Imagery, progressive muscle relaxation and restricted environmental stimulation: enhancing mental training and rowing ergometer performance through flotation REST
Bibliographic record
Abstract
Examining flotation Restricted Environment Stimulation Technique (REST) as a performance enhancement tool in sport has produced positive results. However, earlier studies using flotation REST combined the technique with guided imagery, confounding the effect that REST-only might have on sport performance. Although more recent studies have examined the effects of flotation REST-only on athletic performance, they have only considered fine motor activities. The current study tested the effects of REST, without guided imagery, on rowing ergometer performance, a gross motor, endurance activity. Further more, the study attempted to ascertain why rowers might benefit from including a period of flotation in their training regime, and whether the flotation REST environment was better for mental training than a Progressive Muscle Relaxation (PMR) condition. Subjects (n=24) were male novice and varsity university rowers. Subjects were matched based on previous ergometer scores, imagery use, and imagery ability, and randomly assigned to either a flotation REST condition or a PMR condition. Both groups were exposed to six administrations of one condition over a 7-week period, during which time they completed three 2000-meter ergometer tests, were part of their required training schedule. Results showed a significant improvement in ergometer scores for the REST group, with no significant improvement for the PMR group. The imagery data suggest several alternative explanations for the effect of environment on mental training, while the physical training data suggest a possible link between flotation REST and recovery from physical fatigue.
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 | 0.000 | 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 | 0.000 |
| 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".