Achuthan Krishnashree, Khobragade Sugandh
Center for Cybersecurity Systems and Networks, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, Kerala, India.
PLoS One. 2025 Aug 1;20(8):e0324369. doi: 10.1371/journal.pone.0324369. eCollection 2025.
Attention-Deficit Hyperactivity Disorder (ADHD), a neurodevelopmental disorder affecting children and adults worldwide, has seen a significant rise in diagnoses and medication prescriptions in recent decades. This trend has emphasized the need for non-pharmacological interventions such as music to aid ADHD management. This study explores the musical experiences of individuals with ADHD through a comprehensive analysis of user-generated content from the Reddit r/ADHD community between 2014-2024. Advanced computational techniques, including large language models such as Gemini 1.5 Pro and LLAMA 3.1 were employed for data extraction and categorization. Additionally, APIs from digital streaming platforms were utilized to analyze musical characteristics and lyrical content of 9,215 tracks across three distinct categories: focus music, stuck songs, and general purpose. Insights from selective attention, emotion arousal and mood congruence theories were used to interpret the findings. Statistical analysis revealed significant variations in musical characteristics, with instrumentalness showing the largest effect size across contexts, suggesting unique musical preferences among individuals with ADHD. Correlation analyses uncovered complex interrelationships between musical attributes, particularly in focus music, where energy, speechiness, and instrumental characteristics displayed distinctive patterns. The sentiment and popularity analysis of lyrics further illuminated the emotional landscape of music in ADHD experiences, revealing a strategic approach to musical selection as a potential cognitive and emotional self-regulation mechanism.
注意力缺陷多动障碍(ADHD)是一种影响全球儿童和成人的神经发育障碍,近几十年来,其诊断和药物处方数量显著增加。这一趋势凸显了非药物干预措施(如音乐)对ADHD管理的必要性。本研究通过对2014年至2024年Reddit上的r/ADHD社区用户生成内容进行全面分析,探索了ADHD患者的音乐体验。采用了先进的计算技术,包括Gemini 1.5 Pro和LLAMA 3.1等大语言模型进行数据提取和分类。此外,利用数字流媒体平台的应用程序编程接口(API)分析了9215首歌曲的音乐特征和歌词内容,这些歌曲分为三个不同类别:专注音乐、循环播放歌曲和通用音乐。研究运用选择性注意、情绪唤起和情绪一致性理论的见解来解释研究结果。统计分析显示,音乐特征存在显著差异,其中器乐性在不同情境下的效应量最大,这表明ADHD患者有独特的音乐偏好。相关性分析揭示了音乐属性之间复杂的相互关系,尤其是在专注音乐中,能量、语音性和器乐特征呈现出独特的模式。歌词的情感和流行度分析进一步揭示了ADHD体验中音乐的情感格局,揭示了一种作为潜在认知和情绪自我调节机制的音乐选择策略。