Music is widely used in human–computer interaction (HCI) to enhance engagement, sustain attention, and support cognitive stimulation. Yet its potential for deliberate mood regulation, particularly through personalized memory recall, remains largely unexplored. Music-evoked autobiographical memories (MEAMs) are often elicited by well-known, favorite songs, yielding stronger mood effects than music without personal memory associations. However, songs can also trigger distressing memories, and will never capture all positive personal memories. Since happy personal memories can enhance mood, broader methods for retrieval are needed. To address this, we introduce Constructed Music-Evoked Episodic Memories (CoMEEMs), a framework linking chosen episodic memories to music. By creating a personalized song-memory database, CoMEEMs enable autonomous mood regulation and communication in interactive systems, integrating memory cues—such as people and places—alongside mood congruence, to help choose songs with high mood regulatory impact. In an experiment with 71 Dutch and French adults, participants described 87 positive memories and received song recommendations based on associated people and places, with and without mood matching. Results showed that song familiarity and genre were the strongest predictors of perceived fit, while valence, arousal, tempo, and lyrics played smaller roles. Mood congruence, especially in valence, significantly influenced song relevance. Participants emphasized the need for user input on emotional states and memory context. Based on these findings, we propose design guidelines to improve future music recommendation systems targeting memories.

Music is increasingly used in a wide array of settings, from clinical recovery to sports or well-being interventions. Motivation related to music is often considered as a possible working mechanism for music to facilitate these processes, however this has not previously been systematically evaluated. The current systematic review considered studies that involved music (therapy) interventions, together with motivation-related measures such as wanting to practise, liking the musical activities, or patient adherence to an intervention. Our objective was to examine whether music is related to increased motivation in task performance and/or rehabilitation settings, and whether this is in turn related to better clinical or training outcomes. Seventy-nine studies met the inclusion criteria, the majority of which (85%) indicated an increased level of motivation with music as compared to without. Moreover, in those studies where motivation was increased, clinical or other outcomes were improved in most cases (90%). These results support the notion of motivation as an underlying mechanism of music-based interventions, but more robust evidence is needed to ascertain which mechanisms are crucial in increasing motivation from a behavioural, cognitive, and neurobiological point of view, as well as how motivational mechanisms relate to other factors of effectiveness in music-based paradigms.

The fields of music, health, and technology have seen significant interactions in recent years in developing music technology for health care and well-being. In an effort to strengthen the collaboration between the involved disciplines, the workshop “Music, Computing, and Health” was held to discuss best practices and state-of-the-art at the intersection of these areas with researchers from music psychology and neuroscience, music therapy, music information retrieval, music technology, medical technology (medtech), and robotics. Following the discussions at the workshop, this article provides an overview of the different methods of the involved disciplines and their potential contributions to developing music technology for health and well-being. Furthermore, the article summarizes the state of the art in music technology that can be applied in various health scenarios and provides a perspective on challenges and opportunities for developing music technology that (1) supports person-centered care and evidence-based treatments, and (2) contributes to developing standardized, large-scale research on music-based interventions in an interdisciplinary manner. The article provides a resource for those seeking to engage in interdisciplinary research using music-based computational methods to develop technology for health care, and aims to inspire future research directions by evaluating the state of the art with respect to the challenges facing each field.