JMIR FORMATIVE RESEARCH

Gaur et al

Original Paper

Continuous Monitoring of Heart Rate Variability in Free-Living
Conditions Using Wearable Sensors: Exploratory Observational
Study
Pooja Gaur, PhD; Dorota S Temple, PhD; Meghan Hegarty-Craver, PhD; Matthew D Boyce, BS; Jonathan R Holt,
PhD; Michael F Wenger, BS; Edward A Preble, PhD; Randall P Eckhoff, BS; Michelle S McCombs, BS; Hope C
Davis-Wilson, PhD; Howard J Walls, PhD; David E Dausch, PhD
Research Triangle Institute, Research Triangle Park, NC, United States

Corresponding Author:
Pooja Gaur, PhD
Research Triangle Institute3040 East Cornwallis Road
Research Triangle Park, NC, 27709
United States
Phone: 1 9192481458
Email: pgaur@rti.org

Abstract
Background: Wearable physiological monitoring devices are promising tools for remote monitoring and early detection ofpotential health changes of interest. The widespread adoption of such an approach across communities and over long periods oftime will require an automated data platform for collecting, processing, and analyzing relevant health information.
Objective: In this study, we explore prospective monitoring of individual health through an automated data collection, metrics
extraction, and health anomaly analysis pipeline in free-living conditions over a continuous monitoring period of several months
with a focus on viral respiratory infections, such as influenza or COVID-19.
Methods: A total of 59 participants provided smartwatch data and health symptom and illness reports daily over an 8-monthwindow. Physiological and activity data from photoplethysmography sensors, including high-resolution interbeat interval (IBI)and step counts, were uploaded directly from Garmin Fenix 6 smartwatches and processed automatically in the cloud using astand-alone, open-source analytical engine. Health risk scores were computed based on a deviation in heart rate and heart ratevariability metrics from each individual’s activity-matched baseline values, and scores exceeding a predefined threshold werechecked for corresponding symptoms or illness reports. Conversely, reports of viral respiratory illnesses in health survey responses
were also checked for corresponding changes in health risk scores to qualitatively assess the risk score as an indicator of acuterespiratory health anomalies.
Results: The median average percentage of sensor data provided per day indicating smartwatch wear compliance was 70%,and survey responses indicating health reporting compliance was 46%. A total of 29 elevated health risk scores were detected,of which 12 (41%) had concurrent survey data and indicated a health symptom or illness. A total of 21 influenza or COVID-19
illnesses were reported by study participants; 9 (43%) of these reports had concurrent smartwatch data, of which 6 (67%) had anincrease in health risk score.
Conclusions: We demonstrate a protocol for data collection, extraction of heart rate and heart rate variability metrics, andprospective analysis that is compatible with near real-time health assessment using wearable sensors for continuous monitoring.
The modular platform for data collection and analysis allows for a choice of different wearable sensors and algorithms. Here, we
demonstrate its implementation in the collection of high-fidelity IBI data from Garmin Fenix 6 smartwatches worn by individuals
in free-living conditions, and the prospective, near real-time analysis of the data, culminating in the calculation of health risk
scores. To our knowledge, this study demonstrates for the first time the feasibility of measuring high-resolution heart IBI andstep count using smartwatches in near real time for respiratory illness detection over a long-term monitoring period in free-living
conditions.
(JMIR Form Res 2024;8:e53977) doi: 10.2196/53977

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KEYWORDS
heart rate variability; physiological monitoring; wearable sensors; smartwatch; PPG; photoplethysmography; monitoring;
physiological; heart rate; wearable; wearables; sensor; sensors; observation study; wearable devices; devices; remote monitoring;
community; data platform; data collection; health risk

Introduction
BackgroundThe use of wearable sensors for monitoring health status hasgrown in popularity, in applications ranging from fitness toillness management [1]. Wearable sensors have the potential toprovide continuous, near–real-time monitoring of humanphysiology with minimal discomfort during daily life activities[2]. Among wearable sensors, commercial-off-the-shelf devices,such as smartwatches and smart rings, that integrate heart rate(HR), blood oxygen, and activity monitors are popular devices
for use in real-world physiological monitoring because of theiralready broad owner base and form factor that is compatible
with long-term wear [3].
Heart rate variability (HRV) provides insight into the interplay
between sympathetic and parasympathetic branches of theautonomic nervous system in response to external stressors [4].
HRV measurement using photoplethysmography (PPG) sensors,
such as those embedded in smartwatches, has been tested against
and found to correspond with gold-standard electrocardiogram(ECG) measurements [5]. Although PPG- and ECG-derived
HRV metrics have generally good agreement, PPG-based
measures can differ from ECG-based measures in the presenceof physical activity, cold exposure, and other factors [6-8].
Wearable sensors provide an opportunity for personalized
detection of health changes relative to an individual’s baseline
state, which can differ from normalized or population-levelreference values [9-11]. Interbeat interval (IBI) time-series data
can be used to derive HR and HRV metrics. Changes in aperson’s HR and HRV over time can signal illness, infection,and other health conditions [12-14]. Analysis of HR-based
measures recorded by smartwatches and smart ring sensors has
shown that these measures can indicate the presence of
respiratory infections, such as influenza and COVID-19, before
symptoms start [15-19].
One COVID-19 detection study retrospectively analyzed datafrom smartwatch users over a period of several months using
HR and step summary data provided by the device vendor [18].
Another retrospective study used summary HR, HRV,
respiration rate, temperature, and activity data recorded fromsmart ring users, although HR, HRV, and respiration rate were
only available during sleep periods [19]. A prospective
COVID-19 monitoring study of health care workers focusing
primarily on existing smartwatch users collected
vendor-provided HRV metrics over a period of 5 months [17].
Although vendor-provided HRV measures can be useful tomonitor health trends, high-resolution IBI and activity data aredesirable to enable transparent, open-source calculation of afull set of HRV metrics and their use in a broad range ofalgorithms. A long-term COVID-19 monitoring study of active
military personnel retrieved average HR and step-counts datafrom the device-vendor cloud platform for offline processing,https://formative.jmir.org/2024/1/e53977

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but the availability of these data through the cloud was typicallydelayed by 24-48 hours relative to the time of device recording[15].

Goals of the Study
Unlike the aforementioned studies, we report results fromparticipants in the community under free-living conditions who
were recruited independently of their smartwatch ownership or
use status. That is, we include results from participants who
may or may not have been familiar with smartwatch andaccompanying mobile phone app use prior to involvement inthis study. The validity of continuous monitoring using 4
wearable sensors in a simulated free-living environment
compared with 2 reference devices found that HR accuracy was
generally high when not confounded by physical activity [2].
In this study, we used 1 wearable device to collect data in actual
free-living environments over a monitoring period of 8 months.
Additionally, the data presented in this paper included each IBI
recorded by PPG-based sensors, along with step counts, blood
oxygen level, and respiration rate. The high-resolution data were
processed prospectively in real time using open-source
calculations to yield HR and HRV metrics. Study-provided
Garmin Fenix 6 smartwatches paired to iOS and Android
smartphones running a custom mobile app were used forlong-term continuous monitoring. We explored the applicationof these tools to the monitoring of a cohort of 59 individuals
and reported observations related to the study protocol and dataset.
The objectives for the study were to (1) quantify the quality ofdata (data missingness and artifacts) collected in free-living
conditions, (2) present examples of HR and HRV-derived
metrics during a baseline healthy period, (3) use the healthy
baseline as the foundation for the standardization of HRV
metrics and qualitatively assess if the multivariable anomaly
score (health risk score) shows promise as an indicator of viral
respiratory illness, and (4) demonstrate the data collection,
metrics extraction, and analysis through an automated processing
pipeline for prospective monitoring. The health risk score ispredicated on a difference between physiological metrics relative
to the healthy baseline period, where we implement activity
matching to compare current and baseline metrics within similar
activity bins. Activity matching is an uncommon feature of thistype of data analysis, as most device vendors do not provide
metrics corrected for the level of activity.
Of particular interest in the study was the response to airborne
pathogens, such as COVID-19, influenza, and other respiratory
viruses, self-reported by participants through health survey
questionnaires. Health risk scores exceeding a predefined
threshold were checked for corresponding symptom or illness
reports. Conversely, reports of viral respiratory illnesses inhealth survey responses were checked for corresponding changes
in health risk scores to qualitatively assess the score as anindicator of acute respiratory health anomaly.
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We demonstrated the use of an open-source, modular platform
for the collection of high-resolution, raw data directly fromsmartwatch devices that bypass vendor analytics and cloudservices in real-world conditions. In testing this platform, we
followed participants’ experiences with using this system andgathered feedback to improve functionality and usability. The
data collection and analysis system can be applied to large-scalestudies and can be paired with other choices for devices andalgorithms.

Methods
Study Design
To investigate the real-world use of HRV monitoring throughan automated data collection and processing pipeline in thecontext of prospective illness detection, we conducted an
exploratory observational study combining smartwatch dataand health symptoms reported by each individual through amobile phone app. Participants wore smartwatch devices during
normal daily activity and completed surveys of health symptoms.
This study reports results from data collected over a period of
8 months.

Ethical Considerations
The human participant research protocol was reviewed andapproved by the RTI International Institutional Review Board(STUDY00022001) and the US Army Medical Research andDevelopment Command Office of Human Research Oversight(E02867.5a). Participants were recruited as a conveniencesample from local law enforcement and public healthdepartments. The consent process emphasized that participation
was voluntary and that participants could withdraw at any time
without penalty. Informed consent was provided by allparticipants. Personal identifying information was stored in asecured file and kept separate from data collected once enrolled
in the study. All smartwatch and survey data were deidentified
and labeled by a participant identifier for analysis. The
smartwatch was offered to participants as study compensation
contingent upon their compliance in providing smartwatch andsurvey data at least 60% of the time (excluding any technicalissues).

Wearable and Smartphone Devices
Participants were issued Garmin Fenix 6 smartwatches and were
offered the use of a Samsung Galaxy A12 study phone or the
option to use their phone to pair the watch with the smartphone
app for data collection. The SIGMA+ (S+) Health mobile app
compatible with iOS and Android devices was used for syncing
data from the smartwatch and uploading files to the study
database. Data were transferred from the watch to the phoneusing Bluetooth and from the phone to the cloud-based databaseusing Wi-Fi or cellular connectivity. The data collection
architecture is described by Temple et al [20].

ProtocolStudy demographic and health symptom questionnaires werecollected and managed using REDCap (Research Electronic
Data Capture; Vanderbilt University) [21,22]. At the time of
study enrollment, participants self-reported demographic
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Gaur et al
information including age, sex, height, weight, race, Hispanic
or Latino status, influenza vaccination status, COVID-19
vaccination and booster status, and the presence of any of thefollowing underlying medical conditions: chronic lung disease,
moderate to severe asthma, serious heart condition,
immunocompromised, severe obesity (BMI of ≥40 kg/m2),
cancer, diabetes, chronic kidney disease undergoing dialysis,
liver disease, or smoker.
Participants completed a daily health symptom survey accessible
via a link from the S+ Health app to a REDCap questionnaire.
The questionnaire consisted of symptom severity ratings and atext field for additional notes (eg, stressful events and illness
details). Symptoms were rated on a scale of 0 (no symptoms)
to 3 (severe symptoms) for the following categories: allergies,
runny nose, sore throat, cough, shortness of breath, fever,fatigue, headache, body ache, loss of taste and smell, andgastrointestinal symptoms. A symptom score was calculated
from a sum of the 11 symptom values reported in the survey.
Participants were asked to wear smartwatches continuously
during normal daily activities to the extent possible. Participants
were requested to sync data between the smartwatch and the
S+ Health app at least twice per day. A study coordinator wasavailable to assist participants with technical support during thestudy and to note any feedback shared by participants on using
the smartwatch and the S+ Health app. At the close of the study,
participants were invited to complete a survey on theirexperience.

Data Collection and Processing
Data Collection
Health symptom survey response data were downloaded from
REDCap and processed to extract a total symptom score (ie,the sum of the 11 individual symptoms that were rated on ascale of 0 to 3, with 3 indicating severe symptoms) and theself-reported presence of influenza, COVID-19, or other illness.
Diagnostic testing and reporting were not strictly required, andlaboratory test results were not always available to verify illness
reports. Any other self-reported health events provided as survey
comments were also noted.
IBI, step count, respiration rate, and blood oxygenation data
recorded by the Garmin watches were synced to the S+ Health
app and uploaded to the AWS cloud (Amazon Web Services,
Inc) for storage and computational processing. The analysis in
this study focused on step count and IBI measurements. IBI
data included (ideally) the interval between each individual
heartbeat. Step counts were reported every 1 minute.

Data Quality
To quantify data volume and data artifacts, raw data fraction(RDF), valid data fraction (VDF), and artifact data fraction(ADF) were defined. Data were averaged into 5-minute epochs
based on time of collection within a 24-hour period, with amaximum of 288 epochs per day (24 hours per day×12 epochs
per hour). The RDF represents the ratio of the number of uniquedata point timestamps received to the total number of data points
possible during the monitoring period, in this case, the number

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of measured epochs relative to the maximum of 12 epochs perhour (hourly measure) or 288 epochs per day (daily measure).
Subsets of these measured epochs were labeled as valid if at
least 2 step counts were measured and at least 60 valid IBI data
points (>0.3 and <1.5 seconds) were measured. Two step countreadings were required to ensure that the watch was powered
on for at least 60 seconds (ie, the S+ app acquires a one-stepcount reading every minute). A total of 60 valid IBI points wererequired to ensure good data quality for calculating HRV duringresting states (ie, at least 1 minute of data in each 5-minuteepoch is needed for capturing accurate low-frequency
responses). A total of 60 valid IBI points in each epoch wererequired to calculate a representative HR during higher levelsof activity when motion artifacts can be significant. The VDF
was computed as the ratio of the number of valid epochs to the
maximum of 12 epochs per hour (hourly measure) or 288 epochs
per day (daily measure). ADF was computed as the ratio of thenumber of data points removed during data cleaning to the total
number of data points in the monitoring period for each data
type. A binary daily survey data fraction (SDF) was computed
to quantify survey reporting compliance.

Data Processing
Data were processed to compute “base metrics” and“standardized metrics.” Base metrics included step counts
averaged in 5-minute and 30-minute windows, and HR and
HRV metrics calculated from 5-minute windows of the IBI timeseries, including HR, log of high-frequency power (HF), log of
low-frequency power (LF), LF/HF, median IBI, SD of IBI, androot mean square of successive IBI differences [23].

Gaur et al
The HR and HRV metrics were then standardized using a z
score calculation relative to “matched” historic healthy data.
Metric standardization was performed to minimize the impact
of differences from individual to individual and to incorporate
activity matching to minimize the impact of physical activity
level in the comparison of metrics. Data were “matched” using
the 5- and 30-minute step counts by binning into resting (0 step
per minute), highly active (140+ steps per minute), and all otherstep counts within ±30% (or at least 10 steps per minute). Eachdata point was compared to past data points (“baseline”) at asimilar (“matched”) activity level and a measure of thedeviation—z score—was calculated for IBI, HF, and LF metrics.
We required that the current data be well separated from thebaseline data (eg, by at least 24 hours) that it was matched withbecause a person’s current health state is strongly correlatedwith their recent (eg, within the past hour) health state. We
further required that the baseline exclude data points that werepreviously marked as anomalous (eg, elevated risk score). We
assumed that people were healthy for the first week of datacollection unless otherwise noted.
We smoothed both the standardized baseline and current data
to remove rapid changes from short-term stressors using a 1-hourmoving average. We calculated the Mahalanobis distance [24]between the current and healthy baseline smoothed, standardized
data to obtain the health risk score. The risk score was initiallydeveloped in an influenza-challenge study in which participants
were monitored using wearable ECG and physical activity
sensors and tested for infection status [16]. Figure 1 illustrates
the data processing workflow.

Figure 1. Data collection and algorithm workflow. The smartwatch is paired to a smartphone app, and data are uploaded to the data server. Data cleaning
corrects sensor-specific artifacts and removes periods where data quality is poor or there is not enough data to make a representative calculation. Metrics
are standardized by computing a z score, which corrects for physical activity using an individual’s healthy baseline data and eliminates between-person
variation. The output of the algorithm is a risk score.

Data metrics and risk scores were stored in a Postgres
database. Further details of the operation of the S+ Health app,
data flow from watches to SyncHubs to data servers, and thedata processing methodology are described by Temple et al[20]. Algorithms, data processing, and visualization were
implemented in Python using open-source packages [25-31].

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Results
Cohort Demographics
Health symptoms, illness, and watch data collected from January
through August 2023 are reported for 59 participants. The cohort
consisted of 17 female participants and 42 male participants
with characteristics shown in Table 1. A total of 5 participants
left the study early, and in the last 15 weeks of data collection,an additional 7 participants joined.

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Table 1. Participant characteristics of a convenience sample of 59 individuals who provided smartwatch and health survey data between January 2023
and the end of August 2023.
Characteristics

Male (n=42)

Female (n=17)

Age (years), median (IQR)

41 (32-50)

42 (36-54)

BMI (kg/m2), median (IQR)

30 (27-35)

31 (28-31)

Underlying medical condition, n (%)

6 (14)

1 (6)Influenza

16 (38)10 (59)COVID-19

31 (74)17 (100)

COVID-19 booster

18 (43)14 (82)

Asian

1 (2)

0 (0)

Black or African American

0 (0)

1 (6)

White

40 (95)15 (88)

Two or more

0 (0)

1 (6)

Other

1 (2)

0 (0)

4 (10)

2 (12)

Law enforcement

29 (69)5 (29)

Public health

11 (26)

7 (41)

Other

2 (5)

5 (29)

iOS

23 (55)10 (59)Android19 (45)7 (41)

Vaccination, n (%)

Race, n (%)

Hispanic or Latino
Occupation, n (%)

Smartphone operating system, n (%)

Data Collection and Processing
Figure 2 shows the raw data collected for the 8-monthmonitoring period, which includes periods of no data fromparticipants who either left the study or joined partway through.
To present a more complete picture of the data, we reportcharacteristics both over the whole 8-month period and overthe last 15 weeks, excluding the 5 participants who left the
study.
The median value across participants of the average RDF overthe monitoring window and the IQR are shown in Table 2. The
subset of raw data that met validity criteria is shown in Figure

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RenderX3, with median and IQR values reported in Table 2. From theraw data measured, we quantified the IBI ADF per participant(Figure 4). Because the ADF was calculated relative to themeasured data and no value was computed during data gaps,
we only report values over the 8-month period in Table 2.
Health symptom SDF is shown in Figure 5 and described inTable 2. Participant data are plotted in the same order along they-axis in Figures 2-5 to facilitate comparisons between RDF,
VDF, ADF, and SDF per participant. The average RDF andsubmitted survey percentages are not necessarily similar among
participants (ie, a relatively high RDF may or may notcorrespond to a relatively high survey response rate).

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Figure 2. RDF per participant for a convenience sample of 59 individuals who provided smartwatch data between January 2023 and the end of August2023. Labels on the y-axis indicate the average RDF over the 8-month monitoring period per participant. Each row illustrates daily RDF values per
participant. Symbols × indicate participants who left the study and + indicate participants who joined in the last 15 weeks of the study. RDF: raw data
fraction.

Table 2. Raw, valid, IBIa artifact, and survey data fraction characteristics measured from data collected from 59 individuals between January 2023 andthe end of August 2023. Metrics are reported over the whole 8-month period and over a 15-week period at the end of the study corresponding to when
the last 7 participants were enrolled.

aMetricEight-month period (%), median (IQR)

Last 15 weeks (%), median (IQR)

RDFb

70 (41-84)

81 (60-91)

VDFc

48 (26-68)

60 (40-73)

ADFd

30 (21-42)

—e

SDFf

46 (28-72)

58 (26-75)

IBI: interbeat interval.

b

RDF: raw data fraction.

c

VDF: valid data fraction.

d

ADF: artifact data fraction (calculated for IBI metric).

e

Not available.

f

SDF: survey data fraction.

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Figure 3. VDF per participant for a convenience sample of 59 individuals who provided smartwatch data between January 2023 and the end of August2023. Labels on the y-axis indicate the average VDF over the 8-month monitoring period per participant. Each row illustrates daily VDF values per
participant. The participant order along the y-axis corresponds to the y-axis participant ordering shown in Figure 2. Symbols × indicate participants
who left the study and + indicate participants who joined in the last 15 weeks of the study. VDF: valid data fraction.

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Figure 4. IBI ADF per participant for a convenience sample of 59 individuals who provided smartwatch data between January 2023 and the end of
August 2023. Labels on the y-axis indicate the average ADF calculated from the data measured over the 8-month monitoring period per participant.
Each row illustrates daily ADF values per participant. The participant order along the y-axis corresponds to the y-axis participant ordering shown in
Figure 2. Symbols × indicate participants who left the study and + indicate participants who joined in the last 15 weeks of the study. ADF: artifact data
fraction; IBI: interbeat interval.

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Figure 5. Self-reported health symptoms and illness SDF per participant for a convenience sample of 59 individuals who provided data between January2023 and the end of August 2023. Labels on the y-axis indicate the percentage of daily survey reports submitted relative to the number of days in the
8-month monitoring period per participant. The color map indicates symptom survey reports submitted per day per participant. The participant orderalong the y-axis corresponds to the y-axis participant ordering shown in Figure 2. Symbols × indicate participants who left the study and + indicate
participants who joined in the last 15 weeks of the study. SDF: survey data fraction.

Data Recording Challenges
During the 8-month monitoring period, 6 participants reported
smartwatch-related issues and were offered replacement devices.
Some participants experienced difficulty using the mobile phone
app to upload data from the smartwatch. A total of 10 mobile
phone app updates (combined iOS and Android) were released
to mitigate data-syncing issues, including updates to the Garmin
software development toolkit, and to improve user experience.
Individual help was offered to participants who required
assistance in updating the app on their phones. Additional
challenges to data recording included a dislike of wearing thesmartwatch, reported by 1 participant, and a prolonged loss of
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RenderXa charging cable, reported by another participant. Technicalissues with either the smartwatch or app and diminished study
engagement negatively impacted the data and survey responses
shown in Figures 2 and 5.

Participant Feedback
A participant experience survey was distributed at the end of
the monitoring period. Of the 43 (73%) participants whocompleted the survey, 16 (37%) identified as being not at all
familiar with using a smartwatch prior to joining this study.
Regarding the smartwatch, 30 (70%) responded that the watch
was a good size, 7 (16%) responded that it was not easy to use,
and the most liked features of the watch were access to health
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data (n=15, 35%), activity tracking (n=9, 21%), and sleep
tracking (n=8, 19%). Issues with data syncing and mobile appfunctionality were the most commonly reported problems.
Figure 6 shows example base and standardized HRV metrics,
IBI ADF, and risk scores calculated in 5-minute epochs andhourly averages of RDF and VDF extracted from 2 weeks of a

Gaur et alcleaned data set. Health survey responses during this time
window did not report illness or symptoms. The metrics areshown as a representation of healthy baseline data (ie, z scores
above –3 and no reported symptoms). These data also exemplify
high smartwatch wear compliance and symptom survey
compliance, with occasional short gaps in continuous
monitoring.

Figure 6. Example metrics measured from a participant over a period of 2 weeks in April 2023. All values were calculated in 5-minute epochs except
symptom scores, which were calculated from self-report of individual symptoms. RDF and VDF are averaged by hour. The risk score remained below
the threshold (dotted gray line) and symptom scores were zero for general and respiratory symptom categories throughout the 2 weeks. The risk scores
were detected from analysis of heart rate variability-derived and activity metrics. The symptom scores were calculated from symptom ratings on a scaleof 0 (no symptoms) to 3 (severe symptoms) for the following categories: allergies, runny nose, sore throat, cough, shortness of breath, fever, fatigue,headache, body ache, loss of taste and smell, and gastrointestinal symptoms. ADF: artifact data fraction (calculated for IBI metric); HF: log ofhigh-frequency power; IBI: interbeat interval; LF: log of low-frequency power; RDF: raw data fraction; RMSSD: root mean square of consecutive
heartbeat intervals; SDNN: standard deviation of IBI; Step: step count; VDF: valid data fraction; z: standardized metric.

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Cohort Observations
We explored the correspondence of reported symptoms or illness
with the occurrence of elevated risk scores based on a thresholdvalue of 15. This is the threshold value for which the algorithm
demonstrated high sensitivity and no false alarms in the previous
influenza-challenge study [16]. During the 8-month monitoring
period of this study, 29 elevated score events were noted, of
which 25 had accompanying survey data. Of these, 12 (48%)
reported symptoms or illness of any type, and 13 (52%) did notreport any symptoms. Because the study protocol did not include
diagnostic testing, we were unable to confirm the presence ofasymptomatic cases.
We also explored the occurrence of COVID-19 and influenza
cases and whether an accompanying change in risk score wasobserved. Based on health survey responses, 21 COVID-19 andinfluenza events were reported. A total of 9 (43%) participants
who reported COVID-19 or influenza recorded watch data at

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the time of the reported illness. Of these, 6 (67%) participants
had corresponding increases in risk score (above or below
threshold). Table 3 summarizes the number of above-thresholdrisk scores detected, and influenza and COVID-19 illnesses.
Figure 7 illustrates the events reported per month along withmetrics of the COVID-19 virus detected in county wastewater[32].
Figure 8 shows time series plots of step, IBI, standardized
HRV-derived metrics, S+ health risk scores, and symptom scoresfor 1 participant who reported a positive test for COVID-19
after initially reporting illness with influenza. Symptoms werereported over a period of 15 days and are shown for
respiratory-specific and general (any type) symptoms.
Additionally, illness reports for influenza and COVID-19 areannotated on the bottom panel of Figure 8. An elevated risk
score preceding and coinciding with reported symptoms of
illness is observed.

Table 3. Summary of self-reported influenza and COVID-19 illness or symptoms and elevated risk score events from a convenience sample of 59
individuals who provided smartwatch and health survey data between January 2023 and the end of August 2023. Elevated risk scores were detectedfrom analysis of heart rate variability–derived and activity metrics.
SourceCount, n (%)

Elevated risk score (n=29)
Symptoms reported

12 (41)

No symptoms reported

13 (45)

No survey submitted

4 (14)

Influenza or COVID-19 illness (n=21)
Increase in risk score

6 (29)

No increase in risk score

3 (14)

No smartwatch data

12 (57)

Figure 7. The number of influenza and COVID-19 illness and elevated risk score events observed by month and normalized US county metrics showing
the percent of wastewater samples with detectable COVID-19 virus (noise added). Elevated risk scores were detected from analysis of heart ratevariability-derived and activity metrics of a convenience sample of 59 participants who provided smartwatch and health survey data between January2023 and the end of August 2023. Influenza and COVID-19 illnesses were self-reported by participants through daily surveys and may or may not havebeen ascertained by diagnostic tests.

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Gaur et al

Figure 8. Metrics from a participant who initially reported influenza and ultimately tested positive for COVID-19 in August 2023. Elevated risk scores
relative to a threshold of 15 (dotted gray line) are observed prior to and coinciding with reported symptoms. The risk scores were detected from analysisof heart rate variability-derived and activity metrics. Plots of step count, IBI, RDF, VDF, IBI ADF, standardized IBI metrics, risk score, and healthsurvey symptom score are shown for a period of 1 month. Markers indicating self-reported influenza and COVID-19 illnesses are included alongsidenumeric scores of general (any type) and respiratory-related symptoms. The symptom scores were calculated from symptom ratings on a scale of 0 (nosymptoms) to 3 (severe symptoms) for the following categories: allergies, runny nose, sore throat, cough, shortness of breath, fever, fatigue, headache,body ache, loss of taste and smell, and gastrointestinal symptoms. ADF: artifact data fraction; HF: log of high-frequency power; IBI: interbeat interval;
IBI: interbeat interval; LF: log of low-frequency power; RDF: raw data fraction; VDF: valid data fraction; z: standardized metric.

Discussion
Principal Results
This exploratory study included continuous monitoring ofindividuals to determine HRV metrics over a baseline (healthy)
window, which provides insight into long-term changes in HRV
and a reference range for each metric. For the data collection
and analysis, we used a standalone, vendor-agnostic modular
system that is scalable to large cohorts [20].
We used the multivariate anomaly score (risk score) calculated
relative to the baseline period as an indicator of exposure to
stressors. The risk score was originally developed and tested in
an influenza-challenge study in which participants weremonitored before, during, and after inoculation, and the infection
status was confirmed by a polymerase chain reaction test [16].
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In this study, our objective was to assess if the risk score
calculated using metrics extracted from PPG sensors duringfree-living conditions shows promise as an indicator of
respiratory infection.

Data Missingness and Artifacts
The observed RDF and SDF illustrate the wide variation in
device compliance and study engagement in free-living
conditions over long monitoring periods. The levels of device
compliance and survey compliance were not necessarily related
within participants (Figures 2 and 5). Technical issues with thesmartwatch or use of the mobile phone app were encounteredand may have contributed to reduced user compliance. These
were mitigated with replacement watches, personalized technicalsupport, and app updates. In general, using multiple data sources
such as smartwatches and surveys comes with the risk of
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unbalanced data compliance, which should be considered instudy protocols that rely on both data sources.

Gaur et al

Incorporation of Other Algorithms and Sensors

Along with RDF, we also report VDF and ADF. Inherent indata collection is the possibility that the VDF may be relatively
low even when the RDF over a period is relatively high. This
could occur in a situation where the watch is recording data but
is not actually being worn. For example, in some of these cases,
an increase in IBI ADF can indicate artifactual data recording(Figures 2-4).

The data collection and analysis platform can incorporate otheralgorithms that use HRV metrics obtained from physiologicalsensor data to detect illness. Similarly, other physiologicalsensors could be chosen for data collection. The modular natureof the software platform and the use of transparent, open-source
routines for data cleaning and metrics extraction, bypassing
algorithms proprietary to device vendors, supports theincorporation of other analytical approaches or wearable devices.

Cohort Observations

Limitations

The interpretation of risk algorithm performance depends
fundamentally on wear compliance and quality of ground truth
information, in this case, symptom and illness reporting
compliance. A total of 12 (57%) influenza and COVID-19 cases
reported did not have accompanying smartwatch data. Of the 9(43%) that did have watch data, 6 (67%) were observed to havean increase in risk score. Influenza and COVID-19 infections
were identified from self-reports rather than by diagnostic tests;
and thus, there may have been cases that were not reported andreported cases that were not in actuality influenza or COVID-19.

We have discussed the limitations of cohort size and groundtruth uncertainty for the assessment of illness detection in thisstudy. Additionally, some technical challenges were reported
by participants, which were mitigated by watch replacements
and smartphone app updates. A further shortcoming is the lackof racial diversity in the study cohort. Recent research indicatesthat the accuracy of HR measured by most leading wearable
optical sensors is not significantly affected by skin tone,
although devices themselves vary in their measurement
accuracy, particularly during activity [35]. A 2019 survey of4272 individuals reported only modest differences in smartwatch
use across race, ethnicity, and sex, but larger disparities basedon income level [36]. This has raised the additional concern ofsocioeconomic bias in wearables health monitoring [37].
Algorithm biases based on sex and age have also been reported[19]. To improve health equity, future studies should strive toinclude a more balanced representation of participants.

We explored the prospective use of health anomaly detection
based on a previously determined parameter threshold for thisalgorithm [16]. A total of 12 (48%) elevated risk score events
relative to this threshold that included survey data also reported
symptoms or illness. The choice of value for the thresholdparameter was based on the results of a previous study that used
ECG measurements of patients with confirmed infection status,
and thus may not be optimally tuned for this PPG sensor dataset.
The small sample size and lack of diagnostic tests to confirm
self-reported illnesses limited statistical analysis and the ability
to explore threshold optimization in this study. Similarly, we
did not have enough cases to investigate the application of thealgorithm to health anomalies other than influenza, thecharacteristics of which may or may not be adequately detected.
We did observe similar trends in the number of elevated risk
scores and number of reported influenza and COVID-19 cases
per month, which were qualitatively similar to trends in
COVID-19 virus detected in county wastewater (Figure 7).
Approximately 47% of influenza and 40%-45% of COVID-19
cases are asymptomatic [33,34]. Without a rigid testing protocol
in our study design, and without symptoms to prompt
participants to seek testing, we were unable to confirm
asymptomatic cases. Figure 8 illustrates a symptomatic infection
case that was initially presumed to be influenza but was later
confirmed to be COVID-19 through testing. This example
highlights the importance of ground truth data for researchinterpretation and that, in the absence of health testing, illness
presence or type is not necessarily known.
Vaccination status for influenza and COVID-19, including
COVID-19 boosters, was self-reported at the time of study
onboarding, which preceded the start of the monitoring period
reported in this paper by 2 months. Updates to vaccination status
were not requested during the study. Therefore, it is possiblethat additional vaccinations were obtained by participants andnot recorded in the cohort characteristics listed in Table 1.
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Comparison With Prior Work
Previous research reporting on the use of wearables sensors forlong-term health monitoring has obtained data processed by
vendor-provided analytics, included a cohort based heavily ona smartwatch or smart ring ownership or on active military
status, or performed analysis retrospectively [15,17-19]. To ourknowledge, this is the first study to collect high-resolution IBI
data recorded from the device in a community-based civilian
sample in free-living conditions for prospective analysis that
does not require accessing the device vendor cloud and iscompatible with near real-time monitoring.
Other long-term monitoring studies have incorporated surveys
along with wearables data [15,17-19], but to our knowledge,
studies have not reported metrics for detailed quantification ofdata collection, missingness, and quality for the wearable andsurvey data. Neglecting to wear the device during periods ofillness, which we observed in our cohort, has also been reported
in a previous study in which only 32 of 114 (28%) participants
wore their devices around the time of infection [18]. A studyof 30,529 participants spanning 11.5 weeks in the spring of2020 reported that only 8.7% of participants who experienced
symptoms followed up with a COVID-19 test, reinforcing thereality that diagnostic testing may not always be available orused when monitoring population health [37].

Conclusions
We have described the application of a data collection andanalysis pipeline for continuous wearable–based physiologicalmonitoring and health risk score assessment under free-living
conditions over a period of 8 months. This study used
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JMIR FORMATIVE RESEARCH
commercial-off-the-shelf sensors and open-source algorithms
that provided updated health scores every 5 minutes.
High-resolution data were collected directly from the smartwatch
hardware and processed automatically through an integrated
software platform. The platform architecture is modular, anddifferent sensors or algorithms could be incorporated. Data
quality was quantified for smartwatch data using metrics for
raw, valid, and artifact data fractions and for health survey data.
Study engagement was generally sufficient to calculate healthrisk scores, although inconsistent survey inputs limited
confirmation of illness in relation to elevated risk scores or of

Gaur et alphysiological changes in relation to reported illness in manycases. The risk score calculation shows promise as an indicatorof respiratory infections but needs to be validated in a large-scale
study, in which the illness is confirmed by laboratory tests. To
our knowledge, this study represents the first to collecthigh-resolution, real-time IBI and step count data directly fromthe device (bypassing the device-vendor cloud andvendor-provided analytics) during long-term monitoring of acommunity population in free-living conditions and analysis of
the data to assess acute respiratory health anomalies in real time.

Acknowledgments
The authors acknowledge Mark Wrobel and Peter Larson for their support and guidance and Matthew Blackston, Dan Zygmund-Felt,
and Adam Luxon for their support in data collection and processing. This research was supported in part by the Defense Advanced
Research Projects Agency (contract 140D63-19-C-0023). The views, opinions, and findings expressed are those of the authors
and should not be interpreted as representing the official views or policies of the Department of Defense or the US government.
This paper is approved for public release, and the distribution is unlimited. Artificial intelligence was not used in any portion ofwriting this manuscript.

Data Availability
The data analyzed in this study are not publicly available due to restrictions of the Defense Advanced Research Projects Agency(contract 140D63-19-C-0023) but are available from the corresponding author on reasonable request.

Authors' Contributions
DST, DED, and PG conceptualized the study. DST and MH-C developed the study methodology. EAP, JRH, MFW, MDB, andRPE developed the software and mobile apps. PG, JRH, MH-C, and HCD-W performed formal analysis. EAP performed data
curation. PG and DST wrote the original manuscript. DST, DED, and HJW reviewed and edited the manuscript. PG developed
data visualization. DED performed project administration. MSM managed the study cohort. All authors reviewed the final
manuscript.

Conflicts of Interest
None declared.

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Abbreviations
ADF: artifact data fraction
ECG: electrocardiogram
HF: log of high-frequency power
HR: heart rate
HRV: heart rate variability
IBI: interbeat interval
LF: log of low-frequency power
PPG: photoplethysmography
RDF: raw data fraction
REDCap: Research Electronic Data Capture
SDF: survey data fraction
VDF: valid data fraction

Edited by A Mavragani; submitted 27.10.23; peer-reviewed by JM Lim, H Younes; comments to author 20.03.24; revised version
received 14.05.24; accepted 06.06.24; published 07.08.24
Please cite as:
Gaur P, Temple DS, Hegarty-Craver M, Boyce MD, Holt JR, Wenger MF, Preble EA, Eckhoff RP, McCombs MS, Davis-Wilson HC,
Walls HJ, Dausch DE
Continuous Monitoring of Heart Rate Variability in Free-Living Conditions Using Wearable Sensors: Exploratory Observational
Study
JMIR Form Res 2024;8:e53977
URL: https://formative.jmir.org/2024/1/e53977
doi: 10.2196/53977
PMID:©Pooja Gaur, Dorota S Temple, Meghan Hegarty-Craver, Matthew D Boyce, Jonathan R Holt, Michael F Wenger, Edward A
Preble, Randall P Eckhoff, Michelle S McCombs, Hope C Davis-Wilson, Howard J Walls, David E Dausch. Originally publishedin JMIR Formative Research (https://formative.jmir.org), 07.08.2024. This is an open-access article distributed under the termsof the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work, first published in JMIR Formative Research, is properly
cited. The complete bibliographic information, a link to the original publication on https://formative.jmir.org, as well as thiscopyright and license information must be included.

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