Health changes often start quietly, in sleep, movement, stress or habits, long before symptoms appear. Passive data, gathered through wearables and smartphones, now makes those early shifts visible, without disrupting daily life. Joe Kiani, Masimo and Willow Laboratories founder, saw this potential early. His latest initiative, Nutu™, uses passive tracking to understand behavior, and catch subtle changes before they escalate. By combining biometric and behavioral signals, it offers timely, adaptive support, serving as a quiet partner that notices patterns and knows when to step in.
This kind of unobtrusive intelligence is especially powerful for risk reduction. Instead of reacting to crises, these tools help users stay ahead of them, offering small, timely prompts that fit naturally into daily routines. It’s a fundamental shift from episodic care to continuous insight, providing people with the tools to make healthier choices, before issues arise.
What Is Passive Data?
Passive data is collected without active input from the user. It might come from steps tracked by a smartwatch, heart rate trends recorded overnight or hydration levels inferred from wearable sensors. If used with transparency and consent, it could also include screen time, keyboard usage or voice tone analysis. Unlike active tracking, which requires logging meals or checking in with an app, passive tracking works in the background. That consistency makes it a powerful tool for monitoring gradually developing trends. Passive data doesn’t rely on memory, but it reflects real behavior.
Joe Kiani, Masimo founder, points out, “What’s unique about Nutu is that it’s meant to create small changes that will lead to sustainable, lifelong positive results.” That’s why it is built to meet users where they are, not just when they check in. Passive data allows the system to track meaningful trends, without burdening the user. The result is more accurate insights, better-timed nudges and fewer missed opportunities.
Spotting Change Before It Becomes a Problem
Most people only realize they’re off track once symptoms appear, such as disrupted sleep, low energy or unstable glucose. By that point, a pattern has already formed. Passive data tools detect those patterns earlier. A decline in step count, paired with shorter sleep and higher resting heart rate, might signal rising stress. A shift in meal timing could explain new fatigue. These small shifts might go unnoticed in the moment, but when analyzed together, they tell a clear story. That early awareness allows the system to offer guidance when it matters most, before the issue grows.
No Logging Required
One of the biggest barriers to behavior tracking is the effort required. Logging food, symptoms or mood can help, but many users stop after a few days. Passive data solves this by reducing friction. There’s nothing to enter, no reminders and no guilt.
Passive data supports more consistent tracking across all types of users, including those with time constraints, cognitive overload or low digital literacy. Since there’s no pressure to record everything, it also captures more honest data. When the system listens quietly, it doesn’t interrupt life, but it fits into it.
Nudges That Feel Natural
Passive data reflects what’s really happening, and the system can offer suggestions that feel more natural. It might recognize a stretch of poor recovery and respond with a hydration reminder. Or it may detect decreased movement and suggest a short walk, not to meet a quota, but to support energy. These nudges don’t rely on perfection. They respond to patterns. That responsiveness helps users feel seen, not judged. It also allows the system to support people during low-motivation periods, offering just enough guidance to keep them connected.
Respecting Boundaries and Privacy
Collecting passive data comes with responsibility. It is designed to collect only what’s needed and to explain how that data is used. All insights are framed with user consent and built around transparency. No one wants to feel watched. It avoids that by focusing on trends, not moments. It doesn’t spotlight every missed step or late night. It looks for meaningful shifts, and responds only when it sees an opportunity to help. This balance keeps the system helpful, without being invasive.
Building Long-Term Engagement
People tend to stop using apps that feel demanding. But when a system supports them, even during hard weeks, they’re more likely to stick with it. Relying on passive data reduces the user’s workload. There are no complex dashboards to manage, no daily surveys to complete, just quiet support delivered when needed. Over time, this consistency builds trust. It turns passive data into a foundation for active change.
Helping Clinicians See the Full Picture
Passive data helps users and care teams. When it summarizes months of behavior trends, it gives clinicians context, that lab work alone can’t provide. A user may report fatigue, but passive data might show changes in movement, hydration and screen use that explain it. Because providers have a fuller picture, they can respond with better advice. This context is especially valuable in chronic care, where patterns shape outcomes, as much as prescriptions.
Serving Users Across Demographics
Passive data is also a tool for equity. It supports users who might struggle with manual logging, such as shift workers, busy parents, older adults or people managing multiple conditions. The system becomes more inclusive by requiring less effort. Its adaptive coaching responds quietly based on the data it collects, allowing users to get support, even when they don’t have time to engage daily. This flexibility ensures that more people benefit, regardless of their background, schedule or tech experience.
Listening Is the First Step Toward Change
Not all digital health tools need to talk first. Sometimes, the most powerful feature is the ability to listen quietly, consistently, without interruption. Nutu’s vision reflects that. The platform doesn’t just react. It pays attention. It watches for moments when behavior shifts, and offers support, when that support makes a difference. By combining passive data with timely coaching, systems help people make better choices, one day at a time, without needing to ask for help out loud.
