A possible smart toilet in action.


A smart, internet-connected toilet could offer a surprisingly large wealth of data. That’s according to a study published this week, which claims that building a computerized throne could offer fascinating health insights.

If it calls to mind some of the worst excesses of the Internet of Things — catalogued, perhaps rather aptly, by the “InternetOfShit” Twitter accounts — fear not. The team of metabolism scientists, from the University of Wisconsin-Madison and the Morgridge Institute for Research envision, claim that analyzing urine samples could aid care patients and ensure they’re getting proper medical treatment. Their findings were published this week in the journal Nature Digital Medicine.

“Josh mentioned this at a group meeting one time and it was met with laughter,” Ian Miller, data scientist with the Coon Research Group, said in a statement. “I thought, you know, I kind of like the idea. I already track a lot this stuff in my everyday life.”

Miller’s not wrong. The quantified self movement has quietly grown into an everyday element in many people’s lives. Smartphones contain motion sensors that silently count steps, Fitbits monitor heart rates to check how hard a user is exercising, and the newer Apple Watch contains an electrocardiogram to detect heart conditions. Apple’s latest gadget can even detect when a wearer has fallen, immediately calling the emergency services.

Their research looked at whether frequent urine sample tests could give a real-time picture of a patient’s health, and whether the process could be adapted into a consumer product like a toilet.

Miller teamed up with Joshua Coon, UW-Madison professor of biomolecular chemistry. The pair provided 110 samples collectively over a 10-day period, then sent the samples over for tests. These tests used gas chromatography and mass spectrometry to understand the patient’s metabolism.

The results were promising, and could reveal a pathway to creating a “smart toilet” that could check for metabolic links to over 600 human conditions.

A possible smart toilet in action.

The research flagged up a number of interesting insights into the pair’s routines. One of the two took acetaminophen, a drug used to treat headaches, and this caused a spike in ion intensity in his urine. It could also suggest variations in exercise and sleep, as well as alcohol and coffee consumption.

From here, the team is now aiming to develop a toilet that could include a mass spectrometer that could recognize urine from multiple patients and process samples. These analyses are currently being done on machines around $300,000, but Coon claims it could be done on portable machines at one tenth the cost and prices could drop lower when offered to consumers.

The current prototype is a bit unwieldy — Coon compared it to a Rube Goldberg invention — but it’s functional. They plan to install it in their research building.

“We know in the lab we can make these measurements,” said Coon. “And we’re pretty sure we can design a toilet that could sample urine. I think the real challenge is we’re going to have to invest in the engineering to make this instrument simple enough and cheap enough. That’s where this will either go far or not happen at all.”

The end result could help in-home care patients, ensuring that they’re taking their medications properly. It could also reveal how a patient is metaboloziing certain drugs. On a large scale, it could start to build up a clearer picture of human health and provide new insights?

Internet of Shit? That might be a good thing.

Read the abstract below:

Current healthcare practices are reactive and based on limited physiological information collected months or years apart. By enabling patients and healthy consumers access to continuous measurements of health, wearable devices and digital medicine stand to realize highly personalized and preventative care. However, most current digital technologies provide information on a limited set of physiological traits, such as heart rate and step count, which alone offer little insight into the etiology of most diseases. Here we propose to integrate data from biohealth smartphone applications with continuous metabolic phenotypes derived from urine metabolites. This combination of molecular phenotypes with quantitative measurements of lifestyle reflect the biological consequences of human behavior in real time. We present data from an observational study involving two healthy subjects and discuss the challenges, opportunities, and implications of integrating this new layer of physiological information into digital medicine. Though our dataset is limited to two subjects, our analysis (also available through an interactive web-based visualization tool) provides an initial framework to monitor lifestyle factors, such as nutrition, drug metabolism, exercise, and sleep using urine metabolites.





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