DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection

The increasing use of electronic forms of communication presents new opportunities in the study of mental health, including the ability to investigate the manifestations of psychiatric diseases unobtrusively and in the setting of patients' daily lives. A pilot study to explore the possible connections between bipolar affective disorder and mobile phone usage was conducted. In this study, participants were provided a mobile phone to use as their primary phone. This phone was loaded with a custom keyboard that collected metadata consisting of keypress entry time and accelerometer movement. Individual character data with the exceptions of the backspace key and space bar were not collected due to privacy concerns. We propose an end-to-end deep architecture based on late fusion, named DeepMood, to model the multi-view metadata for the prediction of mood scores. Experimental results show that 90.31% prediction accuracy on the depression score can be achieved based on session-level mobile phone typing dynamics which is typically less than one minute. It demonstrates the feasibility of using mobile phone metadata to infer mood disturbance and severity.Comment: KDD 201

Design and formative evaluation of a virtual voice-based coach for problem-solving treatment: Observational study

BACKGROUND: Artificial intelligence has provided new opportunities for human interactions with technology for the practice of medicine. Among the recent artificial intelligence innovations, personal voice assistants have been broadly adopted. This highlights their potential for health care-related applications such as behavioral counseling to promote healthy lifestyle habits and emotional well-being. However, the use of voice-based applications for behavioral therapy has not been previously evaluated. OBJECTIVE: This study aimed to conduct a formative user evaluation of Lumen, a virtual voice-based coach developed as an Alexa skill that delivers evidence-based, problem-solving treatment for patients with mild to moderate depression and/or anxiety. METHODS: A total of 26 participants completed 2 therapy sessions-an introductory (session 1) and a problem-solving (session 2)-with Lumen. Following each session with Lumen, participants completed user experience, task-related workload, and work alliance surveys. They also participated in semistructured interviews addressing the benefits, challenges and barriers to Lumen use, and design recommendations. We evaluated the differences in user experience, task load, and work alliance between sessions using 2-tailed paired t tests. Interview transcripts were coded using an inductive thematic analysis to characterize the participants\u27 perspectives regarding Lumen use. RESULTS: Participants found Lumen to provide high pragmatic usability and favorable user experience, with marginal task load during interactions for both Lumen sessions. However, participants experienced a higher temporal workload during the problem-solving session, suggesting a feeling of being rushed during their communicative interactions. On the basis of the qualitative analysis, the following themes were identified: Lumen\u27s on-demand accessibility and the delivery of a complex problem-solving treatment task with a simplistic structure for achieving therapy goals; themes related to Lumen improvements included streamlining and improved personalization of conversations, slower pacing of conversations, and providing additional context during therapy sessions. CONCLUSIONS: On the basis of an in-depth formative evaluation, we found that Lumen supported the ability to conduct cognitively plausible interactions for the delivery of behavioral therapy. Several design suggestions identified from the study including reducing temporal and cognitive load during conversational interactions, developing more natural conversations, and expanding privacy and security features were incorporated in the revised version of Lumen. Although further research is needed, the promising findings from this study highlight the potential for using Lumen to deliver personalized and accessible mental health care, filling a gap in traditional mental health services

A Future Research Agenda for Digital Geriatric Mental Healthcare

The proliferation of mobile, online, and remote monitoring technologies in digital geriatric mental health has the potential to lead to the next major breakthrough in mental health treatments. Unlike traditional mental health services, digital geriatric mental health has the benefit of serving a large number of older adults, and in many instances, does not rely on mental health clinics to offer real-time interventions. As technology increasingly becomes essential in the everyday lives of older adults with mental health conditions, these technologies will provide a fundamental service delivery strategy to support older adults’ mental health recovery. Although ample research on digital geriatric mental health is available, fundamental gaps in the scientific literature still exist. To begin to address these gaps, we propose the following recommendations for a future research agenda: 1) additional proof-of-concept studies are needed; 2) integrating engineering principles in methodologically rigorous research may help science keep pace with technology; 3) studies are needed that identify implementation issues; 4) inclusivity of people with a lived experience of a mental health condition can offer valuable perspectives and new insights; and 5) formation of a workgroup specific for digital geriatric mental health to set standards and principles for research and practice. We propose prioritizing the advancement of digital geriatric mental health research in several areas that are of great public health significance, including 1) simultaneous and integrated treatment of physical health and mental health conditions; 2) effectiveness studies that explore diagnostics and treatment of social determinants of health such as “social isolation” and “loneliness;” and 3) tailoring the development and testing of innovative strategies to minority older adult populations

Neural correlates of rumination in adolescents with remitted major depressive disorder and healthy controls

This is the author accepted manuscript. The final version is available from Springer Verlag via the DOI in this record.The aim of the current study was to use fMRI to examine the neural correlates of engaging in rumination among a sample of remitted depressed adolescents, a population at high risk for future depressive relapse. A rumination induction task was used to assess differences in patterns of neural activation during rumination as compared with a distraction condition among 26 adolescents in remission from major depressive disorder (rMDD) and 15 healthy control adolescents. Self-report depression and rumination as well as clinician-rated depression were also assessed among all participants. All participants recruited regions in the default mode network (DMN), including the posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), inferior parietal lobe (IPL), and medial temporal gyrus (MTG) during rumination. Increased activation in these regions during rumination was correlated with increased self-report rumination and symptoms of depression across all participants. Adolescents with rMDD also exhibited greater activation in regions involved in visual, somatosensory, and emotion processing when compared to healthy peers. The current findings suggest that during ruminative thought, adolescents with rMDD are characterized by increased recruitment of regions within the DMN and in areas involved in visual, somatosensory, and emotion processing.The current study was funded by UL1TR00050 (PI:Azar for UIC CCTS) Professional Development award, the Klingenstein Third Generation Foundation, The UIC Campus Research Board, and a Varela award from the Mind and Life Institute (awarded to RHJ). ATP and KLB were supported by National Institute of Mental Health Grant T32- MH067631 (Training in the Neuroscience of Mental Health; PI: Mark Rasenick), and SAL was supported by MH091811 and MH101487. The authors have no conflict of interest to disclose