Psychological measurement is critical to many disciplines. Despite advances in measurement, building nomological networks, theoretical maps of how concepts and measures relate to establish validity, remains a challenge 70 years after Cronbach and Meehl proposed them as fundamental to validation. This limitation has practical consequences: clinical trials may fail to detect treatment effects, and public policy may target the wrong outcomes. We introduce Analysis of Latent Indicators to Generate Nomological Structures (ALIGNS), a large language model-based system trained with validated questionnaire measures. ALIGNS provides three comprehensive nomological networks containing over 550,000 indicators across psychology, medicine, social policy, and other fields. This represents the first application of large language models to solve a foundational problem in measurement validation. We report classification accuracy tests used to develop the model, as well as three evaluations. In the first evaluation, the widely used NIH PROMIS anxiety and depression instruments are shown to converge into a single dimension of emotional distress. The second evaluation examines child temperament measures and identifies four potential dimensions not captured by current frameworks, and questions one existing dimension. The third evaluation, an applicability check, engages expert psychometricians who assess the system's importance, accessibility, and suitability. ALIGNS is freely available at nomologicalnetwork.org, complementing traditional validation methods with large-scale nomological analysis.

Estimating community-specific mental health effects of local events is vital for public health policy. While forecasting mental health scores alone offers limited insights into the impact of events on community well-being, quasi-experimental designs like the Longitudinal Regression Discontinuity Design (LRDD) from econometrics help researchers derive more effects that are more likely to be causal from observational data. LRDDs aim to extrapolate the size of changes in an outcome (e.g. a discontinuity in running scores for anxiety) due to a time-specific event. Here, we propose adapting LRDDs beyond traditional forecasting into a statistical learning framework whereby future discontinuities (i.e. time-specific shifts) and changes in slope (i.e. linear trajectories) are estimated given a location's history of the score, dynamic covariates (other running assessments), and exogenous variables (static representations). Applying our framework to predict discontinuities in the anxiety of US counties from COVID-19 events, we found the task was difficult but more achievable as the sophistication of models was increased, with the best results coming from integrating exogenous and dynamic covariates. Our approach shows strong improvement ($r=+.46$ for discontinuity and $r = +.65$ for slope) over traditional static community representations. Discontinuity forecasting raises new possibilities for estimating the idiosyncratic effects of potential future or hypothetical events on specific communities.

Anxiety and depression are the most common mental health issues worldwide, affecting a non-negligible part of the population. Accordingly, stakeholders, including governments' health systems, are developing new strategies to promote early detection and prevention from a holistic perspective (i.e., addressing several disorders simultaneously). In this work, an entirely novel system for the multi-label classification of anxiety and depression is proposed. The input data consists of dialogues from user interactions with an assistant chatbot. Another relevant contribution lies in using Large Language Models (llms) for feature extraction, provided the complexity and variability of language. The combination of llms, given their high capability for language understanding, and Machine Learning (ml) models, provided their contextual knowledge about the classification problem thanks to the labeled data, constitute a promising approach towards mental health assessment. To promote the solution's trustworthiness, reliability, and accountability, explainability descriptions of the model's decision are provided in a graphical dashboard. Experimental results on a real dataset attain 90 % accuracy, improving those in the prior literature. The ultimate objective is to contribute in an accessible and scalable way before formal treatment occurs in the healthcare systems.

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.

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.

Taipei, Taiwan – Typing "I have anxiety" into ChatGPT, OpenAI's ground-breaking artificial intelligence-powered chatbot gets to work almost immediately. "I'm sorry to hear that you're experiencing anxiety," scrawls across the screen. "It can be a challenging experience, but there are strategies you can try to help manage your symptoms." Then comes a numbered list of recommendations: working on relaxation, focusing on sleep, cutting caffeine and alcohol, challenging negative thoughts, and seeking the support of friends and family. While not the most original advice, it resembles what might be heard in a therapist's office or read online in a WebMD article about anxiety – not least because ChatGPT scrapes its answers from the wide expanse of the internet.

Could playing a video game improve your child's ADHD symptoms? That's the promise fueling a surge in digital therapeutics and other emerging tech tools designed to help individuals manage their ADHD. Virtual reality games with stimuli to punch or avoid. Video games requiring problem-solving strategies to save the galaxy from a meteor storm. A to-do app designed to help kids follow routines.

In this work, we apply information theory inspired methods to quantify changes in daily activity patterns. We use in-home movement monitoring data and show how they can help indicate the occurrence of healthcare-related events. Three different types of entropy measures namely Shannon's entropy, entropy rates for Markov chains, and entropy production rate have been utilised. The measures are evaluated on a large-scale in-home monitoring dataset that has been collected within our dementia care clinical study. The study uses Internet of Things (IoT) enabled solutions for continuous monitoring of in-home activity, sleep, and physiology to develop care and early intervention solutions to support people living with dementia (PLWD) in their own homes. Our main goal is to show the applicability of the entropy measures to time-series activity data analysis and to use the extracted measures as new engineered features that can be fed into inference and analysis models. The results of our experiments show that in most cases the combination of these measures can indicate the occurrence of healthcare-related events. We also find that different participants with the same events may have different measures based on one entropy measure. So using a combination of these measures in an inference model will be more effective than any of the single measures.

Scientists have found a way to control the human brain using a protein lurking inside a creature known as'vampire fish' that has lived on Earth for hundreds of millions of years. US scientists used a protein from a lamprey, which is an ancient lineage of jawless fish similar to eel, to turn off brain circuits associated with addiction, anxiety and depression. Researchers took a gene from the protein, called parapinopsin, and found they were able to control it in the way neurons communicate with each other. Parapinopsin also responds to light, allowing scientists use beams of light to turn off the circuit or reactivate it alter reward behaviors - which could lead to brain implants to deliver treatment. Those suffering with addiction, anxiety and depression may have often wished if they could just turn off their brain and the latest discover could soon make that happen.

Anxiety, stress, overthinking, and trauma are commonly used words to describe people suffering from mental health disorders that appear from work overload, depression, negative feedback, and much more. It's very likely to see people suffering from anxiety faster than coming in contact with positive and uplifting people. Here is how to treat your stress with AI technology. The fact is, the world isn't becoming a safer or stable place. Nevertheless, technology hasn't given up on humankind yet. Researchers and scientists are doing their best to provide aid and stabilize the situation for the greater good.