Ten years ago, the idea of tracking your footsteps or your heartbeat was weird. Those dedicated to the pursuit of quantified self knowledge proselytized in TED Talks, while journalists attended conferences and reported on the strange new trend. Today, over 40% of households in the U.S. own a wearable device, according to statistics service Statista. It is not uncommon to hear retirees comparing or boasting about their step count for the day. The quantified self is ascendant.
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Now, as artificial intelligence’s relentless advance continues, researchers and technologists are looking for ways to take the next step—to build AI health coaches that sift through health data and tell users how to stay fighting fit.
The triumph of the quantified self
There’s a lot of evidence to suggest that wearables do offer at least some benefits. A review of scientific studies from 2022 found that, across over 160,000 participants in all the studies included, people who were assigned to wear activity trackers took roughly 1,800 more steps each day, which translated to a weight loss of around two pounds.
Wearables change behavior in a number of ways—by prompting users to set goals, allowing them to monitor things they care about, by reminding them when they’re not on track to meet their goals—says Carol Maher, a professor of population and digital health at the University of South Australia and a co-author of the review.
These effects often fade with time, however, says Andrew Beam, an assistant professor in the Department of Epidemiology at the Harvard T.H. Chan School of Public Health, who researches medical artificial intelligence.
Accurately detecting the measures that we care about from signal inputs—determining step count from an wrist-worn accelerometer, for example—requires AI, but a banal, unsexy type, says Shwetak Patel, professor in computer science and engineering at the University of Washington and director of health technologies at Google. But, he adds, there is much more it can do already do: “AI can stretch the capability of that sensor to do things that we may not have thought were possible.” This includes features currently available on popular wearable devices, such as fall detection and blood oxygen detection. Some researchers are trying to use the relatively basic health data provided by wearables to detect disease, including COVID-19, although typically not to the same level of accuracy as devices used in clinical settings.
So far, AI has played a supporting role in the rise of the quantified self. Researchers are hoping to make use of recent advances to put AI on center stage.
The coming AI health coaches
Patel recently co-authored a paper in which researchers fed data from wearables into large language models, such as OpenAI’s GPT series, and had the models output reasoning about the data that could be useful for clinicians seeking to make mental health diagnoses. For example, if a study participant’s sleep duration data were erratic, the AI system would point this out and then note that erratic sleep patterns “can be an indicator of various issues, including stress, anxiety, or other disorders.”
The next generation of AI models can reason, says Patel, and this means they could be used for personalized health coaching. (Other researchers argue it’s not yet clear whether large language models can reason). “It’s one thing to say, ‘Your average heart rate is 70 beats per minute,’” he says. “But the thing that we’re focusing on is how to interpret that. The kind of modeling work we’re doing is—the model now knows what 70 beats per minute means in your context.”
The data provided by wearables could also allow AI “coaches” to understand users’ health at a much greater level of depth than a human coach could, says Patel. For example, a human coach could ask you how you slept, but wearables could provide detailed, objective sleep data.
Maher has also helped author a review of the research on the effectiveness of AI chatbots on lifestyle behaviors, which found that chatbot health coaches can help people increase the amount of physical activity and sleep they get and improve their diets, although the effect was smaller than is typically found for wearables. These studies were done using fairly rudimentary chatbots (developed years ago, well before, for example, OpenAI’s ChatGPT) and Maher expects that more sophisticated AI health coaches would be more effective. She notes, however, that there are still challenges that need solving with large language models like ChatGPT—such as the models’ tendency to make up information.
There are reasons to be skeptical about chatbot health coaches, says Beam. First, they suffer from the same drop off in effectiveness over time as wearables. Second, in the realm of health, even human scientists given reams of data about an individual do not yet understand enough to give personalized advice.
Even if the evidence doesn’t yet exist to offer precise recommendations to different people based on their health data, an AI health coach could monitor whether a given action seems to be helping and adjust its recommendations accordingly. For example, heart rate data during a suggested workout could be used to inform future exercise recommendations, says Sandeep Waraich, product management lead for wearable devices at Google.
Google has not announced plans to launch an AI health coach, although it does plan to provide AI-powered insights to FitBit users from early 2024, and in August the New York Times reported that Google DeepMind has been working on an AI “life adviser.” Apple is also reportedly working on an AI health coach, codenamed Quartz, that it plans to release next year.
It’s not just the big tech companies that are trying to take data from wearables and provide continuous, personalized health coaching. Health app Humanity claims to be able to determine a user’s “biological age” to within three years based on movement and heart-rate data. Humanity’s algorithm was developed using data from the U.K. biobank, which had 100,000 participants wear a wrist-worn accelerometer for a week. But Michael Geer, co-founder and chief strategy officer at Humanity, is more excited about the possibility for tracking how biological age changes. ”We’re not trying to say you’re definitely in the body of a 36-year-old. What we’re trying to see is basically over time, did [biological age] generally go up or down, and then that’s feeding back to figure out what actions are making you healthier or not,” he says.
The problem with tracking measures like Humanity’s “biological age” is that there is still no evidence linking those measures to actual health outcomes, like a reduction in all-cause mortality, says Beam. This is a problem with AI’s use in health care more broadly, he says. “In general, caution is the right approach here. Even within clinical medicine, there’s a huge emerging body of literature on how much these AI algorithms know about medicine—we still don’t know how that translates to outcomes. We care about outcomes, we care about improving patient health. And there’s just a paucity of evidence for that as of now.”
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