Modelling and classifying joint trajectories of self-reported mood and pain in a large cohort study

TL;DR

This study models and classifies individual mood and pain trajectories using mobile health data, revealing four distinct patterns that could inform personalized treatment strategies for chronic pain and mood disorders.

Contribution

It introduces a novel mixture of Markov processes approach to identify distinct mood-pain co-evolution patterns in a large cohort.

Findings

Identified four distinct mood-pain trajectory endotypes.

Demonstrated variability in individual-level mood and pain interactions.

Provided insights for personalized treatment approaches.

Abstract

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.