1. Potential Use of Wearable
Activity Trackers to
Measure Detailed Heart
Rate and HRV Responses
for Stress and Stress
Management Activities
ETHAN NG
JULY 22, 2016

2. Introduction
 27.6 million people in America alone suffering from heart disease
 614,000 Americans die from heart disease every year
 Number 1 killer
 20% Millennials/Gen X have anxiety or mental health disorder
 Seek out stress reduction method
 Stress correlation with disorders and negative impact

3. Introduction cont.
 Heart Rate Variability(HRV)
 Method of measuring real time function of autonomic nervous system
 Increases or decreases based on bodies’ threat analysis and response
 Can be used to help with stress management
 Measured by beat to beat intervals

4. Introduction cont.
 Many Benefits for Consumer Electronic
 Portable
 Cheap and easily accessible
 Many applications have been built
• Tips for meditation
• Portable stress management
• GPS tracker for PTSD triggers
• Plan fitness goals

5. Objective
 Purpose
 To determine how accurately these consumer electronics
measure heart rate and heart rate variability
 Problems
 These applications show the levels of stress and recommend
solutions to each of these with unknown accuracy
 Stop companies from misleading consumers and overpromising
with their products

6. Study Subjects
 One healthy volunteer
 Mio Link wristband and MyPatch Holter Monitor
 15 minute of exercise
 10 minute of meditation
Figure 1. An image of the Mio Link wristband properly assembled
onto the volunteer’s wrist
Figure 2. A photo demonstrating the MyPatch monitor properly assembled onto the volunteer’s body.

7. Data Analysis
 Methods
 Data had to be put into a similar form to compare
 MyPatch Holter -> Connected to Computer
♦ Mibf file -> csv
 Mio Link-> Relies on Bluetooth
♦ xls -> csv
 Pearson’s Correlation
 Visualization
 Javascript
Figure 4. Snippet of csv file
Figure 3. Snippet of Mibf file

8. Results
Figure 5. Raw RR Interval Mio (Red) data vs Raw RR Interval Holter (Blue) data Figure 6. 5-min averaged SDRR Mio (Red) Data vs 5-min averaged SDRR
Holter (Blue) Data
 Decent correlation in “normal” heart rates
 Consistent offset with “bad” heart rates
 Mio disconnects

9. Results cont.
 Correlation
▪ Utilized Pearson’s Correlation
• Result of 0.15
• Relatively weak correlation
but still some correlation
Figure 7. 5-min averaged SDRR Mio (Red) Data vs 5-min averaged SDRR Holter (Blue)
Data from first disconnect to second disconnect

10. Discussion
 Importance
 Purpose of study
• Assess accuracy of Mio Link in comparison to Holter (golden standard)
• Make sure companies do not overpromise on what they can deliver
 Fitbit
• Problem with overpromising
• Inaccuracies of up to 20bpm
• Company faced many lawsuits

11. Discussion
 Pitfalls
 Small Sample size
 Healthy volunteers only
 Time restraints allowed for only one analysis
 More controlled experiment
 Future applications
 GPS System to accurately prevent location based triggers
 Prediction of Atrial Fibrillation
• Other heart problem detection
 Better stress management algorithm
• Body temperature sensor

12. Conclusions
 Improvements must be made
 Software
• Algorithm
 Hardware
• Detection

13. Acknowledgements
Special thanks for their guidance and assistance in my research
project:
 Dr. Phyllis Stein: STARS Mentor/Associate Professor of Medicine
Director, Washington University School of Medicine HRV Lab
 Ravi Chacko: STARS Project Lead/MD/PhD Candidate of Brain-
Computer Interface Lab of Washington University, Co-Founder of
Mindset App
 Dr. Aparna Kaul: STARS Advisor/Senior Scientist at Confluence
Discovery Technologies

14. References
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Data are from the Multiple Cause of Death Files, 1999-2013, as compiled from data provided by the 57 vital
statistics jurisdictions through the Vital Statistics Cooperative Program. Accessed Jun. 24, 2016
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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 & Child Neurology, , pp 650-655.
doi:10.1017/S001216220600137X.
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 Measurements. PLoS ONE, 10(9), e0138921
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 Heart J 2015;36:145–7

15. Thank you!