Body Fat, Skin Tone, and the Accuracy of Smartwatch Caloric Expenditure Estimates

Abstract

Smartwatches are widely used to estimate caloric expenditure for weight management, clinical decision making, and public health monitoring. These devices combine photoplethysmography, accelerometry, and proprietary algorithms. However, prior studies report substantial error, and the influence of moderators such as skin tone and body fat percentage (BF) remains underexamined. This study tested whether smartwatch brand, BF, and Fitzpatrick skin type (III to V) predict caloric expenditure error relative to indirect calorimetry. Fifty eight Hispanic adults completed a single laboratory visit including a ten minute recumbent cycling protocol with alternating two minute moderate and vigorous intensity intervals, bracketed by rest and recovery. Participants wore four consumer devices: Apple Watch Series 8, Fitbit Sense 2, Samsung Galaxy Watch 5, and Garmin Forerunner 955. Energy expenditure was measured using a COSMED K5 metabolic system. After device specific data quality filtering, valid participant device pairings ranged from 44 to 52 per brand. One sample tests showed significant mean bias for three devices: Apple, Garmin, and Samsung. Fitbit showed no significant overall bias, although this depended on device specific outlier removal. Mean bias varied by brand, with Garmin and Samsung showing the largest overestimations. Mixed effects models revealed significant effects of device and BF, as well as a device by BF interaction, with physical activity energy expenditure error increasing as adiposity increased. Overall, common smartwatches substantially misestimate caloric expenditure compared with indirect calorimetry. Error varies by brand and worsens with higher body fat, highlighting limitations of current consumer wearables and the need for improved accuracy across diverse body types.