I was diagnosed with cancer last year and found myself drowning in numbers — biomarkers (CEA, LDH), white blood cell counts, hemoglobin, daily pain and fatigue scores, medication logs, bowel movements (colorectal cancer), plus Apple Health data like steps, heart rate, and sleep. No app I tried could handle arbitrary metrics with real analytical tools, so I built one. Meetrics lets you log any numeric, binary (yes/no), or discrete value with a tag, then explore it — rolling averages, 2σ outlier filtering, heatmaps, streak tracking with Bayesian probability estimates, and a Correlate tool that finds relationships between any two tags with adjustable time lag and bucket size.

The Correlate feature is what made it click for me. I found that my chemo side-effects peaked at an 8-day lag — not when the doctors said they would. I showed the chart to my oncologist and it changed the conversation from "I think the side-effects come later than you warned" to "here's the data."

Recently my CT scan showed all tumors shrinking for the first time. Digging into the data, the clearest signal is a sharp increase in daily steps over the past month — partly better weather, partly a new puppy who needed walking. Steps are a known predictor of cancer survival, and the correlation-with-lag tool made it easy to see the timing line up with my scan window.

There's also a premium AI Analyst tab (powered by Claude) that answers natural-language questions about your data and generates daily insight cards. It's how I pay the bills while I'm not working and fighting cancer — the core tracking and analytics are free.

The app is general-purpose. I use it for cancer, but it works for anything — habits, grades, mood, workouts, macro data. I wrote a separate post using it to find the 12-18 month lag between Fed rate changes and unemployment.

Free on iOS: https://apps.apple.com/us/app/meetrics/id6760925743 Full write-up with screenshots: https://github.com/sergeybok/meetrics-support/blob/main/BLOG... Would love feedback from the QS/data crowd here.

As a data scientist I’m probably not your intended audience but a “Correlate tool that finds relationships between any two tags with adjustable time lag and bucket size” sounds like a recipe for spurious correlations. In the context of healthcare in particular I would be cautious about drawing conclusions from that sort of analysis.