Open AccessAccuracy of swimming wearable watches for estimating energy expenditure
Author(s) -
Mihyun Lee,
Hyojin Lee,
Saejong Park
Publication year - 2018
Publication title -
international journal of applied sports sciences
Language(s) - English
Resource type - Journals
eISSN - 2233-7946
pISSN - 1598-2939
DOI - 10.24985/ijass.2018.30.1.80
Subject(s) - energy expenditure , stroke (engine) , activity monitor , statistics , mathematics , physical activity , physical therapy , physical medicine and rehabilitation , medicine , engineering , mechanical engineering , endocrinology
With the recent installation of waterproof function on wearable watches, various sports activities including walking, running and even swimming are monitored. Commercially available swimming wearable watches automatically identified stroke type, swim distance, stroke counts and energy expenditure (EE). Although the accuracy of estimating EE of walking, bilking and activities of daily life on activity monitors have been evaluated, it has not been examined for swimming. Thus, the purpose of the study was to evaluate the accuracy of estimating EE for swimming wearable watches (Apple Watch S2, Apple and Garmin Finex 3HR, Garmin). A total of 78 swimmers aged 20-59 years (female: 48%) participated in the study. All the participants wore Apple and Garmin and completed a set of swimming protocol comprising various speeds (0.4, 0.6, 0.8, 1.0, 1.2 m/s). At each swimming speed they were asked to swim for four minutes. Lap counts, stroke counts and energy expenditure (EE) from the Apple and Garmin were evaluated with the criterion measures. Lap counts and stroke counts were directly counted by the research assistant. The portable respiratory gas analyzer (K4b2, Cosmed, Italy) and a swimming snorkel (Aqua Trainer Snorkel, Cosmed, Italy) was used as the criterion measure of EE. The mean absolute percentage error (MAPE) of lap counting and stroke counts at various swimming speed were within 10% for Apple (lap counts: 0.5-6.1%, stroke counts: 6.2-9.3%) and about 20% for Garmin (lap counts: 0-20.6%, stroke counts: 6.8-17.6%). However, the MAPE of EE was higher for Apple (17.1%-151.7%) than for Garmin (17.9%-32.7%). The accuracy of estimating EE tended to improve with increasing swimming speed for both Apple and Garmin. The EEs from Apple were outside the equivalence zone except for at 1.2 m/s and were overestimated compared to the criteria. On the other hand, EEs from Garmin were within the equivalence zone at all speeds except for 1.2 m/s. In conclusion, Apple and Garmin wearable watches accurately measure lap counts and stroke counts. However, the accuracy of estimating EE are poor at slow to medium swimming speed. Further improvement is needed to estimate energy expenditure of swimming at various speed.