Fig. 2: Overview of digital biomarker exploration and feature engineering for the ITA model development on the AF cohort.

a Time-series plot of the deviation in digital biomarkers (ΔSteps and ΔRHR) in the detection window compared to baseline periods, between the participants diagnosed as COVID-19 positive and negative. The horizontal dashed line displays the baseline median and the confidence bounds show the 95% confidence intervals. b Heatmaps of steps and RHR features that are statistically significantly different (p value <0.05; unpaired t-tests) in a grid search with a different detection end date (DED) and detection window length (DWL) combinations, with green boxes showing p values <0.05 and gray boxes showing p values ≥0.05. The p values are adjusted with the Benjamini–Hochberg method for multiple hypothesis correction. c Summary of the significant features (p value <0.05; unpaired t-tests) from b, with each box showing the number of statistically significant features for the different combinations of DED and DWL. The intersection of the significant features across DWL of 3 and 5 days with a common DED of 1 day prior to the test date (as shown using the black rectangle) was used for the ITA model development. d Box plots comparing the distribution of the two most significant steps and RHR features between the participants diagnosed as COVID-19 positive and negative. The centerlines denote feature medians, bounds of boxes represent 25th and 75th percentiles, whiskers denote nonoutlier data range and the diamonds denote outlier values.