Editor's note: Brian Mondry is the global head of digital innovation at Kantar Health.
The “quantified self” movement can be defined, in its simplest terms, as the use of technology to acquire data on daily aspects of one’s life in terms of inputs, mental and biological states and physical performance. “Inputs” refers to acquiring and tracking data related to what goes into your body: the food you eat, the OTC or pharmaceutical products you take, the quality of the air you breathe. Mental and biological states include mood levels, stress levels, blood pressure, heart rate and blood sugar levels. Physical performance speaks for itself.
Basically, quantified self is self-tracking. However, back in 2007 Wired magazine coined the term “quantified self” and it seems to have become the accepted term. So the quantified self movement is not new, but with the influx of new, affordable, non-intrusive and easy-to-use technology, it seems to be reaching a tipping point.
Passive data collection supplements active data collection
From a market research perspective, this is a key point: the data collected passively through quantified self technology supplements and supports, rather than replaces, data we collect actively through more traditional methodologies such as surveys, online research communities or interviews. By supplementing active data collection with passive we can ask fewer questions, taking some of the burden off research study participants. We also get more accurate data because it’s coming directly from devices such as Internet-connected glucose monitors, heart sensors, fitness sensors and even Internet-connected toothbrushes. In theory more and better data is being generated with less work required by the people whose behavior and attitudes we are studying. By pairing that data with smart analysis, which identifies relevant data stream correlations and builds a compelling narrative around the data, we can get much closer to a 360-degree view of consumers than was possible in the past.
To get to those 360 degrees it is imperative that we not look at quantified self data in isolation but rather in conjunction with other data sets. The quantified self data only tells part of the story. You need to dig deeper to get to the attitudes, emotions, external circumstances and other factors affecting the research subjects. In addition to quantified self technology and mobile app data, it is important to include data coming from qualitative and quantitative research.
To add a layer of passively collected lifestyle data, we can test the idea of having research participants use the research/survey platform to tie in their social media accounts such as Facebook, Twitter, Reddit, Spotify or LinkedIn as these could be strong indicators of lifestyle. Layering in of quantified self data and social media data means a migration is taking place from survey platforms to “lifestyle platforms.”
A big advantage of tying in quantified self data with traditional data is the issue of time. Currently, with traditional methodologies, researchers engage with study participants for a limited amount of time. There is no definitive number, but factoring in attrition rates we get on average 20 minutes of a person’s attention during the course of a study. Twenty minutes represents a small slice of time and people likely answer our questions based on their mind-set during that particular moment when the question is being asked. When respondents are asked, “How often do you check your blood sugar level?” they may try to reflect on their general practices but more likely they are thinking about their more recent practices.
However, linking into quantified self data gives us more time with participants without requiring more of their actual time. Passive, self-tracking data could be collected over weeks or months with minimal effort from participants. In essence, we are drawing on their entire time rather than just the time they are willing to spend providing answers to fixed questions.
We also have found that the quantified self tie-in results in less attrition. Research no longer has to be a one-way conversation in which panelists simply answer our questions and get little back in return. Platform panelists receive a visual dashboard representation of how they’re living their lives. This visual platform for self-reflection and ability to better manage their lifestyle can be perceived as more valuable than the small incentives that are currently doled out. As a result, longitudinal studies become more feasible and it enables us to conduct ethnographic studies more efficiently. This is especially true in the health care space, where patients with chronic conditions such as diabetics or asthmatics stand to benefit from the data we feed back to them more than the average person.
For example, Kantar’s Millward Brown unit ran a study with 200 diabetics over a two-week period. The retention rate was 90 percent, with only five participants not staying the course for the entire program. The study measured a number of factors, such as whether a person’s mood has an impact on monitoring activity. Compliance with diabetes is a multibillion-dollar problem and the improved accuracy from passively collected data via wearable, Internet-connected glucose monitors provides reliable, valuable information that can ultimately improve patients’ outcomes.
Can we go even deeper than this? If, in addition to passively collected glucose data, we can passively collect fitness data and social media data we may be able to identify correlations between glucose levels, exercise, diet and mood. Social media data, which can be a strong indicator of a person’s lifestyle, may help uncover correlations between patients’ hobbies, passions and travel activity with how they treat their condition. Think about how hard it would be to get patients to provide this much information manually, let alone accurately. But because around 70 percent of the data we are asking for can be collected passively, we can get more and higher-quality data during longer periods of time.
From a market research perspective, leveraging quantified self data is still mostly unexplored territory. We’re experimenting and learning. Is too much data coming through? Will enough people tie in their social media accounts and does doing so really provide an accurate gauge of lifestyle? Are higher incentives required to get people to share this information, even passively? The potential benefits mandate that we keep exploring and learning the answers.
There is much to gain by looking at quantified self data in conjunction with other datasets. Here are some top examples of what is now possible:
• Quantified self data is now analyzed along with patient-reported outcome data across various phases of clinical trials.
• For primary patient research, we can minimize survey length while improving accuracy/quality of data. The passive data we collect through Internet connected technology is far more accurate than manually reported data and allows the actual survey questions to focus more on why patients behave in certain ways as well as their perceived mental and physical states of well-being.
• Patient segmentation studies are enhanced by fusing in real behavior and lifestyle data.
• Patient conditions, attitudes and behaviors are tracked longitudinally over longer periods of time.
This self-tracking trend extends beyond the “self.” In addition to quantified self, quantified baby is a rapidly growing industry with Internet-connected onesies that monitor baby sleep patterns, Internet-connected diapers and scales. Not surprisingly, many new moms are dedicated to tracking everything around their babies’ health, food intake, weight, play activity, mood and diaper changes. We currently get limited time with new moms as they tend to have chaotic schedules. By recruiting parents to connect their quantified baby data and occasionally answer some short surveys, we can get more time with this important target market without requiring a lot of their time in terms of active involvement.
Needless to say, there are also numerous tracking products available for dogs. And yes, if you conduct a Web search for quantified dog you will see that that is indeed yet another growing movement. The entire tracking category is not only quite pervasive but expanding far beyond the “self.”
By supporting active data collection with passive data collection through quantified self technology and potentially with social media activity, we can get more and better data. This leads to better insights, which results in smarter, more successful business decisions.