Emotional distress is rising globally. Widely-used music streaming apps, if suitably configured, may offer a scalable intervention to help millions of people manage their emotions more effectively. Services like Spotify already offer mood-based playlists. However, such features are not tailored to individual users’ emotional needs, reducing their effectiveness. We introduce MoodDJ, a Spotify plugin that creates playlists that take users on personalised emotion journeys from current to desired emotion. In a 14-day in-the-wild user study, we explored 22 participants' experiences of using MoodDJ through usage logs, surveys, and interviews. Findings reveal diverse patterns of use in a range of contexts, with participants often using MoodDJ to up- or down-regulate arousal or to feel more pleasant. Qualitative data suggest improvements in emotional awareness. We contribute evidence for the feasibility of embedding emotion-regulation interventions within popular music apps, and present design implications for tools of this kind.

Two decades of focus on User Experience has yielded an array of digital technologies that help people experience, understand and share emotions. Although the effects of specific technologies upon emotion have been well studied, less is known about how people actively appropriate and combine the full range of devices, apps and services at their disposal to deliberately manage emotions in everyday life. We conducted a one-week diary study in which 23 adults recorded interactions between their emotions and technology use. They reported using a diverse range of emotion-shaping tools and strategies as part of coping with daily challenges, managing routines, and pursuing work and social goals. We analyse these data in the light of psychological theories of emotion. Our findings point to the significance of digital emotion regulation as a powerful perspective to inform wider debates about the impacts of technology on social and emotional well-being.

Mobile self-reports are a popular technique to collect participant labelled data in the wild. While literature has focused on increasing participant compliance to self-report questionnaires, relatively little work has assessed response accuracy. In this paper, we investigate how participant context can affect response accuracy and help identify strategies to improve the accuracy of mobile self-report data. In a 3-week study we collect over 2,500 questionnaires containing both verifiable and non-verifiable questions. We find that response accuracy is higher for questionnaires that arrive when the phone is not in ongoing or very recent use. Furthermore, our results show that long completion times are an indicator of a lower accuracy. Using contextual mechanisms readily available on smartphones, we are able to explain up to 13% of the variance in participant accuracy. We offer actionable recommendations to assist researchers in their future deployments of mobile self-report studies.