Wearable devices are widely used for heart rate (HR) monitoring, yet their accuracy across varying BMI and Fitzpatrick skin tones remains uncertain. This study evaluated four wrist-worn devices (Apple, Fitbit, Samsung, Garmin) in 58 Hispanic adults with Fitzpatrick skin types III–V during a cycling protocol alternating moderate (64–76% HRmax) and vigorous (77–95% HRmax) intensities. Criterion HR was obtained via Polar H10 ECG, with agreement assessed using mean absolute error (MAE), mean absolute percentage error (MAPE), mean bias, and intraclass correlation coefficients (ICC), complemented by Bland–Altman visualization. All devices showed small but systematic deviations from the ECG criterion (p < 0.001). Apple and Garmin exhibited the lowest MAE, whereas Fitbit and Samsung showed greater error. Within this cohort, greater dispersion of error around the mean bias line in the Bland–Altman plots was observed among participants with higher BMI and darker (Fitzpatrick V) skin tones, consistent with phenotype-linked agreement drift. These findings support inferences about commercial wrist-PPG devices in Hispanic adults with Fitzpatrick III–V and underscore the need for equity-focused algorithm refinement.

Wearable devices are widely used for heart rate (HR) monitoring, yet their accuracy across diverse body compositions and skin tones remains uncertain. This study evaluated four wrist worn devices (Apple, Fitbit, Samsung, Garmin) in 58 Hispanic adults with Fitzpatrick skin types III to V during a cycling protocol alternating moderate (0.64 to 0.76 HRmax) and vigorous (0.77 to 0.95 HRmax) intensities. Criterion HR was obtained using a Polar H10 ECG, and accuracy was assessed using mean absolute error, mean absolute percentage error (MAPE), bias, and intraclass correlation coefficients. All devices showed significant deviation from criterion measures. Apple and Garmin demonstrated the lowest error, whereas Fitbit and Samsung exhibited greater inaccuracies. Higher BMI and darker skin tones were associated with increased MAPE. These biases disproportionately affect higher risk populations, underscoring the need for improved algorithms to ensure equitable health monitoring.