Objective methods for reliable detection of concealed depression

Recent research has shown that it is possible to automatically detect clinical depression from audio-visual recordings. Before considering integration in a clinical pathway, a key question that must be asked is whether such systems can be easily fooled. This work explores the potential of acoustic features to detect clinical depression in adults both when acting normally and when asked to conceal their depression. Nine adults diagnosed with mild to moderate depression as per the Beck Depression Inventory (BDI-II) and Patient Health Questionnaire (PHQ-9) were asked a series of questions and to read a excerpt from a novel aloud under two different experimental conditions. In one, participants were asked to act naturally and in the other, to suppress anything that they felt would be indicative of their depression. Acoustic features were then extracted from this data and analysed using paired t-tests to determine any statistically significant differences between healthy and depressed participants. Most features that were found to be significantly different during normal behaviour remained so during concealed behaviour. In leave-one-subject-out automatic classification studies of the 9 depressed subjects and 8 matched healthy controls, an 88% classification accuracy and 89% sensitivity was achieved. Results remained relatively robust during concealed behaviour, with classifiers trained on only non-concealed data achieving 81% detection accuracy and 75% sensitivity when tested on concealed data. These results indicate there is good potential to build deception-proof automatic depression monitoring systems

Objective methods for reliable detection of concealed depression

Recent research has shown that it is possible to automatically detect clinical depression from audio-visual recordings. Before considering integration in a clinical pathway, a key question that must be asked is whether such systems can be easily fooled. This work explores the potential of acoustic features to detect clinical depression in adults both when acting normally and when asked to conceal their depression. Nine adults diagnosed with mild to moderate depression as per the Beck Depression Inventory (BDI-II) and Patient Health Questionnaire (PHQ-9) were asked a series of questions and to read a excerpt from a novel aloud under two different experimental conditions. In one, participants were asked to act naturally and in the other, to suppress anything that they felt would be indicative of their depression. Acoustic features were then extracted from this data and analysed using paired t-tests to determine any statistically significant differences between healthy and depressed participants. Most features that were found to be significantly different during normal behaviour remained so during concealed behaviour. In leave-one-subject-out automatic classification studies of the 9 depressed subjects and 8 matched healthy controls, an 88% classification accuracy and 89% sensitivity was achieved. Results remained relatively robust during concealed behaviour, with classifiers trained on only non-concealed data achieving 81% detection accuracy and 75% sensitivity when tested on concealed data. These results indicate there is good potential to build deception-proof automatic depression monitoring systems

Design and evaluation of virtual human mediated tasks for assessment of depression and anxiety

Virtual human technologies are now being widely explored as therapy tools for mental health disorders including depression and anxiety. These technologies leverage the ability of the virtual agents to engage in naturalistic social interactions with a user to elicit behavioural expressions which are indicative of depression and anxiety. Research efforts have focused on optimising the human-like expressive capabilities of the virtual human, but less attention has been given to investigating the effect of virtual human mediation on the expressivity of the user. In addition, it is still not clear what an optimal task is or what task characteristics are likely to sustain long term user engagement. To this end, this paper describes the design and evaluation of virtual human-mediated tasks in a user study of 56 participants. Half the participants complete tasks guided by a virtual human, while the other half are guided by text on screen. Self-reported PHQ9 scores, biosignals and participants' ratings of tasks are collected. Findings show that virtual-human mediation influences behavioural expressiveness and this observation differs for different depression severity levels. It further shows that virtual human mediation improves users' disposition towards tasks

Examining the Effects of Casual Video Gameplay as an Intervention to Alleviate Symptoms of Depression on both Subjective and Objective Measures

Depression can be a debilitating illness that affects more than 300 million people worldwide. Although there are successful treatments for depression with pharmaceuticals and behavioral approaches such as psychotherapy, these approaches are often very costly and may carry a stigma of treatment for some individuals. The purpose of this dissertation study was to compare results of previously collected data that examine whether a prescribed regimen of casual videogame play (CVG) could reduce symptoms associated with depression. This dissertation specifically focused on comparing results of a study group and a comparison group on the self-report instrument, the Patient Health Questionnaire-9 (PHQ-9) as well as objectively measured changes in alpha wave, Electroencephalogram (EEG) data. Participants in the original study were screened for depression using the PHQ-9. There were a total of 57 participants who met the study inclusion criteria. Each participant that met the inclusion criteria was then randomized into either the comparison group (n=29) or the study group (n=28). Experimental group participants were prescribed to play one of three CVGs three times per week (with 24 hours between each session). This process occurred for 30 minutes each session, over a 1-month period. Comparison group participants reviewed the National Institute of Mental Health's webpage on depression during a pre-test and a post-test session. The participants in this group did not engage in any intervention over the one-month period of time between the pre-test and post-test sessions. A repeated-measures analysis of covariance (ANCOVA) was completed to examine three research questions between subjects at Time 1 and Time 3 to compare changes in depression symptoms on both subjective, self-report (PHQ-9) and objective alpha wave EEG measures. The CVGs used as the intervention factor were either Peggle, Bejeweled or Bookworm Adventure. Study analysis revealed significant decreases in depression symptoms reported in the study group on the PHQ-9 self-report scale. Results along the objective, EEG alpha wave scale revealed non-statistically significant changes. Potential reasons for the non-significant findings along with recommendations for future research are also discussed. Conclusions from this study found that a prescribed regimen of CVG may have potential as an intervention to help reduce symptoms of depression as measured on the PHQ-9 scale. Further research should consider examining intricacies of CVG play as a potential intervention to address symptoms related to depression. Findings also revealed that while EEG findings were not statistically significant, participants self-report responses were significant and may underscore the importance of individual's subjective feelings in the therapeutic process