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Upgrading a smartwatch or switching fitness trackers should be a simple decision. New features, better sensors, longer battery life, all of these things sound so exciting. But there is one question many users only start asking after the switch: what happens to all the health data collected over the years?
Modern wearables track an enormous range of metrics. Heart rate, sleep stages, activity levels, stress indicators, and even skin temperature are recorded daily, often building a detailed picture of a person’s health and habits over months and years. For many users, this information becomes a personal health history that should stay between them and their doctors.
The problem is that health data does not always move easily between devices or platforms. Different manufacturers use their own apps, storage formats, and algorithms. When a user changes brands, their historical data may remain locked inside the previous ecosystem, fragmented across apps, or partially lost during transfer.
As wearables become central to preventive health and digital medicine, this raises an important question: how portable is health data really? Understanding how data moves between devices and platforms is becoming just as important as the devices themselves.
Health data portability refers to the ability of users to access, transfer, and maintain their personal health information across different platforms, applications, and devices. In practical terms, it means that users should not lose their health history simply because they change a smartwatch, fitness tracker, or mobile health app.
True portability allows users to:
The concept is closely linked to data ownership. Regulations such as the General Data Protection Regulation (GDPR) in the European Union formally recognize the right to data portability, giving individuals the ability to obtain and reuse their personal data across services. In theory, this means users should be able to request and move their data from one provider to another.
However, there is an important distinction between data ownership and practical usability. Even if users legally own their health data, transferring it is not always simple. Differences in data formats, measurement methods, and platform restrictions can make moving health data between devices far more complicated than it appears.
Although users technically own their health data, in practice, it often becomes tied to the device or platform that collected it. This happens because the wearable ecosystem is highly fragmented, with each manufacturer building its own data environment, formats, and interpretation methods.
Most wearable devices operate inside proprietary ecosystems. Companies such as Fitbit, Garmin, Whoop, and Apple maintain dedicated apps and data infrastructures designed to work best with their own hardware.
Within these ecosystems:
While many platforms allow users to download their data, these exports are rarely optimized for seamless transfer into another system. In some cases, only summarized data is available, leaving out the raw signals needed for deeper analysis.
Even when devices measure similar metrics, the way those metrics are calculated can vary significantly.
For example:
As a result, switching devices does not simply mean continuing the same dataset with a new sensor. The underlying definitions of the metrics may change, making long-term comparisons more difficult and fragmenting what should ideally be a continuous health record.
In theory, switching to a new wearable should simply mean continuing your health journey with better hardware. In reality, what happens to your existing data depends heavily on how closely the new device aligns with the previous ecosystem. There are several common scenarios.
When users upgrade within the same ecosystem, data continuity is usually seamless.
For example, moving from one Apple Watch to a newer Apple Watch typically preserves the entire health history. Because the device continues syncing with the same Apple Health account, historical data such as activity levels, heart rate trends, and sleep records remain intact. The new device simply adds to the existing dataset.
In some cases, switching devices can still maintain partial continuity if both platforms connect to a shared health hub.
For example, moving from Garmin to Apple Watch may allow some data to flow through platforms like:
However, this transition is rarely perfect. Some metrics may not transfer at all, while others may appear but be calculated differently. Historical data may remain visible, but comparisons between devices can become less consistent.
The most difficult scenario occurs when switching between platforms that operate independently.
For example, moving from Whoop to Garmin may lead to:
In these cases, users often end up starting fresh on the new device while their historical data remains stored in the previous platform’s app.
To address fragmentation, several large technology providers have introduced centralized health platforms that act as data hubs between devices and applications. Instead of storing health information only within a single device ecosystem, these platforms collect data from multiple sources and make it accessible in one place.
Well-known examples include:
These platforms allow wearable devices, fitness apps, and digital health services to contribute data to a shared environment. In practice, this means that activity metrics, heart rate readings, sleep data, and other signals can be stored centrally and shared across compatible applications.
The advantages of these hubs include:
However, these solutions do not completely solve the portability problem. Several limitations still exist. Different devices may store inconsistent metadata, making interpretation difficult across platforms. Some systems provide only processed summaries instead of raw sensor signals, which limits deeper analysis. In addition, API access and data permissions vary across ecosystems, restricting how third-party platforms can interact with the data.
As a result, health hubs improve accessibility but do not fully eliminate fragmentation. True portability still depends on deeper standardization across devices and data models.
True health data portability depends on interoperability, meaning that different devices, platforms, and applications can exchange and interpret data in a consistent way.
For this to work effectively, several components must align:
Without these layers, transferring data between systems becomes technically possible but practically unreliable.
There are several challenges that make standardization difficult. Wearables collect data at different sampling frequencies, meaning one device may record heart rate every second while another captures it once per minute. Algorithms used to process signals also vary widely, leading to differences in how metrics are calculated or interpreted. In addition, contextual information such as activity type, environment, or device placement may be missing or inconsistent.
This is why infrastructure plays such a critical role. Interoperable systems must do more than simply move data between platforms. They must translate, harmonize, and contextualize it so that health insights remain reliable even when devices change.
In an ideal digital health ecosystem, changing devices would not disrupt a person’s health history. Just as switching phones does not erase years of photos or messages, switching wearables should not fragment years of physiological data. True health data portability means that individuals can move freely between devices while maintaining a continuous and reliable record of their health.
In practice, this means users should be able to:
This kind of continuity is increasingly important as wearables become part of preventive healthcare, research, and digital health services. Long-term data allows clinicians to identify subtle changes in health, researchers to study real-world behavior at scale, and digital health platforms to personalize interventions.
Wearable devices evolve quickly. New sensors, improved algorithms, and better hardware appear every year, encouraging users to upgrade or switch platforms. But while devices change frequently, a person’s health history should remain continuous.
At Thryve, we focus on making health data truly portable by building infrastructure that harmonizes wearable data across ecosystems. With our API, you get:
If you are building products that depend on continuous health data, explore how interoperable infrastructure can make device switching seamless.
Book a demo with Thryve!
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.
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