mHealth

DESCRIPTION

Building on a concept of “biological age” we go beyond to show that plenty of organism wellness information can be inferred from a locomotor signal on its own. This fact opens wide possibilities for a non-invasive and continuous monitoring of wellness status for a wide population in a completely non-invasive way without interfering with one’s routine activities and on a daily basis.

Gero mHealth technology evaluates age- and wellness-related parameters based on step count tracks recorded with wearable devices or smartphones. First we need to accumulate step counts data during several days - usually a week or so, and these can already be available in your activity history. This is required for averaging activity data to improve signal-to-noise ratio.

Next, we identify parameters of individual locomotor track in the form of histogram - to understand the overall activity levels, and activity transition matrix - to understand frequency of activity bouts. Some additional parameters are extracted such as chronotype to improve the performance of the method. Each of these parameters is related to age and wellness status. For example, if you are more active, you are more healthy - that’s a well known fact. However, it’s not that all. Frequency of activity bouts, statistics of rest intervals, how easy you wake up, how quickly you get tired - all these factors matter. We call the combination of these factors a locomotor activity pattern.

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APPLICATION

Gero mHealth AI engine has been educated on a reference dataset to find an intricate interrelations between locomotor activity patterns to accurately estimate the wellness status of an organism. It can also estimate which age cohort you match based on you locomotor pattern signature!

Wellness status - we also call it Health Score - is a metric of your biological age and overall health status. If you ask whether it can predict your lifespan - the answer is no. Survival is a stochastic process and we can only talk about probability. However, the probability to survive to 70 years, to 80 years, to 90 years and even over 100 years can be greater or less. And it can be measured. The probability to survive strongly depends on different factors among which your genetics, living environment and lifestyle habits are of significant importance. The probability might mean little to individual person, but it becomes enormous on population scale. Thus, the difference between median life expectancy is up to ten years for smokers and non-smokers, which is convincingly a strong manifestation of the effect of lifestyle habits on organism wellness.

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Our technology paves a way to a quantitative metrics of living environment and lifestyle factors. As such it was shown to distinguish between hazardous lifestyles of smoking and obesity in a dose-dependent manner. More than that, it scored improved wellness status for those who gave up smoking. These facts demonstrate that while the technology cannot predict your exact lifespan, it can still accurately measure instant effects of lifestyle that would eventually accumulate in a prolonged or reduced health span. We believe, this may be a promising and motivating tool for those who decide on lifestyle choice bringing a new value to readily available fitness devices and apps.