Blog post by Marco Altini
We are happy to announce that our first paper using HRV4Training users generated data was accepted for publication for EMBC 2016.
When we started developing HRV4Training about 3 years ago one of our main goals was to develop a clinical grade tool to be released in the hands of thousands so that we could crowdsource data collection and shed some light on complex relations between physiology, lifestyle and training, eventually helping individuals improving performance.
One of the key limitations of current studies is often the small sample size, and the consequent difficulties in deriving insights applicable to a broader audience.
Our first small step is our publication below in which we analyzed acute HR and HRV changes in response to self-reported trainings of different intensities. In this paper we show how consistent (small) increases in HR and (greater) reductions in rMSSD show up across a broad range of individuals in response to more intense trainings, validating the use of these parameters to monitor recovery, even when measured in uncontrolled free-living settings.
In this post, I will provide a quick overview of our research process and first paper. The full paper is available here.
As I have briefly explained in other posts we are particularly interested in the opportunity to "outsource" data collection for research to HRV4Training users, so that new models explaining the relation between training, lifestyle factors, physiological parameters and performance can be developed relying on a much broader userbase with respect to regular clinical trials.
This is a trend we are seeing more and more, especially with the release of ResearchKit or CareKit in the past two years, and the involvement of big companies like Apple. The main idea is that instead of running expensive clinical trials on a limited number of participants, we can try to provide clinical grade tools (a bit optimistic) to users/consumers, and acquire a much bigger dataset that can provide a better understanding of the relations between our outcome of interest and other variables.
As we gather data from more people, we can also potentially stratify based on many more parameters, meaning that instead of having homogenous groups of subjects hoping to see similar physiological responses to a specific intervention, we can factor in many individual differences due to the much bigger sample size, for example differences in age, gender, lifestyle, etc.
Things are a bit more complicated than this, but you get the point.
To do so, we need the right reference points (i.e. the Tags in HRV4Training) as well as plenty of data from plenty of users. If you are using the app, try to take the time to provide your Tags accurately every day, and hopefully the whole community will benefit in the future.
Our approach to new scientific discoveries using user generated data
Our approach to new scientific discoveries using user generated data is structured as follows:
We addressed points one and two by developing our unique camera-based technology and working on trying to establish a simple protocol to follow at home (e.g. measurements right after waking up, paced breathing, etc.).
Finally, we choose acute HRV changes as the first aspect to investigate, as such acute HRV changes are probably the most consistent and widely observed phenomena in the relation between HR, HRV and training.
As a matter of fact, even before HRV was readily available due to improvements in technology, heart rate at rest was used to quantify recovery, as we expected a higher HR the days following more intense aerobic workouts.
While things in uncontrolled free living settings get messy easily, by recording the right reference points and relying on a great community of engaged users, we were able to collect a good amount of high quality data and analyze the relation between HR, HRV and training across different age groups and genders. Thus moving past step three in our list above.
Data & results
You can find the details on user's characteristics and data included as well as more details on our signal processing techniques used to acquire HRV using the phone camera in the full text. Here I will just show the main results highlighted in the paper (click the figures to enlarge):
Relation between HR, HRV and training on the entire dataset. HR is consistently increased on days following higher training load, while rMSSD is consistently decreased. Relative changes in rMSSD are bigger, highlighting how HRV can be more discriminative of training load. Error bars indicate the standard error.
Same analysis clustered by gender and age group.
In the figures above you can see how consistent (small) increases in HR and (greater) reductions in rMSSD show up across a broad range of individuals in response to more intense trainings, validating the use of these parameters to monitor recovery, even when measured in uncontrolled free-living settings.
In this blog post I described our first paper in which we investigated the relation between HR,
HRV and training data as acquired in unsupervised free-living settings.
Using HRV4Training we were able to acquire longitudinal data comprising measurements from 797 users that monitored their HR and HRV for a period of 3 weeks to 5 months. Given the greater sample size compared to typical studies we could provide confirmative insights on the feasibility and efficacy of such monitoring in users of different gender and age groups. Our analysis showed small but consistent increases in HR as well as reductions in rMSSD following trainings of higher intensity, regardless of gender and age group. Hence, HR and HRV based training guidance might be effective on a broad set of individuals.
Examples of how we can start being more explorative have been already reported in this blog, where we for example looked at:
As we grow our dataset and we collect more data and additional self-reported annotations (e.g. sleep quality, traveling, perceived physical condition, etc.) we will be extending our analysis to better understand complex relations between physiological, behavioral and lifestyle factors, in uncontrolled free-living settings.
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1. Context & Time of the Day
3. Paced breathing
4. Orthostatic Test
5. Slides HRV overview
6. rMSSD vs SDNN
1a. Acute Changes in HRV
1b. Acute Changes in HRV (population level)
1c. Acute Changes in HRV & measurement consistency
1d. Acute Changes in HRV in endurance and power sports
2a. Interpreting HRV Trends
2b. HRV Baseline Trends & CV
3. Tags & Correlations
4. Ectopic beats & motion artifacts
5. HRV4Training Insights
6. HRV4Training & Sports Science
7. HRV & fitness / training load
8. HRV & performance
9. VO2max models
10. Repeated HRV measurements
11. VO2max and performance
12. HR, HRV and performance
13. Training intensity & performance
14. Publication: VO2max & running performance
15. Estimating running performance
Camera & Sensors
1. ECG vs Polar & Mio Alpha
2a. Camera vs Polar
2b. Camera vs Polar iOS10
2c. iPhone 7+ vs Polar
2d. Comparison of PPG sensors
3. Camera measurement guidelines
4. Validation paper
5. Android camera vs Chest strap
6. Zoom HRV vs Polar
7. Apple Watch and HRV
8. Scosche Rhythm24
9. Apple Watch
1. Features and Recovery Points
2. Daily advice
3. HRV4Training insights
4. Sleep tracking
5. Training load analysis
6a. Integration with Strava
6b. Integration with TrainingPeaks
6c. Integration with SportTracks
6d. Integration with Genetrainer
6e. Integration with Apple Health
6f. Integration with Todays Plan
7. HRV4T Coach advanced view
8. Acute HRV changes by sport
9. Remote tags in HRV4T Coach
10. VO2max Estimation
11. Acute stressors analysis
12. Training Polarization
13. Custom desirable range / SWC
14. Lactate Threshold Estimation
15. Functional Threshold Power(FTP) Estimation for cyclists
16. Aerobic Endurance analysis
1. Intro to HRV
2. HRV normal values
3. HRV by sport
4. HRV, strength & power
5. AngelSensor & HRV
6. HRV 101: How to
7. Top 5 most read articles
8. HRV normalization by HR
9. How to use HRV, the basics