HRV and prescriptive exercise for a citizen

The trouble with a lot of wearable tech and accompanying apps is the bone-jarring lack of clinical context and prosaic health application. They provide no context; how does your exercise pattern specially affect your hypertension? What do your EP & GP think? How reliable and accurate is the data being provided?

We are of the view that basic exercise data and blood pressure and glucose measures from FDA-approved devices is a reasonable basis for information feedback to individuals and their clinicians. We also believe that the higher the fidelity of the data source for example heart rate data, the more useful the potential assessments may be. Which for us, means a measure for heart rate variability that has an appropriate level of fidelity and can be integrated into our machine learning engine such that our clinicians can be assisted in making better informed, in situ adjustments to prescriptive plans.

Heart rate variability is a measurement more often associated with high performance sport than those at risk of developing metabolic disorders. A groundswell of recent research may change this. Heart rate variability (HRV) informs of physiological functioning, and in particular balance in the Autonomic Nervous System (ANS). The vagus nerve, the tenth of 12 paired cranial nerves controls parasympathetic innervation of the heart and will act to lower heart rate. Some of the calculated HRV parameters are measures of sympathetic and parasympathetic tone.

Parasympathetic tone in particular is of interest, since it correlates with several physical, mental and emotional parameters. A high vagal tone is reflected in:

  • better working memory
  • ability to focus attention (concentration)
  • better blood sugar control
  • emotional stabiliy
  • sociability

A low vagal tone is reflected in:

  • diabetes
  • cardiovascular disease.

That's right, I have alluded to an association between vagal tone and diabetes. [3] in a landmark longitudinal study of Chinese workers spanning 4 years found

participants with faster heart rates, suggesting lower automatic function, had increased risk of diabetes, pre-diabetes, and conversion from pre-diabetes to diabetes. Each additional 10 beats per minute was associated with 23 percent increased risk of diabetes, similar to the effects of a 3 kilogram per meter square increase in body mass index.

This is the tip of an iceberg of evidence pointing to the association between HRV and susceptibility to type 2 diabetes. [2] in another significant study with over 9,000 diabetic subjects determined that

Cardiac autonomic impairment appears to be present at early stages of diabetic metabolic impairment, and progressive worsening of autonomic cardiac function over 9 years was observed in diabetic subjects.

The gotcha with tracking heart rate variability currently is the lack of support afforded by wrist wearables such as the best selling fitbit range. It's easy enough to incorporate the necessary cardiac data from chest-strapped based device providers such as POLAR who provide a sound solution. We are planning a HRV function in 2.0 but we have yet to decide the best data source for it. Meanwhile, there is much we can do with our current AI-based analytic capability and resting heart rate (RHR). In a meta-review of RHR and T2DM, [1] found

a strong positive association between high resting heart rate and the risk of type 2 diabetes. As a non-invasive marker of type 2 diabetes risk, resting heart rate may have potential in the clinical setting, especially for interventions aimed at lowering the risk of type 2 diabetes.

As digital health providers acting to improve the quality and reach of clinical exercise services it makes sense then for us to initially incorporate our RHR measures with our metabolic risk tracker slated for the machine-learning based EXPLAIN feature in 2.0

References

[1] Aune, D., B. ó Hartaigh, and L.J. Vatten. "Resting Heart Rate And The Risk Of Type 2 Diabetes: A Systematic Review And Dose–Response Meta-Analysis Of Cohort Studies". Nutrition, Metabolism and Cardiovascular Diseases 25.6 (2015): 526-534. Web.

[2] Schroeder, E. B. et al. "Diabetes, Glucose, Insulin, And Heart Rate Variability: The Atherosclerosis Risk In Communities (ARIC) Study". Diabetes Care 28.3 (2005): 668-674. Web.

[3] Wang, L. et al. "Resting Heart Rate And The Risk Of Developing Impaired Fasting Glucose And Diabetes: The Kailuan Prospective Study". International Journal of Epidemiology 44.2 (2015): 689-699. Web.

[4] Zhang, X. et al. "Resting Heart Rate And Risk Of Type 2 Diabetes In Women". International Journal of Epidemiology 39.3 (2010): 900-906. Web.

Dr Daryl Foy

Dr Daryl Foy is a Behavioural Scientist who specialises in the design of effective health behaviour change apps based on evidence including his own validated models for optimising persistent use. He consults to industry on how-to integrate persuasive design into LEAN product development as well as conversational UI. He can be contacted at dlfoy@mortonlawson.com