Depression is a common mental health illness worldwide that affects our quality of life and ability to work. Although prior research has used EEG signals to increase the accuracy to identify depression, the rates of underdiagnosis remain high, and novel methods are required to identify depression. In this study, we built a model based on single-channel, dry-electrode EEG sensor technology to detect state depression, which measures the intensity of depressive feelings and cognitions at a particular time. To test the accuracy of our model, we compared the results of our model with other commonly used methods for depression diagnosis, including the PHQ-9, Hamilton Depression Rating Scale (HAM-D), and House-Tree-Person (HTP) drawing test, in three different studies. In study 1, we compared the results of our model with PHQ-9 in a sample of 158 senior high students. The results showed that the consistency rate of the two methods was 61.4%. In study 2, the results of our model were compared with HAM-D among 71 adults. We found that the consistency rate of state-depression identification by the two methods was 63.38% when a HAM-D score above 7 was considered depression, while the consistency rate increased to 83.10% when subjects showed at least one depressive symptom (including depressed mood, guilt, suicide, lack of interest, retardation). In study 3, 68 adults participated in the study, and the results revealed that the consistency rate of our model and HTP drawing test was 91.2%. The results showed that our model is an effective means to identify state depression. Our study demonstrates that using our model, people with state depression could be identified in a timely manner and receive interventions or treatments, which may be helpful for the early detection of depression.

GUI49 - Respondents aged 25 years diagnosed or not diagnosed with depression or anxiety. Published by Central Statistics Office. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Respondents aged 25 years diagnosed or not diagnosed with depression or anxiety...

Previous research has shown that depression is associated with a negative thinking style, whereby individuals hold negative views about themselves, the world, and the future. Moreover, it has been argued that holding negative views about the future is a key factor in causing and maintaining depressive episodes (Roepke & Seligman, 2015). The research conducted within this project builds on our lab’s previous findings (Boland et al, 2018) that views about the future can be made less negative by "Positive Simulation Training" (PST), whereby individuals repeatedly engage in positive episodic simulations about potential future events. This research found that PST led to improvements in participants' expectations about the future events, compared to a neutral visualisation task. Positive future events were rated as more likely to occur and negative events less likely, and individuals rated themselves as having more control over both positive and negative future events. These effects were observed in individuals with and without elevated levels of depressive symptomatology. Across six experiments, the project extended our preliminary findings with further explorations of the effects of PST: Experiments 1a and 1b compared the effects of four different versions of PST, relative to a neutral visualisation task, on future event expectancies (likelihood of occurrence, controllability, importance, anticipated happiness, anticipated disappointment). The core aim of these two experiments was to establish whether any one form of Positive Simulation Training modifies future event expectancies more than others. Experiments 2 and 3 extended this to investigate the impact of PST on expectancies for personally relevant vs. irrelevant events (Exp. 2) and for personal goals (Exp. 3). The aims of these experiments were to establish whether PST lead to more positive views about future events that are personally important. Experiments 4 and 5 investigated the impact of PST on dispositional optimism, by exploring whether the effects of PST extend beyond material that is explicitly related to that simulated during training. Experiment 4 explored whether PST modified responses on an implicit measure of future expectancies whilst Experiment 5 examined the effects of PST on anticipated emotions/affective forecasts within a laboratory game of chance involving monetary wins/losses.Depression is a debilitating condition that causes immense psychological distress to those who experience it. Depression also has profoundly negative effects on many other aspects of everyday living, including physical health, educational attainment, and employment status. Understanding the causes of depression and developing interventions to treat it will, therefore, have significant benefits both for individuals and for society. Previous research has shown that depression is associated with a negative thinking style, whereby individuals hold negative views about themselves, the world, and the future. Recent research has indicated that holding negative views about the future is one of the main factors in causing and maintaining depressive episodes. The research we propose builds on our previous findings that views about the future can be made less negative by an intervention we have termed "Positive Simulation Training". In our previous research, participants were presented with a range of potential life events, 15 positive (e.g., people will admire you) and 15 negative (e.g., someone close to you will reject you). For each event, participants predicted how likely it was to occur in the future, how much control they thought they had over it, and how important it would be to them. They then took part in the Positive Simulation Training task in which they were instructed to mentally simulate a series of positive future events as vividly as possible in response to cue words/phrases that appeared on a computer screen. A control group took part in a neutral visualisation task in which they were instructed to imagine neutral scenes (e.g. the layout of their local shopping centre) as vividly as possible. Participants were then presented with a second set of potential life events and asked to rate them for likelihood of occurrence, control, and importance. We found that Positive Simulation Training led to improvements in participants' expectations about the future events, compared to the neutral visualisation task. Positive future events were rated as more likely to occur and negative events less likely, and individuals rated themselves as having more control over both positive and negative future events. These effects were observed in both depressed and non-depressed individuals. We now wish to build on these preliminary findings and establish whether Positive Simulation Training can be used to treat other negative future biases that have been observed in depression. The questions we plan to address include the following: 1. Can Positive Simulation Training lead to more positive views about future events that are personally important to the participants? 2. Can Positive Simulation Training lead to more positive views about how future events will make one feel? 3. Can Positive Simulation Training enhance beliefs about the likelihood of achieving personal goals? 4. Can Positive Simulation Training improve implicit (unconscious) beliefs about the likelihood of future events? This is important because it has been shown that implicit beliefs have a powerful effect on behaviour. 5. Can Positive Simulation Training enhance more general feelings of optimism about the future? Our eventual aim is to develop an intervention based on Positive Simulation Training that will support recovery from depression by reducing the effects of negative thoughts about the future. Six studies all using experimental methodologies with a pre- to post-intervention design. Participants completed pen/paper or computerised tasks measuring future expectancies (this task differed across the six experiments) either side of completing either Positive Simulation Training or a Control Task (Neutral Visual Imagery or Letter Visual Search). The measures of future expectancies served as the dependent variables within all studies.

This publication contains the official statistics about uses of the Mental Health Act ('the Act') in England during 2022-23. Under the Act, people with a mental disorder may be formally detained in hospital (or 'sectioned') in the interests of their own health or safety, or for the protection of other people. They can also be treated in the community but subject to recall to hospital for assessment and/or treatment under a Community Treatment Order (CTO). In 2016-17, the way we source and produce these statistics changed. Previously these statistics were produced from the KP90 aggregate data collection. They are now primarily produced from the Mental Health Services Data Set (MHSDS). The MHSDS provides a much richer data source for these statistics, allowing for new insights into uses of the Act. People may be detained in secure psychiatric hospitals, other NHS Trusts or at Independent Service Providers (ISPs). All organisations that detain people under the Act must be registered with the Care Quality Commission (CQC). In recent years, the number of detentions under the Act have been rising. An independent review has examined how the Act is used and has made recommendations for improving the Mental Health Act legislation. In responding to the review, the government said it would introduce a new Mental Health Bill to reform practice. This publication does not cover: 1. People in hospital voluntarily for mental health treatment, as they have not been detained under the Act (see the Mental Health Bulletin). 2. Uses of section 136 where the place of safety was a police station; these are published by the Home Office.

Comparison of total scores with regard to depression levels.

The mental and physical well-being of healthcare workers is being affected by global COVID-19. The pandemic has impacted the mental health of medical staff in numerous ways. However, most studies have examined sleep disorders, depression, anxiety, and post-traumatic problems in healthcare workers during and after the outbreak. The study’s objective is to evaluate COVID-19’s psychological effects on healthcare professionals of Saudi Arabia. Healthcare professionals from tertiary teaching hospitals were invited to participate in the survey. Almost 610 people participated in the survey, of whom 74.3% were female, and 25.7% were male. The survey included the ratio of Saudi and non-Saudi participants. The study has utilized multiple machine learning algorithms and techniques such as Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (KNN), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The machine learning models offer 99% accuracy for the credentials added to the dataset. The dataset covers several aspects of medical workers, such as profession, working area, years of experience, nationalities, and sleeping patterns. The study concluded that most of the participants who belonged to the medical department faced varying degrees of anxiety and depression. The results reveal considerable rates of anxiety and depression in Saudi frontline workers.