The role of heart rate variability in acute mountain sickness: A meta-analysis
Bibliographic record
Abstract
BACKGROUND: Acute mountain sickness (AMS) is associated with symptoms arising from an individual's failure to acclimatize to high-altitude environments characterized by hypobaric hypoxia. Heart rate variability (HRV) has been proposed as a potential predictor of AMS, but results from individual studies have been inconsistent. Therefore, this study investigated HRV before and after ascent to provide a more comprehensive review of the relationship between HRV and AMS. METHODS: We conducted a systematic search of MEDLINE, PubMed, and Google Scholar from inception to August 2023. Studies measuring HRV in relation to AMS were included. The quality of studies was assessed using the Newcastle-Ottawa scale. RESULTS: Initially, 153 trials were identified through our search strategy. Seven studies met the inclusion criteria, comprising a total of 329 participants. Before ascent, individuals who developed AMS showed significantly higher percentage of successive R-R intervals that differ by more than 50 ms compared with those who did not develop AMS (standardized mean difference = 0.40, 95% confidence interval: [0.11 to 0.69]). After ascent, the AMS group exhibited significantly lower standard deviation of normal-to-normal R-R intervals (standardized mean difference = -0.41, 95% confidence interval: [-0.69 to -0.13]). Other HRV parameters, including low-frequency and high-frequency power, showed trends toward lower values in the AMS group but did not reach statistical significance. CONCLUSION: This meta-analysis proves that certain HRV parameters, particularly percentage of successive R-R intervals that differ by more than 50 ms before ascent and standard deviation of normal-to-normal R-R intervals after ascent, may be associated with AMS development. These findings suggest that HRV analysis could potentially be used as a tool for predicting and monitoring AMS. However, further research is needed to establish definitive clinical guidelines.
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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 | 0.013 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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 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".