Key result
Clustering of daily self-reported mood and pain data identified four distinct endotypes of co-evolution over time, highlighting individual-level variability in the mood-pain relationship.
Why the study?
Although mood and pain are known to interact, individual-level variability in their relationship has been less well quantified than overall associations between low mood and pain.
Cohort (n=9,990)
Model-based clustering of longitudinal self-reported mood and pain data identified four distinct endotypes, highlighting individual-level variability that could inform personalized treatments.
Supports individualized mood-pain monitoring in care; leaves open outcome prediction and interventional targeting in cardiovascular cohorts.
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.
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Das et al. (2023) conducted a cohort in Chronic pain (n=9,990). Daily self-reported mood and pain was evaluated on Clusters of mood-pain dynamics. Clustering of daily self-reported mood and pain data identified four distinct endotypes of co-evolution over time, highlighting individual-level variability in the mood-pain relationship.
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