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

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.

Fig 1

A is the heatmap of probabilities of transitions from one state to another. B is the scatter plot of expected values and the residuals. C shows a histogram of the residuals as well as a blue curve giving the probability density function of a normal distribution having the same mean and variance as the residuals. D is a heatmap of the matrix of residuals based on the model specified by Eq (1).

Fig 2. Heatmaps of the transition probability matrices for the four clusters where G, B, L and H imply good mood, bad mood, low pain and high pain respectively.

Fig 3

A stationary distributions for the four clusters. B age distributions for each cluster. C and D represent log odds ratio of condition and site of pain respectively, per cluster.

Fig 4. Change in stationary distributions by an intervention.

In A, β_M = 0.15 is added to probabilities of transitioning from bad mood to good mood, while in B, β_P = 0.15 has been added to the probabilities of transitions from high pain to low pain.