Recent News & Events

In 2015 we are likely to see the introduction of even more watch-like devices that are capable of gathering fitness data but also serving other personal and productivity needs. Industry will compete to add an increasing number of sensors to the devices, measuring information such as body motion, location, heart rate, galvanic skin response (i.e., sweating), and skin temperature. The newest devices already have sophisticated input/output options, such as touch screens, radio frequency identification tags, and speech input, as well as audio and tactile output. The somewhat bulky devices introduced in 2013-14 will slim down and become more stylish, and developers will figure out user interface conventions that make the devices easier to use.

The biggest surprise in 2015 may not be how consumers use these devices for health, but rather an increasing awareness that the devices improve the utility of the mobile phone. A smartwatch that can automatically detect whether its user is walking, for instance, can make interaction with that person’s mobile phone more pleasant and efficient, such as by automatically changing availability states and the way in which people are notified of messages. The good news is that people will get in the habit of using these devices for everyday tasks, and that will create more opportunities to use the devices to also support health.

Fitness trackers will definitely play a role in the future of our healthcare, as our “sick” care system transitions toward proactive, wellness-based care. Convenient, continuous, and autonomous data gathering on health-related behaviors will be necessary if we are to cost-effectively help hundreds of millions of Americans stay healthy and fit, while at the same time reducing their need for costly clinical and specialist care.

Nevertheless, the PricewaterhouseCoopers study is a somewhat troubling example of how industry and consumer enthusiasm for the commercial devices may exceed the scientific evidence demonstrating their effectiveness. Few well-designed studies have shown that use of wearable fitness technologies leads to long-term, sustainable health and sustained healthy behavior in the general population. In fact, anecdotal evidence suggests that many consumer fitness devices may be abandoned not long after purchase, relegated to the same drawers as pedometers, home exercise videos, food portion measurement cups, and weights. There is a risk that the public and business community will become prematurely disgruntled with the promise of wearable fitness technology before the truly novel uses and benefits of the technology are discovered and definitively proven. As researchers, we have our work cut out for us.

We are working in two areas: improving health behavior measurement using mobile phones and wearable devices, and then using that information to create new just-in-time interventions that help people make and sustain desired behavior changes. In particular, we are exploring how mobile phones and smartwatches can be used to incrementally build up mathematical models of a person’s typical behavior so that we can identify habits. The phone or watch does what it can automatically, inferring some information about physical activity and sleep patterns, but it also asks for information from the person when it needs it. The trick is to figure out ways of doing this so that the user doesn’t feel burdened, even though the automatic sensing will never be perfect.

At the same time, we are developing ideas for how real-time knowledge of what the person is doing can be used to influence behavior by providing computer- and human-generated feedback timed precisely at actionable points of decision. Our goal is to create novel interventions that help people change habits and then maintain those habits for very long periods of time. We want to take advantage of the ability of the computer to patiently and ever-presently measure and model behavior and decision making, and then to use that information to intervene in a compelling way, just when a person is most receptive to help.

The CDC data reports widespread activity in most of the U.S. Also, the intensity of the epidemic is remarkable, retracing the nasty season of 2012-13. However, the most recent data and forecast models are telling us that we are going through the peak right now, and that the activity will likely start decreasing in most of the U.S. This does not mean that we are “out of the woods” yet. Being at the peak of the season means we are just halfway through it. We therefore have to consider several more weeks of sustained flu activity. The usual recommendations about getting the flu shot and not going to work if you feel ill still apply in full.

Concerning Massachusetts, the flu season is seemingly following the national trend with a little delay. However, our region had a very “bumpy” 2013-14, with multiple peaks and irregular activity. Hopefully this year does not have too many surprises in store for us.

We have set up a computational platform, Fluoutlook.org, that allows people to follow the flu season by looking at the real-time data released by the various national flu surveillance systems and by exploring several different forecasting algorithms that project the evolution of the epidemic up to four weeks in advance. The algorithms we use for the forecast span a wide range of techniques, including dynamic generative models that take into account the geographical regions within each specific country and infer the specific epidemic parameters of the season, such as the virus transmissibility. We are considering more than half a dozen countries, including the U.S. and Canada, but we aim at expanding the platform by progressively adding new countries, models, and data. We are also opening the platform to other modeling groups and hope to aggregate more forecasting systems in it in the near future. The aim is to provide a real-time tool with which users can explore data, collect situational awareness, investigate trends, and look at forecasts generally available only to a small number of practitioners in the field. Because we’re operating in real time, we update the platform weekly and issue new forecasts concurrently with any new dataset originated from the surveillance systems. Reliable flu forecasts are still a scientific problem, and we hope that this platform will help in testing, comparing, and evaluating different techniques in different countries.

We go after epidemics by developing large-scale computational epidemic models that integrate socio-demographic and mobility data of the population under study. These models are detailed down to the individual level and provide the dynamic of the epidemic by simulating the infection transmission event in the computer for millions of individuals in their social and geographical settings. In a nutshell, what we do is akin to what is done with computerized weather forecasts. The difference is that the data, model, and algorithms we use are describing the individuals and the biological processes underlying the spread of the disease instead of the physical processes of the meteorological systems.

The flu, although it is a seasonal disease that we know very well, is very elusive from a modeling perspective. It does not have a definite geographical initial condition. The dominant flu strain changes every year, and typically there are several co-circulating strains. These are some of the reasons why we do not have reliable forecasting systems in place—yet. Tools like our Fluoutlook.org are the first attempt, and not the final solution, to solving the problem of real-time epidemic forecasting. Indeed, Fluoutlook.org is an effort that we will continue to support so that the analysis of models, their eventual improvements, and their reliability can be evaluated over the span of several years and in a wide range of geographical and social contexts. There is a lot of work still out there waiting to be done.