JMIR FORMATIVE RESEARCH

Klassen et al

Original Paper

Examining the Light Heart Mobile Device App for Assessing
Human Pulse Interval and Heart Rate Variability: Validation Study
Stephen A Klassen1, PhD; Jesica Jabbar1, BSc; Jenna Osborne1, MSc; Nathaniel J Iannarelli1, MSc; Emerson S Kirby2,
BSc; Deborah D O'Leary1, PhD; Sean Locke1, PhD
1Faculty of Applied Health Sciences, Brock University, St. Catharines, ON, Canada

2Light Heart, Kelowna, BC, Canada

Corresponding Author:
Sean Locke, PhD
Faculty of Applied Health Sciences
Brock University
1812 Sir Issac Brock Way
St. Catharines, ON, L2S 3A1
CanadaPhone: 1 9056885550
Email: slocke@brocku.ca

Abstract
Background: Pulse interval is a biomarker of psychological and physiological health. Pulse interval can now be assessed using
mobile phone apps, which expands researchers’ ability to assess pulse interval in the real world. Prior to implementation,measurement accuracy should be established.
Objective: This investigation evaluated the validity of the Light Heart mobile app to measure pulse interval and pulse ratevariability in healthy young adults.
Methods: Validity was assessed by comparing the pulse interval and SD of normal pulse intervals obtained by Light Heart to
the gold standard, electrocardiogram (ECG), in 14 young healthy individuals (mean age 24, SD 5 years; n=9, 64% female) in aseated posture.
Results: Mean pulse interval (Light Heart: 859, SD 113 ms; ECG: 857, SD 112 ms) demonstrated a strong positive linear
correlation (r=0.99; P<.001) and strong agreement (intraclass correlation coefficient=1.00, 95% CI 0.99-1.00) between techniques.
The Bland-Altman plot demonstrated good agreement for the mean pulse interval measured with Light Heart and ECG withevidence of fixed bias (–1.56, SD 1.86; 95% CI –5.2 to 2.1 ms), suggesting that Light Heart overestimates pulse interval by asmall margin. When Bland-Altman plots were constructed for each participant’s beat-by-beat pulse interval data, all participants
demonstrated strong agreement between Light Heart and ECG with no evidence of fixed bias between measures. Heart ratevariability, assessed by SD of normal pulse intervals, demonstrated strong agreement between techniques (Light Heart: mean 73,
SD 23 ms; ECG: mean 73, SD 22 ms; r=0.99; P<.001; intraclass correlation coefficient=0.99, 95% CI 0.97-1.00).
Conclusions: This study provides evidence to suggest that the Light Heart mobile app provides valid measures of pulse intervaland heart rate variability in healthy young adults.
(JMIR Form Res 2024;8:e56921) doi: 10.2196/56921
KEYWORDS
pulse interval; mobile app; validation; mHealth; mHealth app; app mobile device; mobile device app; pulse; heart; heart rate;validation study; biomarker; psychological; physiological; pulse rate; young adults; youth; linear correlation; heart rate variability;
examining; examine; validity; psychological health; physiological health; interval data; mobile phone

Introduction
Autonomic regulation of cardiac function represents a key
mechanism for homeostatic maintenance in humans. As a resultof autonomic regulation, heart rate demonstrates strong
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beat-to-beat variability, described commonly as heart ratevariability (HRV). Given that the autonomic nervous system iscomprised of a network of nuclei distributed throughout thebrain and brainstem, HRV provides an avenue to explore thebrain-heart connection in humans [1,2].
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HRV represents a biomarker of health status and acutephysiological and mental health [3]. HRV declines strongly
with aging and is reduced in many chronic cardiovascular [4],metabolic [5], and neurological diseases [6]. Thus, HRV
represents a strong predictive biomarker for all-cause mortality,
cardiovascular outcomes, depression, and dementia [7]. HRV
has also been characterized as an index of emotion regulation[8,9].
HRV can be measured in time and frequency domains using
the R-R intervals provided by electrocardiograms (ECGs) [10].
However, ECG-based measures of HRV can be prohibitive dueto the requirement for technical equipment and specialized
expertise to support ECG collection and HRV analysis.
Accordingly, to extend HRV measures beyond laboratory andclinical environments, methods have been developed that relyon photoplethysmography (PPG) to measure pulse interval fromthe pulsatile changes in microcirculatory blood volume in anaccessible anatomical location such as a finger [11].
The recent uptick in wearable technologies capable ofperforming photoplethysmographic measures of pulse interval
has mitigated some of the barriers to population-level HRV
assessment [12]. However, a limited proportion of households
in low-income nations report using wearable technology [13].
Conversely, smartphone use is high (85%+) among residentsof high-income nations such as Canada and the United States[14]. Thus, mobile phones equipped with software capable ofperforming photoplethysmographic measures of HRV will
support the scaling of this measure to the population level. In
this regard, Light Heart is a novel mobile app that uses thecamera and flash of a mobile device to obtain continuous
measures of pulse interval from an individual’s fingertip using
PPG. To date, the validity of pulse interval and HRV indicesmeasured by Light Heart has not been investigated. Like other
HRV apps [15,16], measurement accuracy should be establishedprior to releasing an app to the public.
Many commercially available apps that use PPG to measure
pulse interval provide a single metric of stress but do not provide
researchers with the raw data (eg, Fitbit and Apple). These single
metrics conceal beat-by-beat data and prevent researchers fromexamining raw data points that may skew aggregate indices or
be used to calculate several time- and frequency-domain indicesof HRV. As a result, researchers may be limited in their ability
to obtain beat-by-beat pulse interval data in real-world studies.
Furthermore, there is a reported lack of rigor in evaluating appsthat measure heart rate or HRV [17]. The Light Heart app wasdeveloped to measure pulse intervals outside the laboratory.
The metrics obtained by new research tools require validation
to ensure accuracy and consistency prior to use in appliedresearch settings. Validation is defined as the process ofgathering evidence to suggest a tool is able to accurately
measure what it proports to measure. [18]. An initial step in theprocess of validation is to demonstrate that an assessment is
concurrently valid and provide accurate estimates against a gold
standard like an ECG [19]. Therefore, this study aimed toinvestigate the validity of the Light Heart mobile app to quantify
pulse interval and HRV using ECG as the gold-standard
reference in a cohort of healthy young individuals. This study
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Klassen et altested the hypothesis that pulse interval and HRV would
demonstrate strong agreement between Light Heart and ECG.

Methods
Ethical Considerations
This study received approval from Brock University’s Health
Science Research Ethics Board (#21-118) and conforms to the
Declaration of Helsinki, except for registration in a database.
Each participant provided informed written consent after
receiving detailed written and verbal explanations of study
procedures. Study data are deidentified and stored on an
institutional secure server. Participants were not remunerated
for participation.

ParticipantsThis study tested 14 young healthy, nonsmoking adults (n=9,64% female and n=5, 36% male; mean age 24, SD 5 years;
mean height 169, SD 7 cm; mean weight 68, SD 10 kg; n=11,
79% White; n=1, 7% Hispanic or Latino; and n=2, 14%Southeast Asian). Participants were normotensive and free fromcardiovascular and other diseases and major risk factors.
Participants were not currently taking medications known toaffect autonomic function, heart rate, or blood pressure. Bothoral contraceptive–using and –nonusing female individuals were
included. This study did not control for the menstrual phaseamong female participants. Based on previous studies [20], a
priori sample size calculation revealed a target sample size of
14 based on detecting a minimum correlation of r=0.7, with
α=.01 and power (1 – β) = 0.8.

Experimental Protocol
Participants arrived at the Human Hemodynamics Laboratory
at Brock University after at least a 4-hour fast and abstaining
from alcohol, nicotine, caffeine, cannabis, and vigorous physicalactivity for at least 12 hours prior to the study session. Height(STAT 7X, Ellard Instrumentation Ltd) and body mass
(BWB-800S, Tanita Corporation) were measured.
Participants were instrumented in the supine posture. Following
at least 10 minutes of rest, participants were familiarized withthe mobile device featuring the Light Heart app providing thepulse interval recordings. Participants were asked to perform
at least a 60-second recording with the mobile device to ensurefamiliarization and for investigators to verify optimal pulse
recordings. Participants then transitioned to a seated position
where they assumed a normal seated posture with legs uncrossed
and feet positioned on the floor. The experimental protocol
consisted of a 5-minute seated condition with simultaneousrecordings of pulse intervals via ECG and the Light Heart
mobile app.

Experimental Measures
Lead II from a standard ECG sampled at 500 Hz was used tomeasure the R-R interval (BioAmp FE132, ADInstruments).
LabChart 8 and PowerLab systems (ADInstruments) were used
for data collection and storage. Pulsatile blood volume changes
in the right index finger microcirculation were detected using
PPG by the Light Heart mobile app (v0.0.1-alpha) leveraging
the flash and camera of an iPhone X (iOS 16, Apple).
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Data Processing and Analysis

Finally, to facilitate agreement assessment at the beat-by-beatlevel, the alignment of ECG pulse interval and Light Heart pulseinterval data were verified by visual inspection performed by asingle investigator (SRL) [16]. For both the ECG data and LightHeart PPG data, the SD of normal pulse intervals (SDNN)
provided a measure of HRV.

The data processing specifications were selected a priori andrefined based on trial-and-error pilot testing that was conductedduring app development by the Light Heart research team. Oncethe parameters were established, we did not make any
adjustments to them throughout the conduct of this study. LightHeart collected data at 30 frames per second and produced a
PPG signal by averaging the luminosity for each frame stored
in a time-stamped array. PPG data were filtered using a
Butterworth bandpass filter (0.75-3 Hz) and smoothed using a
5-frame smoothing window based on the convolution of a scaled
window. Data were sampled using a 180 Hz cubic splineinterpolation. Data processing was performed using open-source
Python 3.7.6 algorithms.
Pulse intervals were extracted from the PPG signal by detecting
the local maxima of the signal using a simple neighborcomparison. Local maxima greater than 30% of the mean
prominence of all local maxima detected were considered valid.
During pilot testing, we started with a 25% criterion as suggested
by Plews et al [16] but found that accuracy was increased when
we shifted to 30%. To remove any diastolic peaks that wereincorrectly identified as a systolic peak, a sliding 5-peak window
was applied and any local maxima less than 75% of the mean
prominence within the window were discarded, with theremaining local maxima considered valid for pulse-interval
analysis. Pulse intervals (ms) were computed as the time
duration between valid local maxima.
To identify remaining artifacts, the pulse interval data were
processed using a multistep artifact detection approach. LightHeart performs on-device, real-time signal quality analysis on
windows of 200 frames in order to provide feedback to the user
about the positioning of their finger. Accordingly, pulse interval
data were windowed into shorter segments of 200 intervals, andlow and high threshold cutoff values were set using the first
and third quartile for the respective window. Any values
exceeding the thresholds were removed [21]. In addition,subsequent intervals that differed by 20% were removed as they
were considered physiologically implausible. Finally, any pulseinterval associated with a heart rate less than 20 beats per minute(bpm) or greater than 200 bpm was removed.
ECG data were screened to ensure no ectopic beats. Local
maxima of the ECG signal associated with R-waves weredetected using Python open-source algorithms (Python Software
Foundation) and stored with the associated time stamp.
Beat-by-beat pulse interval was computed as the time between
successive R-R intervals. ECG R-waves were time aligned andcross-correlated with Light Heart–derived PPG local maxima.

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Statistical Analyses
Pearson tests of correlation and intraclass correlation coefficient(ICC; 2-way random effects, absolute agreement) assessed therelationship and agreement, respectively, for the study measures.
Bland-Altman plots assessed agreement between Light Heart–and ECG-based measures [22]. Fixed (paired, 2-tailed t test)
and proportional biases (Pearson correlation) were assessed.
Analyses were performed using SPSS (SPSS Inc) and Prism(GraphPad). All tests were 2-tailed; α=.01. Data are presented
as mean and SD unless otherwise specified.

Results
Comparison of Beat-by-Beat Pulse Interval Between
ECG and Light Heart
This section reports the beat-by-beat data from 5-minute pulseinterval recordings in the seated posture in all individuals. Figure
1 illustrates superimposed recordings of 1 minute of pulseinterval data simultaneously recorded by Light Heart and ECG,
for 1 representative participant. Visual assessment of these data
tracings illustrates that the pulse interval recorded by LightHeart demonstrates strong beat-to-beat agreement with the pulseinterval detected by ECG. This observation was consistent acrossall participants. Table S1 in Multimedia Appendix 1 provides
mean and variability data for pulse intervals acquired by ECG
and Light Heart for all participants.
Base on inspection of the beat-by-beat data in 1 representative
participant (illustrated in Figure 2A), the pulse interval obtainedby ECG and the pulse interval obtained by Light Heartdemonstrated a strong positive correlation and agreement. Table
S2 in Multimedia Appendix 1 provides the Pearson correlation
coefficients and ICCs between pulse intervals collected by ECG
and Light Heart for each participant. When analyses were
performed on 5 minutes of beat-by-beat data, all participants
demonstrated strong positive Pearson correlation coefficients
ranging from 0.89 to 0.99 (P<.001 in 14/14, 100% participants;
Table S2 in Multimedia Appendix 1). All participants
demonstrated strong ICCs ranging from 0.88 to 0.99 (P<.001
for 14/14, 100% participants; Table S2 in Multimedia Appendix
1).

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Figure 1. Time-aligned pulse interval data from ECG and Light Heart from 1 representative participant. ECG: electrocardiogram.

Figure 2. Pulse interval agreement data for the beat-by-beat analysis from 1 representative participant. The (A) scatter plot and (B) Bland-Altman plot
demonstrate agreement between the pulse interval collected by ECG and the pulse interval collected by Light Heart. (A) The dashed black line represents
the line of identity to assess agreement, and the solid red line represents the linear relationship between measures assessed by the Pearson correlation
coefficient (r=0.95; P<.001). (B) The mean difference (2.19, SD 22.78 ms; solid black line) and 95% CIs (–42 to 47 ms; dashed black lines) are plotted.
The regression line (b=–0.05±0.02; P=.009; R2=0.025) fitted to the difference between methods versus the mean of methods is plotted (solid red line).
ECG: electrocardiogram.

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As illustrated by the Bland-Altman plot (Figure 2B) constructedwith beat-by-beat data for 1 representative participant, the pulseintervals measured by ECG and Light Heart demonstrated strong
agreement. For all plots, the scatter of differences was consistentover the range of mean values, and most of the differences
between methods fell within the 95% CI. None of the plots
demonstrated fixed bias, as small differences (range –2.9 to 2.2ms) existed between the methods over the range of mean values
and paired-sample t tests revealed no differences (P≥.11 for
14/14, 100% participants). The test of proportional bias for eachparticipant’s beat-by-beat data, performed by regressing thedifference between methods (ECG–Light Heart) against themean of the methods, revealed proportional bias in 7 (50%) out
of 14 participants (Table S3 in Multimedia Appendix 1).
However, inspection of the R2 values associated with the testof proportional bias revealed demonstrably low values in allparticipants (R2<0.06 in 14/14, 100% participants; Table S3 in
Multimedia Appendix 1), suggesting that less than 6% of thevariability in the difference between methods was explained by
the average value. Table S3 in Multimedia Appendix 1 provides

Klassen et ala summary of the Bland-Altman statistics and tests of fixed andproportional bias for each participant’s beat-by-beat data.

Comparison of Mean Pulse Interval and HRV Metrics
Between ECG and Light Heart
Inspection of the mean pulse interval data for each participant(Figure 3A) revealed that pulse intervals obtained by ECG and
Light Heart demonstrated a strong positive correlation (r=0.99;
P<.001) and strong agreement (ICC=1.00; 95% CI 0.99-1.00).
As illustrated by the Bland-Altman plot (Figure 3B) constructedwith mean data for each participant, pulse intervals measured
by ECG and Light Heart demonstrated strong agreement. The
scatter of differences was consistent over the range of meanvalues and all differences between methods fell within the 95%
CI. This plot illustrated a demonstrably small but statisticallysignificant fixed bias (–1.56, 95% CI –5.2 to 2.1 ms; P=.008),
suggesting that Light Heart overestimated pulse interval by asmall margin. The test of proportional bias performed byregressing the difference between methods (ECG–Light Heart)against the mean of the methods, revealed a trend toward
proportional bias (b=–0.009±0.004; P=.06; R2=0.27).

Figure 3. Pulse interval agreement data for the mean data analysis for the study cohort. The (A) scatter plot and (B) Bland-Altman plot demonstrateagreement between the pulse interval collected by ECG and the pulse interval collected by Light Heart. (A) The dashed black line represents the lineof identity to assess agreement, and the solid red line represents the linear relationship between measures assessed by the Pearson correlation coefficient(r=0.99; P<.001). (B) The mean difference (–1.56, SD 1.86 ms; solid black line) and 95% CIs (–5.2 to 2.1 ms; dashed black lines) are plotted. The
regression line (b=–0.009±0.004; P=.06; R2=0.27) fitted to the difference between methods versus the mean of methods is plotted (solid red line). ECG:
electrocardiogram.

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Inspection of the mean SDNN data from each participant (Figure4A) illustrated a strong positive correlation (r=0.99; P<.001)
and strong agreement (ICC=0.99, 95% CI 0.97-1.00) between
ECG and Light Heart. The Bland-Altman plot (Figure 4B)
constructed with mean SDNN measured by ECG and LightHeart demonstrated strong agreement (Light Heart: mean 73,

Klassen et al
SD 23 ms; ECG: mean 73, SD 22 ms). The scatter of differences
was consistent over the range of mean values, and 1 data point
for the differences between the methods fell beyond the 95%
CI. There was no evidence of fixed (mean difference –0.56, SD
3.34 ms; P=.54) or proportional bias (b=0.062±0.04; P=.14;
R2=0.17).

Figure 4. HRV agreement data for the mean data analysis for the study cohort. The (A) scatter plot and (B) Bland-Altman plot demonstrate agreement
between the SDNNs collected by ECG and Light Heart. (A) The dashed black line represents the line of identity to assess agreement, and the solid redline represents the linear relationship between measures assessed by the Pearson correlation coefficient (r=0.99; P<.001). (B) The mean difference(–0.56, SD 3.34 ms; solid black line) and 95% CIs (–7.1 to 6.0 ms; dashed black lines) are plotted. The regression line (b=0.062±0.04; P=.14; R2=0.17)
fitted to the difference between methods versus the mean of methods is plotted (solid red line). ECG: electrocardiogram; HRV: heart rate variability;
SDNN: SD of normal pulse intervals.

Principal Findings

Bland and Altman [22]. Therefore, Light Heart has utility as ameasure of pulse interval and HRV in young healthy male andfemale individuals under resting seated conditions.

This study generated novel data supporting the validity of theLight Heart mobile app’s PPG-based measures of pulse intervaland HRV in a sample of healthy young individuals. The major
findings of this study are as follows: (1) Light Heart–derived
values of pulse interval demonstrated a strong relationship andagreement with the pulse interval measured with ECG at boththe participant level and the sample level, and (2) SDNN, ameasure of HRV, demonstrated a strong relationship andagreement with the SDNN calculated using ECG. These findings
were based on the collective interpretations of ICCs andagreement plots generated using the technique described by

To our knowledge, this was the first study to assess the validityof the Light Heart mobile app’s PPG-derived indices of pulseinterval and HRV. To achieve this objective, we compared thepulse interval and SDNN obtained using the Light Heart mobile
app with measures collected via ECG. Three pieces of evidence
suggest that Light Heart provides valid measures of pulseinterval in this study. First, the pulse interval obtained from
Light Heart demonstrated a strong linear relationship with thepulse interval measured via ECG. Second, the pulse interval
obtained from Light Heart demonstrated a strong ICC with pulseinterval measured via ECG. Third, the Bland-Altman plot

Discussion

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demonstrated agreement between measures and included no
fixed bias when beat-by-beat data were analyzed for eachparticipant. However, fixed bias was observed when the meanpulse interval data from each participant were used to constructa Bland-Altman plot. This fixed bias suggests that Light Heartoverestimates pulse interval by a demonstrably low value (<2ms). In addition, proportional bias emerged at both thebeat-by-beat analysis and mean data analysis stages, suggestingthat the magnitude of pulse interval overestimation by LightHeart increases as pulse interval increases (ie, heart rate slows).
Again, however, this overestimation is demonstrably low within
the range of mean pulse intervals recorded in the seated posturein this study (679-1020 ms). Similarly, Light Heart provided
valid measures of HRV in this study. This is not surprising giventhat the SDNN was calculated using pulse interval data collectedby Light Heart, which aligned with ECG-derived values. Thisconclusion was based on the evidence of strong correlation
coefficients, no fixed bias, and no proportional bias between
the SDNNs measured by Light Heart and ECG.

Klassen et alsize calculation and the agreement between Light Heart– andECG-based measures was observed at both the individual andcohort levels, suggesting that the sample size likely did not
influence our findings. The participants were familiarized with
the Light Heart app prior to data collection, and data collection
was performed during 5-minute resting seated conditions toimprove the likelihood of high-quality pulse interval datacollection. Future research is encouraged to replicate thesefindings in larger samples of diverse populations.

The observation that Light Heart’s PPG-based measures of pulseinterval and HRV were similar to ECG-derived measures is
consistent with previous research. Indeed, Tarniceriu et al [23]found that a wrist-based plethysmographic method for pulseinterval acquisition demonstrated strong agreement with R-R
interval from ECG in individuals with normal sinus arrhythmia
and atrial fibrillation. In addition, other groups have found good
agreement in the pulse intervals between ECG and smartphone
apps that use the flashlight and camera [15,16]. In our hands,
our data suggest that the Light Heart app represents an additional
simple and convenient PPG-based tool to add to the collectionof devices that enable heart rate and HRV measures beyond
research and clinical settings.

This study assessed the validity of R-R interval and HRV
measured by the Light Heart mobile app in a sitting posturewhile participants were resting and breathing spontaneously.
This posture aligns with instructions for how individuals perform
Light Heart readings in the real world. However, study
participants demonstrated a large range of resting heart rates(59-88 bpm) and proportional bias was low. This suggests that
the Light Heart mobile app may demonstrate validity in otherpostures or conditions imposing elevated heart rates or changes
in a vasomotor tone such as those experienced during dailyliving (eg, physical exercise and mental stress) [26]. Future
research is warranted to validate the Light Heart app undervarious physiological conditions. Apps often take a data-driven
approach during their development to maximize performance.
We made adjustments to certain processing parameters used inprevious research to optimize their accuracy (eg, shifting mean
prominence from 25% [21] to 30% of local maxima). While all
adjustments were made prior to starting this study, future
research could compare the impact of these adjustments on
accuracy once we obtain a larger and more diverse database.
Finally, our conclusions related to the validity of Light Heartfor measuring pulse interval are delimited to the mobile device
and operating system used in this study.

Limitations

Conclusions and Perspectives

Several limitations of this study should be discussed. This study
was performed on a sample of young healthy individuals. Thus,
we are uncertain whether the validity of Light Heart isgeneralizable to other populations such as older individuals or
patients with elevated cardiovascular risk and lower HRV[12,24,25]. The study sample included a high proportion ofWhite individuals and was not powered to examine the validityof Light Heart in cohorts with varying skin tones. However, in
3 individuals who reported race as Southeast Asian and Hispanic
or Latino, the agreement statistics discussed above were notdemonstrably different than the White participants. In addition,
this study was performed in a small sample of individuals(n=14). This sample size was consistent with our a priori sample

Through comparison with ECG, this study provides evidence
to suggest that the Light Heart mobile app provides a valid
measure of pulse interval and HRV in healthy young adults.
HRV is strongly associated with neurological [4], metabolic[5], and cardiovascular diseases [6] and tracks positive
physiological improvements with lifestyle interventions suchas physical exercise [27]. Thus, combined with the highprevalence of smartphone use, our data suggest that the LightHeart app represents a simple, scalable tool that can provide
insight into the human brain-heart axis and overall morbidity
and mortality risk. Future research may consider incorporating
Light Heart into biofeedback interventions designed to improve
HRV on varying time scales.

Acknowledgments
The authors acknowledge the administrative support of Robert Kumar. This work was supported by a Mitacs Accelerate Grant(IT26336 awarded to SL), a Social Sciences and Humanities Research Council Insight Grant (435-2022-0395 awarded to SL),Brock University Investigator Funding (awarded to SL and SAK), a Natural Sciences and Engineering Research Council of
Canada (NSERC) Discovery Grant (2022-05293 awarded to SAK), and an NSERC Discovery Launch Supplement (2022-00320
awarded to SAK).

Data Availability
The data sets generated during or analyzed during this study are available from the corresponding author upon reasonable request.
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Authors' Contributions
All authors contributed to the manuscript revision and approved the final version of the manuscript. SAK contributed to theconception and design of the study, data collection, data analysis, data interpretation, and writing of the manuscript. JJ contributed
to data collection. JO contributed to data collection. NJI contributed to data collection. ESK contributed to data collection, data
analysis, data interpretation, and writing of the manuscript. DDO contributed to the conception and design of the study. SL
contributed to the conception and design of the study, data analysis, data interpretation, and writing the manuscript.

Conflicts of Interest
ESK is an employee of Light Heart Systems.

Multimedia Appendix 1
Mean and variability of R-R interval and pulse interval measures by participant, Pearson correlation coefficients and intraclasscorrelation coefficients between pulse intervals collected by ECG and Light Heart for each participant, and Bland-Atman statistics
of agreement for beat-by-beat pulse interval.
[DOCX File , 25 KB-Multimedia Appendix 1]

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Abbreviations
bpm: beats per minute
ECG: electrocardiogram
HRV: heart rate variability
ICC: intraclass correlation coefficient
PPG: photoplethysmography
SDNN: SD of normal pulse intervals

Edited by A Mavragani; submitted 30.01.24; peer-reviewed by L Dall’Olio; comments to author 18.06.24; revised version received
16.07.24; accepted 18.07.24; published 20.08.24
Please cite as:
Klassen SA, Jabbar J, Osborne J, Iannarelli NJ, Kirby ES, O'Leary DD, Locke S
Examining the Light Heart Mobile Device App for Assessing Human Pulse Interval and Heart Rate Variability: Validation StudyJMIR Form Res 2024;8:e56921
URL: https://formative.jmir.org/2024/1/e56921
doi: 10.2196/56921
PMID:©Stephen A Klassen, Jesica Jabbar, Jenna Osborne, Nathaniel J Iannarelli, Emerson S Kirby, Deborah D O'Leary, Sean Locke.
Originally published in JMIR Formative Research (https://formative.jmir.org), 20.08.2024. This is an open-access article distributed
under the terms of 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 this copyright and license information must be included.

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