Digital Life Tracking: One Man’s Journey to Quantify Everything
A software developer has spent over three years meticulously documenting every aspect of his daily existence, creating what may be one of the most comprehensive personal databases ever assembled. This ambitious quantified-self project has generated nearly 380,000 data points across more than 100 different categories of human activity.
The Genesis of Total Life Documentation
Beginning in 2019, Felix embarked on an extraordinary experiment in self-quantification, tracking everything from his fitness metrics and nutritional intake to mood patterns, sleep quality, and social interactions. His motivation stemmed from a desire to answer fundamental questions about human behavior and lifestyle patterns.
The project aimed to uncover insights such as how different cities affect productivity and happiness levels, the relationship between sleep quality and daily performance, seasonal impacts on wellbeing, and the correlation between work habits and personal life satisfaction.
The Data Collection Framework
The comprehensive tracking system relies on multiple data sources, with the largest contributions coming from automated tools and manual entries. Computer usage tracking through RescueTime accounts for nearly 150,000 entries, while location data from Foursquare Swarm contributes over 126,000 data points. Manual daily entries, covering mood, fitness, sleep, and social activities, represent approximately 67,000 records.
Additional data streams include weather information, Apple Health metrics, and manually entered date ranges covering major life events and periods. This multi-faceted approach ensures a holistic view of daily life patterns and their interconnections.
Technical Architecture and Implementation
The system operates on three core components: a PostgreSQL database using a timestamp-based key-value structure, data input mechanisms primarily through a Telegram bot interface, and custom visualization tools built with Ruby, JavaScript, and Plotly.
The database design allows for flexible addition and removal of tracking categories without structural modifications. Each entry contains a timestamp, descriptive key, and value, enabling complex queries and trend analysis across multiple years of data.
Revealing Patterns and Correlations
The extensive data collection has revealed numerous fascinating correlations. Days marked by happiness and excitement showed 50% higher likelihood of comfort zone expansion and 44% increased meditation frequency. Conversely, these positive mood days correlated with 45% less time spent in video calls.
Geographic location significantly impacts activity levels, with New York City residence resulting in more than double the daily step count compared to other cities. The data also reveals clear seasonal patterns, with summer months showing 33% more daily steps and 40% lower illness rates.
Health and Fitness Insights
Weight management data demonstrates clear correlations between body weight and resting heart rate. During a controlled weight gain period, resting heart rate increased by 9 beats per minute alongside an 8.5-kilogram weight increase. The tracking also revealed that alcohol consumption significantly impacts sleep quality, with resting heart rate increasing by approximately 50% after drinking.
Workout consistency patterns emerge clearly in the data, showing progression from roughly 50 annual gym visits in early years to approximately 200 visits in recent years. The impact of external factors like lockdowns and illness on fitness routines is also clearly visible in the visualizations.
Travel and Lifestyle Documentation
Flight tracking over seven years reveals detailed patterns in travel behavior, with clear disruptions during the COVID-19 pandemic. The data shows Sunday as the primary travel day for international commuting between major cities. Location-based analysis demonstrates how different environments affect various life metrics, from social interactions to productivity levels.
The comprehensive location tracking includes over 126,000 check-ins across multiple countries and cities, providing insights into how geographic changes influence daily routines and overall wellbeing.
Technology Usage and Digital Habits
Computer usage tracking reveals significant shifts in digital behavior over time. Communication and scheduling activities increased from 10% to 30% of computer time over seven years, while software development time decreased from 33% to less than 10% following career changes.
The data emphasizes the value of tracking digital consumption, showing surprising patterns in how time is actually spent versus perceived usage. This awareness has practical applications for productivity optimization and digital wellness.
Privacy Considerations and Data Ownership
Despite the extensive personal data collection, privacy remains a priority through self-hosted infrastructure and careful consideration of what information to share publicly. The system stores all data in a private database not connected to external services, maintaining complete control over personal information.
The public visualizations are designed to show patterns and trends without exposing sensitive details like specific locations, addresses, or personally identifiable information. This approach balances transparency with privacy protection.
Lessons Learned and Future Implications
After three years of intensive data collection and analysis, the project creator acknowledges that while fascinating insights emerged, the time investment may not justify the outcomes for most individuals. The hundreds of hours required to build and maintain such a comprehensive system represent a significant opportunity cost.
However, the project provides valuable awareness of daily patterns and behaviors that might otherwise go unnoticed. The data reveals clear correlations between various lifestyle factors, offering evidence-based insights for personal optimization.
Recommendations for Aspiring Self-Quantifiers
For those interested in personal data tracking, the recommendation is to start with existing services rather than building custom solutions. Key considerations include ensuring data export capabilities, evaluating service sustainability and business models, and focusing on sustainable tracking practices that don’t require excessive time investment.
The project demonstrates that while comprehensive self-tracking is technically feasible, the most valuable insights often come from consistent tracking of a smaller number of key metrics rather than attempting to quantify every aspect of existence.
This ambitious experiment in self-quantification ultimately serves as both an impressive technical achievement and a cautionary tale about the balance between data collection and practical utility in personal optimization efforts.