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.

翻译：众所周知，情绪与疼痛之间存在相互作用，但相较于低情绪与疼痛的整体关联性，两者关系在个体层面的变异程度尚未得到充分量化。本研究利用移动健康数据的潜力，特别是"慢性疼痛天气风险"研究项目——该研究收集了英国慢性疼痛患者居民的纵向数据。参与者通过手机应用记录自我报告指标，包括情绪、疼痛和睡眠质量等变量。这些数据的丰富性使我们能够将数据作为马尔可夫过程的混合模型进行基于模型的聚类分析。通过该分析，我们发现了四种内型，它们具有不同的情绪与疼痛随时间共演化的模式。各内型之间的差异足够显著，可为共病性疼痛与低情绪的个性化治疗临床假设生成提供依据。