Description: 

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

Background/History

The largest long-term study of brain development and child health in the US, the ABCD Study, deployed wearable devices (Fitbit Charge HR 2) to collect real-world physical activity and sleep data.

Purpose

To examine long-term adherence and factors impacting the quality of collected Fitbit data from children in real-world settings.

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)

Benefits

How

Examine the Fitbit-specific data from the ABCD

The Fitbit data and associated QA/QC features were obtained from the ABCD Study data portal. To assess the compliance and quality of data, we used the maximum common observation period of 21 days. We used an unsupervised hierarchical clustering approach to discover latent temporal patterns in each feature. The optimal number of clusters was selected using the Elbow method. Demographic differences between the Fitbit cohort and non-Fitbit cohort, as well as differences between those across clusters per QA/QC feature, were examined through Chi-Squared and Kruskal-Wallis tests.

Our research findings show the presence of bias in real-world wearable data collected from the ABCD Study cohort. The quality and quantity of the data is significantly different between White- and Black/African-American-identifying youth. These underlying significant differences in the quality of wearable data, if not addressed and adjusted for could negatively impact the personalized health trajectories learned wearable data using machine learning algorithms.

Further reading

Additional information

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