AI has invaded fitness apps and wearables, with features claiming to turn data into wisdom. However, many of these are pricey and obvious, missing crucial insights. Users often lose money due to premium subscriptions that offer basic data interpretations. AI fitness lacks context, unable to account for real-life variables affecting performance. True personalization involves understanding unique goals and constraints, which most systems don’t do. AI often repackages data with generic advice, failing to provide genuinely personalized coaching.
Overconfidence in incomplete data occurs, with trackers being inaccurate in metrics like calories burned, leading to misleading AI conclusions. The key is predictive insights, not reactive summaries. Strava and Garmin users experience mixed reactions to their AI features, often finding them redundant and costly.
For better AI fitness insights, Whoop and Oura show promise, offering chatbot-like interactions for more detailed responses. Runna app provides useful context by integrating weather and training schedules. To make the most of AI fitness summaries, consider ignoring them, selectively using data points, disabling summaries, or investing in expert consultations.
Ultimately, fitness trackers already provide the necessary data; learning to interpret it yourself can be more effective and cost-efficient than relying on AI. The current fitness AI technology isn’t meeting its potential and remains costly digital noise.