How the weather affects the pain of citizen scientists using a smartphone app

Abstract

Patients with chronic pain commonly believe their pain is related to the weather. Scientific evidence to support their beliefs is inconclusive, in part due to difficulties in getting a large dataset of patients frequently recording their pain symptoms during a variety of weather conditions. Smartphones allow the opportunity to collect data to overcome these difficulties. Our study Cloudy with a Chance of Pain analysed daily data from 2658 patients collected over a 15-month period. The analysis demonstrated significant yet modest relationships between pain and relative humidity, pressure and wind speed, with correlations remaining even when accounting for mood and physical activity. This research highlights how citizen-science experiments can collect large datasets on real-world populations to address long-standing health questions. These results will act as a starting point for a future system for patients to better manage their health through pain forecasts.

Weather Patterns Associated with Pain in Chronic-Pain Sufferers

“…Even after 200 days, 15% of participants were still entering data nearly every day (Druce et al 2017;Fig. 2b in Dixon et al 2019). This rate of engagement is exceptionally high compared to other mobile health studies where attrition often declines exponentially (e.g., Eysenbach 2005).…”

“…All 124 U.K. postcode areas were represented. Details about the demographics of the population can be found in the supplemental information in Dixon et al (2019).…”

“…As many as 17% of the participants entered only the demographic information and one day's report (Druce et al 2017;Dixon et al 2019). With no subsequent pain report on the next day, these very low engagers could never experience a pain event in our dataset.…”

“…The strength of this method is that it makes only within-participant comparisons, controlling for all timeinvariant participant characteristics (e.g., their subjective experience of pain, gender, medical condition). However, the downside of Dixon et al (2019) is that it required independent sequences of days with and without a pain event within the same month to satisfy the conditions for the case-crossover design which resulted in a substantial thinning of the dataset (from 10,584 to 2,658 eligible participants; supplemental Fig. 1 in Dixon et al 2019), but did not introduce bias.…”

“…However, the downside of Dixon et al (2019) is that it required independent sequences of days with and without a pain event within the same month to satisfy the conditions for the case-crossover design which resulted in a substantial thinning of the dataset (from 10,584 to 2,658 eligible participants; supplemental Fig. 1 in Dixon et al 2019), but did not introduce bias. In addition, the case-crossover method quantified the effect of changes in weather conditions within a calendar month, assuming that the relationships between the weather variables and pain are linear and this relationship did not change between calendar months.…”