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˙O_2max), 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˙O_2max (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.

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) (CP_HYP). Recently, a 3-min all-out test (CP_3MIN) 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 CP_HYP, CP_3MIN, 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. CP_HYP was estimated from 5 TTE. CP_3MIN 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 CP_HYP (253 ± 44 W; CoV, 17%) and CP_3MIN (250 ± 51 W; CoV, 20%) (p < 0.05). Limits of agreement (LOA) from Bland–Altman plots between CP_HYP and CP_3MIN (–39 to 31 W), and CP_3MIN and MLSS (–29 to 62 W) were wide, whereas CP_HYP 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 CP_HYP and CP_3MIN were larger than those observed during MLSS rides. Although CP_HYP and CP_3MIN 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.

Purpose: We investigated the cardiovascular individual response to 6 weeks (3×/week) of work-matched within the severe-intensity domain (high-intensity interval training, HIIT) or moderate-intensity domain (moderate-intensity continuous training, MICT). In addition, we analyzed the cardiovascular factors at baseline underlying the response variability.Methods: 42 healthy sedentary participants were randomly assigned to HIIT or MICT. We applied the region of practical equivalence-method for identifying the levels of responders to the maximal oxygen uptake (V̇O_2max) response. For investigating the influence of cardiovascular markers, we trained a Bayesian machine learning model on cardiovascular markers.Results: Despite that HIIT and MICT induced significant increases in V̇O_2max, HIIT had greater improvements than MICT (p < 0.001). Greater variability was observed in MICT, with approximately 50% classified as “non-responder” and “undecided”. 20 “responders”, one “undecided” and no “non-responders” were observed in HIIT. The variability in the ∆V̇O_2maxwas associated with initial cardiorespiratory fitness, arterial stiffness, and left-ventricular (LV) mass and LV end-diastolic diameter in HIIT; whereas, microvascular responsiveness and right-ventricular (RV) excursion velocity showed a significant association in MICT.Conclusion: Our findings highlight the critical influence of exercise-intensity domains and biological variability on the individual V̇O_2maxresponse. The incidence of “non-responders” in MICT was one third of the group; whereas, no “non-responders” were observed in HIIT. The incidence of “responders” was 11 out of 21 participants in MICT, and 20 out of 21 participants in HIIT. The response in HIIT showed associations with baseline fitness, arterial stiffness, and LV-morphology; whereas, it was associated with RV systolic function in MICT.