Medical-Grade or Consumer Wearable? How to Choose

Since the late 1980s, wearable devices have helped researchers collect data on the physical activity, mobility, and sleep patterns of people in their daily lives. This data captured outside of a laboratory or clinic setting can help provide more holistic and sensitive information about physical functioning, making them an appealing tool for academic, public health, and clinical researchers. Additional aspects contributing to the appeal of including wearables in research studies is that they are relatively easy to use, and they can be deployed at scale.  

With the use of wearables by the general population rising from 9% in 2014 to 33% in 20181 , participants are growing more familiar and comfortable with this technology. A recently published survey of over 1300 participants from a patient advisory group reported that 59% of respondents owned a wearable device2 

With the popularity and demand for wearable devices growing both in the consumer and research spaces, the number of different options available to researchers has increased, prompting a question we hear often - which device is the right choice for my study? This is a complex question with important considerations that will vary for each study, but factors to consider include data security, algorithm transparency, sensor signal quality, raw data access, data visibility, and customer support.  

To help researchers get started answering this question, we’ve created a table that provides information around key questions to consider when deciding between using a medical-grade wearable (a wearable device that is designed for research purposes and often has regulatory clearances) and a consumer wearable (a wearable device that is designed to be used by the general population).  


Click here or on the image below to view the full PDF version.

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To learn more about how to develop a long-term data strategy to extract the greatest value from wearable digital health technologies used in your studies, check out "Maximize Your R&D Investment in DHTs Through the Collection and Retention of Raw Sensor Data.” 




1 Rieder, A., Eseryel, U. Y., Lehrer, C., & Jung, R. (2021). Why Users Comply with Wearables: The Role of Contextual Self-Efficacy in Behavioral Change. International Journal of Human–Computer Interaction37(3), 281–294.

2 Shandhi, M.M.H., Singh, K., Janson, N. et al. Assessment of ownership of smart devices and the acceptability of digital health data sharing. npj Digit. Med. 7, 44 (2024).

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