Apple says watchOS 27 will measure treadmill distance more precisely
Apple's watchOS 27 update promises steadier distance readings on treadmills and during walks, and TechRadar put the claim to a hands-on test.
According to TechRadar, Apple's latest watch software is designed to improve how the device measures distance during treadmill runs and walks. The change was first highlighted when Apple announced watchOS 27 earlier this year, where it competed for attention with a reworked assistant and a Workout Buddy feature that works more independently.
Apple's description, as TechRadar relays it, says improved motion-tracking algorithms should measure treadmill and walking distance more precisely straight from the wrist. The company is primarily refining the calculations that estimate how far a person has traveled once they step onto a treadmill belt, which means the change is mainly a software update rather than a new sensor.
TechRadar's reviewer, who describes themselves as a runner who logs many sessions on a treadmill, tested the update. The piece is a hands-on evaluation, so it reflects one person's experience with the feature rather than a controlled laboratory comparison.
Apple's promise is stated on its own website, and the question for owners is whether the improvement is noticeable in ordinary training. TechRadar's test is aimed at answering that practical question for readers who depend on their watch for indoor workouts.
Background
Distance tracking is one of the hardest problems in wearable fitness. Outdoor runs can use GPS, which measures position directly from satellites. Treadmill sessions give a watch no such signal, because the user stays in one place while the belt moves beneath them. Instead, the device has to estimate distance from motion sensors, usually an accelerometer and gyroscope in the watch, combined with assumptions about stride length and pace.
Those estimates depend heavily on the algorithm. Arm swing, wrist position, running form, and speed all change how motion looks to a sensor, and a model tuned for one style may drift for another. For this reason, many fitness devices let users calibrate stride or compare their results with a reference run to improve accuracy over time.
Apple Watch has built its reputation on health and fitness features since its early models, and the Workout app has grown into one of the platform's central selling points. Each new version of watchOS tends to refine how the device interprets motion, heart rate, and activity, often in small steps that users only notice through repeated use. Treadmill tracking has been a recurring complaint among indoor exercisers, so improvements in this area speak to a long-standing gap rather than a new category of feature.
Because watchOS is the operating system that runs on the watch, changes like this reach existing owners through software updates, provided their hardware is supported. Gains in accuracy are therefore possible without buying a new device, although the size of any improvement will depend on the model, the wrist, and the way each person runs.
Why it matters
For many runners, distance is more than a number on a screen. It shapes pacing plans, training volume, and how progress is measured over weeks and months. A treadmill reading that drifts from reality can make a runner push too hard, skip a target, or misjudge how a training block is going. Better accuracy on a machine many people use for winter training or rainy days has practical value.
The change also highlights how much of modern wearable performance now depends on software. Hardware sensors have been broadly similar across generations, so improvements often come from the code that interprets their data. That shift gives companies room to improve products after purchase, but it also means users cannot always judge a device's ability from its specifications alone.
There is a broader point for the fitness market, too. Consumers increasingly treat wearables as sources of training data, and they expect the numbers to be trustworthy enough to guide decisions. When a manufacturer publicly commits to precision in a specific scenario, it sets a standard that rivals and users can measure against.
Finally, the story shows the value of independent testing. A company's description of an improvement is a starting point. Real-world use, by reviewers and by owners who log the same workouts over time, is what shows whether the promise holds up.
What to watch
The first question is how consistent the improvement is across different runners, paces, and treadmills, since the belt and the machine's own readings can also differ. Readers will want to see whether gains appear in both short walks and longer runs, and whether they hold across seasons and training styles.
Owners should also watch how the update interacts with existing settings, such as stride calibration and workout types. If recalibration is needed, or if results change noticeably from earlier workouts, users may want to understand what has shifted before comparing data over time.
Another thing to follow is whether Apple publishes more detail about the methods behind the change, and whether third-party testers reach similar conclusions. Independent comparisons against a reference measurement would help clarify how large the improvement really is.
Finally, watch how rivals respond. Other wearable makers face the same treadmill problem, and competing claims or updates could shape what buyers expect from fitness trackers in the coming year.
Viewpoints
Commentary from our columns: three angles on this story.
The User what changes for people who use it
If you run on a treadmill a few times a week, the difference between a watch that reads distance well and one that reads it loosely is not abstract. It changes how you judge a workout, whether a training plan feels on track, and whether a hard session actually counted for what you intended. Many people who use an Apple Watch never think about how distance is calculated. They simply trust the number on the screen.
That is why an update like this matters to ordinary owners. You do not need new hardware to benefit, only the software update and a willingness to run your normal sessions and see what changes. The practical advice is simple: pay attention to the numbers you get after updating, compare them against a treadmill display or a known pace, and adjust your expectations if the readings are still off. Better tracking is welcome, but it works best when users understand what the number represents.
The Skeptic open questions and weak points
Apple says its algorithms now measure treadmill and walking distance more precisely from the wrist. That may well be true, but a promise of precision is not the same as proof, and the important questions are still open. Precise compared with what? A treadmill's own display can be off, and a watch that is consistent with itself may still be consistently wrong. Improvement also depends on the runner, since arm swing, stride, and speed all shape how motion looks to a sensor.
Readers should be wary of drawing firm conclusions from a single test, however careful. One person's experience on one machine is useful evidence, but it is not the same as a controlled comparison across many runners and treadmills. The useful response is to ask for clearer methods, independent measurements, and honest reporting of cases where the feature does not help. Until those arrive, the claim deserves a measured reception.
The Builder the engineer's view
From an engineering standpoint, this is a sensible place to invest. A treadmill gives a wrist sensor almost no direct information about distance, because the wearer stays in place while the belt does the moving. Everything has to be inferred from accelerometer and gyroscope data, along with a model that maps motion patterns to speed and stride. The hard part is not collecting the signal but generalizing the model across different people, gaits, and machines without overfitting to a narrow sample.
Software refinement is the right lever here, since the sensor hardware is already capable of capturing the relevant data. The real test is robustness: does the model hold up across warm-up, fatigue, changes in pace, and different wrist positions? Good engineering would show its work through clear validation against a reference measurement and through sensible defaults that let users calibrate when their stride is unusual. Progress will be measured less by headline claims than by how gracefully the system handles the messy variety of real runs.