- Feedback
-
Ideas
An algorithm that adapts more personally to the individual rather than a universal standard
By the time someone gets through about 20 workouts, it would be quite easy to see the convexity of the training load function. Most standard training load charts I see that predict 1RM are quite linear. In my experience, this is not accurate enough and changes based on experience. Also, some people simply have higher endurance which allows them to get a couple more reps at lower loads, but doesn't translate to the typical higher 1RM. In this case, in your app, the person will hit a strength phase having done bench @225 for 8 reps and suddenly it jumps to 250 expecting 5 reps, but that person just has high endurance but lower relative strength at low reps. I've easily plotted this function for myself, so I know a reasonable data scientist can feed an adaptive convexity argument into the AI function.
Customer support service by UserEcho
Totally agree with this.
What you're getting at is that not everyone fits into the same mold when it comes to strength and endurance. Some lifters can grind out a lot of reps at submaximal weights but don't scale linearly when it comes to low-rep max strength. The current 1RM and progression logic assumes a universal curve—but in reality, some people taper off faster, others slower.
What you're proposing is that the app should learn from the user’s own performance—how you actually respond to different loads and rep ranges—and adjust future weight targets based on your profile, not just a fixed formula. After 15–20 workouts, there's enough data to start doing that. That kind of adaptive model would make progression feel way more accurate, especially during strength phases where overshooting can really mess with recovery and momentum.
This would be a great upgrade to Dr. Muscle’s AI engine—more personalization, less guessing.