Effects of Passive or Active Recovery Regimes Applied During Long-Term Interval Training on Physical Fitness in Healthy Trained and Untrained Individuals: A Systematic Review
Bibliographic record
Abstract
Abstract Background Intermittent exercise programs characterized through intensive exercise bouts alternated with passive or active recovery (i.e., interval training), have been proven to enhance measures of cardiorespiratory fitness. However, it is unresolved which recovery type (active or passive) applied during interval training results in larger performance improvements. Objectives This systematic review aimed to summarize recent evidence on the effects of passive or active recovery following long-term interval exercise training on measures of physical fitness and physiological adaptations in healthy trained and untrained individuals. The study protocol was registered in the Open Science Framework (OSF) platform ( https://doi.org/10.17605/OSF.IO/9BUEY ). Methods We searched nine databases including the grey literature (Academic Search Elite, CINAHL, ERIC, Open Access Theses and Dissertations, Open Dissertations, PsycINFO, PubMed/MEDLINE, Scopus, and SPORTDiscus) from inception until February 2023. Key terms as high-intensity interval training, recovery mode, passive or active recover were used. A systematic review rather than a meta-analysis was performed, as a large number of outcome parameters would have produced substantial heterogeneity. Results After screening titles, abstracts, and full texts, 24 studies were eligible for inclusion in our final analysis. Thirteen studies examined the effects of interval training interspersed with passive recovery regimes on physical fitness and physiological responses in trained (6 studies) and untrained (7 studies) individuals. Eleven out of 13 studies reported significant improvements in physical fitness (e.g., maximal aerobic velocity (MAV), Yo-Yo running test, jump performance) and physiological parameters (e.g., maximal oxygen uptake [VO2max], lactate threshold, blood pressure) in trained (effect sizes from single studies: 0.13 < Cohen’s d < 3.27, small to very large) and untrained individuals (effect sizes: 0.17 < d < 4.19, small to very large) despite the type of interval training or exercise dosage (frequency, intensity, time, type). Two studies were identified that examined the effects of passive recovery applied during interval training in young female basketball (15.1 ± 1.1 years) and male soccer players (14.2 ± 0.5 years). Both studies showed positive effects of passive recovery on VO2max, countermovement jump performance, and the Yo-Yo running test. Eleven studies examined the effects of interval training interspersed with active recovery methods on physical fitness and physiological parameters in trained (6 studies) and untrained individuals (5 studies). Despite the type of interval training or exercise dosage, nine out of eleven studies reported significant increases in measures of physical fitness (e.g., MAV) and physiological parameters (e.g., VO2max, blood pressures) in trained (effect sizes from single studies: 0.13 < d < 1.29, small to very large) and untrained individuals (effect sizes: 0.19 < d < 3.29, small to very large). There was no study available that examined the effects of active recovery on physical fitness and physiological responses in youth. Conclusions The results of this systematic review show that interval training interspersed with active or passive recovery regimes have the potential to improve measures of physical fitness and physiology outcomes in trained and untrained adults and trained youth. That is, the applied recovery type seems not to affect the outcomes. Nonetheless, more research is needed on the effects of recovery type on measures of physical fitness and physiological adaptations in youth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".