In 2025, healthcare practitioners still group people into categories that look neat on paper but fail in real life. Age brackets, diagnosis codes, risk scores, and demographic labels were designed for a world where data moved slowly, and patients were only visible at the doctor’s office. But today, two people with the same diagnosis, same medication, and same demographic profile can live entirely different physiological realities. For example, one sleeps well, recovers quickly, stays active, and manages stress, while the other struggles silently in ways no claims dataset will ever capture.
This gap between who people are on paper and how they behave in the real world is one of the biggest blind spots in modern medicine. But it wouldn’t be our blog post if there weren’t a smart solution, and that’s wearables!
With them, it’s possible to review a person’s health trajectory long before clinical systems notice anything. Slowly but surely, segmentation is becoming less static and more dynamic, behavioral, predictive, and deeply personalized.
Previously, we tapped into the topic of target groups in healthcare. Today, we move on and explore why traditional segmentation fails, what wearable data uniquely adds, how to build actionable behavioral segments, and how health organizations can use this approach to deliver smarter, more precise, and more preventive care at scale. We’ll also show how Thryve’s infrastructure enables segmentation models that are both clinically meaningful and operationally scalable.
Most healthcare segmentation models were built for billing, reporting, or actuarial forecasting, not for understanding how people actually live. As a result, they cluster patients into groups that look logical but behave unpredictably.
Traditional segmentation struggles due to:
Static, retrospective data: Claims, diagnostic codes, and annual check-ups reflect clinical events that have already occurred. They offer little insight into day-to-day patterns that influence future health trajectories. As a result, two individuals with identical clinical profiles may follow entirely different risk paths, something these models struggle to capture.
Lack of behavioral and lifestyle data: Sleep consistency, physical activity, recovery patterns, and stress responses meaningfully shape cardiometabolic, mental, and chronic disease risk, yet remain unmeasured in most frameworks.
Lack of sensitivity: Because the data is retrospective, the system reacts late. It flags problems only after symptoms appear, a diagnosis is made, or costs rise. Subtle declines in activity, worsening sleep, or rising resting heart rate go unnoticed.
Unpersonalized approach: Two people with the same disease may have completely different lifestyles, risk profiles, or engagement levels. Traditional segmentation puts them in the same category, losing important differences that could guide more targeted support.
Basically, traditional segmentation explains who a person was, but not how their health is evolving. Wearable-driven segmentation fills this gap by providing continuous, behavior-driven insight that aligns with modern preventive and personalized care.
Wearable adds continuous, real-time insights into how people actually live, move, and recover. This transforms segmentation from a backward-looking into a forward-looking tool for prevention and personalized support.
Let’s go through some major changes:
Real-time, continuous health signals
Wearables track activity, sleep, heart rate, HRV, and more throughout the day. This creates a dynamic picture of health rather than relying on one-time clinical measurements. Segmentation becomes proactive rather than reactive.
Early detection
Changes in resting heart rate, sleep consistency, or activity patterns often signal rising health risks weeks or months before symptoms emerge. Segmentation based on these trends enables early, targeted interventions.
Chronic disease prevention
Most long-term health outcomes are shaped by daily habits. Wearables capture real behavior, how people move, rest, and respond to stress, making segmentation far more meaningful and actionable.
Trend evaluation
Instead of categorizing people based on a single appointment or data point, wearable data supports trend-based segments such as improving, stable, or declining health. This offers more precise and timely insights.
Wearable data shifts segmentation from a static classification process to a dynamic, personalized, and prediction-driven model. This forms the foundation for smarter prevention, better resource allocation, and truly individualized care.
As we already know, this shift allows insurers, providers, and digital health companies to deliver interventions that are more precise, more timely, and far more effective. But what does it really mean? Let’s deep dive into some aspects!
Behavior-based segmentation
This category groups individuals based on daily patterns captured through wearables. Examples include sleep regularity profiles, movement consistency, or stress-related HRV patterns. By segmenting people according to how they live, organizations can tailor recommendations that align with actual routines. For instance, “irregular sleepers” may benefit from circadian-focused nudges, while “low-movement profiles” may need structured activity programs.
Physiology-based segmentation
Here, the focus is on stable physiological indicators such as resting heart rate, HRV baseline stability, or recovery patterns. These markers reflect the body’s internal state, not just daily behavior. This enables segmentation like “high stress load,” “reduced resilience,” or “elevated cardiovascular strain,” each unlocking targeted coaching or monitoring.
Risk trajectory segmentation
Instead of labeling someone as simply “high risk,” wearable data enables tracking whether their condition is improving, declining, or remaining stable. This helps organizations prioritize outreach, allocate resources efficiently, and introduce preventive interventions at the exact moment they are most effective.
Engagement and readiness segmentation
Focusing on user behavior toward the program itself, such as app usage, wearable adherence, or response to nudges, helps identify who is ready for deeper engagement and who may need simplified interventions or motivational support.
Across all categories, dynamic segmentation turns raw data into direction. It allows care teams and digital health systems to meet individuals where they are, anticipate their needs, and deliver support that is relevant, timely, and genuinely impactful.
Dynamic segmentation powered by wearable data is not just a technical upgrade; it reshapes how every part of the healthcare ecosystem operates. For example:
Segmentation enables insurers to move from broad preventive offerings to precision prevention. This allows:
Segmentation helps digital therapeutics deliver personalized experiences that adapt as the user evolves. It can be:
Clinical segments improve patient management inside and outside the clinic, it can be seen in:
Overall, across all sectors, segmentation turns one-size-fits-all strategies into data-driven, personalized support systems.
Now, we’re really giving out secret information here!
To be fair, building an effective segmentation framework requires more than grouping users by age or demographics. A modern segmentation pipeline integrates physiological signals, behavioral patterns, and real-time trends into a dynamic system that updates continuously. Below is a practical, step-by-step guide to designing such a pipeline.
Start with a focused set of wearables-derived metrics that reflect core aspects of health and behavior. Common choices include:
These metrics provide a strong foundation for segmentation, as they correlate well with stress, metabolic health, readiness, and early deterioration.
Segmentation is only as good as the signal quality behind it. Apply smoothing, filtering, and artifact removal to eliminate noise from PPG, accelerometers, or motion-induced distortions.
Better signal quality = more stable and meaningful segments.
Choose the method that fits your product architecture:
Each method can produce visual groupings, such as a 4-quadrant matrix (e.g., high HRV + high sleep → “resilient,” low HRV + low sleep → “overloaded”).
A segment is only valuable if it triggers an action. Define what each group receives: targeted messages, coaching intensity, scheduling changes, clinical follow-ups, or behavioral nudges.
Wearable data changes daily. Your segmentation engine should update classifications automatically as new data arrives, enabling dynamic personalization.
Finally, assess whether segments predict what matters: engagement levels, hospitalization risk, cost trends, therapy adherence, dropout likelihood, or employer wellness participation.
When done correctly, segmentation becomes a living system, one that actually adapts to users, anticipates needs, and unlocks scalable personalization across healthcare.
And now for the fun part. Building segmentation models from wearable data comes with several technical and ethical challenges:
These considerations underscore the importance of responsible design, robust data governance, and transparent methodologies in any segmentation strategy.
Segmentation in healthcare must evolve beyond static demographic buckets and reflect how people actually live, behave, and change over time. Wearable data unlocks this shift, enabling a new generation of dynamic segments that support personalized interventions, early risk detection, and preventive care at scale.
When powered by high-quality signals and thoughtful design, segmentation becomes a strategic tool, not just for insurers and health platforms, but for every organization seeking better outcomes and engagement. At Thryve, we provide the data foundation needed to build accurate, dynamic, and ethically sound segmentation models. Our API offers:
If you’re building a health app, prevention program, or digital therapeutic, now is the time to upgrade your segmentation strategy.
Book a demo to see how real-world wearable data can power smarter, more effective personalization.
Friedrich Lämmel is CEO of Thryve, the plug & play API to access and understand 24/7 health data from wearables and medical trackers. Prior to Thryve, he built eCommerce platforms with billions of turnover and worked and lived in several countries in Europe and beyond.
