Can personal health data motivate behavioral change? It depends.

By MHN Staff
04:50 am

Dave Dickinson HeadshotBy Dave Dickinson, Former CEO, Zeo

After leading Zeo for the last 5 years, I'd like to share some of the key lessons we learned as an early pioneer within the digital healthcare movement. This post includes lessons learned for those who share our mission of improving the health and wellness of mankind by leveraging the awesome power of technology. Hopefully, some thoughts will engage new ways of thinking and serve to help guide other healthcare innovators to keep carrying the flag forward.

One of the critical questions within the connected healthcare movement is whether or not personal health data will actually catalyze and then incentivize lasting behavioral change and better wellness. Will any kind of data serve to motivate behavioral change? Does it matter if we see our data on a smartphone versus a laptop screen? Do all people respond in the same way? Early on, we realized that our ability to transcend our customer base, from highly-engaged, “quantified self” early adopters to the mainstream world of the “frustrated sleepers”, would require far more than just the raw data itself. It turns out that the gift wrapping matters as much as the present inside.

Moving from Quantified Self early adopters to the Mainstream Motivated Self will not be easy, but here are some clues that may help:

All data is not created equal. Some of the health data that various sensors are generating is simply not that provocative or intrinsically motivating. The more intuitively obvious the data is, the lower the consumer engagement will be. Within sleep, we realized that simplistic sleep/wake data is simply not very motivating and will relegate this health metric to the back seat of behavioral change. However, when you are able to help consumers discover a new health unknown, such as the amount and vital roles of their REM, Deep and Light sleep, this new level of shock and awe may provide a better catalyst to engage the mainstream. This is akin to a cholesterol test where the underlying HDL (“good”) and LDL (“bad”) data may be more motivating to encourage statin compliance than just the overall number alone (because I may be more scared). The level of data granularity, and then the scales that are used to convey what is high, low and average, are very important considerations to not only catalyze interest but then to also provide enough statistical significance for the critical positive reinforcement rewards that will be needed later to sustain the changed behavior. Scales of 1-10? Unlikely to motivate change. How about 1-100? Obvious, and better, but don’t assume that this should always be the default. It depends.

Relate the data to something else I care about NOW. Unfortunately, consumers are not motivated enough to take action when the resulting benefits are longer-term or too scary, like the prospect of getting a terrible illness one day in the future. This is one of the greatest challenges of preventative healthcare; however, other ideas may be able to help here. For example, we found that comparing your personal sleep data to others your own age was far more motivating. Most people do not want to age before their time and well understand what being older than your age may imply as it relates to their performance, sex appeal, career development, closeness to the prospect of dementia and more. The cosmetic industry is a very big business, and Real Age built a business model around this powerful consumer insight.

Make your advice as personalized as possible. Knowing about what is best for “people like me” is a good start, but the consumer now has far higher expectations of what is possible with technology. They want a personalized self-assessment that is quickly followed up with far more “prescriptive” advice. This is still relatively new ground for the connected healthcare movement, as the marriage of personal sensors and powerful mobile apps is still in its infancy. I think we ain’t seen nothing yet when it comes to personalization. When we get there, I think we should expect better outcomes than we have now.

The presentation of the data matters. Quantified Selfers revel in being able to demonstrate their data correlations, share their cause & effect discoveries and review new, insightful hypotheses. I know because I am one of them. We are anecdotally discovering some amazing things, but alas, this is not the average consumer. Over the last five years, I have attended many connected health conferences. Unfortunately, I rarely see presentations from behavioral psychologists or, as important, designers and artists who can fire our emotions with the power of their visuals. Some data charts and graphs simply have no chance to capture our fear or to engage our competitiveness. Some data, like trend analysis, is not best displayed on a small smartphone screen, yet another challenge within connected healthcare as we move faster toward mobile viewing versus watching our health wins and losses on bigger screens at home. GE seems to know a lot about data visualization, and there may be some more lessons from their work (read more about it here).

Motivating behavioral change through data visualization can be very powerful, but it is more of an art than a science. We will need far more artists, user interface experts and psychologists to help make our data work harder to motivate better health. Yes, it will take a village of talent, but I believe a far more creatively diverse one than most technology-based innovators may feel comfortable residing within.


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