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Heart Rate Variability & Training Webinar – K3Fitness
Marco Altini, PhD @marco_alt
Lead Data Scientist @ Bloomlife Creator of HRV4Training
Cardiorespiratory Fitness Estimation Energy Expenditure Estimation
Activity Recognition
PhD Applied Machine Learning
BSc, MSc Computer Science Engineering
Making it smaller
Maternal and fetal monitoring during
pregnancy Prediction of pregnancy complications
Labour detection
Load Data Scientist Bloomlife
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
• What is heart rate variability (HRV)?
• How to collect data (technology)
• Best practices
• What to do with the data
• HRV4Training & Coach overview
Quick outline
Beat to Beat Variation
Not a single number: time and frequency domain features. Most meaningful and standardized feature for short measurements: rMSSD (apps typically convert it to something more human-friendly, e.g. 0-10 or 0-100)
Heart Rate Variability (HRV) • Regulated by sympathetic /
parasympathetic branches of the ANS
• HRV (rMSSD) is a clear proxy of 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
• What is heart rate variability (HRV)?
• How to collect data (technology)
• Best practices
• What to do with the data
• HRV4Training & Coach overview
Quick outline
Full ECG: gold standard (see prev image)
Chest strap + app: typically very accurate, recommend Polar H7 (HRV4Training, ithlete, Elite HRV) Validated PPG devices: HRV4Training, ithlete, same as an ECG/Polar. Higher compliancy Wristbands: not there yet, will be soon
Hardware for HRV analysis
All technologies are affected by artifacts (or noise), either in the technology itself or simply actual artifacts in the data due to e.g. ectopic beats. Even an ECG needs to be correctly cleaned up for artifacts, same for Polar data. The app needs to do some extra work, check what you are using.
A note on artifacts
• What is heart rate variability (HRV)?
• How to collect data (technology)
• Best practices
• What to do with the data
• HRV4Training & Coach overview
Quick outline
60 Seconds 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)
• Anything affects the ANS > best practices
Best Practices
• 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?
• Other issues / recommendations
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 unless your heart rate is very low (<40 bpm, in this case sitting or standing might be preferable)
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) – do not breathe at unnatural rate (e.g. too deep).
Consistency is key
• 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)
Other issues
• Do not swallow while measuring, try to relax and avoid swallowing or repeat the measurement (swallowing causes tachicardia and therefore can affect the measurement)
• Empty the bladder • Measure every day: while 4-5 times/
week will get you a good baseline, valuable information is lost (e.g. weekly variability in measurements)
• What is heart rate variability (HRV)?
• How to collect data (technology)
• Best practices
• What to do with the data
• HRV4Training & Coach overview
Quick outline
What to do with HRV data
• Acute HRV changes: day to day variations to acute stressors – intense workout – getting sick – travel – etc.
• Multi parameter trends: long term/chronic changes in baseline values – Maladaptation to training – Tapering – etc.
Acute HRV changes: research
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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
Multi-parameter trends
• HRV baseline and variation
• More variation could be indicative of maladaptation to training (weekly values all over the place)
Multi-parameter trends
• Look for: – Steady or increasing HRV baseline – Steady or decreasing HR baseline – Low CV, unless HRV baseline also
decreasing
Always in the context of your current training/load
Multi-parameter trends
Vesterinen et al. Start an intense training block only when HRV not trending negatively
Other recommendations
Step 1 is always collecting data. Collect for months before you implement any changes.
Understand what affects your physiology. Every person is different, by monitoring physiological responses to your training you can get unique insights on how your body is responding Step 2 is how to optimize training using HRV. Do we always aim for higher values?
HRV will go through cycles of higher and lower values over months, depending on your physiological stress level. By making day to day changes we can try to optimize performance and training in the long term, not necessarily resulting in increased HRV at the end, but in better performance as we trained our body harder at the right moment (see Vesterinen et al). Very different from HR (typically reducing with increased fitness).
• What is heart rate variability (HRV)?
• How to collect data (technology)
• Best practices
• What to do with the data
• HRV4Training & Coach overview
Quick outline
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
• Accessibility & high quality – camera-based data acquisition (validated
with respect to ECG)
• Insights – combine annotations and physiological
data to better understand acute changes, trends, fitness, etc. -> interpretation, more than just data
• User generated data & research – Pushing the boundaries on what we know
about the relations between training, lifestyle, physiology and performance
HRV4Training
AdaptedfromTamuraetal.WearablePhotoplethysmographicSensors—PastandPresent
PPG (measuring blood flow)
AdaptedfromTamuraetal.WearablePhotoplethysmographicSensors—PastandPresent
PPG (measuring blood flow)
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HRV4Training: measurement
Camera view
PPG view
60 seconds timer Breathing bar for paced breathing
Instantaneous heart rate
HRV4Training: main screen
Daily score and advice (color coded)
Baseline
Advice message
Your normal values and where you stand today
HRV4Training: insights
• Acute HRV changes: day to day variability in response to training
• HRV trends: multi-parameter analysis to look at the big picture
• VO2max estimation: based on submaximal HR (requires Strava link)
• Training load analysis: standard ATL/CTL models
• Custom correlations: explore relations between tags and physiological data
HRV4Training: acute HRV changes
• day to day variability in response to training
• Analysis by sport and training intensity.
• How does your physiology respond?
HRV4Training: HRV Trends
• multi-parameter analysis to look at the big picture
• Is it the right time to start intense training blocks?
• Are we adapting well to the current training plan?
• More experimental
HRV4Training: VO2max
• Based on submaximal HR (lower submaximal HR -> higher fitness level)
• Link to Strava to automatically acquire workouts data
HRV4Training: training load analysis
• Pick training load metric (intensity, distance, RPE, TSS, suffer score, etc.)
• Acute training load • Chronic training load • Injury risk (too much
acute training with respect to our chronic?)
HRV4Training: custom correlations
• explore relations between tags and physiological data
• What other factors influence your physiology?
HRV4Training Coach
• Automatically retrieve your athlete’s data right after the measurements
• Analyze insights + explore custom parameters
• Quickly assess physiological stress level based on your athlete historical data (overview of daily advice, HRV and subjective scores)
• Configure remotely your athletes tags & filter them locally
• Export all data as csv files