Abstract
BackgroundActivity trackers are now ubiquitous in certain populations, with potential applications for health promotion and monitoring and chronic disease management. Understanding the accuracy of this technology is critical to the appropriate and productive use of wearables in health research. Although other peer-reviewed validations have examined other features (eg, steps and heart rate), no published studies to date have addressed the accuracy of automatic activity type detection and duration accuracy in wearable trackers.ObjectiveThe aim of this study was to examine the ability of 4 commercially available wearable activity trackers (Fitbits Flex 2, Fitbit Alta HR, Fitbit Charge 2, and Garmin Vívosmart HR), in a controlled setting, to correctly and automatically identify the type and duration of the physical activity being performed.MethodsA total of 8 activity types, including walking and running (on both a treadmill and outdoors), a run embedded in walking bouts, elliptical use, outdoor biking, and pool lap swimming, were tested by 28 to 34 healthy adult participants (69 total participants who participated in some to all activity types). Actual activity type and duration were recorded by study personnel and compared with tracker data using descriptive statistics and mean absolute percent error (MAPE).ResultsThe proportion of trials in which the activity type was correctly identified was 93% to 97% (depending on the tracker) for treadmill walking, 93% to 100% for treadmill running, 36% to 62% for treadmill running when preceded and followed by a walk, 97% to 100% for outdoor walking, 100% for outdoor running, 3% to 97% for using an elliptical, 44% to 97% for biking, and 87.5% for swimming. When activities were correctly identified, the MAPE of the detected duration versus the actual activity duration was between 7% and 7.9% for treadmill walking, 8.7% and 144.8% for treadmill running, 23.6% and 28.9% for treadmill running when preceded and followed by a walk, 4.9% and 11.8% for outdoor walking, 5.6% and 9.6% for outdoor running, 9.7% and 13% for using an elliptical, 9.5% and 17.7% for biking, and was 26.9% for swimming.ConclusionsIn a controlled setting, wearable activity trackers provide accurate recognition of the type of some common physical activities, especially outdoor walking and running and walking on a treadmill. The accuracy of measurement of activity duration varied considerably by activity type and tracker model and was poor for complex sets of activity, such as a run embedded within 2 walking segments.
Highlights
BackgroundAdequate physical activity participation is one of the most important behaviors people can adopt to maintain their health and well-being
Physical activity reduces the risk of several major chronic diseases [1] and early mortality [2], reduces health risks associated with overweight and obesity [3], and improves psychological outcomes, including mood and energy [4]
Previous validation studies have reported high correlations between device step counts and the criterion [13,14] and a general underestimation of energy expenditure as compared with criterion measurements [15], and heart rate validation studies have shown that wearable devices are more accurate during rest than during moderate exercise [16,17,18]
Summary
BackgroundAdequate physical activity participation is one of the most important behaviors people can adopt to maintain their health and well-being. Consumer-grade activity trackers are one tool that may help individuals increase and monitor their physical activity participation These devices are available to consumers at a relatively low cost, with approximately 14 million Fitbits alone sold in 2018 [7] and 120 million devices projected to be sold by 2019 [8]. Such trackers have been shown to support increased physical activity participation in adults [9,10] and are suitable for incorporation into clinical research and health promotion interventions [9,11,12]. The accuracy of measurement of activity duration varied considerably by activity type and tracker model and was poor for complex sets of activity, such as a run embedded within 2 walking segments
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