Mental Health Problems During COVID-19 and Attitudes Toward Digital Therapeutics.

OBJECTIVE: We aimed to elucidate public mental health problems and associated factors during the coronavirus disease-2019 (COVID-19). Furthermore, we evaluated people's attitudes toward digital therapeutics during the pandemic. METHODS: Data was collected online from participants, aged between 20-50 without any history of mental illness, from June 1st to June 30th 2021. The survey consisted of questions regarding demographics, changes during pandemic and attitude towards digital therapeutics, and mental health measures. RESULTS: Among the total of 445 participants, 49.2% reported significant level of stress and 13.5% and 7.0% met the screening criteria for major depressive disorder and generalized anxiety disorder, respectively. Significant predictive factors for mental health problems were-younger age group, female sex, currently being treated for medical or surgical disease, change in the amount of time spent on mobile device or computer after pandemic, change in household income, and change in work environment due to pandemic. Furthermore, 35.1% of participants, considered psychiatric consultation, at least slightly, but were hesitant to receive it due to the fear of contacting COVID-19 at the clinics. Instead, 54.4% of them preferred using digital therapeutics as an alternative to visiting offline clinics. CONCLUSION: We demonstrated that COVID-19 increased mental health problems along with access problems and identified their predictive factors. Digital therapeutics emerged as a viable solution to mental health problems and it was well-received by those in need of psychiatric consultation. Therefore, development and implementation of digital therapeutics should be considered to improve the mental health of people.

Depressive Symptoms Feature-Based Machine Learning Approach to Predicting Depression Using Smartphone.

With the impact of the COVID-19 pandemic, the number of patients suffering from depression is rising around the world. It is important to diagnose depression early so that it may be treated as soon as possible. The self-response questionnaire, which has been used to diagnose depression in hospitals, is impractical since it requires active patient engagement. Therefore, it is vital to have a system that predicts depression automatically and recommends treatment. In this paper, we propose a smartphone-based depression prediction system. In addition, we propose depressive features based on multimodal sensor data for predicting depressive mood. The multimodal depressive features were designed based on depression symptoms defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The proposed system comprises a "Mental Health Protector" application that collects data from smartphones and a big data-based cloud platform that processes large amounts of data. We recruited 106 mental patients and collected smartphone sensor data and self-reported questionnaires from their smartphones using the proposed system. Finally, we evaluated the performance of the proposed system's prediction of depression. As the test dataset, 27 out of 106 participants were selected randomly. The proposed system showed 76.92% on an f1-score for 16 patients with depression disease, and in particular, 15 patients, 93.75%, were successfully predicted. Unlike previous studies, the proposed method has high adaptability in that it uses only smartphones and has a distinction of evaluating prediction accuracy based on the diagnosis.