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  • Author: Nan Zeng x
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Zachary C. Pope, Nan Zeng, Xianxiong Li, Wenfeng Liu and Zan Gao

Background: This study examined the accuracy of Microsoft Band (MB), Fitbit Surge HR (FS), TomTom Cardio Watch (TT), and Apple Watch (AW) for energy expenditure (EE) estimation at rest and at different physical activity (PA) intensities. Method: During summer 2016, 25 college students (13 females; M age = 23.52 ± 1.04 years) completed four separate 10-minute exercise sessions: rest (i.e., seated quietly), light PA (LPA; 3.0-mph walking), moderate PA (MPA; 5.0-mph jogging), and vigorous PA (VPA; 7.0-mph running) on a treadmill. Indirect calorimetry served as the criterion EE measure. The AW and TT were placed on the right wrist and the FS and MB on the left—serving as comparison devices. Data were analyzed in late 2017. Results: Pearson correlation coefficients revealed only three significant relationships (r = 0.43–0.57) between smartwatches’ EE estimates and indirect calorimetry: rest-TT; LPA-MB; and MPA-AW. Mean absolute percentage error (MAPE) values indicated the MB (35.4%) and AW (42.3%) possessed the lowest error across all sessions, with MAPE across all smartwatches lowest during the LPA (33.7%) and VPA (24.6%) sessions. During equivalence testing, no smartwatch’s 90% CI fell within the equivalence region designated by indirect calorimetry. However, the greatest overlap between smartwatches’ 90% CIs and indirect calorimetry’s equivalency region was observed during the LPA and VPA sessions. Finally, EE estimate variation attributable to the use of different manufacturer’s devices was greatest at rest (53.7 ± 12.6%), but incrementally decreased as PA intensity increased. Conclusions: MB and AW appear most accurate for EE estimation. However, smartwatch manufacturers may consider concentrating most on improving EE estimate accuracy during MPA.