Using velocity loss for monitoring resistance training effort in a real-world setting
Bibliographic record
Abstract
The purpose of the present study was to evaluate the changes in movement velocity during resistance training with different loads while the trainees attempted to move the load at a predetermined repetition duration. Twenty-one resistance-trained men (age: 25.7 ± 5 years; height: 177.0 ± 7.2 cm; mass: 85.4 ± 13.56 kg) volunteered to participate in the study. Participants performed 2 test sessions. The first to determine 1-repetition maximum (1RM) load, and the second to evaluate velocity loss during a set to failure performed at 75% and 50% of 1RM using a 2-s concentric and 2-s eccentric repetition duration, controlled by a mobile app metronome. When using 75% 1RM there was a significant loss of movement velocity between the antepenultimate and the penultimate repetition (5.33%, p < 0.05), as well as during the penultimate and the last (22.11%, p < 0.05). At 50% of 1RM the participants performed the set until momentary failure without significant velocity loss. Monitoring velocity loss during high-load resistance training through simple methods can be an important tool for standardize the intensity of effort employed during submaximal training. This can be useful in clinical conditions where maximum exertions are contraindicated or when specific logistics are lacking.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".