Heart rate variability, training & performance
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Transcript of Heart rate variability, training & performance
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Heart Rate Variability, Training & Performance
@marco_alt Lead Data Scientist @ Bloom Technologies
Maker HRV4Training.com PhD Candidate applied Machine Learning @ TU/e
[Marco Altini]
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2012 - 2015
“Runner” / Scientist
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2012 - 2015
“Runner”
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Genetics?
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Yeah, genetics
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Almost there
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2012 - 2015
Scientist
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2009
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Cardiorespiratory Fitness Estimation (VO2max)
Energy Expenditure Estimation (kcals) Activity Recognition
PhD (defense next week!) Applied Machine Learning
Eindhoven University of Technology
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Making it smaller
Prediction of pregnancy
complications Labour detection
Load Data Scientist Bloom Technologies
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Making it smaller
Heart Rate + Heart Rate Variability + Electrohysterography +
Blood Pressure Gestational hypertension prediction
Labour detection Preterm birth
Head of Data Science Bloom Technologies HRV4Training
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60 Seconds PPG Measurements
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Adapted from Tamura et al. Wearable Photoplethysmographic Sensors—Past and Present
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Adapted from Tamura et al. Wearable Photoplethysmographic Sensors—Past and Present
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• 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
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• 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
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2012 - 2015
What is HRV?
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Beat to Beat Variation
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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
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Higher HRV
Less physiologically stressed
Ready to perform
Lower HRV
More physiologically stressed
Tiredness
This slide is an oversimplification
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• 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
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2012 - 2015
How to get the most out of HRV measurements?
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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!
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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?
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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!
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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
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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)
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Paced Breathing (2/3)
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Paced Breathing (3/3)
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Paced Breathing (3/3)
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Consistency!
• Choose: – A body position – A paced breathing rate – Waking time (more or less) / measurement
routine
Stick to those
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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)
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Are 60 seconds really enough?
• Yes. Just follow the best practices
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HRV4Training - measurement
Camera view
PPG view
60 seconds timer Breathing bar for paced breathing
Instantaneous heart rate
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• 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
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2012 - 2015
What can we do with the data in the context of training &
performance?
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What to do with HRV data
• Acute HRV changes
• Multi parameter trends
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Acute HRV changes Day to day variability
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Acute HRV changes Rest or easy trainings
Higher HRV
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Acute HRV changes Average or intense trainings
Lower HRV
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Acute HRV changes
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Acute HRV changes
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Acute HRV changes
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Acute HRV changes
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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
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Multi-parameter trends
• HRV baseline and variation
• More variation could be indicative of maladaptation to training (weekly values all over the place)
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Multi-parameter trends
• Detects: – Coping well – Maladaptations – Accumulated
fatigue
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• 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
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2012 - 2015
User generated data
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User generated data Dataset
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User generated data Acute HRV changes
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User generated data Acute HRV changes
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User generated data Acute HRV changes & consistency
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User generated data Acute HRV changes & consistency
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User generated data Acute HRV changes & consistency
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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
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Questions?
HRV4Training.com/faq
@marco_alt