Wearable technology

Best practice

Quantum mechanics

Scale (ratio)

Statistics

Data collection

Cartography

Embedded system

Economics

Computer science

Internet privacy

Physics

Mathematics

Management

Geography

Wearable computer

Data science

Smartwatch

Medicine

Observational study

Human–computer interaction

Activity tracker

Pathology

npj Digital Medicine

Smartphone apps and wearable devices for tracking physical activity and other health behaviors have become popular in recent years and provide a largely untapped source of data about health behaviors in the free-living environment. The data are large in scale, collected at low cost in the "wild", and often recorded in an automatic fashion, providing a powerful complement to traditional surveillance studies and controlled trials. These data are helping to reveal, for example, new insights about environmental and social influences on physical activity. The observational nature of the datasets and collection via commercial devices and apps pose challenges, however, including the potential for measurement, population, and/or selection bias, as well as missing data. In this article, we review insights gleaned from these datasets and propose best practices for addressing the limitations of large-scale data from apps and wearables. Our goal is to enable researchers to effectively harness the data from smartphone apps and wearable devices to better understand what drives physical activity and other health behaviors.

Best practices for analyzing large-scale health data from wearables and smartphone apps