Gina Neff and Brittany Fiore-Silfvast – “Pictures of health: Does the future of wellness need us?”
Panel: Bodies and Bits
As part of our project on health hacking—technological disruption and the meaning and metrics of care—one of us (Gina) attended The MIT Future of Health and Wellness conference. The conference, organized by MIT’s Industrial Liaison Program, was part of an on-going series to connect MIT faculty and industry, and it brought together policy, science, social media, medicine, economics and wellness. In other words, it perfectly captured the current buzz about technology-driven health and wellness, or “Health 2.0,” that is happening at conferences like TedMed, mHealth Summit, and Stanford’s Medicine X. Underlying these conversations is the hope that new forms of data can transform clinical care and motivate people to be healthier.
Several people, and even more start-ups, are betting that ubiquitous and pervasive sensing from consumer mobile devices can translate into better health and wellness outcomes. The Nike Fuelband, Striiv, Fitbit and other similar devices track physical activity. Mobile apps like MyFitnessPal, The Eatery, and Lose It! play on people having their phones handy to track food. Many people are now asking how such data might transform clinical care. For example, the Mayo Clinic recently launched an app called MyCare that integrates a personalized “Plan of Stay” in the hospital with a roadmap for recovery at home and integrates Fitbit data and other remote monitoring devices. HealthTap, backed by Google’s Eric Schmidt, is another example of a mobile platform promising to connect patients/consumers directly to expert care and information without the “waiting room,” while developers will get high-quality health content and data. The FDA recently approved AliveCor’s iPhone-based ECG heart monitor, another example of the how rapidly consumer, mobile, and medical realms are reshaping each other.
From this perspective, the MIT conference squarely fit with rhetoric about data’s power to transform health and wellness that is happening both within lab settings and in startups. The promise of always-on data about our behavior offers a tantalizing possibility of making accidental connections or discoveries about ourselves, our bodies, our biology. Fascinating presentations by MIT faculty Ros Picard who is working on ubiquitous sensors and Damon Centola who is running experiments on the structure of online networks to support healthy behaviors highlight these promises. This is part of the promise of the Quantified Self movement: that through tracking, one can discover knowledge impossible to gain without the aid of self-tracking. As Sandy Pentland from MIT’s Media Lab and an advisor to big data health startup Ginger.io, said at the conference, said “data means you no longer need to see a patient” to know what is going on with their health. There is enormous potential for population-level data from mobile devices to describe and potentially predict our health outcomes, and Ginger.io’s platform is designed to connect a consumer-slash-patient with health care expertise through a data-at-a-distance model. “Big data, better health,” in their words.
These are fascinating and promising ways to think about what we know and don’t know about bodies, and the power of data-intensive technologies to collect information in surprising ways. But at the heart of these attempts at data-driven health and wellness is a seductive—and perhaps flawed—model of the relationship of data to knowledge, sense-making and action. That’s what we’ll be talking about at Theorizing the Web. These conversations about data-intensive health and wellness posit data as black boxed solutions versus how people actually use, stretch, and adjudicate their data in their practices with it. The stories we tell ourselves about what our numbers might mean for us may or may not fit these models that new tools are being built around.
Our paper explores the gap between the discourses of data and the practices of, with, around and through these data. This gap is particularly stark across the communities of technology designers, “e-health” providers and advocates, and so-called users of health and wellness data. In the discourses of health care technology designers and advocates, data comes to represent a notion of actionability, the potential of data to be used for social and material performances. In these discourses, possessing data serves as a catalyst for behavioral change: as one advocate put it, “data leads to knowledge and knowledge leads to change.” This seemingly innocuous data-behavior model forms the core logic behind technology development in health and wellness applications and digital health sites. For technologists, this framework means they try to solve the seemingly inextricable problems of healthcare within the United States with so-called well-designed, usable, personalized, and beautifully visualized interfaces for this data. The self becomes a platform and thus programmable.
Is this a data-behavior model that fits what people actually do with their data? This model of data leaves no room for storytelling, fudging, and personal adjustments. Susannah Fox and Maeve Duggan, the co-authors of a recent report on self-tracking for health from the Pew Internet and American Life project, found that of the 69% of U.S. adults who report keeping track of their health, half of them did so “in their heads.” Fox likens this to “skinny jeans” tracking—just as women may not own a scale, they know when they can fit into the skinny jeans they keep in their closet. In other words, health tracking for many people does not mean data, but means memories, stories, and perceptions.
Nor does this data-behavior model fit with current understandings of behavioral change. As one psychologist responded at Stanford’s Medicine X conference, if data indeed led to change, we’d have no need for the entire field of psychology. The experiences with data and conversations in which data is being employed suggest that people are tapping into a wide range of aspects, cadences, and valences of health data, allowing data to perform in different ways in different communities and for different purposes.
There are two contradictory ways people talk about data. First, data are presented as if data equal single, clear, unambiguous, and stable answers. This concept of data flies in face of how data get cleaned, massaged, interpreted and interpolated and goes against what we know about how people make sense of and tell stories about their data. The second way people talk about data is in highly contextualized and personal ways. The hopes for the data transformation of health lie in the intersection between this idea of “ultimate transparency” and one of “ultimate personalization.” As Kevin Kelly, a founder of the Quantified Self movement, noted in the closing plenary of the 2012 QS Conference, “Data wants to be linked and used.” Otherwise, it is “naked data,” not yet related to anything else, not yet valuable, and certainly not monetized.
When we look at the practices people have around health and wellness data we find instead that data are perspectival, fluid, and relational. But, people in online health communities talk about data as a stable material object, rather than as discursively enacted in multiple emergent ways that resist such stability. These conversations complicate our understanding of the production and consumption of data-intensive technologies by bringing back into the picture the practices, communities, and networks of data that are generated—sometimes as byproducts—in the socio-technical assemblages we study. Second, health and wellness data help us to frame notions of user and use differently, as relationships with data are inscribed, enacted, and discursively labeled as problematic or appropriate. Third, the notion data stability is questioned as the context for data generation, storage, and interpretation is evoked to make sense of data within these communities. Finally, our work is looking at how and when people use data as a starting point for conversation, especially when the entities that are they cast as “talking” are nutrients, proteins, genes, and bodies. This future scenario of health no longer needs people to talk for their bodies. Our research on how people relate to their health and wellness data makes us skeptical that such a future will ever fully arrive.
Gina Neff (@ginasue) is an associate professor of communication at the University of Washington and author of “Venture Labor: Work and the Burden of Risk in Innovative Industries.” Her website can be found here.
Brittany Fiore-Silfvast (@brittafiore) is a doctoral candidate in Communication at the University of Washington. Her PhD research, funded by an NSF dissertation improvement grant, looks at the design and implementation of new health information technologies in healthcare settings in the U.S. and in India. Her website can be found here.