Abstract
The prevalence of individuals suffering from mental health issues is on the rise, now as common as catching the flu. Human resources in mental health care remain limited across mental health care units. How can we provide 24/7 services and a non-invasive approach in such situations? This thesis investigates the therapeutic benefits of algorithmic music and music video selection applied to multisensory and single-sensory passive music therapy approaches. This thesis also covers with highlights on walking, music video, and music listening approaches.The algorithmic selection methods are based on the energy and mood of music, considering music in four general categories: Happy, Angry, Sad, and Relaxed. The study of active music therapy approach integrates single category music selection into daily walking routines through our research app, "Emotion Equalization App". It reveals how categorized music impacts participants' emotions, even manipulating the use of negative mood music. Additionally, it explores the influence of music videos on emotional well-being, emphasizing the strategies to enhance emotional impact with the optimal number of music videos should be watched and using dynamic sequencing methods for distraction and relaxation. Moreover, the passive listening approaches also promote mental health and mood improvements across severity of depression and anxiety.
By investigating the impact of these approaches on mental health balance, particularly in depression and anxiety conditions, this thesis aims to provide the directions of how digital music therapy interventions can be implemented for supporting mental health care agencies in 24/7 mode and staying connected and being responsive to our users' emotions, daily routines, and current trends.
| Date of Award | 2025 |
|---|---|
| Original language | English |
| Awarding Institution |
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| Supervisor | Andrew Brian HORNER (Supervisor) |
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