This study explores the intersection of information technology-based self-monitoring (ITSM) and emotional responses in chronic care. It critiques the lack of theoretical depth in current ITSM research and proposes a dynamic emotion process theory to understand ITSM's impact on users' emotions. Utilizing computational grounded theory and machine learning analysis of hypertension app reviews, the research seeks to extend emotion theory by examining ITSM stimuli and their influence on emotional episodes, moving beyond discrete emotion models towards a continuous, nuanced understanding of emotional responses.

翻译：本研究探讨了基于信息技术的自我监测（ITSM）与慢性护理中的情绪反应之间的交叉领域。它批判了当前ITSM研究缺乏理论深度的现状，并提出了动态情绪过程理论来理解ITSM对用户情绪的影响。通过运用计算扎根理论和对高血压应用程序评论的机器学习分析，本研究旨在通过考察ITSM刺激因素及其对情绪片段的影响来扩展情绪理论，超越离散情绪模型，趋向对情绪反应的连续、细致入微的理解。