Introduction There are few published empirical data on the effects of COVID‐19 on mental health, and until now, there is no large international study. Material and methods During the COVID-19 pandemic, an online questionnaire gathered data from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively. Statistical analysis Descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses and Factorial Analysis of Variance (ANOVA) tested relations among variables. Results Probable depression was detected in 17.80% and distress in 16.71%. A significant percentage reported a deterioration in mental state, family dynamics and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (31.82% vs. 13.07%). At least half of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop depression was associated with history of Bipolar disorder and self-harm/attempts (RR = 5.88). Suicidality was not increased in persons without a history of any mental disorder. Based on these results a model was developed. Conclusions The final model revealed multiple vulnerabilities and an interplay leading from simple anxiety to probable depression and suicidality through distress. This could be of practical utility since many of these factors are modifiable. Future research and interventions should specifically focus on them.

The purpose of this paper was to compare the ideal body weight (IBW) formulas and published height-weight tables for healthy adults in the United States with the body mass index (BMI) of 22 kg/m2, which is associated with lowest mortality. There are numerous formulas and published height-weight tables available to determine IBW, but there are no published studies comparing the validity of formulas with each other or comparing formulas with BMIs. Data from height-weight tables, weight for specific heights determined by IBW formulas, and weight for BMIs of 20, 22, 25, and 30 kg/m2 at different heights were plotted for both men and women. Slopes based on a range of heights were determined for each formula using relational database software. The value for each slope obtained by linear regression was compared with the BMIs to determine which fit best with BMI of 22 kg/m2. Most height-weight tables and formulas predicted IBWs within the range of BMI of 20-25 kg/m2. However, for shorter heights the formulas were closer to BMI 20 kg/m2 and for taller heights, were closer to BMI 25 kg/m2. Height-weight tables' slopes were closer to the BMI slopes than formula slopes. Robinson's formula appears to be the best equation for calculating desirable/healthy weights in men; however, no formula predicted close to a BMI of 22 kg/m2 for women. Thus, in practice it might be more useful to use BMI ranges instead of IBW formulas for men and women.

Importance: Virtual reality (VR) is a promising tool with the potential to enhance care of cognitive and affective disorders in the aging population. VR has been implemented in clinical settings with adolescents and children; however, it has been less studied in the geriatric population. Objective: The objective of this study is to determine the existing levels of evidence for VR use in clinical settings and identify areas where more evidence may guide translation of existing VR interventions for older adults. Design and measurements: We conducted a systematic review in PubMed and Web of Science in November 2019 for peer-reviewed journal articles on VR technology and its applications in older adults. We reviewed article content and extracted the number of study participants, study population, goal of the investigation, the level of evidence, and categorized articles based on the indication of the VR technology and the study population. Results: The database search yielded 1554 total results, and 55 articles were included in the final synthesis. The most represented study design was cross-sectional, and the most common study population was subjects with cognitive impairment. Articles fell into three categories for VR Indication: Testing, Training, and Screening. There was a wide variety of VR environments used across studies. Conclusions: Existing evidence offers support for VR as a screening and training tool for cognitive impairment in older adults. VR-based tasks demonstrated validity comparable to some paper-based assessments of cognition, though more work is needed to refine diagnostic specificity. The variety of VR environments used shows a need for standardization before comparisons can be made across VR simulations. Future studies should address key issues such as usability, data privacy, and confidentiality. Since most literature was generated from high-income countries (HICs), it remains unclear how this may be translated to other parts of the world.

No abstract

Introduction Deinstitutionalization has led to various changes in the utilization of healthcare services. The increased focus on treating patients within the community has led to variations in the utilization patterns of inpatient units. Shifts in demographic variables and disease-related, system-based, and economic factors have been observed. Due to the paucity of recent literature, this study was planned to assess the characteristics and treatment patterns in an acute inpatient psychiatric unit of a university hospital. Methods A retrospective observational study reviewing electronic medical records of patients in the context of demographic, disease-related, treatment-related, and system-based data was conducted over five years. Quantitative data were analyzed through descriptive statistics. Linear regression was used to study each variable across time. Results There was an increase in neurodevelopmental disorders (p = 0.024), substance use disorders (p = 0.041), and trauma and stressor-related disorders (p = 0.012), with a decrease in depressive disorders (p = 0.047). The use of restraints (p = 0.035) has increased significantly during the same period. Conclusion This study gives us an insight into the changing trends in patient characteristics which have the potential to inform the creation of improved services.