论文标题
在一项大型队列研究中，建模和分类自我报告的情绪和疼痛的联合轨迹
Modelling and classifying joint trajectories of self-reported mood and pain in a large cohort study
论文作者
论文摘要
众所周知，情绪和疼痛彼此相互作用，但是这种关系中的个体级别的可变性比低情绪和疼痛之间的整体关联量不佳。在这里，我们利用移动健康数据提出的可能性，尤其是“有疼痛的可能性”研究，该研究以慢性疼痛状况收集了英国居民的纵向数据。参与者使用一个应用程序记录了自我报告的因素度量，包括情绪，疼痛和睡眠质量。这些数据的丰富性使我们能够将基于模型的数据聚类作为马尔可夫过程的混合物。通过这项分析，我们发现了四种具有不同情绪和痛苦随着时间的共同进化的内部型号。内型之间的差异足够大，可以在临床假设产生中发挥作用，以便对合并症疼痛和情绪低落进行个性化治疗。
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.
