Predicting Bipolar Episodes Before They Happen
Your data knows before you do. Research shows that patterns in sleep, energy, and irritability can predict bipolar episodes days in advance. Steadyline's AI finds these patterns so you can act early.
Your data sees it first
After months of tracking mood and sleep, a pattern emerges that no amount of self-reflection would catch: your sleep starts shifting 3-6 days before you feel any mood change. Energy climbs while you still feel "fine." Irritability creeps up before you recognize it.
These are the early warning signs of a bipolar episode — and they're hiding in data you're already generating. The problem is that humans are terrible at spotting gradual shifts in their own behavior. That's where AI helps.
What the research says
Episode prediction isn't science fiction. Published research supports it:
- Sleep variability predicted hypomania onset with sensitivity 0.94 — meaning it caught 94% of approaching episodes (Ortiz et al., 2025)
- Energy and psychomotor changes precede mood shifts, often by 2-4 days
- Multi-dimensional tracking (mood + sleep + energy + irritability) outperforms single-signal tracking for prediction accuracy
How Steadyline predicts patterns
Steadyline's AI doesn't just log your data — it analyzes it across dimensions to find early warning patterns:
- Sleep variability detection — flags when your night-to-night consistency breaks down
- Energy trend analysis — catches gradual climbs or drops before they become obvious
- Irritability correlation — connects irritability spikes to sleep and energy changes
- Stability scoring — a composite metric that shows whether your baseline is holding or shifting
The AI finds what you'd miss. Then you and your psychiatrist decide what to do about it.
What AI shouldn't do
Steadyline has hard limits on what AI does. It detects patterns and flags concerns — it doesn't diagnose, it doesn't prescribe, and it doesn't replace your psychiatrist. The AI is a pattern-finding tool, not a clinician.
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