Daylio App Review: Analyzing 1000+ Days of Data

A 1,000-day mood log in Daylio suggests a stable personal baseline rather than a steady upward trend: the average mood settles around 3.7 on a 1–5 scale, with typical day-to-day variation of about 1.2 (so most entries fall roughly between 2.5 and 4.9). After an initial dip and a gradual leveling period, the cumulative mood curve flattens—meaning the long-run question “am I getting happier over time?” doesn’t show a clear improvement, and the “am I getting more stable?” question also lands on “not really.”

The analysis starts with how mood is recorded. Daylio entries map a user’s overall mood to a 1–5 rating (with custom sub-moods such as “life is good,” “grateful,” “meaningful,” “zen,” “mad,” and “angry”). The log includes multiple entries on some days—capturing different moods at different times—so the daily mood is treated as the average of that day’s entries (for example, combining a 1 and a 4 yields a 2.5 average). The most common daily rating lands on 4 out of 5, followed by 5, then 2, then 3, with 1 the least frequent. Subcategory breakdowns reinforce that “life is good” and positive frames appear more often than “zen,” while “angry” dominates the low-end category.

To answer the two core questions, the creator uses cumulative averages and dispersion measures. The cumulative average mood is computed day by day: each new day’s rating updates the running mean of all prior days, producing a curve that can reveal whether the baseline is drifting upward or downward. Over the multi-year span (roughly 2018 to 2021), the cumulative mood appears to stabilize after about six months, hovering near 3.7. A parallel look at cumulative standard deviation—used as a measure of variability—stays steady around 1.2, indicating that mood volatility doesn’t meaningfully shrink as time passes.

A key nuance is scale. Even when plots look like they fluctuate, the changes are small in absolute terms: the smoothed trend moves only from about 3.8 down to roughly 3.6–3.7. On a 1–5 axis, that’s a narrow band, and the error bars remain relatively constant. The practical takeaway is a quantified baseline: mood is “about 3.7 ± 1.2,” not a steadily rising line.

The discussion then broadens beyond statistics into interpretation. The creator questions whether different “types” of happiness (for instance, “meaningful” versus “blissful” versus “grateful”) are interchangeable on a single 1–5 scale, whether an “ideal” distribution would be all 5s (or whether variability is inherently human), and whether modern improvements in external conditions translate into greater subjective happiness. The final personal reflection is existential: if life’s baseline is stable rather than improving, what does it mean to keep striving—and is chasing happiness a dead end or a necessary pursuit of something other than mood alone?

Overall, the dataset turns a vague self-assessment into a measurable claim: long-term mood stability is real, but long-term improvement is not obvious in this record.