RESUMEN
Objective: 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. Methods: 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. Results: 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. Conclusion: 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.
RESUMEN
BACKGROUND: Fibromyalgia patients can benefit from music approaches as complementary treatments. In the literature, it was shown that these interventions managed pain conditions as well as reduced complaints, increased relaxation, and improved moods. OBJECTIVE: This study aimed at evaluating music therapy, in the form of therapeutic music listening, specifically for patients with fibromyalgia, to treat chronic pain by reducing pain perception, increasing well-being, and improving quality of life. METHODS: Twenty-four patients with fibromyalgia were recruited to take part in this feasibility pilot study that adopted a between-subject and within-subject design. Participants were randomised into three groups: (1) standard care, (2) standard care plus preferred music listening, (3) standard care plus Melomics-Health music listening, composed by an algorithm. Participants in experimental groups listened to 30 min of music at home, twice a day for a month. Patients' perceptions of changes following the listening, the intensity of pain and its interference in their lives, physical and mental well-being, and reported attitudes towards listening to music were evaluated respectively through the patients' global impression of change, the brief pain inventory, the Short Form Healthy Survey-12, and the cognitive behavioural assessment-outcome evaluation. RESULTS: The study showed that music listening can significantly affect mental well-being compared to no music. Moreover, the effects in the Melomics-Health group are maintained at follow-up. No significant effect on pain perception was noted. CONCLUSIONS: The study provides information supporting a possible role of music listening in improving well-being of patients with fibromyalgia.