What else should you track besides mood for bipolar?

Track energy levels, sleep duration and quality, irritability, medication adherence, psychomotor activity (restlessness or slowness), and notable stressors or triggers. These additional dimensions are what psychiatrists actually assess when evaluating bipolar stability.

Why is energy important to track for bipolar disorder?

Energy changes often precede mood changes by 1 to 2 days. Increased energy without elevated mood can signal approaching hypomania, while decreased energy before mood drops can indicate depression onset. Energy tracking catches these shifts earlier than mood tracking alone.

What dimensions do psychiatrists assess for bipolar?

Psychiatrists evaluate mood state, energy levels, sleep patterns, irritability, psychomotor activity (physical restlessness or slowing), concentration, appetite changes, and risk-taking behavior. A comprehensive mood tracker should capture as many of these clinical dimensions as possible.

Is a single mood scale enough for bipolar tracking?

No. A single scale from sad to happy misses critical bipolar indicators. Mixed episodes involve simultaneous depression and agitation. Irritable mania presents without euphoria. These clinically important states are invisible on a one-dimensional mood scale.

Why Mood Alone Isn't Enough for Bipolar

Tracking mood alone misses the picture. Bipolar needs energy, sleep, irritability, and stability tracked together. Here's what most mood apps leave out.

Tracking mood alone provides an incomplete picture of bipolar disorder. Psychiatrists assess multiple dimensions including energy levels, sleep quality and duration, irritability, psychomotor activity, and concentration. A mood tracker that captures only a single mood scale misses the clinical signals that differentiate bipolar episodes from normal emotional variation.

How do you feel today?

Every mood app on the planet starts with some version of this question. Pick a number. Choose a face. Tap an emoji. And then you’re done. Your entire internal state, compressed into a single data point.

For general wellbeing, maybe that’s fine. For anyone managing a real mood disorder (bipolar, major depression, anxiety disorders), it’s not even close to enough. The complete guide to bipolar mood tracking goes deeper into what a serious tracking setup looks like.

One number hides everything

Let me give you three different days, all with the same mood score of “5 out of 10”:

Day A: Mood 5, energy 3, sleep 10 hours, stability 4. This person is in the early stages of a depressive episode. The oversleeping, low energy, and moderate stability loss point to a downward trend. Mood hasn’t crashed yet, but it’s coming.

Day B: Mood 5, energy 8, sleep 4 hours, stability 2. This person is in a dangerous mixed state. This is exactly why tracking irritability and other clinical signals matters. High energy with low mood and low stability, running on almost no sleep. This is the state where impulsive decisions happen. Where things get said that can’t be unsaid. This person needs intervention.

Day C: Mood 5, energy 5, sleep 7 hours, stability 7. This person is having a perfectly average day. Mood is middling but everything else is solid. No clinical concern at all.

Same mood score. Three completely different situations. One is fine, one is concerning, one is an emergency. If your app only captured mood, all three look identical.

The axes that matter

After tracking my own data for a while, I’ve settled on four things that I think actually matter for understanding your mental state day to day:

Mood. The obvious one. How happy or sad you feel. But it’s just the surface.

Energy. How much drive you have, physically and mentally. This is separate from mood in important ways. Depression can come with low energy (typical) or agitated high energy (atypical/mixed). Hypomania almost always comes with elevated energy. Tracking energy separately from mood catches states that mood alone misses.

Sleep. Both duration and quality. Not just “I slept 7 hours” but “I slept 7 hours and woke up three times.” For people with mood disorders, sleep disruption is often the first signal that something is changing, days before mood follows.

Stability. This is the one nobody else tracks, and I think it’s the most important. Stability is your subjective sense of how solid the ground is. Can you trust your own reactions today? If something stressful happened, would you handle it proportionally? Or are you one small trigger away from an outsized response?

Stability is hard to quantify, I know. It’s not as clean as “hours of sleep” or “mood from 1 to 10.” But after months of tracking it, I’ve found it to be the single most useful predictor of whether a day is going to go well or badly. High stability absorbs shocks. Low stability amplifies them. Everything else being equal, stability is what determines whether a stressor becomes a bad hour or a bad week.

Why apps don’t do this

The reason most apps stick to a single mood score is simple: engagement. The fewer fields in your daily check-in, the more people will complete it. The more people complete it, the better the retention metrics look. The better the metrics, the more funding the company gets. It’s the same trade-off I discuss in my Daylio comparison for bipolar users.

Multi-axis tracking has higher friction. It takes 30 seconds instead of 5. Some people will drop off. The daily active user count goes down.

But here’s the thing: the people who drop off from a multi-axis tracker are probably the people who don’t need it. They’re the “I use this app for fun” crowd. The people who need it, who are actively managing a condition and want real data, they’ll take the extra 30 seconds. Because for them, the difference between a single mood number and a four-axis snapshot is the difference between useless and genuinely informative.

I’d rather build for the second group.

What the extra data actually tells you

When you track multiple axes daily, you start seeing relationships that a single mood score would never reveal:

Energy leads mood. In my data, energy changes tend to precede mood changes by about a day. This is a core part of how bipolar episode prediction works. If my energy spikes or drops, my mood follows. This gives me a 24-hour warning window that I wouldn’t have if I only tracked mood. That window is how you catch the early warning signs of a manic episode before they escalate.

Sleep predicts stability, not mood directly. Bad sleep doesn’t reliably tank my mood the next day. But it reliably tanks my stability. Which means I’m more vulnerable to everything the next day, even if I wake up feeling “fine.”

Mismatches are the danger zone. When all four axes move together, all up or all down, that’s usually coherent and manageable. When they diverge (high energy, low mood, crashing stability), that’s when I need to pay attention. Those mismatches are the fingerprint of mixed states, and they’re invisible if you’re only tracking one dimension.

None of this is complicated. It’s just more than one number. But that “more” is the whole difference between a toy and a tool.

The design challenge

I think about this a lot as I build Steadyline. How do you ask for four data points daily without it feeling like homework? How do you make multi-axis tracking feel as easy as tapping a smiley face?

My answer so far: sliders. Quick, visual, no typing required. You drag four sliders, maybe add a note if you want to, and you’re done in under a minute. The app handles the rest: the charts, the correlations, the flags.

It’s a tradeoff. It’ll never be as frictionless as a single emoji. But it’ll be infinitely more useful for the people who actually need it, the ones navigating daily life with bipolar. And I’ve made my peace with that tradeoff.

Related reading:

If your current mood tracker asks you one question and calls it done, it’s giving you one pixel of a picture that needs four. Steadyline tracks mood, energy, sleep, and stability, because your mental health is more than a smiley face.