If you use this method in your work, please cite the DBDP: dbdp.org and the references at the bottom of this page.
This method is featured in our recent publication on the DBDP:
Bent, B., Wang, K., Grzesiak, E., Jiang, C., Qi, Y., Jiang, Y., Cho, P., Zingler, K., Ogbeide, F.I., Zhao, A., Runge, R., Sim, I., Dunn, J. (2020). The Digital Biomarker Discovery Pipeline: An open source software platform for the development of digital biomarkers using mHealth and wearables data. Journal of Clinical and Translational Science, 1-28. doi:10.1017/cts.2020.511 (Link to Open Access Article)
Objectives: Estimate the resting heart rate biomarker using:
- Personal data (i.e. different estimates for different individuals)
- Accessible wearable device data, specifically Fitbit data
- Transparent and meaningful model/logic based on background literature
Organizations: This project is part of the Big Ideas Lab at Duke University. We collaborated with the DISCOVeR Lab at Stanford University, which conducted the Strong-D study. The Strong-D Fitbit data set was used to develop, evaluate and publish this resting heart rate estimation model.
Publication:
C. Jiang, L. Faroqi, L. Palaniappan and J. Dunn, "Estimating Personal Resting Heart Rate from Wearable Biosensor Data," 2019 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Chicago, IL, USA, 2019, pp. 1-4.
doi: 10.1109/BHI.2019.8834554
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8834554&isnumber=8834448\
Please contact the contributors of this repository if you cannot access this publication.
RHR_estimation.R: helper functions for estimating resting heart rate from Fitbit data (heart rate and steps)
plotting.R: helper functions for plotting
example.R: example usage of the functions in this directory; the associated output plots are included in the example_output subdirectory
R packages:
data.tablemagrittrRcppRollggplot2
Sources: