I’ve been interested in the growing population of folks who self-track objective data for health purposes. The phenomenon is referred to either as personal informatics or the Quantified Self. Both concepts have a following and both are intimately tied into the value of connected health. Connected Health adds value in two fundamental ways: self–care and just-in-time care. In both cases, objective, quantified data is a critical piece of success. For those individuals who are even a bit motivated to improve their health, quantified, objective information leads to insights that prompt behavior change.
I had a chance the other day to catch up with Gary Wolf, who is one of the founders of Quantifiedself.com, a frequent contributor to the New York Times Sunday Magazine and a Contributing Editor at Wired. We had an inspiring discussion about the intersections of Quantified Self and Connected Health.
Gary was a bit out of breath, having just wrapped up the first Quantified Self Conference in Mountain View, CA. Gary was very excited about the conference and its impact. More than 100 projects were presented, 60 talks were given and more than 25% of participants presented. When I asked him what was ‘the hook,’ i.e. why is QS taking off so fast, his response was that, “people are reaching the realization/hope that personal data have personal meaning.” We both agree that the growing interesting in quantification is bringing us beyond the ‘data is geeky’ stage to an era where there is a real movement around the collection of data and the use of that data to gain insight about health and affect behavior change.
Sadly, our experience dealing with real-world patients at the Center for Connected Health is varied. One example is our Diabetes Connect program which until recently involved a device that measured glucomenter readings and moved them over an analog phone line to our database. For a disappointingly high fraction of our patients, the step of plugging in a device to the glucometer, to the phone line and then pushing a single button to upload glucose readings was more work than they were willing to do. Even the opportunity to see their glucose readings quantified and shared with their health care provider was not enough motivation for some individuals. This experience calls into mind several interesting hypotheses re: the gulf between the Quantified Selfers and our ‘average Joe’ patients.
One explanation could be that that managing chronic disease, especially diabetes, can be complex and too overwhelming for someone to take on anything more.
A second explanation could be that it is the health care provider’s conscious or unconscious doing by failing to create the expectation that patients should take charge of their health. We have given patients the message that once you have a diagnosis, it’s too complex to self-manage. Our insurance plans and politicians have a hand in this too by sending out the message that sick people are victims and health care is an entitlement.
A third explanation could be that the technologies are only mature enough to attract an early adopter crowd. As Gary noted, the ‘geeky’ users are very forgiving of technical challenges and rise above them, often without thinking twice. However, the average health consumer might struggle with quantification, as systems are possibly too complex.
It is important to know more about these (and other) roadblocks because the power of quantification in chronic disease management is evident. It is one of the primary strategies we’ll need to lower the services burden on an already beleaguered primary care work force.
I have a good friend, a former senior executive at Partners HealthCare, who told me he was ‘put on a program by my insurer. I have to walk 10,000 steps and interact with a health coach once a week about my progress.’ Next time I saw him, he had an inexpensive pedometer on his belt. No other technology was employed. But he was just one week into the program and by 9 am he’d already done his 10,000 steps for the day. He did so by taking his first few conference calls while out on a walk.
It was after this experience that I decided that we need to turn as many of our patients as possible into quantified selfers.
When I asked Gary about this challenge, he was thoughtful in his response. He spoke of segmenting folks beyond the simple binary classification of quantified selfers and couch potatoes. He suggested that as we learned about the various segments, we’d glean corresponding strategies to inspire them to quantify and use their self-generated data to improve their health.
We also talked about the willingness of health care providers to embrace data from self-quantifiers and we both agreed that this needs to improve. He alluded to a component of the recent QS conference where the topic of discussion was ‘quant-friendly docs.’ While I know we have a lot of distance to travel here, I am encouraged on two levels. One is that doctors are beginning to realize how much data their patients generate when out of the office and the value that data can bring to healthcare decision making. The second is that we have software solutions (decision support) that can plow through reams of banal, normal data and pull out those data points that are worthy of a highly trained professional’s analysis. I’m confident this problem will be overcome quicker than the passivity that I see in chronically ill individuals.
What’s your feeling? Should we convert as many patients as possible into quantified selfers? How should we do it?
Dr. Joseph Kvedar is the Director of the Center for Connected Health at Partners Healthcare. He blogs regularly at THCB and also at the cHealth Blog.