Principle
A momentum model that mirrors biology
Training builds adaptation. Rest lets it fade. The app's math follows the same curve.
Binary tracking lies about the body
A missed day does not erase a month of work. Yet streak systems treat it that way, producing feedback that is emotionally loud and physiologically wrong.
Streak-based systems try to overcome this with elaborate schemes revolving around forgiveness of breaks, like "lives" in a video game, often with a monetised element. We're not here to play games.
Stimulus adds, time decays
Each logged workout adds momentum in proportion to its volume. Between sessions, momentum decays exponentially — fast near the top, slower as it approaches your base. That shape matches how adaptation behaves in reality.
We can imagine that momentum slowly fades second-by-second, though FitMo recalculates the score only once per day for simplicity.
The feedback always fits your current state
Because momentum is a living variable, the next target adjusts to where you are right now. There is no fixed plan to fall behind on — only a present state and the most useful next step. The daily goal never falls below what you've set as you baseline (what you can manage even when unfit).
One high-signal metric is enough
Many fitness apps pile on data: exact per-session volume, calorie burn estimated from heart rate, calorie balance from meal logging, recovery scores derived from sleep wearables. For most people, that level of crunching is not sustainable once the initial enthusiasm wears off, and most of it adds very little to actual training decisions.
A lot of that complexity exists for reasons that have nothing to do with your fitness: selling biometric data, pushing wearable hardware, or upselling extra app features and diet programs. We are not interested in any of that.
FitMo deliberately tracks one high-signal input — training volume — and turns it into a biological analog of fitness state through the momentum score. If you are not chasing an Olympic edge, that is all you need to make the next useful training decision. And less is more if it makes it easier to sustain.
Questions
Why exponential decay instead of linear?
Exponential decay matches how adaptation fades in reality: quickly near the peak, then slowly as it approaches your base. It also forces you to keep working harder to prevent your score from plateauing.
How is momentum added from a workout?
Your logged volume (reps, weight, distance, or time) is converted through a movement-specific factor and added to your current momentum.
Why no calorie tracking, heart rate, or wearable integration?
Because they are not necessary for the job this app is doing. Volume is already a high-signal input, and the momentum score turns it into a meaningful picture of your training state. Layering on biometric data and meal logging mostly adds friction and abandonment risk without changing the actual training decision in front of you.
What is the "baseline"?
The baseline is what you can do even when unfit. It is the starting point for the momentum algorithm and the daily goal. It is the value you see when you first add an exercise. Some typical default baselines are set for the templated exercises, but you can change them to anything you like if you find this starting point too easy or too hard.