Monitoring the Prescribed and Experienced Heart Rate–Derived Training Loads in Elite Field Hockey Players
Bibliographic record
Abstract
Perrotta, AS, Taunton, JE, Koehle, MS, White, MD, and Warburton, DER. Monitoring the prescribed and experienced heart rate-derived training loads in elite field hockey players. J Strength Cond Res 33(5): 1394-1399, 2019-This study examined the congruence between the prescribed and experienced heart rate-derived training loads over a 5-week periodized mesocycle. Twenty-four elite female field hockey players training as part of a national team were monitored before an (FIH) Hockey World League tournament. Three on-field training sessions per week were prospectively designed focusing on technical, tactical, and physiologically oriented hockey drills. A training load value, modeling the periodized weekly loading scheme, was prescribed for each training session and was calculated using normative training load responses from performing on-field hockey drills. Magnitude-based inferences focusing on the effect size (ES) and a Pearson correlation coefficient (r) were used to examine the degree of difference and the strength of correlation between the prescribed and experienced training loads. A significant correlation was observed between the experienced and prescribed training loads over the 5-week mesocycle (r = 0.92, 90% confidence limit [CL] [0.84-0.96]). The percentage difference and the ES between the achieved and prescribed training loads were as follows: week 1 demonstrated a 2.0% difference (ES = 0.10, 90% CL [-0.22-0.41]), week 2 a -5.4% difference (ES = -0.41, 90% CL [-0.75 to -0.07]), week 3 a -1.5% difference (ES = -0.09, 90% CL [-0.37 to 0.20]), week 4 a 7.1% difference (ES = 0.46, 90% CL [0.14-0.78]), and week 5 a 3.5% difference (ES = 0.18, 90% CL [-0.17 to 0.53]). This investigation demonstrates the efficacy for coaches to prospectively design on-field training sessions using normative training load data to enhance the congruence between the prescribed and experienced training loads over a periodized mesocycle.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".