Comparison of Polar M600 Optical Heart Rate and ECG Heart Rate during Exercise
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to evaluate the accuracy of the Polar M600 optical heart rate (OHR) sensor compared with ECG heart rate (HR) measurement during various physical activities. METHODS: Thirty-six subjects participated in a continuous 76-min testing session, which included rest, cycling warm-up, cycling intervals, circuit weight training, treadmill intervals, and recovery. HR was measured using a three-lead ECG configuration and a Polar M600 Sport Watch on the left wrist. Statistical analyses included OHR percent accuracy, mean difference, mean absolute error, Bland-Altman plots, and a repeated-measures generalized estimating equation design. OHR percent accuracy was calculated as the percentage of occurrences where OHR measurement was within and including ±5 bpm from the ECG HR value. RESULTS: Of the four exercise phases performed, the highest OHR percent accuracy was found during cycle intervals (91.8%), and the lowest OHR percent accuracy occurred during circuit weight training (34.5%). OHR percent accuracy improved steadily within exercise transitions during cycle intervals to a maximum of 98.5% and during treadmill intervals to a maximum of 89.0%. Lags in HR calculated by the Polar M600 OHR sensor existed in comparison to ECG HR, when exercise intensity changed until steady state occurred. There was a tendency for OHR underestimation during intensity increases and overestimation during intensity decreases. No statistically significant interaction effect with device was found in this sample on the basis of sex, body mass index, V˙O2max, skin type, or wrist size. CONCLUSIONS: The Polar M600 was accurate during periods of steady-state cycling, walking, jogging, and running, but less accurate during some exercise intensity changes, which may be attributed to factors related to total peripheral resistance changes and pulse pressure.
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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 | 0.005 |
| 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) | 0.001 | 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 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".