You may have heard about “calm technology” – but do you know where it came from and what it’s really all about?

In the 1990s, Silicon Valley scientist Marc Weiser defined “calm technology” in his work articulating the futuristic concept of “ubiquitous computing”. He and his colleague John Seely Brown described the process of “fitting technology to our lives.”

They imagined a future of always-on data streams. They defined calm technology as that which provides helpful information while easily moving between being attention-grabbing and unobtrusive.

” As technology becomes more imbedded and invisible, it calms our lives by removing annoyances while keeping us connected with what is truly important.”

In 1996, many found this the stuff of fantasy. But a quarter-century later, it seems obvious. Ubiquitous computing is the norm. As you read this, there are probably several computers within your reach. Calm technology can be found everywhere, from motion sensors in public restrooms, to email that auto-suggests recipients, to cash registers that offer shoppers coupons on the items they’re probably running low on.

In 2014, Brown pointed out that the particular gift of calm technology was its potential to be anticipatory, to predict when it was likely to be needed and act appropriately. Calm tech remains quietly aware until it “knows” when it should “come alive” – and as this sentence shows, it’s difficult not to anthropomorphize.

Maybe that’s why there’s a hint of discomfort in many reactions to calm tech. It can live in the “uncanny valley” between machine and sentience. We want its help but we don’t always entirely trust it. And so developers must tread carefully. A tool must be limited enough to make the user feel in control. It must feel unthreatening, not policing or controlling. But it must also be robust enough to serve usefully, and appealing enough to be invited into the user’s life. It’s a difficult balance to strike.

Calm tech already exists in healthcare: just look at implanted defibrillators. And its promise is easily visible, too: for example, many are working on a device that combines an insulin monitor and pump into one implant.

Calm tech in health works best the more “tells” a condition has. The more indicators or predictors a condition has, and the easier they are to measure, the easier it may be to develop calm tech that can track, analyze, predict and help it. These indicators can be big or small, visible or microscopic. But the clearer they are, the easier an algorithm will be. Conditions like weight management, smoking cessation and seizure disorders may be prime candidates, as may be mental illnesses like depression.

And it’s possible that even more discreet signs may be able to be harnessed. As one example, ShareCare is a mHealth company investigating the potential of language analysis to monitor stress levels. What if your mobile phone could ambiently track your health, using its awareness of where you were, what was on your calendar, who you were having conversations with by phone or text, to gently advise you on how best to handle the stressful people in your life?

The frontiers of calm tech in healthcare will expand as we gain the ability to understand predictors, symptoms and signs, to quantify and track them. Each innovation will bring us closer to a day where we can feel confident – dare I say calm? – in the “care” that calm tech will provide.