Catellani Patrizia, Biella Marco, Carfora Valentina, Nardone Antonio, Brischigiaro Luca, Manera Marina Rita, Piastra Marco
Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy.
University of Pavia - Istituti Clinici Scientifici Maugeri IRCCS - Neurorehabilitation and Spinal Units, Pavia, Italy.
Front Psychol. 2023 Jul 27;14:1200304. doi: 10.3389/fpsyg.2023.1200304. eCollection 2023.
We investigated how physical activity can be effectively promoted with a message-based intervention, by combining the explanatory power of theory-based structural equation modeling with the predictive power of data-driven artificial intelligence.
A sample of 564 participants took part in a two-week message intervention via a mobile app. We measured participants' regulatory focus, attitude, perceived behavioral control, social norm, and intention to engage in physical activity. We then randomly assigned participants to four message conditions (gain, non-loss, non-gain, loss). After the intervention ended, we measured emotions triggered by the messages, involvement, deep processing, and any change in intention to engage in physical activity.
Data analysis confirmed the soundness of our theory-based structural equation model (SEM) and how the emotions triggered by the messages mediated the influence of regulatory focus on involvement, deep processing of the messages, and intention. We then developed a Dynamic Bayesian Network (DBN) that incorporated the SEM model and the message frame intervention as a structural backbone to obtain the best combination of in-sample explanatory power and out-of-sample predictive power. Using a Deep Reinforcement Learning (DRL) approach, we then developed an automated, fast-profiling strategy to quickly select the best message strategy, based on the characteristics of each potential respondent. Finally, the fast-profiling method was integrated into an AI-based chatbot.
Combining the explanatory power of theory-driven structural equation modeling with the predictive power of data-driven artificial intelligence is a promising strategy to effectively promote physical activity with message-based interventions.
我们通过将基于理论的结构方程模型的解释力与数据驱动的人工智能的预测力相结合，研究如何通过基于信息的干预有效地促进体育活动。
564名参与者通过移动应用程序参与了为期两周的信息干预。我们测量了参与者的调节焦点、态度、感知行为控制、社会规范以及参与体育活动的意图。然后，我们将参与者随机分配到四种信息条件（获得、非损失、非获得、损失）。干预结束后，我们测量了信息引发的情绪、参与度、深度处理以及参与体育活动意图的任何变化。
数据分析证实了我们基于理论的结构方程模型（SEM）的合理性，以及信息引发的情绪如何介导调节焦点对参与度、信息深度处理和意图的影响。然后，我们开发了一个动态贝叶斯网络（DBN），将SEM模型和信息框架干预作为结构主干，以获得样本内解释力和样本外预测力的最佳组合。使用深度强化学习（DRL）方法，我们随后开发了一种自动化的快速剖析策略，根据每个潜在受访者的特征快速选择最佳信息策略。最后，快速剖析方法被集成到一个基于人工智能的聊天机器人中。
将理论驱动的结构方程模型的解释力与数据驱动的人工智能的预测力相结合，是通过基于信息的干预有效促进体育活动的一种有前景的策略。
