Replication Data for: Using machine learning methods to predict physical activity types with Apple Watch and Fitbit data using indirect calorimetry as the criterion.

Objectives There is considerable promise for using commercial wearable devices for measuring physical activity at the population level. The objective of this study was to examine whether commercial wearable devices could accurately predict lying, sitting, and different physical activity intensity in a lab based protocol. Methods We recruited a convenience sample of 46 participants (26 women) to wear three devices, a GENEActiv, and Apple Watch Series 2, a Fitbit Charge HR2. Participants completed a 65-minute protocol with 40-minutes of total treadmill time and 25-minutes of sitting or lying time. Indirect calorimetry was used to measure energy expenditure. The outcome variable for the study was the activity class; lying, sitting, walking self-paced, 3 METS, 5 METS, and 7 METS. Minute-by-minute heart rate, steps, distance, and calories from Apple Watch and Fitbit were included in four different machine learning models. Results Our analysis dataset included 3656 and 2608 minutes of Apple Watch and Fitbit data, respectively. We test decision trees, support vector machines, random forest, and rotation forest models. Rotation forest models had the highest classification accuracies at 82.6% for Apple Watch and 89.3% for Fitbit. Classification accuracies for Apple Watch data ranged from 72.5% for sitting to 89.0% for 7 METS. For Fitbit, accuracies varied between 86.2 for sitting to 92.6% for 7 METS. Conclusion This study demonstrated that commercial wearable devices, Apple Watch and Fitbit, were able to predict physical activity type with a reasonable accuracy. The results support the use of minute by minute data from Apple Watch and Fitbit combined machine learning approaches for scalable physical activity type classification at the population level.