Daylio App - Advanced Statistics Explained

Mood tracking becomes actionable when Daily turns raw check-ins into comparisons: which moods show up most, which activities tend to move mood up or down, and how those patterns vary by streaks and days of the week. In a 30-day snapshot, the user’s “life is good” mood appears 9 times out of 30 days, up from 5 times in the prior 30-day window—an increase of about 80%. But the app’s own context matters: “life is good” is also rated around 4/5 for them, so frequency alone doesn’t automatically mean things are improving; it’s a starting signal that needs follow-up.

Daily’s advanced stats also track how long a mood lasts and what it tends to cluster with. For “life is good,” the longest streak runs from Feb 25 to Feb 27, and the “longest streak without that mood” is about 8 days—an expected outcome when multiple moods can exist within a mood group. The app then links moods to related activities. For example, “paying proper attention” (including walking in silence and meditating) sits around 40, while most other activities fall in the 33–29 range. The user notes that patterns become more reliable only after enough data accumulates; with sparse activity tracking, correlations can look strong but carry low confidence.

The stats extend from single moods to mood groups—especially useful for asking when someone feels their best. For a top mood group (5/5), the user reports 362 times per month since tracking began (about 8.5 entries per month, roughly two days per week), with a longest 5/5 streak of four days and 23 days without feeling really good. Still, the app’s correlation charts can be misleading when activities are too rare or too specific. The user says they eventually narrowed tracking to about 10 standardized activities to improve statistical signal.

Day-of-week patterns add another layer. For the 5/5 mood group, Monday is the lowest incidence day (41 times), while Friday peaks and the weekend dips—Sunday and Saturday are close, with Sunday slightly lower (55 vs 58). These weekly rhythms don’t prove causation, but they help identify routines worth testing.

At the activity level, Daily compares average mood on days with an activity versus days without it, plus “previous day” and “next day” effects. Walking is the clearest example: across all time, mood averages 3.9 on walking days versus 3.6 on non-walking days (about a 9% relative boost). Confidence is described as high because both groups have enough samples, though the user cautions that “previous day” and “next day” effects can be hard to interpret—sometimes they may reflect anticipation rather than the activity itself. Social shows a larger contrast: 3.9 on social days versus 3.1 on non-social days, with additional carryover into adjacent days. Finally, when there’s no data for a mood group (like 1/5 in the last 30 days), Daily withholds comparisons and flags the need for more entries.