Determination of Vertical Jump as a Measure of Neuromuscular Readiness and Fatigue
Bibliographic record
Abstract
Watkins, CM, Barillas, SR, Wong, MA, Archer, DC, Dobbs, IJ, Lockie, RG, Coburn, JW, Tran, TT, and Brown, LE. Determination of vertical jump as a measure of neuromuscular readiness and fatigue. J Strength Cond Res 31(12): 3305-3310, 2017-Coaches closely monitor training loads and periodize sessions throughout the season to create optimal adaptations at the proper time. However, only monitoring training loads ignores the innate physiological stress each athlete feels individually. Vertical jump (VJ) is widely used as a measure of lower-body power, and has been used in postmatch studies to demonstrate fatigue levels. However, no pretraining monitoring by VJ performance has been previously studied. Therefore, the purpose of this study was to determine the sensitivity of VJ as a measure of readiness and fatigue on a daily sessional basis. Ten healthy resistance-trained males (mass = 91.60 ± 13.24 kg; height = 179.70 ± 9.23 cm; age = 25.40 ± 1.51 years) and 7 females (mass = 65.36 ± 12.29 kg; height = 162.36 ± 5.75 cm; age = 25.00 ± 2.71 years) volunteered to participate. Vertical jump and BRUNEL Mood Assessment (BAM) were measured 4 times: pre-workout 1, post-workout 1, pre-workout 2, and post-workout 2. Workout intensity was identical for both workouts, consisting of 4 sets of 5 repetitions for hang cleans, and 4 sets of 6 repetitions for push presses at 85% 1 repetition maximum (1RM), followed by 4 sets to failure of back squats (BSs), Romanian deadlift, and leg press at 80% 1RM. The major finding was that VJ height decrement (-8.05 ± 9.65 cm) at pre-workout 2 was correlated (r = 0.648) with BS volume decrement (-27.56 ± 24.56%) between workouts. This is important for coaches to proactively understand the current fatigue levels of their athletes and their readiness to resistance training.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".