Search

Background: Cognitive symptoms are common during and following episodes of depression. Little is known about the persistence of self-reported and performance-based cognition with depression and functional outcomes. Methods: This is a secondary analysis of a prospective naturalistic observational clinical cohort study of individuals with recurrent Major Depressive Disorder (MDD; N=623). Participants completed app-based self-reported and performance-based cognitive function assessments alongside validated measures of depression, functional disability, and self-esteem every three months. Participants were followed-up for a maximum of 2-years. Multilevel hierarchically nested modelling was employed to explore between- and within-participant variation over time to identify whether persistent cognitive difficulties are related to levels of depression and functional impairment during follow-up.Results: 508 individuals (81.5%) provided data (mean age: 46.6, SD: 15.6; 76.2% female). Increasing persistence of self-reported cognitive difficulty was associated with higher levels of depression and functional impairment throughout the follow-up. In comparison to low persistence of objective cognitive difficulty (75% of timepoints) reported significantly higher levels of depression (B=5.17, SE=2.21, p=0.019) and functional impairment (B=4.82, SE=1.79, p=0.002) over time. Examination of the individual cognitive modules shows that persistently impaired executive function is associated with worse functioning, and poor processing speed is particularly important for worsened depressive symptoms.Conclusions: We replicated previous findings of greater persistence of cognitive difficulty with increasing severity of depression and further demonstrate that these cognitive difficulties are associated with pervasive functional disability. Difficulties with cognition may be an indicator and target for further treatment input.

BACKGROUND Tracking and visualizing health data through mobile health applications (apps) can be an effective self-management tool for mental health conditions. However, little evidence is available to guide the design of health tracking and data visualization mechanisms. OBJECTIVE Analyze the content of user reviews of depression self-management apps to guide the design of data tracking and visualization mechanisms in future apps. METHODS We systematically reviewed depression self-management apps on the 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, and reviews which referenced health tracking and data visualization were included in sentiment analysis as well as qualitative framework analysis. RESULTS Searches identified 130 unique apps, 26 of which were eligible for inclusion. We included 783 reviews in framework analysis. Five themes emerged: “impact of app-based health tracking,” “tracking mental health: data recording and data visualization,” “a larger health ecosystem,” “cost, finance, and paywalls,” and “technical issues.” Reviews that discussed previous experiences with health tracking apps, the responsiveness of the developer, and personalization had higher ratings and more positive sentiment scores, while those which discussed data accuracy, interoperability, limited ability to display multiple data streams, and technical issues had lower ratings and scores. CONCLUSIONS App-based health data tracking supports depression self-management when features align with users’ individual needs and goals. Heterogeneous preferences pose a challenge for app developers and further research should prioritize features based on importance and impact to users.

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.