Mazin Abdalla;Parya Abadeh;Zeinab Noorian;Amira Ghenai;Fattane Zarrinkalam;Soroush Zamani Alavijeh
{"title":"音乐对焦虑、抑郁和创伤后应激障碍患者压力水平的影响:混合效应模型和倾向评分分析","authors":"Mazin Abdalla;Parya Abadeh;Zeinab Noorian;Amira Ghenai;Fattane Zarrinkalam;Soroush Zamani Alavijeh","doi":"10.1109/TCSS.2025.3561073","DOIUrl":null,"url":null,"abstract":"The intersection of music and mental health has gained increasing attention, with previous studies highlighting music’s potential to reduce stress and anxiety. Despite these promising findings, many of these studies are limited by small sample sizes and traditional observational methods, leaving a gap in our understanding of music’s broader impact on mental health. In response to these limitations, this study introduces a novel approach that combines generalized linear mixed models (GLMM) with propensity score matching (PSM) to explore the relationship between music listening and stress levels among social media users diagnosed with anxiety, depression, and posttraumatic stress disorder (PTSD). Our research not only identifies associative patterns between music listening and stress but also provides a more rigorous examination of potential causal effects, taking into account demographic factors such as education level, gender, and age. Our findings reveal that across all mental health conditions, music listening is significantly associated with reduced stress levels, with an observed 21.3% reduction for anxiety, 15.4% for depression, and 19.3% for PTSD. Additionally, users who listened to music were more likely to report a zero stress score, indicating a stronger relaxation effect. Further, our analysis of demographic variations shows that age and education level influence the impact of music on stress reduction, highlighting the potential for personalized interventions. These findings contribute to a deeper understanding of music’s therapeutic potential, particularly in crafting interventions tailored to the diverse needs of different populations.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 5","pages":"3816-3830"},"PeriodicalIF":4.5000,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Impact of Listening to Music on Stress Level for Anxiety, Depression, and PTSD: Mixed-Effect Models and Propensity Score Analysis\",\"authors\":\"Mazin Abdalla;Parya Abadeh;Zeinab Noorian;Amira Ghenai;Fattane Zarrinkalam;Soroush Zamani Alavijeh\",\"doi\":\"10.1109/TCSS.2025.3561073\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The intersection of music and mental health has gained increasing attention, with previous studies highlighting music’s potential to reduce stress and anxiety. Despite these promising findings, many of these studies are limited by small sample sizes and traditional observational methods, leaving a gap in our understanding of music’s broader impact on mental health. In response to these limitations, this study introduces a novel approach that combines generalized linear mixed models (GLMM) with propensity score matching (PSM) to explore the relationship between music listening and stress levels among social media users diagnosed with anxiety, depression, and posttraumatic stress disorder (PTSD). Our research not only identifies associative patterns between music listening and stress but also provides a more rigorous examination of potential causal effects, taking into account demographic factors such as education level, gender, and age. Our findings reveal that across all mental health conditions, music listening is significantly associated with reduced stress levels, with an observed 21.3% reduction for anxiety, 15.4% for depression, and 19.3% for PTSD. Additionally, users who listened to music were more likely to report a zero stress score, indicating a stronger relaxation effect. Further, our analysis of demographic variations shows that age and education level influence the impact of music on stress reduction, highlighting the potential for personalized interventions. These findings contribute to a deeper understanding of music’s therapeutic potential, particularly in crafting interventions tailored to the diverse needs of different populations.\",\"PeriodicalId\":13044,\"journal\":{\"name\":\"IEEE Transactions on Computational Social Systems\",\"volume\":\"12 5\",\"pages\":\"3816-3830\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Computational Social Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11037659/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, CYBERNETICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Social Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/11037659/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
The Impact of Listening to Music on Stress Level for Anxiety, Depression, and PTSD: Mixed-Effect Models and Propensity Score Analysis
The intersection of music and mental health has gained increasing attention, with previous studies highlighting music’s potential to reduce stress and anxiety. Despite these promising findings, many of these studies are limited by small sample sizes and traditional observational methods, leaving a gap in our understanding of music’s broader impact on mental health. In response to these limitations, this study introduces a novel approach that combines generalized linear mixed models (GLMM) with propensity score matching (PSM) to explore the relationship between music listening and stress levels among social media users diagnosed with anxiety, depression, and posttraumatic stress disorder (PTSD). Our research not only identifies associative patterns between music listening and stress but also provides a more rigorous examination of potential causal effects, taking into account demographic factors such as education level, gender, and age. Our findings reveal that across all mental health conditions, music listening is significantly associated with reduced stress levels, with an observed 21.3% reduction for anxiety, 15.4% for depression, and 19.3% for PTSD. Additionally, users who listened to music were more likely to report a zero stress score, indicating a stronger relaxation effect. Further, our analysis of demographic variations shows that age and education level influence the impact of music on stress reduction, highlighting the potential for personalized interventions. These findings contribute to a deeper understanding of music’s therapeutic potential, particularly in crafting interventions tailored to the diverse needs of different populations.
期刊介绍:
IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.