Depression and anxiety co-occur with chronic pain, and all three are thought to be caused by dysregulation of shared brain systems related to emotional processing associated with body sensations. Understanding the connection between emotional states, pain, and bodily sensations may help understand chronic pain conditions. We developed a mobile platform for measuring pain, emotions, and associated bodily feelings in chronic pain patients in their daily life conditions. Sixty-five chronic back pain patients reported the intensity of their pain, 11 emotional states, and the corresponding body locations. These variables were used to predict pain 2 weeks later. Applying machine learning, we developed two predictive models of future pain, emphasizing interpretability. One model excluded pain-related features as predictors of future pain, and the other included pain-related predictors. The best predictors of future pain were interactive effects of (a) body maps of fatigue with negative affect and (b) positive affect with past pain. Our findings emphasize the contribution of emotions, especially emotional experience felt in the body, to understanding chronic pain above and beyond the mere tracking of pain levels. The results may contribute to the generation of a novel artificial intelligence framework to help in the development of better diagnostic and therapeutic approaches to chronic pain.

抑郁和焦虑与慢性疼痛同时发生，这三者都被认为是由与身体感觉相关的情绪处理相关的共享大脑系统失调引起的。了解情绪状态、疼痛和身体感觉之间的联系可能有助于了解慢性疼痛状况。我们开发了一个移动平台，用于测量慢性疼痛患者日常生活条件下的疼痛、情绪和相关身体感受。 65 名慢性背痛患者报告了他们的疼痛强度、11 种情绪状态以及相应的身体部位。这些变量用于预测两周后的疼痛。应用机器学习，我们开发了两种未来疼痛的预测模型，强调可解释性。一种模型排除了与疼痛相关的特征作为未来疼痛的预测因素，另一种模型则包含了与疼痛相关的预测因素。未来疼痛的最佳预测因素是（a）疲劳身体图与负面影响和（b）正面影响与过去疼痛的交互作用。我们的研究结果强调了情绪，尤其是身体感受到的情绪体验，对于理解慢性疼痛的贡献，而不仅仅是跟踪疼痛水平。研究结果可能有助于生成新型人工智能框架，以帮助开发更好的慢性疼痛诊断和治疗方法。