Mental health disturbance in preclinical medical students and its association with screen time, sleep quality, and depression during the COVID-19 pandemic.

BACKGROUND: Affected by various hurdles during COVID-19, preclinical medical students are at an elevated risk for mental health disturbances. However, the effects of modern mental health problems on preclinical medical students have not been adequately researched. Thus, this study was aimed to identify the proportions and implications of current mental health problems for depression, sleep quality and screen time among Indonesian medical preclinical students during the COVID-19 pandemic. METHODS: This cross-sectional study was conducted using crowdsourcing between October 2020 and June 2021. During the study period, 1,335 subjects were recruited, and 1,023 datasets were identified as valid. General Health Questionnaire-12 (GHQ-12) was used to measure current mental health disturbances (categorized as without current mental health disturbances, psychological distress, social dysfunction, or both). The Patient Health Questionnaire-9 (PHQ-9) was used to assess depression, the Pittsburgh Sleep Quality Index (PSQI) was employed to assess sleep quality, and a questionnaire devised for this study was used to assess screen time length per day. Multivariate data analysis was conducted using SPSS version 24 for Mac. RESULTS: According to the findings, 49.1% of the 1,023 participants had current mental health disturbances: 12.8% had psychological distress, 15.9% had social dysfunction, and the rest (20.4%) had both psychological distress and social dysfunction. The statistical analysis provided strong evidence of a difference (p < 0.001) between the medians of depression and sleep quality with at least one pair of current mental health disturbance groups, but the difference for screen time was not significant (p = 0.151). Dunn's post-hoc analysis showed that groups without current mental health problems had significantly lower mean ranks of depression and sleep quality compared to groups that had current mental health problems (p < 0.001). CONCLUSION: Current mental health disturbances during the COVID-19 pandemic were significantly associated with preclinical medical students' depression and sleep quality in preclinical medical students. Thus, mental health programs for this specific population should be tailored to integrate mindfulness therapy, support groups, stress management, and skills training to promote mental wellbeing.

Developing attention deficits/hyperactivity disorder-virtual reality diagnostic tool with machine learning for children and adolescents.

The traditional diagnosis of Attention Deficits/Hyperactivity Disorder (ADHD) is through parent-child interviews and observations; therefore, innovative ADHD diagnostic tools that represent this digital era are needed. Virtual reality (VR) is a significant technology that can present a virtual immersive environment; it can provide an illusion of participation in an artificial milieu for children with ADHD. This study aimed to develop an ADHD-VR diagnostic tool construct (Research Domain Construct/RDC) based on the DSM5 ADHD diagnostic criteria, and using the RDC to develop a diagnostic tool with a machine learning (ML) application that can produce an intelligent model to receive some complex and multifaceted clinical data (ADHD clinical symptoms). We aimed to expand a model algorithm from the data, and finally make predictions by providing new data (output data) that have more accurate diagnostic value. This was an exploratory qualitative study and consisted of two stages. The first stage of the study applied the Delphi technique, and the goal was to translate ADHD symptoms based on DSM 5 diagnostic criteria into concrete behavior that can be observed among children in a classroom setting. This stage aimed to gather information, perceptions, consensus, and confirmation from experts. In this study, three rounds of Delphi were conducted. The second stage was to finalize the RDC of the ADHD-VR diagnostic tool with ML, based on the first-stage results. The results were transformed into concrete activities that could be applied in the programming of the ADHD-VR diagnostic tool, followed by starting to input data that were required to build the diagnostic tool. The second stage consisted of more than ten focus-group discussions (FGDs) before it could be transformed into the ADHD-VR diagnostic tool with the ML prototype. First-stage data analysis was performed using Microsoft Excel for Mac. Qualitative data were analyzed using conceptual content analysis with a manifest/latent analysis approach. From the first stage of the study, there were 13 examples of student behaviors that received more than 75% totally agreed or agreed from the experts. The RDC of the ADHD-VR diagnostic tool with machine learning application consisted of three domains and was divided into six sub-domains: reward-related processing, emotional lability, inhibitory, sustained attention, specific timing of playing in order, and arousal. In conclusion, the results of this study can be used as a reference for future studies in a similar context and content, that is, the ADHD-VR diagnostic tool with machine learning based on the constructed RDC.