Validity of heart rate measurements in wrist-based monitors across skin tones during exercise
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Bibliographic record
Abstract
PURPOSE: To evaluate the accuracy of a wrist-based heart rate (HR) monitor at different exercise intensities across different skin tones. METHODS: Using a cross-sectional design, we compared HR measures from the wrist-based photoplethysmography Fitbit Charge 5 to the Polar H10 chest strap at rest and during the YMCA Protocol using a recumbent cycle ergometer. Participant were grouped into three skin tone categories: light (Fitzpatrick Scale Skin Types 1+2), medium (Types 3+4), and darker skin tone (Types 5+6). HR measures using the Polar chest strap during the exercise test were categorized as <40%, 40-60%, or >60% HR reserve (HRR). Absolute error in beats per minute (bpm) between the two devices was calculated for each measure. A linear mixed effects model was used to assess interaction effects between skin tone and exercise intensity, with participants as the random effect. Bland-Altman plots were used for visual analyses. RESULTS: Twenty-five participants [mean (SD): 25.8 (1.9) years old; 64% female] were included with 495 observations of simultaneous Fitbit and Polar HR recordings collected during exercise. During exercise, we observed a statistically significant interaction effect between skin tone and exercise intensity. Compared with light skin tone at <40% HRR, mean error was greater for medium skin tone at >60% HRR [mean error (95%CI): 11.8 (5.6-17.9) bpm, p<0.001] and darker skin tone at 40-60% HRR [7.6 (1.7-13.5) bpm, p = 0.011] and >60% HRR [11.7 (5.3-18.0) bpm, p<0.001]. CONCLUSION: HR measurement error using a wrist-based device was greater with increasing exercise intensity for people with darker skin tones.
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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 | 0.000 | 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 it