#14. Wearable Technology And Patient-led Innovation With Dr. David Putrino Make Visible: Understanding Complex Illness podcast

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#14. Wearable technology and patient-led innovation with Dr. David Putrino

1d ago 49:06

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Data gathered from wearable technology can warn of impending symptom exacerbation in complex chronic illness a new study has found.

In this week’s episode Dr. David Putrino discusses the findings. He, in collaboration with leading immunologists, microbiologists and data scientists, tracked data points from 5000 Visible app users (who enrolled in the study) to establish that HRV and resting heart rate can be used to predict a crash. Analyzing more than 55,000 readings over 1000 days they were able to see changes in the autonomic nervous system of contributors using this biometric data. The largest study of its kind, these findings have the potential to provide the basis for individualised care strategies for this enormous cohort of patients.

Here we discuss the function and dysfunction of the autonomic nervous system. Dr. Putrino gives us an overview of heart rate variability, the way in which it fluctuates, what its readings can determine about our health and ways in which these can be influenced. We talk through the power of breathwork including two of Dr Putrino’s studies – on resonant breathing and hypocapnia – showing the way in which breathing impacts our physiology but determining that we have innate power to influence our health.

As Director of Rehabilitation Innovation Mount Sinai Health, Dr. Putrino‘s focus is on real-world solutions for this patient cohort. In a world where new drug approval takes 10 years and technologies in this field can take 17 years to reach market, his aim is to bring patients tangible treatments and protocols in a practical timeframe. Currently involved in clinical trials for rapamycin and vagal nerve stimulation, Dr. Putrino endeavours to re-purpose drugs and technologies that are already approved in other situations to create personalised strategies for this engaged cohort.

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