The physiological signature of sadness: A comparison between text, film and virtual reality.

Studies focused on the ubiquitous emotion of sadness demonstrate substantial variability in physiological responses during sadness elicitation, with no consensus regarding the physiological pattern of sadness. Variability in findings could be attributed to (a) the use of different induction techniques across studies or (b) the existence of subtypes of sadness with distinct physiological activation patterns. Typically, studies have used text and film to elicit sadness. However, virtual reality (VR) confers advantages over more traditional methods by allowing individuals a subjective sense of "being there" or presence. We compared participants' physiological responses to the same narrative presented via VR, Film and Story (n = 20 each) and collected their subjective responses to the stimuli. Results confirmed that participants in all conditions experienced the discrete emotion of sadness. Moreover, participants in the VR condition experienced the highest degree of presence. Regarding psychophysiological responses, participants in the VR condition had the lowest degree of baseline-adjusted parasympathetic activation in comparison to participants in the Film condition. Furthermore, while participants in the VR group showed diminished baseline-adjusted respiration rate and parasympathetic activation with an increase in presence, the opposite pattern was true for participants in the other conditions. The data suggest that the VR condition may elicit an activating pattern of sadness; whereas Film and Story conditions may elicit a deactivating pattern of sadness. Our results have implications for research using the discrete model of emotion, highlighting that different emotion elicitation techniques may result in differing expressions of what is considered a unitary emotion.

Health Tracking via Mobile Apps for Depression Self-management: Qualitative Content Analysis of User Reviews.

BACKGROUND: Tracking and visualizing health data using mobile apps can be an effective self-management strategy for mental health conditions. However, little evidence is available to guide the design of mental health-tracking mechanisms. OBJECTIVE: The aim of this study was to analyze the content of user reviews of depression self-management apps to guide the design of data tracking and visualization mechanisms for future apps. METHODS: We systematically reviewed depression self-management apps on Google Play and iOS App stores. English-language reviews of eligible apps published between January 1, 2018, and December 31, 2021, were extracted from the app stores. Reviews that referenced health tracking and data visualization were included in sentiment and qualitative framework analyses. RESULTS: The search identified 130 unique apps, 26 (20%) of which were eligible for inclusion. We included 783 reviews in the framework analysis, revealing 3 themes. Impact of app-based mental health tracking described how apps increased reviewers' self-awareness and ultimately enabled condition self-management. The theme designing impactful mental health-tracking apps described reviewers' feedback and requests for app features during data reporting, review, and visualization. It also described the desire for customization and contexts that moderated reviewer preference. Finally, implementing impactful mental health-tracking apps described considerations for integrating apps into a larger health ecosystem, as well as the influence of paywalls and technical issues on mental health tracking. CONCLUSIONS: App-based mental health tracking supports depression self-management when features align with users' individual needs and goals. Heterogeneous needs and preferences raise the need for flexibility in app design, posing challenges for app developers. Further research should prioritize the features based on their importance and impact on users.