Improving Reading Habits with Local Analytics: Building a Data-Driven Cadence

“How many books or deep articles did you genuinely master this year?”—most people can only offer vague guesses. Fewer still know how many days they maintained focused reading, which hours produced their highest-quality reflections, or what percentage of highlights converted into structured knowledge.

Sustainable reading habits should not rely on fleeting bursts of willpower. Yomitomo quantifies key cognitive behaviors locally, giving you verifiable data to optimize your reading cadence.

Local Reading Analytics Metrics Model

Dimension	Data Source	Calculation Logic	Visual Presentation	Habit Diagnostic Value
Focused Time	Local reader focus tracking	Excludes idle time; logs active engagement	Daily/Weekly charts and historical trends	Pinpoints true focus time without self-deception
Cognitive Density	Highlights (`A`), thoughts, discussions	Tracks frequency across 5 cognitive categories	Category breakdown donut chart & hourly distribution	Evaluates thinking depth (high Question/Assumption ratio = active critique)
Distillation Ratio	Published distillation cards (`T`)	Ratio of `Distillation Cards ÷ Total Highlights`	Conversion funnel & weekly distillation velocity	Warns against the “highlighting without synthesizing” trap
Consistency Grid	70-day rolling activity	Daily cognitive score mapped to color density	GitHub-style 70-day activity matrix	Provides visual positive feedback to sustain momentum

The Local Analytics Dashboard

Daily Activity: Active reading time, imported articles, highlight count, and distillation submissions;
Streaks & Consistency: Total recorded days, active days per week, and peak cognitive time blocks;
Conversion Velocity: Historical curve comparing raw highlights to finalized knowledge cards;
AI Collaboration Distribution: Call frequency breakdown across various specialist personas.

These metrics expose hidden habit signals: if active days drop from 5 to 2, your routine is slipping; if late-night highlights are never referenced in distillation drafts, move deep reading to the morning.

70-Day Consistency Matrix

The 70-day heatmap uses varying green shades to visualize reading density over the past two months. This continuous positive feedback builds internal momentum far more effectively than abstract resolutions.

WeRead Data Integration: Breadth Meets Depth

When configured with your WeRead API Key, the analytics center switches seamlessly to visualize your WeRead reading duration across weekly, monthly, and yearly horizons. WeRead represents input breadth (hours spent, pages turned), while Yomitomo metrics represent processing depth (questions raised, knowledge distilled).

Target Audience & Usage Boundaries

Who This Is For

Readers seeking to build a sustainable analytical reading routine backed by quantitative data;
Knowledge workers monitoring their highlight-to-distillation conversion efficiency;
WeRead power users wishing to combine input volume with deep desktop synthesis.

What This Is Not For

Social leaderboard competition: Analytics are 100% private to your local device without public leaderboards or social sharing buttons;
Artificial time farming: Focus tracking detects idle windows and pauses automatically when inactive.

Frequently Asked Questions (FAQ)

Q1: Is my reading analytics data uploaded to any server?

Answer: Never. All telemetry, timestamps, and heatmaps live exclusively inside your local SQLite database.

Q2: Does the timer keep running if I step away from my desk?

Answer: No. Yomitomo includes intelligent idle detection. When the window loses focus or experiences no interaction, the timer pauses automatically.

Q3: Will my 70-day heatmap transfer when switching computers?

Answer: Yes. Exporting your database backup under Settings > General and importing it on your new computer restores all historical analytics and heatmaps completely.