u/Complete-Big-7364

How much do you trust data like heart rate and sleep tracking? I know heart rate is only measured about every 5 minutes, so do you still trust the metrics derived from that heart rate data?

Sure, the watch still 'works' without updates. But for a software-driven device, that's not the whole story. watchOS 27 is just the beginning—OS 28, 29, 30 will keep coming, and who's to say the currently supported AWU3 won't get dropped just as quickly?

Most people buy Apple products with the expectation of long-term software support, which is exactly why we pay such a premium. Look at the iPhone 11; it supports iOS 27. That’s the kind of company Apple is supposed to be. Dropping the AWU1 from watchOS 27 is nothing short of a slap in the face to consumers. I seriously don't get why everyone is being so forgiving about them ending support for the AWU1.

My English isn't the best, so I used Gemini to help me translate this.

Even though I'm subscribed to Gemini Pro, Coach AI doesn't work if I use an Apple Watch or Garmin. I'm disappointed.

Considering the data from the article you provided and the measurement methods of wearable sensors, if the goal is to derive results like 'Recovery' and 'Training Load', I believe that a slightly less accurate but frequent, continuous measurement approach provides more reliable insights.

The reason lies in the intrinsic characteristics of recovery and physical load.

1. The Continuity of Sleep and Recovery Data (HRV) Heart Rate Variability (HRV) during sleep and resting heart rate are key indicators showing the recovery state of the autonomic nervous system. Depending on the sleep cycle (light sleep, deep sleep, REM sleep), your heart rate and HRV fluctuate significantly throughout the night. A method that measures infrequently but with very high sensor accuracy provides near-perfect data for that specific moment. However, if that measurement happens to occur during REM sleep or while you are tossing and turning, there is a risk of distorting the entire night's sleep data and recovery status due to timing bias. On the other hand, a method that measures continuously throughout the night to derive averages and trends—even with slight sensor errors—offsets temporary noise and presents the overall flow of recovery much more stably.

2. Everyday Stress and the Value of Accumulated Data Recovery is influenced not just by the time spent sleeping, but also by the everyday context while you are awake. By tracking heart rate closely 24/7, you can continuously accumulate and calculate physiological changes from work stress, short breaks, or even light family walks and commuting on foot. To comprehensively evaluate the balance between training load and recovery status, this 'seamless context of data' holds greater significance than the extreme accuracy of a single isolated measurement.

3. Complementing the Accuracy of Training Load Of course, during moments of rapid heart rate changes, such as high-intensity intervals on an indoor bike or outdoor running, the absolute accuracy of the sensor is crucial in determining the training load. However, this drawback can be easily overcome by pairing a chest strap heart rate monitor just during your workouts, bypassing the wrist's optical sensor to maximize accuracy. In contrast, you cannot arbitrarily increase or alter a device's measurement frequency or background algorithms during daily life and sleep.

In Conclusion: If your goal is capturing the 'absolute value' of a single heart rate reading or using it for medical reference, a highly accurate sensor is advantageous, even if it only captures brief moments. However, when painting the big picture of Training Readiness—asking questions like, "How much have I recovered today from yesterday's training and daily stress?"—a continuous measurement ecosystem that reads an unbroken, constant flow provides a more practical and reliable baseline.

What are your thoughts?