Reduced Exertion High Intensity Interval Training (REHIT) on the Rowing Ergometer Elicits Lower Blood Lactate Accumulation and Higher Enjoyment and Affective Valence Versus Cycle Ergometry
Bibliographic record
Abstract
Reduced exertion high intensity interval training (REHIT) is an extremely time-efficient version of sprint interval training requiring two to three 20 s “all-out” sprints within a 10 min exercise session. Findings from laboratories in the UK (Metcalfe et al. 2012) and Canada (Gillen et al. 2016) show significant increases in VO2max, glycemic control, and body composition in inactive adults completing 6 - 12 wk of cycling based REHIT. A prior study (Astorino et al. 2020) in 85 adults having diverse VO2max values shows positive affective valence despite substantial blood lactate (BLa) accumulation. Whether REHIT can be performed using other exercise modes is unknown. PURPOSE: To compare changes in BLa, enjoyment, and affective valence between rowing and cycling REHIT. METHODS: Healthy, active adults (N = 20, age = 27 ± 6 yr) unfamiliar with REHIT completed four sessions using a within-subjects, repeated measures design. Sessions were separated by > 24 hr and held at the same time of day within participants. On 2 separate days, they underwent a VO2max test on the cycle ergometer and rowing ergometer and were familiarized to REHIT. Subsequently, they completed REHIT on both modes consisting of three “all-out” 20 s sprints separated by 3 min active recovery. Resistance was equal to 5 % body mass and a drag factor = 135 or 145 for women and men, respectively. BLa, affective valence, rating of perceived exertion (RPE 6 – 20), and enjoyment were acquired during exercise. RESULTS: Results showed no difference in peak HR (98 ± 6 vs. 95 ± 4 %HRmax, p = 0.06) or maximal workload (191 ± 34 vs. 204 ± 29 %Wmax, p = 0.25) between rowing and cycling REHIT. Rowing REHIT exhibited significantly lower BLa (p < 0.001) at 2, 30, and 60 min post-exercise (7.0 ± 2.2 vs. 11.6 ± 2.8 mM, p < 0.001, d = 1.9; 3.1 ± 1.5 vs. 6.2 ± 2.8 mM, p < 0.001, d =1.4; and 2.0 ± 0.7 vs. 3.1 ± 1.3 mM, p = 0.02, d = 1.2) versus cycling. In addition, results showed significantly greater enjoyment (101 ± 12 vs. 89 ± 17, p < 0.001, d = 0.84) and lower delta RPE (7.8 ± 2.0 vs. 9.5 ± 2.1, p = 0.001, d = 0.85) and affective valence (-1.2 ± 1.5 vs. -2.4 ± 2.7, p = 0.036, d = 0.54) with rowing versus cycling REHIT. CONCLUSION: Overall, rowing REHIT represents a suitable alternative to cycling as it maintains the supramaximal intensity while optimizing the perceptual response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".