I want my wearables!

The layer of quantified self (QS) gadgetry starting to surround us (but not encircle us in the Heideggerian sense) is driving both the mindset shift (e.g.; disruption in the notion of the identity of the individual) and technical practicalities (e.g.; new health informatics business models and technology tools) required for a next generation of health science and technology innovation.

The adoption of the current cell phone-based QS applications and level-one QS devices (e.g.; Fitbit pedometers, myZeo sleep trackers, and Nike+ and Jawbone UP fitness trackers) could give way to the potentially rapidly arriving era of wearable computing. Mobile phones have been one of the fastest adopted technology platform to date, and the wearable computing platform could have even faster adoption and allow for even more self-quantification.

The ‘So What’ Interface – a Prescriptive Layer

When we see a calendar, we know what the information means, and readily map this to potential action-taking. This is not as clear with data displayed from the new era of the Health Internet of Things - the increasingly ubiquitous information climate of consumer biophysical monitoring and quantified self-tracking that surrounds us.

So far the main focus has been on Apple-y/iOS-y design and data visualization in health data streams such as 23andMe genomic data, Fitbit data, the Eatery food diary summarization data, or EKG data tracking across a smartphone screen when held up to the chest. However, we are all still scratching our heads as to what to do as a result of seeing the information.

The Prescriptive Interface
A whole new conceptual category of ‘what to do with this data’ needs to be articulated, named, and implemented – an intuitively-apparent prescriptive layer of suggested action-taking as a result of viewing the data. The two biggest challenges are first, these data streams are only nascently available and therefore meaning has not yet been determined in many cases, and second, there is little effort in determining and implementing a core set of principles for behavior influence and ambient suggestioning. Having these challenges solved could help constitute what is missing in the health data streams but not the calendar – knowing what the information means and what action to take as a result.