Learning to manage stress and enhance well-being: A heartmath cardiac coherence intervention with university student-athletes
Bibliographic record
Abstract
Due to the challenge of balancing academic and sport training demands, student-athletes experience higher levels of stress (Gould & Whitly, 2009). The purpose of this study was to examine the impact of a self-regulation intervention on the stress and well-being of 11 Human Kinetics undergraduate student-athletes. Over the course of 6 weekly 15-minute intervention sessions, student-athletes learned how to self-regulate and manage their stress using the emWave cardiac coherence (CC) training software program; CC represents a physiological state in which the nervous, cardiovascular, hormonal and immune systems are working efficiently and harmoniously (HeartMath, 2010). During each intervention session, participants focused on maintaining desired breathing patterns and positive emotions and thoughts, while receiving visual feedback from the emWave program regarding their heart rate variability and CC level. They also practiced sustaining CC on their own once per day for 3-5 minutes and completed a log. In a final interview, they shared their perceptions regarding the impact of the intervention. Results indicated that 6 of the 11 student-athletes considerably improved their ability to sustain CC, 3 showed moderate improvement, and 2 had little to no improvement. Moreover, 9 of the 11 student-athletes reported lower levels of stress, and 6 reported increased well-being including an enhanced ability to control emotions, focus, relax, and maintain a positive attitude.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".