No Arabic abstract
Objective: This study aims to identify the social determinants of mental health among undergraduate students in Bangladesh, a developing nation in South Asia. Our goal is to identify the broader social determinants of mental health among this population, study the manifestation of these determinants in their day-to-day life, and explore the feasibility of self-monitoring tools in helping them identify the specific factors or relationships that impact their mental health. Methods: We conducted a 21-day study with 38 undergraduate students from seven universities in Bangladesh. We conducted two semi-structured interviews: one pre-study and one post-study. During the 21-day study, participants used an Android application to self-report and self-monitor their mood after each phone conversation. The app prompted participants to report their mood after each phone conversation and provided graphs and charts so that participants could independently review their mood and conversation patterns. Results: Our results show that academics, family, job and economic condition, romantic relationships, and religion are the major social determinants of mental health among undergraduate students in Bangladesh. Our app helped the participants pinpoint the specific issues related to these factors as participants could review the pattern of their moods and emotions from past conversation history. Although our app does not provide any explicit recommendation, participants took certain steps on their own to improve their mental health (e.g., reduced the frequency of communication with certain persons). Conclusions: Overall, the findings from this study would provide better insights for the researchers to design better solutions to help the younger population from this part of the world.
The recent growth of digital interventions for mental well-being prompts a call-to-arms to explore the delivery of personalised recommendations from a users perspective. In a randomised placebo study with a two-way factorial design, we analysed the difference between an autonomous user experience as opposed to personalised guidance, with respect to both users preference and their actual usage of a mental well-being app. Furthermore, we explored users preference in sharing their data for receiving personalised recommendations, by juxtaposing questionnaires and mobile sensor data. Interestingly, self-reported results indicate the preference for personalised guidance, whereas behavioural data suggests that a blend of autonomous choice and recommended activities results in higher engagement. Additionally, although users reported a strong preference of filling out questionnaires instead of sharing their mobile data, the data source did not have any impact on the actual app use. We discuss the implications of our findings and provide takeaways for designers of mental well-being applications.
In cognitive psychology, automatic and self-reinforcing irrational thought patterns are known as cognitive distortions. Left unchecked, patients exhibiting these types of thoughts can become stuck in negative feedback loops of unhealthy thinking, leading to inaccurate perceptions of reality commonly associated with anxiety and depression. In this paper, we present a machine learning framework for the automatic detection and classification of 15 common cognitive distortions in two novel mental health free text datasets collected from both crowdsourcing and a real-world online therapy program. When differentiating between distorted and non-distorted passages, our model achieved a weighted F1 score of 0.88. For classifying distorted passages into one of 15 distortion categories, our model yielded weighted F1 scores of 0.68 in the larger crowdsourced dataset and 0.45 in the smaller online counseling dataset, both of which outperformed random baseline metrics by a large margin. For both tasks, we also identified the most discriminative words and phrases between classes to highlight common thematic elements for improving targeted and therapist-guided mental health treatment. Furthermore, we performed an exploratory analysis using unsupervised content-based clustering and topic modeling algorithms as first efforts towards a data-driven perspective on the thematic relationship between similar cognitive distortions traditionally deemed unique. Finally, we highlight the difficulties in applying mental health-based machine learning in a real-world setting and comment on the implications and benefits of our framework for improving automated delivery of therapeutic treatment in conjunction with traditional cognitive-behavioral therapy.
Despite extensive use in related domains, Virtual Reality (VR) for generalised anxiety disorder (GAD) has received little previous attention. We report upon a VR environment created for the Oculus Rift and Unreal Engine 4 (UE4) to investigate the potential of a VR simulation to be used as an anxiety management tool. We introduce the broad topic of GAD and related publications on the application of VR to this, and similar, mental health conditions. We then describe the development of a real time simulation tool, based upon the passive VR experience of a tranquil, rural alpine scene experienced from a seated position with head tracking. Evaluation focused upon qualitative feedback on the application. Testing was carried out over the period of two weeks on a sample group of eleven students studying at Nottingham Trent University. All participants were asked to complete the Depression, Anxiety and Stress Scale - 21 Items (DASS21) at the beginning and at the end of the study order to assess their profile, and hence suitability to comment upon the software. Qualitative feedback was very encouraging, with all participants reporting that they believed the experience helped and that they would consider utilising it if it was available. Additionally, a psychologist was asked to test the application to provide a specialist opinion on whether it would be appropriate for use as an anxiety management tool. The results highlight several areas for improvement but are positive overall in terms of its potential as a therapeutic tool.
We describe a set of experiments for building a temporal mental health dynamics system. We utilise a pre-existing methodology for distant-supervision of mental health data mining from social media platforms and deploy the system during the global COVID-19 pandemic as a case study. Despite the challenging nature of the task, we produce encouraging results, both explicit to the global pandemic and implicit to a global phenomenon, Christmas Depression, supported by the literature. We propose a methodology for providing insight into temporal mental health dynamics to be utilised for strategic decision-making.
Data-driven methods for mental health treatment and surveillance have become a major focus in computational science research in the last decade. However, progress in the domain, in terms of both medical understanding and system performance, remains bounded by the availability of adequate data. Prior systematic reviews have not necessarily made it possible to measure the degree to which data-related challenges have affected research progress. In this paper, we offer an analysis specifically on the state of social media data that exists for conducting mental health research. We do so by introducing an open-source directory of mental health datasets, annotated using a standardized schema to facilitate meta-analysis.