Pre-symptomatic detection of COVID-19 from smartwatch data

Abstract

Consumer wearable devices that continuously measure vital signs have been used to monitor the onset of infectious disease. Here, we show that data from consumer smartwatches can be used for the pre-symptomatic detection of coronavirus disease 2019 (COVID-19). We analysed physiological and activity data from 32 individuals infected with COVID-19, identified from a cohort of nearly 5,300 participants, and found that 26 of them (81%) had alterations in their heart rate, number of daily steps or time asleep. Of the 25 cases of COVID-19 with detected physiological alterations for which we had symptom information, 22 were detected before (or at) symptom onset, with four cases detected at least nine days earlier. Using retrospective smartwatch data, we show that 63% of the COVID-19 cases could have been detected before symptom onset in real time via a two-tiered warning system based on the occurrence of extreme elevations in resting heart rate relative to the individual baseline. Our findings suggest that activity tracking and health monitoring via consumer wearable devices may be used for the large-scale, real-time detection of respiratory infections, often pre-symptomatically.

Fig. 1 |. Overview of the study design, cohort and data.

A total of 5,262 participants were recruited, including individuals who were: (1) sick and tested positive for COVID-19 (dark red); (2) sick and tested positive for other illnesses (gold); (3) sick without a confirmed diagnosis (dark grey); and (4) not sick but were at high risk of exposure (light grey). Participants were asked to log daily symptoms and to share their fitness tracker data via the study app, MyPHD. The data types collected included heart rate, steps and sleep over a period of several months. Two infection detection algorithms were developed (RHR-Diff and HROS-AD). The bottom two panels represent derived heart rate metrics from the two algorithms over a period of months in one individual, centred around the onset of symptoms (day 0). The earliest detected abnormal heart rate elevations are marked by red stars. The anomaly periods detected by RHR-Diff are spanned by red arrows. The anomaly time points detected by HROS-AD are marked by red dots. The symptom onset day and diagnosis day are indicated by vertical dashed red and purple lines, respectively.

Fig. 2 |. Association of heart rate with COVID-19 illness.

a, Summary of data collected from 32 study participants who reported a confirmed diagnosis of COVID-19 with a symptom onset and/or test date. Each row along the yaxis represents one participant, with prediction groups labelled to the left. The plot shows sick periods for COVID-19 (between black arrows, with dashed lines for unknown bounds where the symptom onset or recovery day was unclear), COVID-19 test dates (red crosses), sick periods for other illnesses (between orange arrows) and the days for which participants filled in the daily survey (diamonds), with purple diamonds representing days when symptoms were reported and blue diamonds representing days when symptoms were not reported. b, Overlapping bar plots depicting heart rate metrics and timings of infection detection from RHR-Diff and HROS-AD with respect to the infection detection window for COVID-19-positive participants. The plots are manually grouped into three groups: group I (single region; blue); group II (early and multiple regions; red); and group III (other; gold), focusing mainly on the single-region group. One additional participant who reported a diagnosis of influenza B is also shown for comparison (purple). Summary plots for all COVID-19-positive participants from all three groups are shown in Supplementary Fig. 2a, and all participants with other illnesses are shown in Supplementary Fig. 2b. The xaxis shows days during the infection detection window and the yaxis shows standardized residual values from RHR-Diff (brown bars) and standardized (0 to −1) HROS values from HROS-AD (transparent green bars) in the intervals during which COVID-19 infection was detected by each algorithm. These values are plotted separately for each participant. This window is a period of time centred around symptom onset at day 0 (substituted by the diagnosis day wherever the day of symptom onset was unavailable). The infection detection window spans a period of 15 d before day 0 and 7 d after day 0.

Fig. 3 |. Examples of heart rate metrics during COVID-19 illness.

a-d, Examples of heart rate metrics during COVID-19 infection for four individual participants, two from group I (a and b) and one each from group II (c) and group III (d). Red and purple vertical dashed lines indicate the days of symptom onset and diagnosis, respectively. Shown are the standardized HROS from the HROS-AD method (bottom plot in each panel; dark blue lines) and the standardized heart rate residuals from the RHR-Diff method (top plot in each panel; black lines). For RHR-Diff, the green dashed line is at 0. Gold solid triangles mark the infection detection window used to score detections as a hit or a miss. Also indicated are time intervals when the heart rate residuals were significantly elevated from RHR-Diff (red arrows in the top plots of each panel) and times when anomalies were detected by HROS-AD (red dots in the bottom plots of each panel).

Fig. 4 |. Summary of detection timing and heart rate during COVID-19 illness.

a,b, Histograms summarizing the distribution of early-detected COVID- 19 events compared with the first day of self-reported symptoms (a) and the reported diagnosis day (b). If multiple RHR-Diff intervals existed within or intersecting the COVID-19 event (−14/+7 d versus the symptom day), the first day of the closest interval ahead of this event was used. If no interval before the event was observed, the closest interval after the event was used. If the symptom day was not available or there was no interval detected within the 21 d surrounding the symptom onset, the closest interval within 28 d of the diagnosis onset (−21/+7 d) was used. The colours represent the number of individuals in each group (group I: blue; group II: red; group III: gold). The purple line shows the kernel probability density estimate. Also shown are individuals for whom the algorithm missed detecting COVID-19 infection (separated from the quantitative part of the graph by grey dashed lines). c, As in a, but for participants with other illnesses. d, Boxplots summarizing the hourly ARHR of the detected COVID-19 or other illness interval compared with the baseline RHR of the same individual. These boxplots exclude individuals for whom the RHR-Diff algorithm missed detecting infection. Central lines represent median values, box limits represent upper (third) and lower (first) quartiles, whiskers represent 1.5x the interquartile range above and below the upper and lower quartiles, respectively, and red crosses represent outliers. The number above each boxplot represents the median value of ARHR for the indicated individual.

Fig. 5 |. Summary of steps and sleep during COVID-19 illness.

a, Heatmap showing standardized daily steps per participant (that is, zscores of daily steps) for the 22 participants we have steps data for, and for whom RHR-Diff detected a change of RHR any day between 14d before the symptom onset date and 2 d after. Tile colours indicate the z score and asterisks represent the first day of detection for each participant. b, Boxplot showing the change in daily steps between days before and after the detection start date in a window of −21 d before to +7 d after the symptom onset date. c, Heatmap showing the standardized sleep duration per participant (that is, z scores of total sleep duration) for the 13 participants we have sleep data for and for whom RHR-Diff detected a change of RHR any day between 14d before the symptom onset date and 2 d after. d, Boxplot showing the change in total sleep duration between days before and after the detection start date in a window of −21 d before to +7 d after the date of symptom onset. For the heatmaps in a and c, black rectangles highlight the period after symptom onset. The boxplots in b and d include data with imputation (see Methods). Data without imputation are shown in Supplementary Figs. 4a,b. For both b and d, central lines represent median values, box limits represent upper (third) and lower (first) quartiles and whiskers represent 1.5x the interquartile range above and below the upper and lower quartiles, respectively.

Fig. 6 |. Association of COVID-19 symptoms with heart rate signal.

a-d, Plots of four individual participants (APGIB2T (a), AQC0L71 (b), A0VFT1N (c) and A1K5DRI (d)) over the course of COVID-19 infection. Vertical columns along the x axes each represent a single day of symptoms (from early illness (leftmost) to late illness) and are aligned with the heart rate metrics below. Columns showing symptoms are only present for the days when the daily survey was completed, while heart rate metrics progress continually below. ‘Overall feeling’ indicates how the participants reported feeling on a particular day, with a bar plot above indicating the measured temperature if reported, and specific symptoms highlighted below as a heatmap depicting the severity. Black vertical lines below the symptoms and descending into the heart rate metrics are labelled to highlight significant days during the illness course, and align with the symptoms above. The RHR-Diff plots show standardized heart rate residuals from RHR-Diff (black lines) and time intervals when the heart rate residuals were significantly elevated (red lines with arrowheads). The bottom plots in each panel show standardized HROS using the HROS-AD method (black line), and each detected anomaly is indicated by a red oval. e, Summary of symptoms data for individuals who provided surveys on a past COVID-19 illness. Each column represents a study participant, as labelled below. Shown are (from top to bottom): a bar plot of average temperatures in °C reported during illness; overall feelings (see legend above); total duration between reported symptom onset and recovery (if provided); a boxplot (showing numerical median values) of ARHR in beats per minute when heart rate residual alarms were raised; and a plot of individual symptoms (where black boxes indicate reported symptoms and white boxes indicate no reported symptoms). For the boxplot, central lines represent median values, box limits represent the upper (third) and lower (first) quartiles, the whiskers represent 1.5x the interquartile range above and below the upper and lower quartiles, respectively, and red crosses are outliers.

Fig. 7 |. Online detection of COVID-19 infection.

a-d, Examples of online prediction performance during COVID-19 infection for two participants with long-term data (a and b), one example of other (non-COVID-19) illness (c) and one example from the healthy group (d; note the smaller scale compared with a-c). For each plot, the xaxis is the number of days pre- or post-symptom onset. The red and purple vertical dashed lines indicate days of symptom onset and diagnosis, respectively, and the blue dashed vertical lines indicate the alarming time from online detection (see Methods).e, Alarm counts per 30 d for participants with COVID-19. The blue and red bars indicate the alarm counts before and after the COVID-19 event. Average alarm counts are 0.29 versus 1.35 before and after infection, respectively. f, Early detection comparison between offline detection (RHR-Diff) and online detection (CuSum). Detection days are compared with the symptom day. Each red circle indicates one participant. The black dashed line is the identity line and the blue dashed lines surrounding it are at a distance of ±1d from the identity line. The grey dotted lines separate the quantitative part of the graph from the missed cases. g, Comparison of the total alarm duration across the COVID-19 positive group, other illness group and potentially healthy group. h, Comparison of the alarm peak height across the different groups described in g. For each sickness case in g and h, the alarms are further assigned to three categories: pre-sickness, during sickness and post-sickness. Only P values with a significance of <0.05 are shown. In addition, a slight increase in alarm frequency was observed, but it did not achieve significance (Supplementary Fig. 7 and Supplementary Table 29). For the boxplots in g and h, central lines represent median values, box limits represent the upper (third) and lower (first) quartiles, whiskers represent 1.5x the interquartile range above and below the upper and lower quartiles, respectively and black dots represent outliers.