Why:

• No existing study that looks at the Fitbit data in terms of the quality of the:
  • Density of the data (i.e., how much data)
  • Longitudinality of the data (i.e., for how long)
  • Bias of the data (underlying social cohort, intra and/or inter-individual differences)

What:

• We will explore the existing Fitbit data from the ABCD study and seek to understand:
  
  • Sociodemographic data
  • The Fitbit data that is available
• Stemming from the data exploration:
  • Bias analysis

How:

• Examine the Fitbit-specific data from the ABCD

Resource Stack

Reference Papers:

1. Concurrent and prospective associations between fitbit wearable-derived RDoC arousal and regulatory constructs and adolescent internalizing symptoms
2. Current, future and potential use of mobile and wearable technologies and social media data in the ABCD study to increase understanding of contributors to child health
3. Performance of a commercial multi-sensor wearable (Fitbit Charge HR) in measuring physical activity and sleep in healthy children
4. ABCD Study Main Paper - The ABCD study: understanding the development of risk for mental and physical health outcomes
5. Tutorial - https://abcd-repronim.github.io/
6. https://www.nature.com/articles/s41746-020-0224-8