Comparison of smartwatch-derived energy expenditure estimation during outdoor activities

Smartwatches are commonly used to estimate energy expenditure, but studies have reported errors of 25% to 70%, possibly due to factors such as activity type, use case, and sensor data. We compared three readily available configurations for estimating energy expenditure during outdoor walking and running: a smartwatch operated in its activity-specific mode that incorporated GPS, IMU, and heart rate data; a smartwatch operated in its generic, non-activity-specific mode that incorporated IMU and heart rate data; and a pre-defined heart rate model. Thirty adults (17 men, 13 women; 34 ± 14 years) completed outdoor activities while wearing a portable respirometry system and both smartwatches. Participants performed self-paced walking and running conditions, and followed ecologically relevant audio prompts to elicit real-world walking. Cumulative energy expenditure from each method was compared with ground-truth respirometry. The activity-specific smartwatch achieved 13% error during outdoor walking with 2.2-2.8 times lower error than the generic smartwatch and heart rate model, which could be attributed to its activity-specific algorithm, GPS availability, hardware, or a combination of these factors. The heart rate model yielded a lower error than both the activity-specific and non-activity-specific smartwatches during outdoor running in this cohort. This study reflects practical differences between commercially available setups as device, sensor suite, and proprietary algorithms differ across these tools. Estimates from non-activity-specific smartwatches, such as the Fitbit Charge 4 tested here, should be interpreted cautiously by researchers, clinicians, and users when monitoring physical activity.