An undergraduate laboratory to study exercise thresholds.

In exercise physiology, laboratory components help students connect theoretical concepts to their own exercise experiences and introduce them to data collection, analysis, and interpretation using classic techniques. Most courses include a lab protocol that involves exhaustive incremental exercise during which expired gas volumes and concentrations of oxygen and carbon dioxide are measured. During these protocols, there are characteristic alterations in gas exchange and ventilatory profiles that give rise to two exercise thresholds: the gas exchange threshold (GET) and the respiratory compensation point (RCP). The ability to explain why these thresholds occur and how they are identified is fundamental to learning in exercise physiology and requisite to the understanding of core concepts including exercise intensity, prescription, and performance. Proper identification of GET and RCP requires the assembly of eight data plots. In the past, the burden of time and expertise required to process and prepare data for interpretation has been a source of frustration. In addition, students often express a desire for more opportunities to practice/refine their skills. The objective of this article is to share a blended laboratory model that features the "Exercise Thresholds App," a free online resource that eliminates postprocessing of data and provides a bank of profiles on which end-users can practice threshold identification skills with immediate feedback. In addition to including prelaboratory and postlaboratory recommendations, we present student accounts of understanding, engagement, and satisfaction following completion of the laboratory experience and introduce a new quiz feature of the app to assist instructors with evaluating student learning.NEW & NOTEWORTHY We present a laboratory to study exercise thresholds from gas exchange and ventilatory measures that features the "Exercise Thresholds App," a free online resource that eliminates postprocessing of data and provides a bank of profiles on which end-users can practice threshold identification skills. In addition to including prelaboratory and postlaboratory recommendations, we present student accounts of understanding, engagement, and satisfaction and introduce a new quiz feature of the app to assist instructors with evaluating learning.

Faster V̇O2 kinetics after priming exercises of different duration but same fatigue.

This study compared the responses of two priming exercises of similar fatigue on the adjustment of the oxygen uptake time constant (τV̇O2) in cycling. Ten healthy young adults (25 ± 3 yr) performed: three step transitions from a 20-W baseline to the power output (PO) below the gas exchange threshold (MOD, MODPRE); a 3-min bout (P3MIN) at 90% of peak PO (POpeak), followed by MOD (MOD3MIN); and a 6-min bout (P6MIN) at 80% of POpeak, followed by MOD (MOD6MIN). The O2 supply-to-O2 demand ([HHb]/V̇O2) ratio was calculated for MODPRE, MOD3MIN, and MOD6MIN. Neuromuscular fatigue was measured isometrically pre- and post-priming exercise. Reductions in maximal voluntary contraction (-29 ± 6 vs -34 ± 7%) and high-frequency doublet amplitude (-48 ± 13 vs -43 ± 11%) were not significantly different between P3MIN vs P6MIN, suggesting similar fatigue. τV̇O2 for MOD3MIN and MOD6MIN were similar, being ~25% smaller than MODPRE. The [HHb]/V̇O2 ratio was significantly greater in MODPRE (1.13 ± 0.12) compared to MOD3MIN (1.02 ± 0.04) and MOD6MIN (1.02 ± 0.04). This study showed that priming exercise of shorter duration and higher intensity, was sufficient to accelerate V̇O2 kinetics similarly to that observed subsequent to P6MIN when the muscle fatigue was similar.

The relationship between oxygen uptake kinetics and neuromuscular fatigue in high-intensity cycling exercise.

PURPOSE: In theory, a slow oxygen uptake ([Formula: see text]) kinetics leads to a greater accumulation of anaerobic by-products, which can, in turn, induce more neuromuscular fatigue. However, the existence of this relationship has never been tested. METHODS: After two sessions to measure peak [Formula: see text], peak power output (POpeak), and [Formula: see text] kinetics responses in the unfatigued state (τ [Formula: see text] MOD), 10 healthy young adults performed a 6-min cycling bout at 80% POpeak (INT6-min). [Formula: see text] kinetics responses were also measured during INT6-min. Neuromuscular fatigue was measured isometrically pre- and post-INT6-min (immediately post- and 15-s post-INT6-min) with an innovative cycle ergometer. RESULTS: Maximal voluntary contraction (MVC) force, high-frequency doublet amplitude, and the ratio of low- to high-frequency doublet amplitudes decreased by 34 ± 7, 43 ± 11, and 31 ± 13%, respectively (all P < 0.01). A significant Spearman's rank correlation was observed between the change in low-frequency doublet force (ΔDb10) immediately after INT6-min and both τ [Formula: see text] MOD and τ [Formula: see text] INT6-min (ρ = -0.68 and ρ = -0.67, both P < 0.05). When considering the largest responses from the two neuromuscular evaluations post-INT6-min, significant correlations were also found between τ [Formula: see text] MOD and ΔDb10 (ρ = -0.74; P < 0.05) and between τ[Formula: see text] INT6-min and both ΔDb10 and low-frequency fatigue (ρ = -0.70 and ρ = -0.66; both P < 0.05). CONCLUSION: The present results suggest that subjects with slow [Formula: see text] kinetics experience more peripheral fatigue, in particular more excitation-contraction coupling failure, likely due to a greater accumulation of protons and/or inorganic phosphates.

Skeletal Muscle MicroRNA Patterns in Response to a Single Bout of Exercise in Females: Biomarkers for Subsequent Training Adaptation?

microRNAs (miRs) have been proposed as a promising new class of biomarkers in the context of training adaptation. Using microarray analysis, we studied skeletal muscle miR patterns in sedentary young healthy females (n = 6) before and after a single submaximal bout of endurance exercise ('reference training'). Subsequently, participants were subjected to a structured training program, consisting of six weeks of moderate-intensity continuous endurance training (MICT) and six weeks of high-intensity interval training (HIIT) in randomized order. In vastus lateralis muscle, we found significant downregulation of myomiRs, specifically miR-1, 133a-3p, and -5p, -133b, and -499a-5p. Similarly, exercise-associated miRs-23a-3p, -378a-5p, -128-3p, -21-5p, -107, -27a-3p, -126-3p, and -152-3p were significantly downregulated, whereas miR-23a-5p was upregulated. Furthermore, in an untargeted approach for differential expression in response to acute exercise, we identified n = 35 miRs that were downregulated and n = 20 miRs that were upregulated by factor 4.5 or more. Remarkably, KEGG pathway analysis indicated central involvement of this set of miRs in fatty acid metabolism. To reproduce these data in a larger cohort of all-female subjects (n = 29), qPCR analysis was carried out on n = 15 miRs selected from the microarray, which confirmed their differential expression. Furthermore, the acute response, i.e., the difference between miR concentrations before and after the reference training, was correlated with changes in maximum oxygen uptake (V̇O2max) in response to the training program. Here, we found that miRs-199a-3p and -19b-3p might be suitable acute-response candidates that correlate with individual degrees of training adaptation in females.

Identification of Non-Invasive Exercise Thresholds: Methods, Strategies, and an Online App.

During incremental exercise, two thresholds may be identified from standard gas exchange and ventilatory measurements. The first signifies the onset of blood lactate accumulation (the lactate threshold, LT) and the second the onset of metabolic acidosis (the respiratory compensation point, RCP). The ability to explain why these thresholds occur and how they are identified, non-invasively, from pulmonary gas exchange and ventilatory variables is fundamental to the field of exercise physiology and requisite to the understanding of core concepts including exercise intensity, assessment, prescription, and performance. This review is intended as a unique and comprehensive theoretical and practical resource for instructors, clinicians, researchers, lab technicians, and students at both undergraduate and graduate levels to facilitate the teaching, comprehension, and proper non-invasive identification of exercise thresholds. Specific objectives are to: (1) explain the underlying physiology that produces the LT and RCP; (2) introduce the classic non-invasive measurements by which these thresholds are identified by connecting variable profiles to underlying physiological behaviour; (3) discuss common issues that can obscure threshold detection and strategies to identify and mitigate these challenges; and (4) introduce an online resource to facilitate learning and standard practices. Specific examples of exercise gas exchange and ventilatory data are provided throughout to illustrate these concepts and a novel online application tool designed specifically to identify the estimated LT (θLT) and RCP is introduced. This application is a unique platform for learners to practice skills on real exercise data and for anyone to analyze incremental exercise data for the purpose of identifying θLT and RCP.

miRNAs as markers for the development of individualized training regimens: A pilot study.

Small, non-coding RNAs (microRNAs) have been shown to regulate gene expression in response to exercise in various tissues and organs, thus possibly coordinating their adaptive response. Thus, it is likely that differential microRNA expression might be one of the factors that are responsible for different training responses of different individuals. Consequently, determining microRNA patterns might be a promising approach toward the development of individualized training strategies. However, little is known on (1) microRNA patterns and their regulation by different exercise regimens and (2) possible correlations between these patterns and individual training adaptation. Here, we present microarray data on skeletal muscle microRNA patterns in six young, female subjects before and after six weeks of either moderate-intensity continuous or high-intensity interval training on a bicycle ergometer. Our data show that n = 36 different microRNA species were regulated more than twofold in this cohort (n = 28 upregulated and n = 8 downregulated). In addition, we correlated baseline microRNA patterns with individual changes in VO2 max and identified some specific microRNAs that might be promising candidates for further testing and evaluation in the future, which might eventually lead to the establishment of microRNA marker panels that will allow individual recommendations for specific exercise regimens.

Does Becoming Fit Mean Feeling (f)it? A Comparison of Physiological and Experiential Fitness Data From the iReAct Study.

Regular exercise fosters fitness-enhancing benefits. We assume that exercise interventions become successful and sustainable if physiological benefits of exercise are also subjectively perceivable. The goal of this study was to examine how young inactive adults physiologically respond to an exercise intervention and how those responses are subjectively experienced. Furthermore, we aimed to assess whether the sequence of two distinct endurance-based exercise modes has an impact on physiological and subjectively experienced physical fitness. Thirty-one young inactive adults were assessed for this substudy of the larger iReAct study. Participants were randomly assigned to a high-intensity interval training (HIIT) or a moderate-intensity continuous training (MICT) group for 6 weeks and subsequently switched groups for a second training period. Physiological fitness data was collected at baseline, follow-up I, and follow-up II using a graded cardiopulmonary exercise test. Subjectively reconstructed (i.e., retrospective constructions) experiences relating to physical fitness were assessed at follow-up II using a biographical mapping method. A repeated-measures one-way ANOVA on each training group was performed to see whether physiological and subjectively experienced fitness differed across training periods. The rate of change between all variables was calculated for the first and the second training period in order to compare the agreement between physiological and subjective fitness improvements. Participants increased their fitness across the intervention period both physiologically and subjectively. However, the rate of change depended on the sequence of the two training modes. While VO2max increased significantly in both training periods in the MICT-HIIT sequence, a significant increase in VO2max in the HIIT-MICT sequence was only observed in the HIIT period. Participants similarly perceived those increases subjectively in their exercise-related physical fitness, although they experienced a significant decrease in the second period of the HIIT-MICT sequence. For subjectively perceived physical fitness relating to everyday activities, significant increases were only observed for the first period of the MICT-HIIT sequence. Young inactive adults can improve both their physiological and their subjectively perceived fitness through regular exercise. However, exercise modes and their sequence can make a substantial difference regarding measured and perceived physical fitness. Additionally, despite a favorable tendency toward HIIT over MICT, inter- and intra-individual variability, particularly in the subjective experiences of fitness, reiterates the necessity of individualized approaches to exercise.

Effectiveness of HIIE versus MICT in Improving Cardiometabolic Risk Factors in Health and Disease: A Meta-analysis.

PURPOSE: We aimed to investigate differences between high-intensity interval exercise (HIIE, including high-intensity interval training and sprint interval training) and moderate-intensity continuous training (MICT) on physical fitness, body composition, blood pressure, blood lipids, insulin and glucose metabolism, inflammation, and endothelial function. METHODS: Differences between HIIE and MICT were summarized using a random-effects meta-analysis on the effect size (Cohen's d). A meta-regression was conducted using the following subgroups: population, age, training duration, men ratio, exercise type, baseline values (clinical relevant ranges), and type of HIIE. Studies were included if at least one of the following outcomes were reported: maximal oxygen uptake (V˙O2max), flow-mediated dilation (FMD), body mass index (BMI), body mass, percent body fat, systolic and diastolic blood pressure, high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglycerides, total cholesterol, C-reactive protein (CRP), fasting glucose and insulin, glycated hemoglobin (HbA1c), and insulin resistance (HOMA-IR). A total of 55 studies were included. RESULTS: Overall, HIIE was superior to MICT in improving V˙O2max (d = 0.40, P < 0.001) and FMD (d = 0.54, P < 0.05). Oppositely, MICT was superior to HIIE in improving HbA1c (d = -0.27, P < 0.05). No differences were observed in BMI (d = -0.02), body mass (d = -0.05), percent body fat (d = 0.04), systolic blood pressure (d = -0.04), diastolic blood pressure (d = 0.03), HDL (d = -0.05), LDL (d = 0.08), triglycerides (d = 0.03), total cholesterol (d = 0.14), CRP (d = -0.11), fasting insulin (d = 0.02), fasting glucose (d = 0.02), and HOMA-IR (d = -0.04). Moderator analyses indicated that the difference between HIIE and MICT was affected by different subgroups. CONCLUSION: Overall, HIIE showed to be more effective in improving cardiovascular health and cardiorespiratory fitness, whereas MICT was superior in improving long-term glucose metabolism. In the process of personalized training counseling, health-enhancing effects of exercise training may be improved by considering the individual risk profiles.

Different Endurance Exercise Modalities, Different Affective Response: A Within-Subject Study.

Affect experienced during an exercise session is supposed to predict future exercise behavior. However, empirical evidence reveals high variability in affective response to different exercise modalities. Thus, the purpose of the present study was to compare acute affective response and its variation during three different endurance exercise modalities: (a) moderate-intensity continuous exercise (MICE), (b) vigorous-intensity continuous exercise (VICE), and (c) high-intensity interval exercise (HIIE). Using the dual-mode theory as a theoretical framework, cognitive and interoceptive factors were considered as potential predictors of in-task affective response. In a within-subject design, 40 insufficiently active healthy participants (aged from 20 to 40 years) attended three sessions per exercise modality on a cycle ergometer. Affective valence (measured by the Feeling Scale), two cognitive factors (perceived competence and awareness of interoceptive cues), and one interoceptive factor (heart rate) were assessed before, during, and after each exercise session. Mixed models with three levels (subject, exercise session, and time point) revealed more positive affective valence during MICE compared with VICE (p < 0.001) and HIIE (p < 0.01), while there was no significant difference between the latter two. Levene's test results showed the highest variability of in-task affective valence during VICE (ps < 0.01). Regarding the course across the session, MICE was associated with a constant slight increase in affective valence from pre- to post-exercise (p < 0.05), whereas VICE and HIIE caused a decline in pleasure, followed by an affective rebound immediately after exercise termination (ps < 0.01). The highest importance of cognitive and interoceptive factors for in-task affective valence was observed in VICE (ps < 0.05). The current findings provide support for the tenets of the dual-mode theory, however, indicating that there may be differences in the affect-intensity relationship between continuous and interval exercise. In conclusion, the study results concerning previously insufficiently active individuals extend the knowledge of how exercise can positively shape affective well-being depending on exercise modality and psychophysiological influences. This knowledge enables public health practitioners to design more individualized activity recommendations, thereby improving the subjective experience of exercise.

Responders and non-responders to aerobic exercise training: beyond the evaluation of V˙O2max.

The evaluation of the maximal oxygen uptake ( V˙O2max ) following exercise training is the classical assessment of training effectiveness. Research has lacked in investigating whether individuals that do not respond to the training intervention ( V˙O2max ), also do not improve in other health-related parameters. We aimed to investigate the cardiovascular and metabolic adaptations (i.e., performance, body composition, blood pressure, vascular function, fasting blood markers, and resting cardiac function and morphology) to exercise training among participants who showed different levels of V˙O2max responsiveness. Healthy sedentary participants engaged in a 6-week exercise training program, three times a week. Our results showed that responders had a greater increase in peak power output, second lactate threshold, and microvascular responsiveness, whereas non-responders had a greater increase in cycling efficiency. No statistical differences were observed in body composition, blood pressure, fasting blood parameters, and resting cardiac adaptations. In conclusion, our study showed, for the first time, that in addition to the differences in the V˙O2max , a greater increase in microvascular responsiveness in responders compared to non-responders was observed. Additionally, responders and non-responders did not show differences in the adaptations on metabolic parameters. There is an increasing need for personalized training prescription, depending on the target clinical outcome.

Sex differences in a chronometric mental rotation test with cube figures: a behavioral, electroencephalography, and eye-tracking pilot study.

In chronometric mental rotation tasks, sex differences are widely discussed. Most studies find men to be more skilled in mental rotation than women, which can be explained by the holistic strategy that they use to rotate stimuli. Women are believed to apply a piecemeal strategy. So far, there have been no studies investigating this phenomenon using eye-tacking methods in combination with electroencephalography (EEG) analysis: the present study compared behavioral responses, EEG activity, and eye movements of 15 men and 15 women while solving a three-dimensional chronometric mental rotation test. The behavioral analysis showed neither differences in reaction time nor in the accuracy rate between men and women. The EEG data showed a higher right activation on parietal electrodes for women and the eye-tracking results indicated a longer fixation in a higher number of areas of interest at 0° for women. Men and women are likely to possess different perceptual (visual search) and decision-making mechanisms, but similar mental rotation processes. Furthermore, men presented a longer visual search processing, characterized by the greater saccade latency of 0°-135°. Generally, this study could be considered a pilot study to investigate sex differences in mental rotation tasks while combining eye-tracking and EEG methods.

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').

The near-infrared spectroscopy-derived deoxygenated haemoglobin breaking-point is a repeatable measure that demarcates exercise intensity domains.

OBJECTIVES: A breaking-point in the near-infrared spectroscopy (NIRS)-derived deoxygenated haemoglobin ([HHb]) profile towards the end of a ramp incremental (RI) cycling test has been associated to the respiratory compensation point (RCP). Despite the physiological value of this measure, its repeatability remains unknown. The aim was to examine the repeatability of the [HHb] breaking-point ([HHb]BP) and its association to RCP during a RI cycling test. DESIGN: A repeated measures design was performed on 11 males (30.5±8.4 year; 76.5±8.4kg) and 4 females (30.5±5.9 year; 61.9±4.4 Kg). METHODS: Gas exchange and NIRS [HHb] data were collected during RI tests performed on two different days separated by 48h. The [HHb]BP and the RCP were determined and compared for each trial. RESULTS: The [HHb]BP and the respiratory compensation point (RCP) occurred at the same VO2 in test 1 and test 2 ([HHb]BP: 3.49±0.52Lmin-1 test 1; 3.48±0.45Lmin-1 test 2; RCP: 3.38±0.40Lmin-1 test 1; 3.38±0.44Lmin-1 test 2) (P>0.05). The VO2 associated with the [HHb]BP and the VO2 at RCP were not significantly different from each other either in test 1 as well as in test 2 (P>0.05). Neither test 1 nor test 2 showed significant mean average error between the VO2 at the [HHb]BP and RCP using Bland & Altman plots. CONCLUSIONS: The [HHb]BP is a repeatable measure that consistently occurs towards the end of a RI test. The association between the [HHb]BP and the RCP reinforces the idea that these parameters may share similar mechanistic basis.

Critical power testing or self-selected cycling: Which one is the best predictor of maximal metabolic steady-state?

Critical power (CP) demarcates the boundary between heavy and very heavy exercise intensity domains, and therefore, the power output (PO) that can be sustained at the maximal metabolic steady-state during constant-PO exercise (i.e., maximal lactate steady-state (MLSS)). However, the estimated CP does not always reflect a sustainable intensity of exercise, where blood lactate concentration ([La]) and oxygen uptake (V˙O2) reach a plateau. OBJECTIVES: To test cyclists' ability to predict their highest PO associated with metabolic steady-state based on their own perception of effort. DESIGN: Repeated measures. METHODS: Thirteen healthy young cyclists (26±3years; 69.0±9.2kg; 174±10cm) were tested. Five time-to-exhaustion trials were used to derive CP based on a 2-parameter hyperbolic model (CPHYP). Participants performed two 30-min rides at a self-selected PO that they considered their highest sustainable exercise intensity (CPSELF). Additionally, MLSS was determined as the highest PO at which variation in [La] ≤1.0mmolL-1 between the 10th and 30th min was observed during a 30-min ride. RESULTS: Mean PO at CPSELF (233±42W) was similar (p>0.05) to MLSS (233±41W), whereas CPHYP (253±44W) consistently overestimated (p<0.05) the PO associated to metabolic steady-state. The limits of agreement (LOA) between MLSS and CPSELF were -20 to +20W (bias=0W, p>0.05), whereas the LOA between CPHYP and CPSELF were -40 to 0W (bias=-20W, p<0.05). CPSELF and MLSS presented similar (p>0.05) metabolic response (i.e., V˙O2, [La], and HR). CONCLUSIONS: Compared to CPHYP, CPSELF may offer a more precise approach to predict the constant-PO associated with maximal physiological steady-state.

Can measures of critical power precisely estimate the maximal metabolic steady-state?

Critical power (CP) conceptually represents the highest power output (PO) at physiological steady-state. In cycling exercise, CP is traditionally derived from the hyperbolic relationship of ∼5 time-to-exhaustion trials (TTE) (CPHYP). Recently, a 3-min all-out test (CP3MIN) has been proposed for estimation of CP as well the maximal lactate steady-state (MLSS). The aim of this study was to compare the POs derived from CPHYP, CP3MIN, and MLSS, and the oxygen uptake and blood lactate concentrations at MLSS. Thirteen healthy young subjects (age, 26 ± 3years; mass, 69.0 ± 9.2 kg; height, 174 ± 10 cm; maximal oxygen uptake, 60.4 ± 5.9 mL·kg-1·min-1) were tested. CPHYP was estimated from 5 TTE. CP3MIN was calculated as the mean PO during the last 30 s of a 3-min all-out test. MLSS was the highest PO during a 30-min ride where the variation in blood lactate concentration was ≤ 1.0 mmol·L-1 during the last 20 min. PO at MLSS (233 ± 41 W; coefficient of variation (CoV), 18%) was lower than CPHYP (253 ± 44 W; CoV, 17%) and CP3MIN (250 ± 51 W; CoV, 20%) (p < 0.05). Limits of agreement (LOA) from Bland-Altman plots between CPHYP and CP3MIN (-39 to 31 W), and CP3MIN and MLSS (-29 to 62 W) were wide, whereas CPHYP and MLSS presented the narrowest LOA (-7 to 48 W). MLSS yielded not only the maximum PO of stable blood lactate concentration, but also stable oxygen uptake. In conclusion, POs associated to CPHYP and CP3MIN were larger than those observed during MLSS rides. Although CPHYP and CP3MIN were not different, the wide LOA between these 2 tests and the discrepancy with PO at MLSS questions the ability of CP measures to determine the maximal physiological steady-state.