habit

To analyze user habit patterns, you summarize wellbeing, presence, mood, and activity logs to detect habitual times and strengths. This process identifies recurring behavioral patterns to enable habit-aware nudging.

Behavioral patterns enrich threshold nudges with habit-aware phrasing and timing for posture and wellbeing. They also feed music personalization hints via music_patterns to tailor genre suggestions based on historical activity.

Yes, habit pattern detection enriches wellbeing and posture threshold nudges with context-aware phrasing rather than firing its own standalone nudge. It uses historical data to ensure nudges are delivered at habitual times.

The best way to store detected habit patterns is by satisfying the on-disk patterns.json schema. This ensures outputs remain privacy-preserving while providing structured data for downstream personalization.

Yes, habit pattern tracking works by reading and building per-user patterns from mood, presence, posture, and activity data. This multi-source approach allows accurate detection of habitual routines and strengths.