Examiners' perceptions of forensic mental health assessments conducted via videoconferencing.

The COVID-19 pandemic led to an acceleration in the adoption of videoconferencing (VC) for conducting forensic mental health evaluations (forensic mental health assessments [FMHA]). Two years into the COVID-19 pandemic, we administered a survey to 71 Minnesota-licensed forensic evaluators. Approximately two-thirds (65.7%) had started using VC for FMHA only after the pandemic, though a combined 84.5% reported performing FMHA via VC frequently at present. A striking 43.7% of respondents preferred VC for FMHA over in-person evaluation, and another 22.5% expressed no preference between modalities. Further, nearly 70% of respondents denied there were any populations for which they would never use VC to complete an FMHA. We conclude that the widespread adoption of VC for FMHA with the advent of the COVID-19 pandemic has induced a lasting change in the practice of FMHA. We postulate that with further advancements in technology and the development of testing instruments that can be administered online, the use of VC for FMHA will become standard practice.

A measurement method for mental health based on dynamic multimodal feature recognition.

Introduction: The number of college students with mental problems has increased significantly, particularly during COVID-19. However, the clinical features of early-stage psychological problems are subclinical, so the optimal intervention treatment period can easily be missed. Artificial intelligence technology can efficiently assist in assessing mental health problems by mining the deep correlation of multi-dimensional data of patients, providing ideas for solving the screening of normal psychological problems in large-scale college students. Therefore, we propose a mental health assessment method that integrates traditional scales and multimodal intelligent recognition technology to support the large-scale and normalized screening of mental health problems in colleges and universities. Methods: Firstly, utilize the psychological assessment scales based on human-computer interaction to conduct health questionnaires based on traditional methods. Secondly, integrate machine learning technology to identify the state of college students and assess the severity of psychological problems. Finally, the experiments showed that the proposed multimodal intelligent recognition method has high accuracy and can better proofread normal scale results. This study recruited 1,500 students for this mental health assessment. Results: The results showed that the incidence of moderate or higher stress, anxiety, and depression was 36.3, 48.1, and 23.0%, which is consistent with the results of our multiple targeted tests. Conclusion: Therefore, the interactive multimodality emotion recognition method proposed provides an effective way for large-scale mental health screening, monitoring, and intervening in college students' mental health problems.

StudentSADD: Rapid mobile depression and suicidal ideation screening of college students during the coronavirus pandemic

The growing prevalence of depression and suicidal ideation among college students further exacerbated by the Coronavirus pandemic is alarming, highlighting the need for universal mental illness screening technology. With traditional screening questionnaires too burdensome to achieve universal screening in this population, data collected through mobile applications has the potential to rapidly identify at-risk students. While prior research has mostly focused on collecting passive smartphone modalities from students, smartphone sensors are also capable of capturing active modalities. The general public has demonstrated more willingness to share active than passive modalities through an app, yet no such dataset of active mobile modalities for mental illness screening exists for students. Knowing which active modalities hold strong screening capabilities for student populations is critical for developing targeted mental illness screening technology. Thus, we deployed a mobile application to over 300 students during the COVID-19 pandemic to collect the Student Suicidal Ideation and Depression Detection (StudentSADD) dataset. We report on a rich variety of machine learning models including cutting-edge multimodal pretrained deep learning classifiers on active text and voice replies to screen for depression and suicidal ideation. This unique StudentSADD dataset is a valuable resource for the community for developing mobile mental illness screening tools. © 2022 ACM.

DepreST-CAT: Retrospective smartphone call and text logs collected during the COVID-19 Pandemic to Screen for Mental Illnesses

The rates of mental illness, especially anxiety and depression, have increased greatly since the start of the COVID-19 pandemic. Traditional mental illness screening instruments are too cumbersome and biased to screen an entire population. In contrast, smartphone call and text logs passively capture communication patterns and thus represent a promising screening alternative. To facilitate the advancement of such research, we collect and curate the DepreST Call and Text log (DepreST-CAT) dataset from over 365 crowdsourced participants during the COVID-19 pandemic. The logs are labeled with traditional anxiety and depression screening scores essential for training machine learning models. We construct time series ranging from 2 to 16 weeks in length from the retrospective smartphone logs. To demonstrate the screening capabilities of these time series, we then train a variety of unimodal and multimodal machine and deep learning models. These models provide insights into the relative screening value of the different types of logs, lengths of log time series, and classification methods. The DepreST-CAT dataset is a valuable resource for the research community to model communication patterns during the COVID-19 pandemic and further the development of machine learning algorithms for passive mental illness screening. © 2022 ACM.

Associations among Self-Reported Mental Health, Physical Activity, and Diet during the COVID-19 Pandemic.

Background: Understanding the relationship between physical activity, diet, and mental health during the COVID-19 pandemic may help inform resources encouraging healthy lifestyle choices during the time of an increased threat to health and wellbeing. Aim: Our objective was to examine how self-rated mental health was associated with engagement in physical activity and consumption of fruits and vegetables during the COVID-19 pandemic. Methods: The study utilized cross-sectional survey data from adults (≥18 years of age) living, working, and/or receiving healthcare in Arkansas (n = 754). Multivariable regression models were used to examine the associations between self-rated mental health and the number of days respondents engaged in 30 min of physical activity and the number of days respondents consumed five or more servings of fruits and vegetables. Results: Respondents who reported somewhat poor/poor mental health reported engaging in at least 30 min of physical activity fewer days per week (ß = -.77, p = .018) compared with those reporting excellent mental health, after controlling for sociodemographic factors and self-rated health. The significant association observed in the first two models between mental health and number of days consuming five or more servings of fruits and vegetables became non-significant after inclusion of self-rated health. Conclusion: The relationship between mental health and physical activity and diet reaffirms a need for healthcare providers to promote the importance of maintaining both a healthy physical activity level and a nutrient-rich diet in the face of challenging circumstances, such as a global pandemic.

The Gaps Between the Self and Professional Evaluation in Mental Health Assessment of COVID-19 Cluster Cases.

Objective: To analyze the discrepancy between self-rating and professional evaluation of mental health status in coronavirus disease 2019 (COVID-19) cluster cases. Method: A total of 65 COVID-19 cluster cases admitted to Beijing Ditan Hospital Capital Medical University from June 14, 2020 to June 16, 2020 were included in the study. Mental health assessment was completed by self-rating and professional evaluation. The gaps between self-rating and professional evaluation in different demographic characteristics were compared. Results: The results of self-rating were inconsistent with those of professional evaluation. The gap was statistically different among certain demographic subgroups. As for anxiety, the gaps had remarkable statistics differences in subgroups of sex, monthly income, infection way, and anxiety/depression medical history. Similarly, in the terms of depression, the gaps had significant statistic differences in the subgroups of the medical history of anxiety/depression, history of physical disease, employment status and the insurance type, marriage, education (year), residing in Beijing (year), and the monthly income. Conclusion: Compared to the professional evaluation, patients had a higher self-rating, which may be related to some demographic characteristics. It suggests that screening can be conducted in patients with COVID-19 by self-rating first, and then professional evaluation should be carried out in the patients with suspicious or positive results.

Assessing the mental health condition of home-confined university level students of Bangladesh due to the COVID-19 pandemic.

Aim: The COVID-19 pandemic drove the Government of Bangladesh to shut down educational institutions, which had an enormous effect on the psychological health of students. This study aimed to assess the mental health status of Bangladeshi university students during the lockdown period. Subject and methods: Through an online-based questionnaire, information was collected from 509 university students of Bangladesh from June 19, 2020, to June 28, 2020, using convenient sampling. K-means clustering was applied to organize students according to their psychological health score, and confirmatory factor analysis (CFA) was also conducted to determine the association among the student's activities and their mental health during the pandemic. In addition, these associations were examined through chi-square test and ordinal logistic regression. Results: Students were categorized into four categories where 4.32% had mild, 72.7% had moderate, 12.57% had moderately severe, and 10.41% suffered from severe mental health imbalance. The results showed that having family members affected by the coronavirus, facing insecurity, using social media, and smoking habits increased the mental health imbalances of students; in contrast, being worried about studying, future career, spending more time with family members, and participation in household chores reduced the mental health disturbances of students. On the other hand, the results of the ordinal logistic regression indicated that sleeping time and participation in household chores were preventive factors for students. Conclusion: This study reveals that a large proportion of University students of Bangladesh suffered from mental health disturbances during the lockdown period. Implementing mental health plans and providing job security, improved communication approaches toward family members, not flattening illusive news, and preoccupation in household activities may assist to enhance the mental health status of the university students. The authors believe that this study's findings will be helpful to expedite the rate of attaining the sustainable development goal associated with health status in Bangladesh.