Assessment of Heart Rate Variability Among Plant Nursery Workers
Bibliographic record
Abstract
OBJECTIVES: Work-related stress has become prevalent among workers in various occupational sectors, including agriculture. This study assessed stress among seasonal migrant workers at a plant nursery throughout their full work shift. METHODS: The study included nine participants who performed a variety of tasks during the measurement day. Their beat-to-beat heart intervals were continuously collected using a heart rate monitor and mobile application. Their stress at work was then characterized in terms of heart rate variability (HRV) parameters (Mean RR, SDNN [Standard Deviation of NN], RMSSD [Root Mean Square of Successive Differences], VLF [Very Low Frequency] Power, LF [Low Frequency] Power, HF [High Frequency] Power, Total Power, VLF%, LF%, HF%, LF n.u. HF n.u. and LF:HF Ratio) using the Kubios HRV software. Generalized linear mixed-effects models were used to determine the effect of time of day since work began (partitioned into 15-min windows) and the assigned job tasks. RESULTS: Time of day was found to reduce Mean RR, SDNN, RMSSD, LF, HF, and Total Power, while increasing the LF:HF ratio, indicating stress increased as the workday progressed. Some job tasks had a significant impact on workers' stress levels. The task of field maintenance, including trimming bushes and sweeping cut branches, resulted in an increase in workers' stress. Conversely, weeding appeared to decrease SDNN, RMSSD, and VLF Power, suggesting this activity reduced stress. CONCLUSION: This study demonstrated the feasibility of assessing heart rate variability in the field, providing objective data to decision-makers. To the authors' knowledge, this study is among the first to evaluate agricultural workers' stress using direct physiological measures such as heart rate variability.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".