Heart rate variability, training & performance

transcript

Heart Rate Variability, Training & Performance

@marco_alt Lead Data Scientist @ Bloom Technologies

Maker HRV4Training.com PhD Candidate applied Machine Learning @ TU/e

[Marco Altini]

2012 - 2015

“Runner” / Scientist

2012 - 2015

“Runner”

Genetics?

Yeah, genetics

Almost there

2012 - 2015

Scientist

Cardiorespiratory Fitness Estimation (VO2max)

Energy Expenditure Estimation (kcals) Activity Recognition

PhD (defense next week!) Applied Machine Learning

Eindhoven University of Technology

Making it smaller

Prediction of pregnancy

complications Labour detection

Load Data Scientist Bloom Technologies

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

60 Seconds PPG Measurements

Adapted from Tamura et al. Wearable Photoplethysmographic Sensors—Past and Present

•  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

•  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

2012 - 2015

What is HRV?

Beat to Beat Variation

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

Higher HRV

Less physiologically stressed

Ready to perform

Lower HRV

More physiologically stressed

Tiredness

This slide is an oversimplification

Quick outline

2012 - 2015

How to get the most out of HRV measurements?

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!

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?

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!

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

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)

Consistency!

•  Choose: – A body position – A paced breathing rate – Waking time (more or less) / measurement

routine

Stick to those

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)

Are 60 seconds really enough?

•  Yes. Just follow the best practices

HRV4Training - measurement

Camera view

PPG view

60 seconds timer Breathing bar for paced breathing

Instantaneous heart rate

Quick outline

2012 - 2015

What can we do with the data in the context of training &

performance?

What to do with HRV data

•  Acute HRV changes

•  Multi parameter trends

Acute HRV changes Day to day variability

Acute HRV changes Rest or easy trainings

Higher HRV

Acute HRV changes Average or intense trainings

Lower HRV

Acute HRV changes

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

•  HRV baseline and variation

•  More variation could be indicative of maladaptation to training (weekly values all over the place)

•  Detects: –  Coping well –  Maladaptations –  Accumulated

fatigue

Quick outline

2012 - 2015

User generated data

User generated data Dataset

User generated data Acute HRV changes

User generated data Acute HRV changes & consistency

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

Questions?

HRV4Training.com/faq

@marco_alt

Heart rate variability, training & performance

Engineering