It’s been two years since I started tracking my Long Covid with the Visible app, and a year since I last sat down with the data here on the blog. This post is a follow-up — same data, but now with twice as much of it, a clearer improvement arc, and some new plots and analyses I hadn’t tried before. Calendar heatmaps, ridgeline plots, change-point detection, and a phase-by-phase look at how my morning stability score moves across the menstrual cycle all make an appearance, alongside a quick check on whether last year’s symptom clusters still hold up.

This blog post delves into the my personal Long Covid data, collected via the Visible app, using Principal Component Analysis (PCA). I detail four key clusters: Menstruation, Exertion, Emotional, and Neurological and then compare these clusters to the principal components derived from PCA. The analysis aims to provide a validated, data-driven framework for exploring my symptoms during this condition, using clear visualisations to showcase the results.

This post dives into a year of tracking Long Covid symptoms and progress using the Visible app. Dr. Mowinckel, a quantitative scientist, shares their experience monitoring heart rate, heart rate variability (HRV), daily symptoms, and functional capacity using the app’s features and the FUNCAP27 questionnaire. Discover how this data provides insights into pacing strategies, identifies potential warning signs like HRV spikes, and reveals meaningful clusters of interconnected symptoms and experiences. Explore the journey of living with and managing Long Covid through the lens of personal health data.