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Heart rate variability, training & performance

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Heart rate variability, training & performance

  1. 1. Heart Rate Variability, Training & Performance @marco_alt Lead Data Scientist @ Bloom Technologies Maker HRV4Training.com PhD Candidate applied Machine Learning @ TU/e [Marco Altini]
  2. 2. 2012 - 2015 “Runner” / Scientist
  3. 3. 2012 - 2015 “Runner”
  4. 4. Genetics?
  5. 5. Yeah, genetics
  6. 6. Almost there
  7. 7. 2012 - 2015 Scientist
  8. 8. 2009
  9. 9. Cardiorespiratory Fitness Estimation (VO2max) Energy Expenditure Estimation (kcals) Activity Recognition PhD (defense next week!) Applied Machine Learning Eindhoven University of Technology
  10. 10. Making it smaller Prediction of pregnancy complications Labour detection Load Data Scientist Bloom Technologies
  11. 11. Making it smaller Heart Rate + Heart Rate Variability + Electrohysterography + Blood Pressure Gestational hypertension prediction Labour detection Preterm birth Head of Data Science Bloom TechnologiesHRV4Training
  12. 12. 60 Seconds PPG Measurements
  13. 13. Adapted  from  Tamura  et  al.  Wearable  Photoplethysmographic  Sensors—Past  and  Present  
  14. 14. Adapted  from  Tamura  et  al.  Wearable  Photoplethysmographic  Sensors—Past  and  Present  
  15. 15. •  Accessibility – camera-based data acquisition •  User generated data & research – Pushing the boundaries on what we know about the relations between training, lifestyle, physiology and performance – More users, more parameters, more stratifications (lifestyle factors) HRV4Training
  16. 16. •  What is heart rate variability (HRV)? •  How to get the most out of HRV (best practices) •  What can we do with the data •  Opportunities from user generated data Quick outline
  17. 17. 2012 - 2015 What is HRV?
  18. 18. Beat to Beat Variation
  19. 19. Heart Rate Variability (HRV) •  Regulated by sympathetic / parasympathetic branches of the ANS •  Clear proxy to parasympathetic activity / recovery / body functions at rest – Understand how we react to stressors Autonomic Nervous System
  20. 20. Higher HRV Less physiologically stressed Ready to perform Lower HRV More physiologically stressed Tiredness This slide is an oversimplification
  21. 21. •  What is heart rate variability (HRV)? •  How to get the most out of HRV (best practices) •  What can we do with the data •  Opportunities from user generated data Quick outline
  22. 22. 2012 - 2015 How to get the most out of HRV measurements?
  23. 23. 60 Seconds PPG Measurements •  Quick snapshot of your physiology (HR +HRV) –  Parasympathetic activity •  Low barrier (fast, convenient, no sensors) •  Insightful –  day to day variability due to external stressors (training, travel, etc.), long term baseline changes (physical condition, chronic stress) –  If done properly!
  24. 24. Best Practices for 60 seconds PPG Measurements •  When to take the measurement –  Morning, during the day?, etc. •  What type of measurement –  Lying down, sitting, orthostatic? •  Paced breathing –  Constrained, unconstrained? •  What metric to use? –  Time domain, frequency domain? •  Are 60 seconds really enough?
  25. 25. When to take the measurement •  First thing after waking up – Relaxed physiological state – Limit all external stressors – Closest to what we do in research / clinical studies – Don’t read your email before the measurement!
  26. 26. What type of measurement •  Lying down while still in bed – Limits other factors like not waiting enough after standing up – Performed in clinical studies – Sitting/Standing also valid, however for simplicity I’d recommend lying down
  27. 27. Paced Breathing (1/3) •  Improves reliability and repeatability of the measurement – Breathing patterns and RSA have an impact on HRV values – Using paced breathing provides more consistent settings (same context!) – Use what works for you (8-12 breaths per minute typically)
  28. 28. Paced Breathing (2/3)
  29. 29. Paced Breathing (3/3)
  30. 30. Paced Breathing (3/3)
  31. 31. Consistency! •  Choose: – A body position – A paced breathing rate – Waking time (more or less) / measurement routine Stick to those
  32. 32. What metric to use? •  HRV is not a single number •  Use rMSSD or ln rMSSD – Marker of parasympathetic activity (only thing you can reliably measure). There is no clear sympathetic marker – HF, LF, HF/LF or other frequency domain features require more time (and are computed differently by everyone, difficult to generalize/compare)
  33. 33. Are 60 seconds really enough? •  Yes. Just follow the best practices
  34. 34. HRV4Training - measurement Camera view PPG view 60 seconds timer Breathing bar for paced breathing Instantaneous heart rate
  35. 35. •  What is heart rate variability (HRV)? •  How to get the most out of HRV (best practices) •  What can we do with the data •  Opportunities from user generated data Quick outline
  36. 36. 2012 - 2015 What can we do with the data in the context of training & performance?
  37. 37. What to do with HRV data •  Acute HRV changes •  Multi parameter trends
  38. 38. Acute HRV changes Day to day variability
  39. 39. Acute HRV changes Rest or easy trainings Higher HRV
  40. 40. Acute HRV changes Average or intense trainings Lower HRV
  41. 41. Acute HRV changes
  42. 42. Acute HRV changes
  43. 43. Acute HRV changes
  44. 44. Acute HRV changes
  45. 45. Multi-parameter trends •  In the long term things get more complicated •  Higher HRV not necessarily linked to better condition/performance •  Understanding the big picture requires more parameters and context – Training load, other stressors
  46. 46. Multi-parameter trends •  HRV baseline and variation •  More variation could be indicative of maladaptation to training (weekly values all over the place)
  47. 47. Multi-parameter trends •  Detects: –  Coping well –  Maladaptations –  Accumulated fatigue
  48. 48. •  What is heart rate variability (HRV)? •  How to get the most out of HRV (best practices) •  What can we do with the data •  Opportunities from user generated data Quick outline
  49. 49. 2012 - 2015 User generated data
  50. 50. User generated data Dataset
  51. 51. User generated data Acute HRV changes
  52. 52. User generated data Acute HRV changes
  53. 53. User generated data Acute HRV changes & consistency
  54. 54. User generated data Acute HRV changes & consistency
  55. 55. User generated data Acute HRV changes & consistency
  56. 56. User generated data •  What’s next – Better understand relation between physiological parameters and physical condition in the long term – Build better individual models – Stratify on more parameters / include different samples of the population
  57. 57. Questions? HRV4Training.com/faq @marco_alt

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