day measurement sessions separated by 10 days. During each session, participants had their body composition measured, and they performed two exercise tests. The first test was an incremental cycling test to measure aerobic fitness, and the second was a cross-training specific FGB workout performed the next day. Reliability and repeatability were calculated from the three measurements. The total FGB Score (FGBTOTAL) showed excellent reliability (ICC 0.9, SEM 6%). Moreover, FGBTOTAL was strongly correlated with aerobic fitness (i.e., time to exhaustion (Texh, R2 = 0.72), maximal workload (Wmax, R2 = 0.69), time to gas exchange threshold (TGET, R2 = 0.68), and peak oxygen uptake (VO2peak, R2 = 0.59). We also found that agreement between standardized FGB and standardized aerobic performance indices such as Texh, VO2peak, Wmax, maximum heart rate, TGET, and workload at gas exchange threshold was high by the Bland–Altman method. In conclusion, FGB is a reliable test that can be used in order to measure changes in cross-training performance caused by an intervention. Moreover, FGB is strongly correlated to aerobic fitness.

Constant Load Exercise at Gas Exchange Threshold is Accompanied by a Distinct Elevation in Blood Lactate Level

Background: The gas exchange threshold (GET) is determined during incremental exercise (Inc-Ex) testing. It is generally considered to be a safe training intensity, with little or no elevation in blood lactate (BLa). However, actual exercise training at GET is carried out primarily as a constant load exercise (CL-Ex). The dynamics of BLa during CL-Ex at GET have not been studied. This study was conducted particularly among the elderly population. Methods: We recruited 20 healthy elderly individuals (H: age 69.4±6.8 years) and 10 patients with cardiovascular diseases or under medication for cardiovascular risk factors (P: age 73.0±8.8 years). On day 1, we determined GET during symptomatic maximal Inc-Ex. On day 2, CL-Ex at GET intensity was performed for 20 min. Arterialized blood lactate levels were determined. Results: The mean BLa at GET during Inc-Ex was 1.51±0.29 mmol/L in H and 1.78±0.42 mmol/L in P (p < 0.05). During CL-Ex, BLa increased significantly more than that at GET, reaching a steady state level of 2.65±1.56 (H) and 2.53±0.95 (P) mmol/L (ns), with a mean respiratory exchange ratio (RER) of 0.94±0.05 (H) and 0.93±0.05 (P) (ns). Oxygen uptake (VO2) also reached a steady state in all participants. All participants were able to complete CL-Ex with mean perceived exertion ratings (Borg/20) of 11.8±1.3 (H) and 12.2±1.3 (P) (ns). Conclusions: CL-Ex at GET occurred at distinctly increased BLa levels; however, BLa reached a steady state, together with VO2 and RER, indicating that exercise intensity was metabolically moderate.

Acute respiratory muscle unloading improves time-to-exhaustion during moderate- and heavy-intensity cycling in obese adolescent males

Obesity significantly impairs breathing during exercise. The aim was to determine, in male obese adolescents (OB), the effects of acute respiratory muscle unloading, obtained by switching the inspired gas from ambient air (AIR) to a normoxic helium + oxygen gas mixture (HeO2) (AIR → HeO2) during moderate [below gas exchange threshold (GET)] and heavy [above GET] constant work rate cycling. Ten OB [age 16.0 ± 2.0 years (mean ± SD); body mass index (BMI) 38.9 ± 6.1 kg/m2] and ten normal-weight age-matched controls (CTRL) inspired AIR for the entire exercise task, or underwent AIR → HeO2 when they were approaching volitional exhaustion. In OB time to exhaustion (TTE) significantly increased in AIR → HeO2 vs. AIR during moderate [1524 ± 480 s vs. 1308 ± 408 (P = 0.024)] and during heavy [570 ± 306 s vs. 408 ± 150 (P = 0.0154)] exercise. During moderate exercise all CTRL completed the 40-min task. During heavy exercise no significant differences were observed in CTRL for TTE (582 ± 348 s [AIR → HeO2] vs. 588 ± 252 [AIR]). In OB, but not in CTRL, acute unloading of respiratory muscles increased TTE during both moderate- and heavy-exercise. In OB, but not in CTRL, respiratory factors limit exercise tolerance during both moderate and heavy exercise.

Quantifying the Training-Intensity Distribution in Middle-Distance Runners: The Influence of Different Methods of Training-Intensity Quantification

Purpose: To compare the training-intensity distribution (TID) across an 8-week training period in a group of highly trained middle-distance runners employing 3 different methods of training-intensity quantification. Methods: A total of 14 highly trained middle-distance runners performed an incremental treadmill test to exhaustion to determine the heart rate (HR) and running speed corresponding to the ventilatory thresholds (gas-exchange threshold and respiratory-compensation threshold), as well as fixed rating of perceived exertion (RPE) values, which were used to demarcate 3 training-intensity zones. During the following 8 weeks, the TID (total and percentage of time spent in each training zone) of all running training sessions (N = 695) was quantified using continuous running speed, HR monitoring, and RPE. Results: Compared with the running-speed-derived TID (zone 1, 79.9% [7.3%]; zone 2, 5.3% [4.9%]; and zone 3, 14.7% [7.3%]), HR-demarcated TID (zone 1, 79.6% [7.2%]; zone 2, 17.0% [6.3%]; and zone 3, 3.4% [2.0%]) resulted in a substantially higher training time in zone 2 (effect size ± 95% confidence interval: −1.64 ± 0.53; P < .001) and lower training time in zone 3 (−1.59 ± 0.51; P < .001). RPE-derived TID (zone 1, 39.6% [8.4%]; zone 2, 31.9% [8.7%]; and zone 3, 28.5% [11.6%]) reduced time in zone 1 compared with both HR (−5.64 ± 1.40; P < .001) and running speed (−5.69 ± 1.9; P < .001), whereas time in RPE training zones 2 and 3 was substantially higher than both HR- and running-speed-derived zones. Conclusion: The results show that the method of training-intensity quantification substantially affects computation of TID.

Energy Expenditure Equation Choice: Effects on Cycling Efficiency and its Reliability

Purpose: There are several published equations to calculate energy expenditure (EE) from gas exchanges. The authors assessed whether using different EE equations would affect gross efficiency (GE) estimates and their reliability. Methods: Eleven male and 3 female cyclists (age 33 [10] y; height: 178 [11] cm; body mass: 76.0 [15.1] kg; maximal oxygen uptake: 51.4 [5.1] mL·kg−1·min−1; peak power output: 4.69 [0.45] W·kg−1) completed 5 visits to the laboratory on separate occasions. In the first visit, participants completed a maximal ramp test to characterize their physiological profile. In visits 2 to 5, participants performed 4 identical submaximal exercise trials to assess GE and its reliability. Each trial included three 7-minute bouts at 60%, 70%, and 80% of the gas exchange threshold. EE was calculated with 4 equations by Péronnet and Massicotte, Lusk, Brouwer, and Garby and Astrup. Results: All 4 EE equations produced GE estimates that differed from each other (all P < .001). Reliability parameters were only affected when the typical error was expressed in absolute GE units, suggesting a negligible effect—related to the magnitude of GE produced by each EE equation. The mean coefficient of variation for GE across different exercise intensities and calculation methods was 4.2%. Conclusions: Although changing the EE equation does not affect GE reliability, exercise scientists and coaches should be aware that different EE equations produce different GE estimates. Researchers are advised to share their raw data to allow for GE recalculation, enabling comparison between previous and future studies.

Exercise Testing of Adolescents and Young Adults With Sickle Cell Disease: Perceptual Responses and the Gas Exchange Threshold

For individuals with sickle cell disease (SCD), mild to moderate exercise is advised, but self-regulation of these intensities is difficult. To regulate intensity, one SCD recommendation is to stop exercising at the first perception of fatigue. However, perceived effort and affect (how one feels) are perceptual cues that are commonly used to guide exercise intensity. This study (a) examined perceived effort, affect, and fatigue in relation to metabolic state (gas exchange) in adolescents and young adults (AYAs) with SCD, (b) explored guidelines AYAs use to self-regulate exercise, and (c) compared perceived effort and affect at gas exchange threshold (GET) with healthy counterparts. Twenty-two AYAs with SCD completed an incremental cycle test. Perceived effort, affect, and fatigue were assessed every 2 minutes. A mixed-effects linear model was conducted to model changes in effort, affect, and fatigue across time. Mean scores of effort and affect at GET were compared with published data of healthy counterparts. Participants were queried about self-regulation exercise strategies. Findings indicated that both perceived fatigue and effort at GET was lower than expected. Perceived effort was lower ( p < .0001), and perceived affect was significantly higher ( p = .0009) than healthy counterparts. Interviews revealed that most participants (95%) do not stop exercising until fatigue is moderate to severe, and many (73%) do not stop until symptoms are severe (chest tightness, blurry vision). Nurses should review guidelines for safe exercise with AYAs with SCD. Exercise training may be beneficial to AYAs with SCD for learning how to interpret bodily responses to exercise to improve self-regulation.