High-intensity interval training (HIIT), high-intensity continuous training (HICT), moderate-intensity interval training

The gap

Evidence profile

Sourced from the future work and recommendations and abstract of the source papers, classified as general, spanning 2 journals.

Supporting evidence — 3 representative gaps

• Validity and agreement of the 30–15 Intermittent Fitness Test for estimating aerobic and performance-related parameters: a systematic review and meta-analysis (2026) · BMC Sports Science, Medicine and Rehabilitation · doi

  While the present meta-analysis supports the 30–15IFT as a valid and practical field-based tool for estimating VO₂max, HRmax, and MRV in athletes, several meth- odological limitations of the included studies warrant attention. According to QUADAS-2 and QUADAS-C assessments, only Paravlic et al. (2022) demonstrated low risk of bias in participant selection, whereas most other studies showed unclear or moderate risk in key domains such as index test and reference standard application. PEDro scores ranged from 3 to 5, reflecting moderate methodological quality, with common limitations includ- ing lack of blinding and incomplete reporting of alloca- tion procedures. Future research should prioritize improving study qual- ity by ensuring rigorous participant selection, blinding of assessors, and standardized administration of both index and reference tests. Larger and more diverse samples, combined with direct physiological measurements (e.g., gas analyzer VO₂max) alongside predictive equations Ghazzagh et al. BMC Sports Science, Medicine and Rehabilitation (2026) 18:228 Page 18 of 20 tailored to intermittent running protocols, could reduce heterogeneity and improve the precision of findings. The findings of the present meta-analysis provide a unique opportunity to refine VO₂max prediction models based on the 30–15IFT. Specifically, the observed pooled bias (1.88 ml·kg⁻¹·min⁻¹) and 95% limits of agreement (− 5.28 to + 9.04 ml·kg⁻¹·min⁻¹) can inform the develop- ment of more precise, population- and protocol-specific predictive equations. For example, regression-based models can incorporate the pooled bias as a group-level correction term, such that predicted VO₂max values are adjusted upward by the mean bias to account for systematic underestimation. Additionally, the limits of agreement can be used to quantify individual-level vari- ability, either by providing prediction intervals for single measurements or by incorporating this variance as a ran- dom effect in mixed-effects models. This approach allows future predictive equations to systematically correct for both average bias and individual variability, thereby enhancing the accuracy and applicability of VO₂max esti- mations derived from the 30–15IFT. In practice, incorporating bias as a fixed effect and the limits of agreement as a random effect or as uncertainty intervals would allow researchers and practitioners to generate VO₂max estimates that are both more accu- rate and individualized. For instance, consider a semi- professional soccer player performing the 30–15IFT. Using a standard predictive approach, the test might esti- mate their VO₂max at 52 ml·kg⁻¹·min⁻¹. Our meta-anal- ysis indicates that the 30–15IFT tends to overestimate VO₂max by 1.88 ml·kg⁻¹·min⁻¹ compared with treadmill- based ITRT. By incorporating this pooled bias as a fixed effect in a regression or mixed-effects model, the esti- mate can be adjusted downward to 50.12 ml·kg⁻¹·min⁻¹. Additionally, applying the 95% limits of agreement (− 5.28 to + 9.04 ml·kg⁻¹·min⁻¹) as a random effect or uncertainty interval allows for individualized prediction ranges—for example, 44.84 to 59.16 ml·kg⁻¹·min⁻¹ for this player. Incorporating further athlete-specific covariates, such as intermittent running experience or fitness level, would refine predictions even more, enhancing the prac- tical utility of the 30–15IFT for individualized training prescription. Moreover, combining these adjustments with athlete- specific factors—such as fitness level, sport-specific adaptations, and intermittent running proficiency—could further enhance predictive accuracy. Such refined model- ing approaches would strengthen the ecological validity of the 30–15IFT and improve its utility for athlete moni- toring and individualized training prescription.

  generalfuture work
  Keywords: bias predictive effect based limits agreement specific level incorporating individualized meta equations intermittent running prediction
• Effects of high-intensity interval training versus continuous aerobic training on body weight, body mass index, and body fat percentage in youth with overweight or obesity: a systematic review and meta-analysis (2026) · Frontiers in Pediatrics · doi

  at 60%–70% peak HR 35 min CAT, continuous aerobic training; CT, continuous training; ET, endurance training; HIIE, high-intensity interval exercise; HIIT/HIT, high-intensity interval training; HR, heart rate; HRmax, maximal heart rate; HRR, heart-rate reserve; LI/HI, low-/high-intensity continuous training; MAS, maximal aerobic speed; MICE/MICT, moderate-intensity continuous exercise/training; vVO2max, velocity at maximal oxygen uptake. aLazzer et al.: the two eligible continuous-training arms were pooled. Dias et al.: available-case sample sizes varied by outcome. Su et al.: 44 participants were randomized and 43 completed the intervention. 3.3 Risk of bias assessment Risk-of-bias assessments for the included trials are presented in Supplementary Table S1. Of the 14 trials, one (7.1%) was judged to be at low risk of bias overall, 13 (92.9%) raised some concerns, and none was judged to be at high risk. The most frequent concerns involved the randomization process in 10 trials (71.4%), mainly because the allocation process was incompletely reported, and missing outcome data in six trials (42.9%), primarily because of attrition and complete-case analyses. All trials were judged to be at low risk for deviations from intended interventions, outcome measurement, and selection of the reported result. 3.4 Effects on body-composition outcomes All effect estimates were calculated as HIIT minus continuous aerobic training; therefore, negative mean differences favored HIIT. The pooled results are presented in Figure 2, and detailed estimates are provided in Table 2. 3.4.1 Body weight Thirteen trials involving 409 participants reported body weight. The pooled estimate slightly favored HIIT, but the difference between HIIT and continuous aerobic training was not statistically significant (MD = −0.37 kg, 95% CI −0.89 to 0.15; P = 0.146). No between-study heterogeneity was detected (τ2 = 0.000; I2 = 0%; Cochran’s Q = 7.74, P = 0.805), indicating statistically consistent findings across the included trials. 3.4.2 Body mass index Fourteen trials involving 432 participants contributed data for BMI. The pooled estimate showed no statistically significant difference between HIIT and continuous aerobic training (MD = −0.14 kg/m2, 95% CI −0.31 to 0.02; P = 0.089). Between- study heterogeneity was not detected (τ2 = 0.000; I2 = 0%; Cochran’s Q = 12.86, P = 0.458). 3.4.3 Body fat percentage statistically Eleven trials involving 331 participants reported body fat percentage. Although the pooled estimate favored HIIT, the (MD = −0.33 difference was not percentage points, 95% CI −1.33 to 0.67; P = 0.481). Moderate- to-substantial heterogeneity was observed (τ2 = 1.166; I2 = 60.3%; Cochran’s Q = 24.02, P = 0.008), indicating variability in the effects cautious trials interpretation of the pooled estimate. and warranting significant reported across 3.5 Sensitivity analyses to Leave-one-out analyses showed that omitting any individual trial did not materially alter the pooled estimates, and all include zero. After corresponding 95% CIs continued excluding the reconstructed body weight and BMI effects from Su et al. (32), the results remained non-significant for body weight (MD = −0.36 kg, 95% CI −1.17 to 0.45; P = 0.354) and BMI (MD = −0.21 kg/m2, 95% CI −0.48 to 0.07; P = 0.125). Including the direct-comparison study by Leite et al. (33), in which random allocation was unclear, also did not change the conclusions for body weight (MD = −0.36 kg, 95% CI −0.87 to 0.15), BMI (MD = −0.13 kg/m2, 95% CI −0.31 to 0.05), or body fat percentage (MD = −0.18 percentage points, 95% CI −1.18 to 0.81). These findings indicated that the primary results were robust the sensitivity analyses. Detailed primary and sensitivity-analysis estimates are presented in Table 2. to

  generalrecommendations
  Keywords: training trials body continuous hiit pooled aerobic risk reported weight percentage high intensity participants analyses
• Comparing four training modalities on body composition, cardiorespiratory fitness and cardiovascular responses in sedentary young men: a randomized trial (2026) · BMC Sports Science, Medicine and Rehabilitation · doi

  Abstract Background Although high-intensity interval training (HIIT), high-intensity continuous training (HICT), moderate-intensity interval training (MIIT), and moderate-intensity continuous training (MICT) have all been shown to improve health outcomes, few studies have compared these four modalities within the same framework.

  generalabstractevidence 5/5
  Keywords: intensity training high interval continuous moderate abstract background hiit hict miit mict improve health outcomes

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