Dan Parry and Nana Parry.
A study analyzing daily data from 2658 patients over 15 months found significant yet modest relationships between pain and relative humidity, pressure, and wind speed, highlighting the potential of citizen-science experiments to collect large datasets on real-world populations to address long-standing health questions.
The study found significant associations between pain and relative humidity, pressure, and wind speed, with relative humidity having the strongest association with pain.
The study found significant relationships between relative humidity, pressure, wind speed, and pain, with correlations remaining even when accounting for mood and physical activity.
The study estimates the odds ratio for a pain event in response to changes in weather variables, including temperature, wind speed, relative humidity, and pressure.
The odds of a pain event was 12% higher per one standard deviation increase in relative humidity (9 percentage points) (OR 1.119 (1.084–1.154), compared to 4% lower for pressure (OR 0.958 (0.930–0.989) and 4% higher for wind speed (OR 1.041 (1.010–1.073) (11 mbar and 2 m s−1, respectively)
The analysis has demonstrated significant relationships between relative humidity, pressure, wind speed and pain, with correlations remaining even when accounting for mood and physical activity
The study aimed to investigate the relationship between weather and pain, overcoming limitations of prior weather–pain studies such as small populations, short follow-up, and assumptions about participant location and weather exposure.
The study used a smartphone app to collect daily data from participants, including pain symptoms, mood, physical activity, and weather data from nearby weather stations. The data was analyzed using a case-crossover design, comparing the weather on pain-event days to weather on control days within a risk set of a calendar month.
The study used a case-crossover design, where participants served as their own control, eliminating confounding by time-invariant factors. Participants were asked to collect daily symptoms for six months, and weather data were obtained by linking hourly smartphone GPS data to the nearest UK Met Office weather stations.
The study uses a conditional logistic regression model to estimate the odds ratio for a pain event in response to changes in weather variables. The model includes the preceding day’s pain score, mood, physical activity, and time spent outside as covariates.
The study found significant associations between pain and relative humidity, pressure, and wind speed, with relative humidity having the strongest association with pain. The odds of a pain event were higher than other variables.
The study retained 65% of participants for the first seven days and 44% for the first month, with over 2600 participants contributing to the analysis. The results showed significant relationships between weather variables and pain, with correlations remaining even when accounting for mood and physical activity.
The results of the study are presented as odds ratios for a pain event in response to changes in weather variables.