THE ULTIMATE GUIDE TO HEART RATE VARIABILITY ​​

I've put together a series of posts that cover many aspects of HRV measurement, data interpretation as well as plenty of examples that you can look at to better understand how to make use of the data, and how HRV relates to training and lifestyle stressors.

​Check them out at these links:

• Part 1: Measurement setup, best practices and metrics
• Part 2: Interpreting your data
• Part 3: Case studies and practical examples
• Part 4: Common misconceptions

Heart Rate Variability tools

Since 2012, I've been developing mobile apps using the phone's sensors to provide unique insights on the user's physiological status (such as heart rate and heart rate variability) without the need for external hardware.

Whether you are interested in measures of resting physiology and chronic stress, biofeedback exercises, would like to design your own research study or to embed our camera-based technology in your product, we can help.

You can learn more about these solutions at HRV.tools

HRV4Training Pro: Web platform

HRV4Training Pro is the ultimate platform for individuals and teams that want to learn more about how their body is responding to different stressors associated to training and / or lifestyle. You can learn more here and here.

Strava: Relative effort

I have helped Strava developing their current Relative Effort, a metric used to quantify training effort, combining intensity and duration. You can read Strava's official launch blog post here as well as an interview I gave here. 

HRV4Biofeedback

Improve self-regulation and better cope with stress using HRV4Biofeedback, the camera-based Heart Rate Variability (HRV) Biofeedback app. Learn more here.

bloomlife

At Bloomlife I was mainly taking care of data science activities. However, I had also developed an app that implemented part of my work around preterm birth risk assessment and prediction, using open data provided by the CDC. You can read more about our approach on another post I wrote on Medium.

The app uses twenty parameters to feed a Bayesian model and determine preterm birth risk. It is free and can be used by expecting mothers as well as by clinicians. For expecting mothers, the app provides awareness and perspective with respect to people with similar characteristics. On the other hand, by tuning the different parameters, clinicians can use the app to dynamically explore the dataset, therefore understanding the impact of different parameters without the need for downloading the dataset and further statistical analysis.

37

37 estimates your risk of preterm delivery, gestational diabetes and gestational hypertension based on your characteristics. Your risk factor is computed based on more than 3 million deliveries registered in the United States between 2012 and 2014 by the Center for Disease Control and Prevention. The app is no longer available.