  It is well-known that mood and pain interact with each other, however
individual-level variability in this relationship has been less well quantified
than overall associations between low mood and pain. Here, we leverage the
possibilities presented by mobile health data, in particular the "Cloudy with a
Chance of Pain" study, which collected longitudinal data from the residents of
the UK with chronic pain conditions. Participants used an App to record
self-reported measures of factors including mood, pain and sleep quality. The
richness of these data allows us to perform model-based clustering of the data
as a mixture of Markov processes. Through this analysis we discover four
endotypes with distinct patterns of co-evolution of mood and pain over time.
The differences between endotypes are sufficiently large to play a role in
clinical hypothesis generation for personalised treatments of comorbid pain and
low mood.
