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HRV & Monitoring the Machine

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Tomas Gutierrez

on 26 March 2015

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Transcript of HRV & Monitoring the Machine

Human Performance
Nutrition
Physical Fitness
Sleep
Mental Fitness
Stress Management
Monitoring the Machine with HRV
@togume
what?
Daily tracking of waking HRV against stressors
HRV?
Variability between R-R intervals
Higher HRV value = better
how?
Bluetooth HRM + iOS Apps (currently iThlete)
+
Ancestral/paleo/primal
Quantitative
Sleep length (maths & Sleep Cycle app)
Physical output/exertion (Suunto/HRM)

Qualitative
Sleep quality
Nutrition
Psychological Stress
how?
how?
lessons
Sleep: Consistent quantity and quality is most important factor in HRV scores
HRV + Qualitative & Quantitative Markers
Carb backloading
Intermittent fasting
Ketosis
High-fat
Squash
Backpacking
Mountain Biking
Trail Running
Kayaking
Adult Gymnastics
Sun-cycle
8+hrs
Minimal food @ T-2hrs
Minimize blue
Brain ramp-down
Managed uppers
Cortisol
Juggling
Moto riding
RC flying machines
Business, work, tech
Meditation
Digital diets
Perspective travel
Reading
Controlled tech/media
GTD
Most insightful & unobtrusive biomarker available today*
Baseline normal and identify aberrations
Good for iterating and improving variables/results
HRV tl; dr.
http://en.wikipedia.org/wiki/Heart_rate_variability
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3916914/
http://www.marksdailyapple.com/have-you-checked-your-heart-rate-variability-lately/
@togume's data for HRV: https://docs.google.com/spreadsheets/d/1blJHTeob-1GkahGYqg7xtZMyVcU0fsYCbZfyV0-51Uc/edit?usp=sharing - you can comment!
resources
how?
Data analysis
lessons
Physical stress: HRV shows me when I'm throwing too much load on the system
lessons
Stress management: meditation practice to calm the system is key. Low HRV = low "coherence" (HeartMath)
HR... What?
Athletes exposed to high training loads and demanding competition schedules are at risk of experiencing unintentional overreaching, illness and injury when sufficient recovery is unattained. Heart rate variability (HRV), a non-invasive marker of autonomic status extrapolated from successive R peaks obtained by an electrocardiograph (ECG), is emerging as a valuable training status biometric used to objectively measure stress levels in athletes. A growing body of evidence supports the utility of HRV’s efficacy in sports training for the purposes of guiding periodization (Hautala et al., 2009; Kiviniemi et al., 2007; Kiviniemi et al., 2010), assisting in the diagnosis of over-trained states (Baumert et al., 2006; Tian et al., 2012); predicting physical performance (Chalencon et al., 2012; Manzi et al., 2009); and reflecting recovery status and training load (Chen et al., 2011; Iellamo et al., 2002; Pichot et al., 2000; Sartor et al., 2013).

Traditional HRV recordings are often performed in specialized laboratories and involve considerable time demand (i.e., at least 5-minute recordings) and a qualified technician for interpretation. These requirements make HRV assessment within athletic field settings difficult. Thus, practical measures capable of providing interpretable HRV data quickly, easily and affordably are desired. At present, there are various HRV field tools commercially available, though few have been validated. For example, several heart rate monitors have been shown to provide accurate R-R interval data, such as the Polar S810 (Gamelin et al., 2006; Gamelin et al., 2008; Nunan et al., 2009; Porto and Junqueira, 2009; Vanderlei et al., 2008; Weippert et al., 2010), the Polar RS800 (Wallén et al., 2012), and the Suunto T6 (Weippert et al., 2010). Though heart rate monitors provide more practicality than traditional HRV measures, they still require manual exportation of the raw data to a personal computer for software analysis and interpretation by an informed individual.
Heart Rate Variability
Linked to fight-flight response
More stressed = lower HRV
Less stressed = higher HRV
Source: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3916914/
#NOPE
The Machine =
The Human =
The Primate

@togume
flaws/no time
App asks for data points after reading - could be psychosomatic?
Sleep quality/quantity becomes second nature - no need for fancy tech to quantify - where's the quant!?
Would have liked to export PTE of workouts vs. HRV of the next day from Suunto - not easy... :/
Would have liked to export HeartMath scores for "coherence" vs HRV of the same day
Q & A
@togume
about.me/tomas.gutierrez
.
.
.
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SLEEP!
* IMO
Sun?
Full transcript