The Epidemiology of Stress II

In my previous post on the cost of stress the number of people behind clinical denominations of stress and anxiety such as Generalised Anxiety Disorder, I wrote that even though the scale of the problem is tremendous, and recent studies point to more than 40% of diagnosed patients going untreated, not much has been done to look for epidemiologically-meaningful data behind stress.

I said I would, in a second post, outline what we (at BioBeats) and our partners mean to do about this. As often happens, things took a little while to set up, but we are about to conduct our first pilot study with a major private health insurer, and will shortly go into open beta with a couple of pieces of technology.

Before I lead you to where you can help us conduct the largest experiment on stress epidemiology ever, let me outline our thoughts.

The Perpetual Beta

In Designing for Collective Intelligence, Dawn Gregg refers to the seventh principle of collective intelligence application requirements as ‘The Perpetual Beta’, in which the changing needs of a user community are serviced on an on-going basis, the application adding and removing features as those needs change.

In the case of people who suffer from stress (and that’s a large percentage of any population), those needs are complex: most people come to their doctor with complaints of symptoms generated –by- a stress disorder, but they aren’t actually aware that the cause is stress.

They are, in effect, suffering from a lack of contextual awareness. My personal story bears witness to this phenomenon: after being resuscitated in an ambulance following a cardiac incident at an airport, I spent years trying to figure out where that attack came from; what its nature was. In the years that elapsed, I heard countless stories from other people, who didn’t realise they had an anxiety disorder, and they all told me they thought it was something else. A dislocated jaw that wasn’t dislocated at all, an arm that ‘didn’t feel right’, chest pains, stomach pains, joint pains, headaches and crippling migraines, and violent, amoral thoughts that cause great suffering because they come from dark, unexpected places in one’s consciousness, and feel alien. None of it was described to me as a ‘disorder’: every person really believed there was something wrong with their limbs and nervous systems, and it took them a long time to transition to the realisation that it was ‘just’ the effects of chronic anxiety.

We Must Collaborate

Perhaps the most helpful thing that software makers, designers, product people and engineers could tackle right now is this: perception and cultural awareness of the medical facts behind anxiety. Education of the public by both creating interventions that illustrate the problems and potential solutions, and refinement of the doctor/patient interaction to remove obstacles in primary care diagnosis and treatment of stress/anxiety conditions.

This can’t be driven by technological solutionism: no app is going to completely ‘cure’ someone seriously suffering from stress. Instead, our work must focus on creating technologies that gently, transparently usher people to coping mechanisms rooted in evidence-based health care practice, and to the people and carers behind that evidence. In other words, the technology must be a gateway to real care.

To do this, we have to collaborate. We must innovate together, in Open ground, and use not only Open Innovation methods to create new algorithms and platforms, but farm collective intelligence in order for those algorithms to be informed by more than the typical, pharma-comissioned clinical study. They must be informed by sufferers themselves.

A Systemic Approach to Open Innovation

It’s impossible to approach physiological data on stress without some kind of cardiovascular and/or skin conductivity data that gives the researcher an objective view of the person’s condition. It’s also impossible to quantify what that physiological data means to the person without some subjective mirror of it: a psychological, personal perspective on how it –feels- to be them at that point in their lives, both in time and in space.

We can (and BioBeats has begun trying this with our Pulse experiment) instrument smartphone apps that gather this from a user over time, by asking them or enticing them to check in often enough for the data to be analysed for triggers and locations.

But to be ultimately effective, we need continuous monitoring. It’s possible that platforms such as Withing’s Pulse, with their nearly ubiquitous distribution (Walmart, etc.) could be the answer, but it appears Pulse doesn’t offer continuous cardiovascular data streams, and relies on checkins. Back to the smartphone app problem. The Basis band could also be the answer, and it certainly offers continuous monitoring not only of blood flow pulse but also skin temperature (disclosure: the original founder for Basis is also the founder here at BioBeats), but huge demand and ramping up times means that Basis is not yet ubiquitously available, and not available at all outside of the U.S., which is simply problematic if one is (as we are) trying to gather data from millions across 100+ countries.

Open Source / Open Hardware / Open Innovation

Many of us here at BioBeats came from Open Source communities such as the Apache Software Foundation, and the Linux (GNU/Debian) community. Our lead product designer, Ifung Lu, wrote an entire PhD thesis on the democratisation and Open Sourcing of surgical instrument design. Davide Morelli and myself met and worked together in the Puredata community, an Open Source dataflow programming environment for media, and we still use Puredata architecture to build BioBeats products.

This spirit of openness allows to take inspiration from work like Rumi Chunara’s epidemiological patterns during the 2010 Haitian cholera outbreak, and to Open Hardware platforms such as the Angel Sensor (to whose successful Indiegogo campaign we recently contributed) as a possible way forward for collective intelligence solutions to the epidemiology of stress and anxiety. In their spirit, and following our own internal process whereby sensors and algorithms (and the filters through which they reach people and eventually, healthcare processes), we are going to put in place our own Open Innovation strategy.

What’s being done?

Collective Intelligence, as a design process, needs to ask a few important questions (what? who? why? how?) in order to find out what building blocks (“genes”) are needed in order to create the right kind of system to solve a problem.

Let’s summarise ours: stress and anxiety represent an enormous percentage of doctor visits and hospitalisations, but a lack of education (about stress) and participatory service design means patients don’t actually realise what is actually happening to them. We need systems that will draw potential patients in, engage them in the process, use their data to model better diagnosis, and funnel them to the right treatment earlier and quicker, thus saving trillions to healthcare systems worldwide.

So firstly, what’s being done is the characterisation and analysis, objectively and subjectively, of psycho-physiological user data about stress and anxiety, at a massively-multiplayer scale.

Who is doing it? Ideally, everyone with a smartphone who has felt stress impair them, and wants to explore their condition.

Why would they do this? Most people understand that something is wrong when they start to feel that overwhelmed by daily life, or begin to fear crowds and social situations, or feel like they won’t ever catch a break from email (or even catch their breath).

The problem is this: we, the hyper-connected animal, don’t ultimately understand the need for that connection, which is superimposed on top of the –actual-, physical and emotional connections we make with the world (whether virtual or physical, ironically). More than ever, we are living the Prometheal myth, in which (as paraphrased by recent philosophers such as Stiegler) we deviate from the equilibrium of animals, as a result of Epimetheus’ mistake (stealing fire and giving it to us, causing the release of Pandora), but are left to wonder at the technologically divine nature of fire, which we can hardly control. We are connected to everything, but that very connection makes us feel naked and defenseless.

So again, why would people help us do this? Because they understand the Prometheal problem inherently. Linda Stone isn’t the only one pointing at email apnea and other problems of this nature. Most people, by now, feel technology is hurting us as much as it is helping (if not more).

How are we going to help? Starting right now, we will stage trials and ask you, our readers and users, to help us test our approaches in Beta tests and applications, so that can take our first steps in the Perpetual Beta of stress mapping.

Please join us for the Android Beta test of Pulse, or download the iOS version to help us gather tons of data while we make music out of your heart!

Finally, please sign up to our mailing list because we’re very close to releasing a whole new app that will actually enable us to test music and breathing-based coping techniques for stress. We’ll need alpha and beta testers for that too, and we’ll be emailing people shortly!

References
  • Allgulander, C. (2006). Generalized anxiety disorder: What are we missing? European neuropsychopharmacology the journal of the European College of Neuropsychopharmacology, 16 Suppl 2, S101–S108. Retrieved from http://www.europeanneuropsychopharmacology.com/article/S0924-977X(06)00070-8/abstract
  • Ballenger, J. C., Davidson, J. R. T., Lecrubier, Y., & Nutt, D. J. (2001). A Proposed Algorithm for Improved Recognition and Treatment of the Depression/Anxiety Spectrum in Primary Care. Primary Care Companion to the Journal of Clinical Psychiatry, 3(2), 44–52. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/15014615
  • Gregg, D. G. (2010). Designing for collective intelligence. Communications of the ACM, 53(4), 134. doi:10.1145/1721654.1721691
  • Kessler, R. C., Chiu, W. T., Demler, O., & Walters, E. E. (2005). Prevalence, severity, and comorbidity of twelve-month DSM-IV disorders in the National Comorbidity Survey Replication (NCS-R). Archives of General Psychiatry, 62, 617–627. doi:10.1001/archpsyc.62.6.617.Prevalence
  • Lieb, R., Becker, E., & Altamura, C. (2005). The epidemiology of generalized anxiety disorder in Europe. European Neuropsychopharmacology, 15(4), 445–452. doi:http://dx.doi.org/10.1016/j.euroneuro.2005.04.010
  • Malone, T. W., Laubacher, R., & Dellarocas, C. (2010). The collective intelligence genome. IEEE Engineering Management Review, 38(3), 21–31. doi:10.1109/EMR.2010.5559142
  • Wittchen, H. U. (2002). Generalized anxiety disorder: prevalence, burden, and cost to society. Depression and Anxiety, 16, 162–171.