It has recently been reported that identifying the depression severity of a person requires involvement of mental health professionals who use traditional methods like interviews and self-reports, which results in spending time and money. In this work we made solid contributions on short-term depression detection using every-day mobile devices. To improve the accuracy of depression detection, we extracted five factors influencing depression (symptom clusters) from the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders), namely, physical activity, mood, social activity, sleep, and food intake and extracted features related to each symptom cluster from mobile devices’ sensors. We conducted an experiment, where we recruited 20 participants from four different depression groups based on PHQ-9 (the Patient Health Questionnaire-9, the 9-item depression module from the full PHQ), which are normal, mildly depressed, moderately depressed, and severely depressed and built a machine learning model for automatic classification of depression category in a short period of time. To achieve the aim of short-term depression classification, we developed Short-Term Depression Detector (STDD), a framework that consisted of a smartphone and a wearable device that constantly reported the metrics (sensor data and self-reports) to perform depression group classification. The result of this pilot study revealed high correlations between participants` Ecological Momentary Assessment (EMA) self-reports and passive sensing (sensor data) in physical activity, mood, and sleep levels; STDD demonstrated the feasibility of group classification with an accuracy of 96.00% (standard deviation (SD) = 2.76).

Kyong-Mee Chung, Eunsun Chung, and Hoomyung Lee. J Korean Acad Child Adolesc Psychiatry 2024;35:29-38. https://doi.org/10.5765/jkacap.230019

Cancer patients experience various psychological and social difficulties, the most common being depression and anxiety. The purpose of this study was to develop and evaluate the effectiveness of an app-based cognitive behavioral therapy program for depression and anxiety in cancer patients. For this purpose, 63 participants who met the inclusion criteria were randomly assigned to either a mobile-application-based cognitive behavioral therapy program (HARUToday), a simple information-provision mobile-application-based program (HARUCard), or a waitlist control group. Self-report questionnaires including the Beck Depression Inventory, State-Trait Anxiety Inventory, Health-Related Quality of Life Scale, Dysfunctional Attitude Scale, and two computer tasks including the dot-probe task and the Implicit Association Test, were administered before and after 66 days of intervention. The results showed that the Beck Depression Inventory and State-Trait Anxiety Inventory scores of the cognitive behavioral therapy program (HARUToday) group decreased significantly after the intervention compared to the attention control (HARUCard) and waitlist control groups. However, there were no significant changes in scores of the Health-Related Quality of Life Scale and Dysfunctional Attitude Scale, and the two computer tasks. Such results suggest that a mobile-application-based cognitive behavioral therapy program may be an effective intervention for alleviating depression and anxiety, but not the general quality of life of cancer patients. Taking into consideration that psychosocial problems may not the topmost priority for cancer patients who are facing a chronic and possibly mortal disease, a mobile-application cognitive behavioral therapy program may be a possible solution for the alleviation of depression and anxiety in cancer patients who have many restraints in terms of time and space.

The purposes of this study were to develop the Yonsei Face Database (YFace DB), consisting of both static and dynamic face stimuli for six basic emotions (happiness, sadness, anger, surprise, fear, and disgust), and to test its validity. The database includes selected pictures (static stimuli) and film clips (dynamic stimuli) of 74 models (50% female) aged between 19 and 40. Thousand four hundred and eighty selected pictures and film clips were assessed for the accuracy, intensity, and naturalness during the validation procedure by 221 undergraduate students. The overall accuracy of the pictures was 76%. Film clips had a higher accuracy, of 83%; the highest accuracy was observed in happiness and the lowest in fear across all conditions (static with mouth open or closed, or dynamic). The accuracy was higher in film clips across all emotions but happiness and disgust, while the naturalness was higher in the pictures than in film clips except for sadness and anger. The intensity varied the most across conditions and emotions. Significant gender effects were found in perception accuracy for both the gender of models and raters. Male raters perceived surprise more accurately in static stimuli with mouth open and in dynamic stimuli while female raters perceived fear more accurately in all conditions. Moreover, sadness and anger expressed in static stimuli with mouth open and fear expressed in dynamic stimuli were perceived more accurately when models were male. Disgust expressed in static stimuli with mouth open and dynamic stimuli, and fear expressed in static stimuli with mouth closed were perceived more accurately when models were female. The YFace DB is the largest Asian face database by far and the first to include both static and dynamic facial expression stimuli, and the current study can provide researchers with a wealth of information about the validity of each stimulus through the validation procedure.