Predictive Modeling of The Chronic Pain-Induced Depression in Old Adults Based on Music Intervention

Abstract

Chronic pain is a prevalent concern for older individuals, often leading to a decline in mental well-being, especially through conditions like depression. This study explores the potential effectiveness of Music Intervention (MI) as a non-pharmacological approach to alleviate depressive symptoms in those experiencing chronic pain. Existing methodologies lack predictive accuracy, prompting the introduction of the Predicting Chronic Pain-based Music Intervention (PCP-MI) model. Utilizing machine learning, the PCP-MI model customizes music treatments based on individual characteristics and preferences, showcasing promising results across various metrics related to pain, anxiety, heart and respiratory rates, pain tolerance, emotional well-being, quality of life, and depression severity. The PCP-MI method demonstrated a mean performance across multiple metrics, encompassing pain intensity (17.75%), anxiety level (27.79%), heart rate (78.30 bpm), respiratory rate (15.90 bpm), pain tolerance threshold (59.37 seconds), emotional well-being (75.56%), quality of life (74.81%), and depression severity (65.27%). This research suggests a promising avenue for enhancing the psychological well-being of a vulnerable group, representing a significant advancement in comprehensive pain treatment approaches.