Differences in data output from two leading devices in the consumer-grade wearables market have been examined, namely Jawbone UP4 and Fitbit Charge HR devices, by comparing measurements that were conducted while participants wore both devices in tandem. Aggregate daily totals of steps and calories were shown to be highly correlated between devices (0.82–0.93 correlation coefficient for steps and 0.71–0.85 for calories); however, at the hourly level, differences in data output are evident, especially during hours of vigorous activity. These differences lead to both under- and over-estimation of measures such as hourly step-counts. Heart rate measurement with Jawbone and Fitbit is shown to be significantly different even at the daily level (p-value < 0.00001), which could be due to hardware differences in sensor type and possibly due to unknown differences in proprietary algorithms. Models were trained to enable adjustment of data collected from one device to the equivalent value in terms of the other device's measurement. This approach to data integration is recommended for researchers who are comparing data from multiple wearable devices, for individual users who have switched from one device to another and could use this method to adjust their wearables data history to be comparable with the new device, or for users who are comparing data with a user who has another type of device, or for groups organizing fitness challenges and health initiatives that can track users by comparing diverse wearables data.
All Science Journal Classification (ASJC) codes
- Computer Science(all)