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HRV & Monitoring the Machine
Transcript of HRV & Monitoring the Machine
Monitoring the Machine with HRV
Daily tracking of waking HRV against stressors
Variability between R-R intervals
Higher HRV value = better
Bluetooth HRM + iOS Apps (currently iThlete)
Sleep length (maths & Sleep Cycle app)
Physical output/exertion (Suunto/HRM)
Sleep: Consistent quantity and quality is most important factor in HRV scores
HRV + Qualitative & Quantitative Markers
Minimal food @ T-2hrs
RC flying machines
Business, work, tech
Most insightful & unobtrusive biomarker available today*
Baseline normal and identify aberrations
Good for iterating and improving variables/results
HRV tl; dr.
@togume's data for HRV: https://docs.google.com/spreadsheets/d/1blJHTeob-1GkahGYqg7xtZMyVcU0fsYCbZfyV0-51Uc/edit?usp=sharing - you can comment!
Physical stress: HRV shows me when I'm throwing too much load on the system
Stress management: meditation practice to calm the system is key. Low HRV = low "coherence" (HeartMath)
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
The Machine =
The Human =
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