The study assessed the accuracy of the Mio Link wristband in measuring heart rate and heart rate variability (HRV) compared to the standard MyPatch Holter device. Results showed the Mio Link correlated well with the Holter under steady conditions but poorly during moderate to high activity. Therefore, the Mio Link is not adequate for real-time stress management during higher activity. However, with refinement, wearable trackers may realize this goal of stress measurement.

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Potential Use of Wearable Activity Trackers to Measure Detailed
Heart Rate and HRV Responses for Stress and Stress Management
Activities
Ng E., Stein P.K., Chacko R.
Abstract
Heart rate variability has been shown to have a direct correlation with stress. Stress is
directly correlated with many disorders and has been shown to cause damage to organ systems.
The purpose of this study was to assess the accuracy of the Mindset Mio Link wristband in terms
of collecting heart rate and heart rate variability data, in comparison to the standard MyPatch
Holter device. The Holter device collects the electrocardiogram from which fully accurate heart
rate and heart rate variability data can be derived using commercial Holter analysis system.
Overall, under steady state conditions (low activity), results from the Mio Link wristband
correlated well with the results from the MyPatch Holter device. However, during periods of
moderate to higher activity, results from the Mio Link wristband correlated poorly with results
from the MyPatch Holter device. Therefore, we conclude that the current version of this activity
tracker is not adequate for real time stress management under conditions of moderate to higher
activity. However, it is likely that with further refinement of the Mio Link wristband’s software
and hardware capabilities the goal can be realized.
Introduction
There are 27.6 million people in America alone suffering from heart disease. Every year,
614,000 Americans die from heart disease, making it the leading cause of death in America [1].
Around 20% of Millennials and Gen X's have an anxiety or mental health disorder [2]. This
causes many Millennials and Gen X’s to seek out stress reduction methods. Furthermore, studies
show that stress has a negative effect on organ systems and can be directly correlated with other
disorders such as insomnia [3]. Heart Rate Variability (HRV) is a way to measure the real time
functioning of the autonomic nervous system, which is the body’s built in operating system for
managing threat and returning to safety. Acute stress is a threat response whereas chronic stress
results when people are physiologically unable to come down from the activated threat response
and return to a relaxed state. For example, if someone goes from calm to upset, HRV will
decrease. If they perform a stress reducing activity, and if they calm down, that would result in
an increase in HRV. The assumption that average heart rate (HR) alone is enough to track stress
response and relaxation is too simplistic, so we are assessing the potential application of an
existing app and wrist-based heart rate tracking device for real time measurement of HRV for
stress management.

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Consumer electronics now allow scientists to study HRV. The introduction of consumer-
based devices provides a great leap in the medical field, because it helps to connect the doctor to
the patient in a more reliable and non-invasive manner. Consumer applications like the Mio and
the Fitbit allow consumers to keep track of data while they are not in the hospital being
monitored. It is crucial to determine the accuracy of these devices because there may be a
tendency to overpromise in terms of what they can actually do. Although these devices are much
cheaper and more accessible, they are less accurate than devices used in clinical studies.
Evaluating and improving the accuracy of these consumer electronics allows us to build
applications onto these devices. The applications can be further developed to better integrate
with smartphones and to include services such as a GPS to help identify and avoid locations that
would trigger someone’s stress.
Currently, these applications show the levels of stress and recommend solutions to each
of these with unknown accuracy. The purpose of this study was to determine how accurately
these consumer electronics measure heart rate and heart rate variability [4]. To do this, we
compared the data from Mio Link to the data collected by a Holter monitor, i.e., a monitor that
collects and stores the actual electrocardiogram. The comparison will help with stress
management app development by providing information about how accurate the consumer
electronic device is at this point.
Methods and Materials
Materials and Software:
1. Mio Link Wristband by Mio Global Inc.
2. MyPatch Holter Monitor by DMS Service
3. Java by Sun Microsystems
4. Mindset App developed by DataDog Inc.
5. Python by Guido van Rossum in Centrum Wiskunde & Informatica
6. MatLab by Mathworks
7. HRVInteractive (custom PC-based software developed in the HRV lab at Washington
University)
Subjects:
Data were analyzed from two healthy volunteers who collected their heart rate data
simultaneously on a Holter device (MyPatch) and using a Mio wristband. Each volunteer started
by placing a Mio Link wristband on to his or her wrist. Figure 1 shows a Mio monitor on a wrist.
At the same time, the volunteer also put on the Holter monitor. The MyPatch is a Holter device
in the form of an adhesive patch. The device is applied on the top of the chest and measures up to
3 channels of continuous ECG as shown in Figure 2.

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Both volunteers agreed to perform at least 15 minutes of exercise and 10 minutes of
meditation, and wear both the Mio Link wristband and Holter monitor for 24 hours. The Mio
uses a Bluetooth connection with a smartphone app (Mindset) to record and store data. The
MyPatch Holter device uses an attached device to record and store data. This device is later
connected to a computer so that it can be processed with the Cardioscan Holter analyzed
software.
Data Analysis:
1. Development of comparable beat-to-beat data files from the Mio and the Holter recorder.
After completion of the study, the recorded data from the Mio Link wristband were
collected from the Mindset website. These data were provided in an .xls file (Figure 3).
As can be seen in Figure 3, the data were shown in three columns. The first was a time
stamp column that had to be converted to a numerical format, millisecond (Figure 4) to
match the data generated via the Holter. The second column was the instantaneous heart
rate and the third column was the interval in milliseconds from the last detected heart
beat (interbeat interval). The Holter data were processed using a system that is designed
for clinical Holter analyses (Cardioscan, DMS). After the HRV Lab technician had made
sure that the time of each normal heartbeat was accurately detected, a beat-to-beat file
(.mibf, machine-independent beat file) was exported. The mibf has a header with
information about the subject, date and start time and then a list of the beat types
(normal=Q) and interbeat intervals in ms. (Figure 5). To enable direct comparison, this
mibf file from the Holter recorder was also converted to a .csv file that had the same
format as the Mio file shown in Figure 4. This data is then exported into a csv file with
HRVInteractive.
2. Synchronization of data from the Mio and Holter recorders. After both data sets were
processed so that they were in the same format, they were displayed in a graph as seen in
the Results section. In order to process the data, another JavaScript was written to parse
the Mio data in csv format and synchronize it with the Holter data. After the beat-to-beat
csv files were aligned, the standard deviation of interbeat intervals (SDRR) for every 5
minutes for both the Mio and Holter beatfiles was compared to check the correlation
between these two datasets [5]. The objective was to determine how well the Mio data
matched up with the Holter data and also whether or not the Mio could accurately predict
and classify volunteer’s physical states, i.e. rest, stress, meditation, exercise, etc. The data
timestamps were collected by converting the 24 hour timestamps in the Holter into
millisecond timestamps from Epoch time. From there, because the Mio timestamps were
already given in epoch time so we could compare the two sets of data.
3. Visualization of data. The code used to compare the two sets of data was written in Java.
The code parsed the csv files and added each data set to a respective array. This array was
then graphed using the public JFreeChart library to visualize the results.

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4. Statistical methods. The correlation of the dataset was done with MatLab in which a
covariance function was utilized to determine how much relation there was between the
two sets of data.
Figure 1. An image of the Mio Link wristband properly assembled onto the volunteer’s wrist
linked to the phone via Bluetooth
Figure 2. A photo demonstrating MyPatch monitor properly assembled onto the volunteer’s body

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Figure 3. XLS File exported by Mindset website Figure 4. CSV File after processing
Figure 5. Mibf file produced by the Cardioscan Holter software

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Results
Graphs:
Figure 6. Raw RR Interval Mio (Red) data vs Raw RR Interval Holter (Blue) data
Figure 7. 5-min averaged SDRR Mio (Red) Data vs 5-min averaged SDRR Holter (Blue) Data

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Analysis:
Both Figure 6 and Figure 7 show a clear correlation between the data collected by the
Mio Link wristband and the data collected by the MyPatch Holter device. The correlation
between the Mio Link wristband and the MyPatch Holter device seems to be stronger during the
day when the heart rate is within a regular range (60-120 bpm). However, the correlation seems
to be weaker during periods of high intensity exercise and during sleep. Throughout these
periods the heart rate is outside of the 60-120 bpm range and the correlation between the Mio
Link wristband and the MyPatch Holter device becomes weaker. Furthermore, there are several
drops or disconnects with the Mio Link wristband. These drops are either due to a weak
Bluetooth connection with a mobile device or not being able to detect extremely slow heart rates.
Another issue that could be affecting the constant data collection is the way the Mio Link
wristband communicated and stores data. The Mio Link wristband utilizes a Bluetooth
connection with a mobile device to store data. However, Bluetooth signals only update once a
second meaning that they are unable to provide a constant data collection. This indicates that the
Mio Link wristband is unable to be as accurate as the MyPatch Holter device because it cannot
register many of the low band frequencies that the heart produces.
Discussion
General:
The Mio Link wristband and MyPatch Holter device were used to simultaneously collect
data from volunteers. The purpose of this experiment was to determine the accuracy of the
consumer available Mio Link wristband in comparison to the clinically proven MyPatch Holter
monitor. The accuracy of a consumer device is important because consumers need to know what
the product is able to provide and the company needs to know what it can safely advertise.
Furthermore, third party applications such as the Mindset App by DataDog Inc. rely on the
accuracy of consumer monitors like the Mio Link wristband in order to create applications that
can actually determine the heart rate variability and stress of consumers. These applications
utilize the raw data that the Mio Link wristband collects to create algorithms to detect states of
activity, i.e. stress, relaxation, meditation, exercise. Without an accurate enough source, these
third party applications are unable to properly perform the tasks they set out to accomplish.
The experimental data indicates that the Mio Link wristband remains fairly accurate
while recording data from a subject who remains idle or in a low intensity exercise and with
regular heart rate patterns (within 60-120 bpm). However, the Mio Link wristband does not
reliably record data when the subject exhibits a more irregular heart rate outside of the 60-120
bpm range or during moderate to high intensity exercise. Furthermore, the Mio Link wristband
experienced several drops in connection with the smartphone throughout the day and a long

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period of disconnection during sleep. This indicates that many hardware improvements must be
made to the Mio Link wristband so that it is able to collect data despite low heart rates.
While the Mio Link wristband is able to detect heart beats, it is unable to distinguish
between the different parts of a beat. For example, the Mio Link wristband does not detect a
difference between the P-wave, QRS-complex, and T-wave. When compared to the MyPatch
Holter device, the Mio Link wristband is unable to accurately predict medical conditions such as
Atrial Fibrillation [6].
Importance:
It is crucial that a consumer device be as accurate as possible to avoid misleading
consumers. Any sort of inaccuracy may pose a risk for the health of consumers, especially in a
medical device. This is why the study was crucial to determine what data the Mio Link wristband
could reliably gather. To test this, we analyzed data over extended periods of time. This data
showed a good correlation despite the extended period of time.
The importance of this is to avoid scandals such as the one involving Fitbit in May this
year. The Fitbit was reportedly shown to have inaccuracies of over 20 beats per minute during
exercise [7]. This inaccuracy poses a serious threat to the health of consumers, especially those
who are unaware of a medical condition. By studying the Mio Link wristband and it’s correlation
with the gold standard of the MyPatch Holter device, we hope to avoid scandals like this.
Pitfalls:
While we gained important insights on the Mio Link wristband from this study, we
acknowledge that there are several areas of improvement. First, the small sample size limits the
accuracy and generalizability of the data, however, future studies can be designed with a bigger
sample size to address this issue.
Second, this study was conducted only on healthy volunteers. This indicates that for a
consumer with an existing medical condition, the accuracy of the Mio Link wristband may not be
what was concluded from the experiments in this study. As mentioned in the results section, the
Mio Link wristband was not reliable in detecting signals that were either too high or too low.
Many heart conditions cause irregular heart rate patterns. Because of the inaccuracy that the Mio
Link wristband experiences with irregular heart rate patterns consumers with certain heart
conditions cannot rely on the Mio Link wristband.
Finally, the components of the Mio Link wristband need further improvement. The Mio
Link wristband stores its data via a Bluetooth connection with a mobile device. Without this
connection, the Mio Link wristband is unable to record any data. This is a major problem

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because a poor Bluetooth connection could cause a recording with frequent drops and a lot of
interference. Not only is there an issue with drops and interference, the Bluetooth signal only
updates once a second. This means that the source of data is not constant, compared to the
MyPatch Holter device which has a constant signal. Furthermore, the relatively short battery life
of the Mio, lasting around 8 hours means that it has to be switched out causing problems for a
study lasting 24 hours. However, these problems can be improved upon by creating a newer and
more stable version of the Mio Link wristband.
Future Applications:
Based on the data collected from this experiment, the current Mio Link wristband serves
as a fairly accurate Heart Rate monitor among healthy individuals. The correlation shows that the
Mio Link wristband detects Heart Rate quite well during normal activities and low intensity
forms of exercise. However, the Mio Link wristband does not accurately detect irregular heart
rate patterns. Until improvements are made to the Mio Link wristband itself, it seems that the
device it not suitable for consumers who have medical conditions involving the heart.
The current Mindset application on mobile devices alerts users when they are stressed.
Utilizing the GPS system, the application can mark where users are stressed out. If the Mindset
application detects many increases of stress nearby a certain location, it could notify the user.
These locations could be marked on a map, which could be noted in the phone’s navigation
system. The navigation system would create routes to avoid these locations. This would help to
deal with a person's stress.
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compiled from data provided by the 57 vital statistics jurisdictions through the Vital
Statistics Cooperative Program. Accessed Jun. 24, 2016
2. Kessler RC, Chiu WT, Demler O, Walters EE. Prevalence, severity, and comorbidity of
twelve-month DSM-IV disorders in the National Comorbidity Survey Replication (NCS-
R). Archives of General Psychiatry, 2005 Jun;62(6):617-27
3. Sylvia Doo and Yun Kwok Wing (2006). Sleep problems of children with pervasive
developmental disorders: correlation with parental stress. Developmental Medicine &
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4. Sacha J (2014) Interaction between heart rate and heart rate variability. Ann Noninvasive
Electrocardiol. 2014 May; 19(3):207-16.
5. Munoz, M. L., van Roon, A., Riese, H., Thio, C., Oostenbroek, E., Westrik, I., …
Snieder, H. (2015). Validity of (Ultra-) Short Recordings for Heart Rate Variability
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6. Lamkin, Paul. "Fitbit Heart Rate Tech 'puts Consumers at Risk'
According to Lawsuit Scientist." Wearable. Wearable, 24 May 2016. Web. 14 July
2016.
7. John RM, Stevenson WG. Predicting atrial fibrillation: can we shape the future? Eur
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Acknowledgements
Dr. Phyllis Stein is gratefully acknowledged for her assistance and guidance as a mentor
throughout this study. Ravi Chacko is gratefully acknowledged for his assistance as a learned
student with a lot of experience. Dr. Aparna Kaul is greatly acknowledged for her assistance in
writing this paper and providing support when needed. Finally, the STARS program is greatly
appreciated for giving me the opportunity to pursue research in this field.