The effects of HeartMath Heart Lock‐In on elementary students' HRV and self‐reported emotion regulation skills
Bibliographic record
Abstract
Abstract It is important to address social and emotional concerns early on, as they can adversely affect learning at all levels. The classroom is an ideal context for fostering healthy social and emotional development. For example, emotion regulation can be reinforced through simple daily practices within schools. The current applied research project was in collaboration with multiple community partners and assessed the effectiveness of a classroom‐based HeartMath practice (Heart Lock‐In) on resting heart rate variability (HRV) and self‐reported emotional benefits in elementary students. This repeated‐measures study was conducted in central Alberta, Canada, in 2020 and involved obtaining pre–post HRV measurements from N = 24 grade five students who participated in a teacher‐led 5‐min Heart Lock‐In (like loving‐kindness—radiating love to oneself and others) daily for 4 weeks. We hypothesized that the practice would increase resting HRV compared to a 4‐week relaxation control. Qualitative questions were included to capture perceptions of the utility and impact of the practice. Univariate analysis of variance revealed that the HeartMath intervention significantly increased HRV compared to the relaxation control. Students reported enhanced emotional stability, feeling more positive about themselves, and improved interpersonal relationships. They expressed that the practice gives them better focus, which helps us to improve their performance (e.g., in academics and athletics). These findings provide evidence that a simple and short HeartMath ER practice can be practical for school educators, administrators, and counselors to implement in the classroom.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 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) | 0.000 | 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 teacher head, 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".