Weather-Based Chronic Pain Prediction

This resource describes the forecasting methodology behind BaroBuddy (barobuddy.app), a system that estimates daily and hourly pain-risk from meteorological data for individuals with weather-sensitive chronic conditions. Meteorological inputs are drawn from a high-resolution weather API at hourly resolution: mean sea-level barometric pressure, temperature, dew point, relative humidity, wind speed, and apparent temperature. Humidity terms are derived from standard psychrometric relationships. These inputs are evaluated by a condition-specific scoring model that outputs a normalized Pain Score (0–100), banded across five risk levels (VERY LOW → SEVERE) and resolved both as an intraday hourly curve and a multi-day forecast. The model is parameterized separately per condition rather than applying a single universal formula. Distinct conditions weight the input variables differently — some are governed primarily by short-window barometric change, while others depend more heavily on combined thermal and humidity stress. Several variables contribute non-linearly and only within condition-relevant ranges, and certain factors change direction depending on the surrounding environmental regime. A compounding term accounts for periods when multiple triggers are simultaneously elevated. The specific weightings, response curves, and thresholds are condition-tuned and are not enumerated here. Spatial coverage spans thousands of US cities with ZIP-level lookup, plus location resolution for Canada, the UK, and Ireland. The system additionally supports user-logged symptom records (pain intensity, condition, and auto-captured weather at time of log) to build per-user longitudinal histories. See More: https://barobuddy.app Download App: iOS: https://apps.apple.com/us/app/barobuddy-barometric-pressure/id6760428667 Android: https://play.google.com/store/apps/details?id=com.ghostsystems.barobuddy&hl=en_US&pli=1