10 Years of Wearables: What's Actually Worth Tracking (And What to Ignore) | 344 — Garage Gym Athlete

Most wearables are a Pandora's box of useful data and confusing inaccuracies — but for serious athletes and gym enthusiasts, the real value lies in what actually influences your training and lifestyle. In this eye-opening episode, Jerred Moon and Dave break down their decades-long experience with wearables like Garmin, Whoop, and Apple Watch to reveal what truly works — and what's just noise. Jerred shares rare insights from testing over 31 different sources and reveals how even the most advanced tech can fall short of the accuracy of a simple chest strap. Dave discusses the essentials he's tracked for years, from daily steps and heart rate zones to long-term trends in resting heart rate, emphasizing how these basic metrics can drive real behavior change. Discover:

The surprising benefits of low-tech features like a flashlight or vibration alarms that keep your life simple and effective
Why most advanced recovery scores and proprietary algorithms can do more harm than good by encouraging unnecessary stress or over-optimization
How the quest for perfect data can detour you from actual progress — and the moment to hit "pause" and focus on intuitive training
Which metrics are objectively helpful, and which are just digital distraction
Jerred's next evolution: turning Garmin into a "dumb watch" to reconnect with instinctive movement and breathe easier with tech boundaries

Whether you're a seasoned athlete, a garage gym enthusiast, or someone curious about tech's role in health, this episode offers a balanced perspective: more data isn't always better. It's about understanding what matters, what influences your habits, and when to trust your own body over the latest gadgetry. Stay tuned for tips on how to use wearables without falling into the trap of over-optimization and learn how to leverage technology to support natural, sustainable fitness. This conversation is essential listening for anyone eager to cut through the noise and get back to real results — no fancy algorithms required.