Monitoring A Canadian Recreational Rowing Athlete’s Psychophysiological Response To Training Load: A Case Study
Bibliographic record
Abstract
There has been growing attention to the monitoring of elite athlete training load, aiming to optimize performance and reduce injury/illness. However, research designs have yet to combine evidence-based practices (i.e., validated surveys) with popular methodology used by recreational athletes (ie., wearables) to support self-management of training loads and recovery. PURPOSE: To comprehensively describe a Canadian rowing athlete’s standardized performance and psychophysiological responses to training load across a 6-week training block. METHODS: 1 male Canadian rowing athlete recruit (20 years), volunteered to participate in the current study. Rowing performance (i.e., anaerobic 4x500m sprint, aerobic 10-minute row for distance) was assessed on weeks 1, 6. Daily surveys and/or wearables data were collected to assess: sleep (hours), session rate of perceived exertion (sRPE), training days lost to injury, session heart rate maximum (HRmax) and average (bpm), and training volume in minutes. The Recovery Stress Questionnaire for Athletes (RESTQ-Sport-36) was used to assess athletes’ perception of stress and recovery. Descriptive statistics were computed for each of the collected variables. RESULTS: Anaerobic performances observed across time were as follows: Week 1 = 1:37.7 min/500 m, Week 6 = 1:36.9 min/500 m. Aerobic performances observed across time were as follows: Week 1 = 1:42.0 min/500 m, Week 6 = 1:43.2 min/500 m. Across the 6-week period, the participant reported the following on average: sleep = 7.5 hours per night, sRPE = 4.8 ± 3.2, 7 training days lost due to illness/injury, session HRmax = 153 ± 28 bpm, session average HR = 110 ± 30 bpm, and training volume = 59.5 ± 41.2 min/session. On average, the participant reported low stress (1.1-2.5), high general recovery (3.0-3.3), low sport stress (0.9-2.7), and high sport recovery (2.6-3.2). CONCLUSIONS: Despite growing attention to self-monitor athlete training load and recovery to optimize performance and reduce injury/illness, there were no observed improvements in performances from week 1 to week 6. With these lack of findings, future research should address interventions to focus on performance improvement through sufficient training stimulus and adequate recovery among recreational athletes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".