Doctors might not condone many of the ways pop stars choose to blow off steam. However, scientists say that Raye's unusual daily habit could be the secret to beating the stress of the superstar lifestyle. In a recent interview, the award-winning singer claimed that a daily dose of video games helps her to'escape from her everyday life'. While it might seem strange, scientists say that developing a healthy gaming habit really could be the key to a clean bill of mental health. Studies have shown that gaming can boost emotional well-being, help fight stress, and even tackle the symptoms of depression and anxiety.

In this study, we introduce ANGST, a novel, first-of-its kind benchmark for depression-anxiety comorbidity classification from social media posts. Unlike contemporary datasets that often oversimplify the intricate interplay between different mental health disorders by treating them as isolated conditions, ANGST enables multi-label classification, allowing each post to be simultaneously identified as indicating depression and/or anxiety. Comprising 2876 meticulously annotated posts by expert psychologists and an additional 7667 silver-labeled posts, ANGST posits a more representative sample of online mental health discourse. Moreover, we benchmark ANGST using various state-of-the-art language models, ranging from Mental-BERT to GPT-4. Our results provide significant insights into the capabilities and limitations of these models in complex diagnostic scenarios. While GPT-4 generally outperforms other models, none achieve an F1 score exceeding 72% in multi-class comorbid classification, underscoring the ongoing challenges in applying language models to mental health diagnostics.

Digital phenotyping offers a novel and cost-efficient approach for managing depression and anxiety. Previous studies, often limited to small-to-medium or specific populations, may lack generalizability. We conducted a cross-sectional analysis of data from 10,129 participants recruited from a UK-based general population between June 2020 and August 2022. Participants shared wearable (Fitbit) data and self-reported questionnaires on depression (PHQ-8), anxiety (GAD-7), and mood via a study app. We first examined the correlations between PHQ-8/GAD-7 scores and wearable-derived features, demographics, health data, and mood assessments. Subsequently, unsupervised clustering was used to identify behavioural patterns associated with depression or anxiety. Finally, we employed separate XGBoost models to predict depression and anxiety and compared the results using different subsets of features. We observed significant associations between the severity of depression and anxiety with several factors, including mood, age, gender, BMI, sleep patterns, physical activity, and heart rate. Clustering analysis revealed that participants simultaneously exhibiting lower physical activity levels and higher heart rates reported more severe symptoms. Prediction models incorporating all types of variables achieved the best performance ($R^2$=0.41, MAE=3.42 for depression; $R^2$=0.31, MAE=3.50 for anxiety) compared to those using subsets of variables. This study identified potential indicators for depression and anxiety, highlighting the utility of digital phenotyping and machine learning technologies for rapid screening of mental disorders in general populations. These findings provide robust real-world insights for future healthcare applications.

It's well known that spending too much time gaming could lead to weight gain and trouble sleeping. However, it could spell trouble in the bedroom, a study suggests. Researchers in China studied more than 200,000 men while they performed'leisure' activities like watching TV, going for a drive, and playing games on the computer. The team measured participants' sex hormones, as well as feelings of depression and anxiety. They found that every 1.2 hours spent playing video games or doing other leisure activities per day at the computer led to a three times greater risk of erectile dysfunction (ED).

We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression and Anxiety Predictor (CoDAP) to predict mental health trends, particularly anxiety and depression, during the COVID-19 pandemic. Our method utilizes a comprehensive dataset, which tracked mental health symptoms weekly over ten weeks during the initial COVID-19 wave (April to June 2020) in a diverse cohort of U.S. adults. This period, characterized by a surge in mental health symptoms and conditions, offers a critical context for our analysis. Our focus was to extract and analyze patterns of anxiety and depression through a unique lens of qualitative individual attributes using CoDAP. This model not only predicts patterns of anxiety and depression during the pandemic but also unveils key insights into the interplay of demographic factors, behavioral changes, and social determinants of mental health. These findings contribute to a more nuanced understanding of the complexity of mental health issues in times of global health crises, potentially guiding future early interventions.

Summary: Utilizing data from Twitter and applying natural language processing artificial intelligence algorithms, researchers created a new, accurate prediction model for depression and anxiety. Researchers at the University of São Paulo (USP) in Brazil are using artificial intelligence (AI) and Twitter, one of the world's largest social media platforms, to try to create anxiety and depression prediction models that could in future provide signs of these disorders before clinical diagnosis. The study is reported in an article published in the journal Language Resources and Evaluation. Construction of a database, called SetembroBR, was the first step in the study. The name is a reference to Yellow September, an annual suicide awareness and prevention campaign, and also to the fact that data collection for the study began one day in September. The second step is still in progress but has provided some preliminary findings, such as the possibility of detecting whether a person is likely to develop depression solely on the basis of their social media friends and followers, without taking their own posts into account.

Toronto, Ontario--(Newsfile Corp. - October 27, 2022) - Datametrex AI Limited (TSXV: DM) (FSE: D4G) (OTCQB: DTMXF) (the "Company" or "Datametrex") is pleased to announce that its wholly-owned subsidiary, Nexalogy Environics Inc. ("Nexalogy"), is in the process of developing an artificial intelligence ("AI") and machine learning ("ML") tool to detect depression and anxiety. The tool explores sets of data and text to analyze and distinguish emotions and moods to determine one's mental state and if the creator of that text is suffering specifically from depression or anxiety. Nexalogy designed their tool to focus on depression and anxiety detection. The latest research suggests that the most prevalent mental illnesses are depression and anxiety which are estimated to affect nearly one in ten people worldwide, over 676 million people. Identifying these conditions in their early stages is critical in addressing the significant public health concerns surrounding these illnesses that lead to self-harm and suicide.

For the past several decades, psychedelics have been widely stigmatized as dangerous illegal drugs. But a recent surge of academic research into their use to treat psychiatric conditions is spurring a recent shift in public opinion. Psychedelics are psychotropic drugs: substances that affect your mental state. Other types of psychotropics include antidepressants and anti-anxiety medications. Psychedelics and other types of hallucinogens, however, are unique in their ability to temporarily induce intense hallucinations, emotions and disruptions of self-awareness.

The burden of depression and anxiety in the world is rising. Identification of individuals at increased risk of developing these conditions would help to target them for prevention and ultimately reduce the healthcare burden. We developed a 10-year predictive algorithm for depression and anxiety using the full cohort of over 400,000 UK Biobank (UKB) participants without pre-existing depression or anxiety using digitally obtainable information. From the initial 204 variables selected from UKB, processed into > 520 features, iterative backward elimination using Cox proportional hazards model was performed to select predictors which account for the majority of its predictive capability. Baseline and reduced models were then trained for depression and anxiety using both Cox and DeepSurv, a deep neural network approach to survival analysis. The baseline Cox model achieved concordance of 0.813 and 0.778 on the validation dataset for depression and anxiety, respectively. For the DeepSurv model, respective concordance indices were 0.805 and 0.774. After feature selection, the depression model contained 43 predictors and the concordance index was 0.801 for both Cox and DeepSurv. The reduced anxiety model, with 27 predictors, achieved concordance of 0.770 in both models. The final models showed good discrimination and calibration in the test datasets.We developed predictive risk scores with high discrimination for depression and anxiety using the UKB cohort, incorporating predictors which are easily obtainable via smartphone. If deployed in a digital solution, it would allow individuals to track their risk, as well as provide some pointers to how to decrease it through lifestyle changes.

X, Alphabet's experimental R&D lab, today detailed Project Amber, a now-disbanded project which aimed to make brain waves as easy to interpret as blood glucose. The goal was to develop objective measurements of depression and anxiety that could be used to support diagnoses, treatment, and therapies. An estimated 17.3 million adults in the U.S. have had at least one major depressive episode, according to the U.S. National Institutes of Health. Moreover, the percentage of adults in the U.S. experiencing serious thoughts of suicide increased 0.15% from 2016-2017 to 2017-2018 -- 460,000 more people than last year's dataset. Today's assessments mostly rely on conversations with clinicians or surveys like the PHQ-9 or GAD-7. The Amber team sought to marry machine learning techniques with electroencephalography (EEG) to measure telling electrical activity in the brain.