Improve Your Reading Habits With Data: Becoming a Data-Driven Reader

“How many books did I read this year?” — most people can’t answer this. Let alone the follow-ups: How many days last month did I actually read? What time of day do I read most effectively? How many notes does a typical book produce? These answers don’t require guesswork — you already have the data; you just haven’t had a tool that surfaces it.

Yomitomo’s Stats page aggregates behavioral data scattered across your reading sessions, letting you understand your reading state through numbers rather than feelings.

Local Reading Stats: Track Every Step

Yomitomo automatically records all key reading behaviors. The Stats page surfaces these metrics:

• Today’s Activity: How many articles you imported today, how many highlights you created, how many distillations you published
• Days Recorded: How many days have had reading activity since you installed Yomitomo
• Weekly Active Days: Over the past week, how many days you opened Yomitomo and read
• Peak Activity: When your reading activity level hit its highest point

The value of these metrics isn’t “looking at nice numbers” — it’s surfacing signals. If your weekly active days drop from 6 to 2, your reading habit is slipping and needs active correction. If your peak activity consistently falls at 11 PM, but the quality of highlights from that time slot (measured by how many later get referenced in distillations) is low, maybe you should shift reading to a higher-energy time of day.

Activity Charts: See Trends, Not Just Numbers

The Stats page provides two visualization dimensions:

Activity Trend Chart shows curves for your recent highlight count, distillation count, and discussion comment count over time. If highlight counts stay steady but distillation counts keep dropping, it might mean you’ve been “passively highlighting” lately — marking a lot but not spending time to process. This is a warning signal: input volume stays the same, but processing depth is declining.

Activity Calendar uses a heatmap to show your reading activity level over the past 70 days. At a glance, you can see which days were “deep reading days” (multiple highlights + discussions + distillations) and which were “just opened and glanced” (activity recorded but no output). This kind of visual feedback is more motivating than a table of numbers — seeing five consecutive green squares makes you inclined not to leave the sixth one blank.

WeRead Stats: Two Sources, One View

If you’ve configured a WeRead API key, the Stats page can switch to the “WeRead” data source, showing weekly, monthly, yearly, or cumulative reading duration, books finished, highlight counts, and more.

When you switch time periods, Yomitomo prefers locally cached data — it won’t re-request the API every time. Only when you manually click “Query” or “Re-query” does it pull fresh data. This design avoids excessive API calls and ensures you can still browse historical stats while offline.

Local reading stats and WeRead stats coexist — they don’t replace each other, they complement each other. Think of WeRead data as breadth on the input side, and Yomitomo’s local data as depth on the processing side — the former tells you how long you read, the latter tells you what you produced while reading.

From Data to Action

The point of stats isn’t “just looking.” It’s driving action. A few approaches you can try:

• Set a weekly minimum for active days (say, 4 days) and track it with the activity calendar
• Watch the distillation-to-highlight ratio — if it stays below 10%, you’re over-highlighting and under-processing
• At the end of each month, review the activity trend chart against the previous month and identify one behavior to improve

Reading is a slow-feedback activity — you rarely feel cognitive change the day after finishing a book. Stats fill this feedback gap, letting you see every footprint on the long journey of sustained reading.

Who This Is For

Anyone who wants to turn reading from “occasional impulse” into “stable habit.” If your reading frequency is erratic, if you read and immediately forget, if you want to use data to drive continuous improvement in your reading behavior.