Interesting! Mood tracking for bipolar disorder is a meaningful space with real impact potential.

Let me make sure I understand what you're exploring. There are a few different angles you might be researching:

Key dimensions I'm seeing:

• Mood granularity — How people experience and describe their emotional states (beyond just "up" or "down")
• Temporal patterns — When and how often people track, and what triggers check-ins
• Context capture — What factors people associate with mood shifts (sleep, meds, events, etc.)
• Self-awareness journey — How tracking helps people recognize patterns over time
• Communication needs — Sharing data with care teams, loved ones, or just for self

Before I suggest research questions, I want to understand your goal better:

Got it — you're designing a new mood tracking app for people with bipolar disorder. That means we want to deeply understand how potential users experience and think about tracking.

Here are some research questions that could drive meaningful product decisions:

Great choice — this is foundational. If you understand how people naturally describe and differentiate their mood states, you can design an interface that actually matches their mental model (rather than forcing clinical scales that feel foreign).

Let me check what interactive tools I have available to design an exercise for this:

I have drag-and-drop, animations, and icons available. Here are some exercise ideas that could help you understand how people naturally describe and experience their mood states:

Love it! The Energy-Emotion Mapper is a great choice because bipolar experiences often involve both an energy dimension (high/low activation) and a valence dimension (pleasant/unpleasant) — but we want to see how users naturally place and label their feelings rather than assuming a clinical model.

Here's what I'm thinking:

Exercise concept: A 2D canvas where users drag mood tokens onto an energy (vertical) × pleasantness (horizontal) space. They can place pre-made tokens and see where they cluster — revealing their mental model of how moods relate to each other.

This will prompt interviewees to:

• Think aloud about why certain moods go together
• Reveal which distinctions matter to them (e.g., is "irritable" high or low energy? Is it always unpleasant?)
• Surface vocabulary and nuances the clinical scales might miss

Let me build it:

Your exercise is live! View the Energy-Emotion Mapper →

How it works:

• Users drag mood tokens from the tray onto a 2D canvas
• Vertical axis = Energy level (high ↔ low)
• Horizontal axis = Pleasantness (unpleasant ↔ pleasant)
• Tokens shrink into compact pills once placed
• Double-click to remove and re-place

What you'll learn from interviews:

• Where people place ambiguous moods like "Irritable" or "Restless" — and why
• Whether the energy/pleasantness model matches their experience
• Clusters that reveal related states (e.g., does "Euphoric" sit near "Anxious" for some people?)
• Language gaps — moods they wish were included, or labels they'd rename

Tips for the interview:

• Ask them to think aloud as they place each token
• When they hesitate, probe: "What are you considering?"
• At the end: "Are there moods you experience that aren't represented here?"

Want me to adjust anything — different mood words, colors, or axis labels?