Combining Numerical and Visual Approaches in Validating Sleep Data Quality of Consumer Wearable Wristbands

Abstract

The recent rise of the Quantified Self movement has witnessed a significant increase in the adoption of consumer wearable wristbands for sleep tracking. Nevertheless, data quality of these devices has been a main concern. This study aimed to validate a most popular consumer wristband, i.e. Fitbit Charge 2™, against medical devices. We proposed a new validation approach that combines numerical technique with visual aid for epoch-by-epoch comparison on sleep stages. We found that Fitbit Charge 2™ had low accuracy in detecting wake and reasonable accuracy in detecting light, deep, and REM sleep stages. The visual aid of scatter plots showed that Fitbit was more accurate in detecting deep sleep stage in the first half of a night and more accurate in detecting REM sleep stage in the second half of a night. Our results indicate that consumer wearable wristbands are not able to produce high quality data of sleep stages in ecological settings. Future studies should consider the effect of time on device accuracy and may resort to segmented modelling techniques to improve data quality.