Evaluation of Heart Rate in Daily Life Based on 10 Million Samples Database
Bibliographic record
Abstract
This research uses a very-large amount of heart rate data (approximately 10 millions entries) collected on one million persons to investigate heart rate trends in daily life. Trends were examined regarding both human characteristics (gender, age, BMI), and environmental properties (hour of the day, day of the week, season). Concerning the relation between heart rate and gender, women’s heart rate trend was significantly lower compared to men’s. As for the relation between heart rate and age, on one hand, women’s heart rate tends to become lower with aging. On the other hand, men’s heart rate tends to keep increasing until the 50’s. Also, men’s heart rate is lower than women’s in the 10’s and 20’s, the trend becomes opposite from the 30’s. In the relation between heart rate and BMI (Body Mass Index), heart rate is significantly higher for users with higher BMI. Regarding diurnal fluctuation of heart rate, it has been confirmed to take its lowest between 3 A.M. and 6 A.M., then increasing regularly until the peak between noon and 3 P.M., after which it decreases during the evening through the night. As for in-week fluctuation, the trend of higher heart rate during the week and lower heart rate during week-ends and holidays has been comfirmed. Specialy, heart rate remarkably increases from Sunday to Monday. Finally, concerning seasonal fluctuation trend, heart rate is decreasing from summer to autumn, and is at his highest level in winter. The results of this research tend to correspond with previous studies that have been carried out on smaller samples and fewer subjects, demonstrating the credibilitay and validity of the data measured by our system. Moreover, on a public health point of view, we can say that it is important to take into account various human properties and spatio-temporal characteristics when having a comparative analysis of people’s health distribution. We think the knowledge obtained in this research can be applied practically to the whole society as an indication for health care and various diseases prevention.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".