Smart watches are commonly used to provide continuous feedback on activity and caloric expenditure and are leveraged for weight management, clinical decisions, and public health strategies. Most wrist-worn wearables combine photoplethysmography, accelerometry, and proprietary algorithms to estimate caloric expenditure. Prior research indicates significant errors, yet the roles of potential moderators, specifically skin tone and body fat percentage (BF%), remain insufficiently examined. Therefore, the primary objective of this study was to quantify the accuracy of smartwatch-derived physical activity energy expenditure (PAEE) estimates relative to indirect calorimetry and to examine whether error varies by device brand, body fat percentage, and skin tone. We tested whether brand, BF%, and Fitzpatrick skin type (III–V) predict caloric expenditure error versus indirect calorimetry. Hispanic adults (
n
= 58) completed a single 10-minute recumbent-cycle protocol with alternating 2-minute intervals at ~64–76% and ~77–95% HRmax (Tanaka formula), bracketed by 5-minute rest/recovery. Participants wore Apple Watch Series 8, Fitbit Sense 2, Samsung Galaxy Watch 5, and Garmin Forerunner 955; COSMED K5 metabolic system provided the criterion. After device-specific data quality filters, analyzable participant–device pairings were Apple = 52, Garmin = 51, Samsung = 50, Fitbit = 44. One-sample tests indicated significant mean bias for three of four devices,
p
< .05. Importantly, the non-significant Fitbit bias depended on device-specific outlier removal. Bias (
M
,
SD
) and 95% CI (kcal): Apple 21.60 (36.63), 11.59–31.60; Garmin 68.61 (55.86), 53.28–83.94; Samsung 56.76 (42.03), 45.11–68.41; Fitbit 3.14 (40.95), −8.96 to 15.24. Mixed-effects models showed a device main effect (
p
< .001), a BF% main effect (
p
< .01), and a device by BF% interaction (
p
= .02): Physical activity energy expenditure (PAEE) error increased with adiposity across all brands (
p
< .01). Common smart watches substantially misestimate PAEE relative to indirect calorimetry, with error magnitude increasing as BF% rises and varying by brand. Current consumer devices do not yet provide reliable caloric monitoring for individuals or for research; improving accuracy across body types is essential for clinical and public health applications.
