"When you sleep well, your completion rate jumps 40%."
Our intelligence layer uses Spearman rank correlation to detect relationships between your wellness signals. It learns what works for you specifically, not what works in a study.
Eight signal domains, one engine
Sleep. Mood. Movement. Nutrition. Completion. Stress. Energy. Cycle. All feeding one correlation engine, one insight generator, one daily plan adaptation.
Why Spearman over Pearson?
Wellness data is messy. Outliers happen. Non-linear relationships exist. We test 18 signal pairs at same-day and lag-1 offsets. Minimum 10 samples. Minimum |r| of 0.25. Robust statistics for real-world health data.
Pearson assumes normal distributions and linear relationships. Real human wellness data rarely meets those assumptions. Spearman works on ranks, which makes it resilient to the kind of noise that daily life produces.
Energy: the score you never log
Energy is not something you log. It is something we calculate. A synthetic 0-to-10 score derived from your sleep, mood, and completion data. When two of three signals are present, we compute the missing dimension. Derived intelligence from existing data.
The habits engine
After 14 days, the app knows you better than you think. The Rhythm Pattern Engine analyses your completion events and builds a behavioural fingerprint. Morning person or evening heavy? Strong pillars? Skip days?
Our habits engine uses Exponentially Weighted Moving Averages for pattern detection. Shift detection, confidence scoring, histogram analysis. Behavioural science meets real-time data. It reshapes your plan to fit how you actually live.
One model that deepens every day
Rolling 30-day baselines. 14-day trend directions. Cross-domain correlations. Day-of-week risk scores. The wellbeing model is the brain behind every recommendation. It never stops learning.