Hasil untuk "Mental healing"

D. W. Sue, D. Sue

H. Grossman

Mu Mu, Aria Banazadeh, Michael Cauchi et al.

Background: Flow FL-100 is a transcranial direct current stimulation (tDCS) device self-administered by users at home. The retrospective analysis examined real-world usage and effectiveness of Flow tDCS treatment using data from 14,726 users collected by Flow Neuroscience AB between 2020 and 2024. Methods: Self-reported user background information and Montgomery-Åsberg Depression Rating Scale Self (MADRS-S) scores up to week 50 of the treatment were analysed. Effectiveness metrics (remission, response, and relapse rates) were assessed at pre-treatment baseline followed by week 3, 6, 10, 15, 20, and 50. Repeated-measures ANOVA, post-hoc and between-subject analysis were used to examine the change in MADRS-S over time and the influence of user background factors. Outcomes: The reported mean MADRS-S score decreased from a moderate level before treatment to a mild level at week 10 and then plateaued thereafter. Response and remission rates increased between week 3 and week 10, then stabilised through to week 50. A repeated measures ANOVA and post-hoc test reveal statistically significant differences (p < 0.0001) in MADRS-S scores across multiple time points. Reduction in reported depressive symptoms was observed during the initial weeks of treatment, these levels were maintained throughout the subsequent weeks. User adherence and background factors were significantly associated (p ≤ 0.005) with changes in MADRS-S scores during the first 10 weeks of treatment. Interpretations: Users of self-administered Flow tDCS treatment reported a rapid initial improvement and long-term reduction of depressive symptoms. Observed variability in adherence and response across diverse user groups suggests potential benefits from personalised treatment protocols and support.

Ritika Upadhyay, Biswajeet Champaty, Suraj Kumar Nayak

Purpose: This study aims to determine whether Himalayan singing bowl vibrations could lead to deeper and faster relaxation than supine silence. Numerous civilizations have used singing bowls, gongs, bells, didgeridoos, and voice sounds and chants as instruments for sound healing for ages in religious rites, festivals, social celebrations, and meditation activities. Materials and Methods: The effect of sound vibrations on physical and mental wellness is supported by scientific research. Although various pieces of research have demonstrated the effect of meditation on humans, very few studies have been done on the beneficial effects of singing bowls on the body and the mind (decrease in unease and temperament, Electroencephalogram, etc.). This study suggests two Machine Learning (ML) models for the automatic classification of the meditative state from the normal state using the Heart Rate Variability (HRV) data. Results: To pick suitable inputs for the ML models a statistics-based t-test and Principal Component Analysis (PCA) was applied. In the statistics-based t-test method, the HRV parameters were subjected to choose appropriate input for the ML model. Conclusion: In this case study there are two models that were considered the most effective models based on their accuracy, that are MLP 31-13-2 and RBF 31-17-2 model having a training accuracy of 83.75% and 68.75% respectively. In the second case study, the PCA approach was applied to the HRV parameters, and as a result MLP 4-6-2 and MLP 4-10-2 were the most effective models, with an accuracy of 69.6% and 71.4% respectively.

Abdelrahaman A. Hassan, Abdelrahman A. Ali, Aya E. Fouda et al.

The increasing global prevalence of mental disorders, such as depression and PTSD, requires objective and scalable diagnostic tools. Traditional clinical assessments often face limitations in accessibility, objectivity, and consistency. This paper investigates the potential of multimodal machine learning to address these challenges, leveraging the complementary information available in text, audio, and video data. Our approach involves a comprehensive analysis of various data preprocessing techniques, including novel chunking and utterance-based formatting strategies. We systematically evaluate a range of state-of-the-art embedding models for each modality and employ Convolutional Neural Networks (CNNs) and Bidirectional LSTM Networks (BiLSTMs) for feature extraction. We explore data-level, feature-level, and decision-level fusion techniques, including a novel integration of Large Language Model (LLM) predictions. We also investigate the impact of replacing Multilayer Perceptron classifiers with Support Vector Machines. We extend our analysis to severity prediction using PHQ-8 and PCL-C scores and multi-class classification (considering co-occurring conditions). Our results demonstrate that utterance-based chunking significantly improves performance, particularly for text and audio modalities. Decision-level fusion, incorporating LLM predictions, achieves the highest accuracy, with a balanced accuracy of 94.8% for depression and 96.2% for PTSD detection. The combination of CNN-BiLSTM architectures with utterance-level chunking, coupled with the integration of external LLM, provides a powerful and nuanced approach to the detection and assessment of mental health conditions. Our findings highlight the potential of MMML for developing more accurate, accessible, and personalized mental healthcare tools.

Jinwen Tang, Qiming Guo, Wenbo Sun et al.

Long-form mental health assessments pose unique challenges for large language models (LLMs), which often exhibit hallucinations or inconsistent reasoning when handling extended, domain-specific contexts. We introduce Stacked Multi-Model Reasoning (SMMR), a layered framework that leverages multiple LLMs and specialized smaller models as coequal 'experts'. Early layers isolate short, discrete subtasks, while later layers integrate and refine these partial outputs through more advanced long-context models. We evaluate SMMR on the DAIC-WOZ depression-screening dataset and 48 curated case studies with psychiatric diagnoses, demonstrating consistent improvements over single-model baselines in terms of accuracy, F1-score, and PHQ-8 error reduction. By harnessing diverse 'second opinions', SMMR mitigates hallucinations, captures subtle clinical nuances, and enhances reliability in high-stakes mental health assessments. Our findings underscore the value of multi-expert frameworks for more trustworthy AI-driven screening.

Keqi Chen, Zekai Sun, Yuhua Wen et al.

The in-context learning capabilities of large language models (LLMs) show great potential in mental health support. However, the lack of counseling datasets, particularly in Chinese corpora, restricts their application in this field. To address this, we constructed Psy-Insight, the first mental health-oriented explainable multi-task bilingual dataset. We collected face-to-face multi-turn counseling dialogues, which are annotated with multi-task labels and conversation process explanations. Our annotations include psychotherapy, emotion, strategy, and topic labels, as well as turn-level reasoning and session-level guidance. Psy-Insight is not only suitable for tasks such as label recognition but also meets the need for training LLMs to act as empathetic counselors through logical reasoning. Experiments show that training LLMs on Psy-Insight enables the models to not only mimic the conversation style but also understand the underlying strategies and reasoning of counseling.

Benjamin W. Nelson, Celeste Wong, Matthew T. Silvestrini et al.

Large language models often mishandle psychiatric emergencies, offering harmful or inappropriate advice and enabling destructive behaviors. This study evaluated the Verily behavioral health safety filter (VBHSF) on two datasets: the Verily Mental Health Crisis Dataset containing 1,800 simulated messages and the NVIDIA Aegis AI Content Safety Dataset subsetted to 794 mental health-related messages. The two datasets were clinician-labelled and we evaluated performance using the clinician labels. Additionally, we carried out comparative performance analyses against two open source, content moderation guardrails: OpenAI Omni Moderation Latest and NVIDIA NeMo Guardrails. The VBHSF demonstrated, well-balanced performance on the Verily Mental Health Crisis Dataset v1.0, achieving high sensitivity (0.990) and specificity (0.992) in detecting any mental health crises. It achieved an F1-score of 0.939, sensitivity ranged from 0.917-0.992, and specificity was >= 0.978 in identifying specific crisis categories. When evaluated against the NVIDIA Aegis AI Content Safety Dataset 2.0, VBHSF performance remained highly sensitive (0.982) and accuracy (0.921) with reduced specificity (0.859). When compared with the NVIDIA NeMo and OpenAI Omni Moderation Latest guardrails, the VBHSF demonstrated superior performance metrics across both datasets, achieving significantly higher sensitivity in all cases (all p < 0.001) and higher specificity relative to NVIDIA NeMo (p < 0.001), but not to OpenAI Omni Moderation Latest (p = 0.094). NVIDIA NeMo and OpenAI Omni Moderation Latest exhibited inconsistent performance across specific crisis types, with sensitivity for some categories falling below 0.10. Overall, the VBHSF demonstrated robust, generalizable performance that prioritizes sensitivity to minimize missed crises, a crucial feature for healthcare applications.

Praveen Anugula, Avdhesh Kumar Bhardwaj, Navin Chhibber et al.

Contemporary DevSecOps pipelines have to deal with the evolution of security in an ever-continuously integrated and deployed environment. Existing methods,such as rule-based intrusion detection and static vulnerability scanning, are inadequate and unreceptive to changes in the system, causing longer response times and organization needs exposure to emerging attack vectors. In light of the previous constraints, we introduce AutoGuard to the DevSecOps ecosystem, a reinforcement learning (RL)-powered self-healing security framework built to pre-emptively protect DevSecOps environments. AutoGuard is a self-securing security environment that continuously observes pipeline activities for potential anomalies while preemptively remediating the environment. The model observes and reacts based on a policy that is continually learned dynamically over time. The RL agent improves each action over time through reward-based learning aimed at improving the agent's ability to prevent, detect and respond to a security incident in real-time. Testing using simulated ContinuousIntegration / Continuous Deployment (CI/CD) environments showed AutoGuard to successfully improve threat detection accuracy by 22%, reduce mean time torecovery (MTTR) for incidents by 38% and increase overall resilience to incidents as compared to traditional methods. Keywords- DevSecOps, Reinforcement Learning, Self- Healing Security, Continuous Integration, Automated Threat Mitigation

Rijul Magu, Arka Dutta, Sean Kim et al.

Large Language Models (LLMs) have been shown to demonstrate imbalanced biases against certain groups. However, the study of unprovoked targeted attacks by LLMs towards at-risk populations remains underexplored. Our paper presents three novel contributions: (1) the explicit evaluation of LLM-generated attacks on highly vulnerable mental health groups; (2) a network-based framework to study the propagation of relative biases; and (3) an assessment of the relative degree of stigmatization that emerges from these attacks. Our analysis of a recently released large-scale bias audit dataset reveals that mental health entities occupy central positions within attack narrative networks, as revealed by a significantly higher mean centrality of closeness (p-value = 4.06e-10) and dense clustering (Gini coefficient = 0.7). Drawing from an established stigmatization framework, our analysis indicates increased labeling components for mental health disorder-related targets relative to initial targets in generation chains. Taken together, these insights shed light on the structural predilections of large language models to heighten harmful discourse and highlight the need for suitable approaches for mitigation.

Eduard Kuric, Peter Demcak, Matus Krajcovic

To keep card sorting with a lot of cards concise, a common strategy for gauging mental models involves presenting participants with fewer randomly selected cards instead of the full set. This is a decades-old practice, but its effects lacked systematic examination. To assess how randomized subsets affect data, we conducted an experiment with 160 participants. We compared results between full and randomized 60\% card sets, then analyzed sample size requirements and the impacts of individual personality and cognitive factors. Our results demonstrate that randomized subsets can yield comparable similarity matrices to standard card sorting, but thematic patterns in categories can differ. Increased data variability also warrants larger sample sizes (25-35 for 60% card subset). Results indicate that personality traits and cognitive reflection interact with card sorting. Our research suggests evidence-based practices for conducting card sorting while exposing the influence of study design and individual differences on measurement of mental models.

Qinjun Jian, Jing Hu, Lihe Yan et al.

By numerically solving the nonlinear Schrödinger equation, we theoretically study the nonlinear propagation dynamics and self-healing properties of elliptical Airy beams (EABs) propagating in water under Kerr nonlinearity. Compared to linear propagation, EABs exhibit extended propagation distances and enhanced stability in nonlinear media. Furthermore, particular emphasis is placed on the impact of Kerr nonlinearity strength on the propagation and self-healing properties of EABs. By varying the input power, it is found that EABs within a moderate power range can propagate longer distances while maintaining higher intensity and exhibit improved robustness after being blocked, indicating better self-healing performance. Based on this analysis, we propose an optimal input power for EABs through a quantitative analysis of the impact of Kerr nonlinearity, enabling them to achieve the greatest propagation distance and maintain the highest stability. Our work provides a comprehensive theoretical understanding of the nonlinear propagation dynamics and self-healing properties of EABs, with their superior characteristics potentially applicable to long-distance laser transmission.

Yue Xin, Jionghong Liang, Lantu Ren et al.

Ion conductive hydrogels (ICHs) have attracted great interest in the application of ionic skin because of their superior characteristics. However, it remains a challenge for ICHs to achieve balanced properties of high strength, large fracture strain, self-healing and freezing tolerance. In this study, a strong, stretchable, self-healing and antifreezing ICH was demonstrated by rationally designing a multiphysically cross-linked network structure consisting of the hydrophobic association, metal-ion coordination and chain entanglement among poly(acrylic acid) (PAA) polymer chains. The deliberately designed Brij S 100 acrylate (Brij-100A) micelle cross-linker can effectively dissipate energy and endow hydrogels with desirable stretchability. The self-healing ability of hydrogels originates from the reversible hydrophobic association in micelles and Fe3+-COO- coordination. After the addition of NaCl, the chain-entangled physical network caused by the salting-out effect can both enhance mechanical strength and promote electron transport. With the synergy of hydrophobic association, mental-ligand coordination and chain entanglement, the PAA/Brij-100A/Fe3+/NaCl (PAA/BA/Fe3+/NaCl) hydrogels exhibited a high tensile strain of 1140%, a tensile strength of 0.93 MPa and a toughness of 3.48 MJ m-3. Besides, the PAA/BA/Fe3+/NaCl hydrogels exhibited a high conductivity of 0.43 S m-1 and good freezing resistance. The ionic skin based on the PAA/BA/Fe3+/NaCl hydrogels showed high sensitivity (GF = 5.29), wide strain range (0-950%), fast response time (220 ms) and good stability. Also, the self-healing ability of the ionic skin can significantly prolong its service time, and the antifreezing property can broaden its applicable temperature. This study offers new insight into the design of multifunctional ionic skin for wearable electronics.

Ducel Jean-Berluche

This review examines the transformative impact creativity has on mental health. Creative expression has the potential to promote the cognitive, emotional, physical, and social well-being of individuals of all ages. Drawing from various scholarly sources, including empirical studies and theoretical frameworks, this review synthesizes the current knowledge on the relationship between creativity and mental health. The review elucidates how creativity influences emotional regulation, cognitive flexibility, and social connectedness. Through a detailed literature search utilizing databases such as PubMed, PsycINFO, PsychARTICLES, and Google Scholar, research findings from articles across different creative activities, including visual arts, writing, music, and crafts/DIY projects, are discussed in conjunction with reported benefits on mental health and well-being. Furthermore, the review discusses the practical implications of the positive link between creative expression and mental health, emphasizing the relevance of this for therapeutic interventions and community programs. The findings highlight the need for further research to explore the underlying mechanisms, long-term effects, and potential cultural variations of the creativity-mental health relationship. This review provides a comprehensive overview of the positive influences, inviting researchers, practitioners, and policymakers to harness the healing power of creative expression.

Krishnapriya Vishnubhotla, Daniela Teodorescu, Mallory J. Feldman et al.

We are united in how emotions are central to shaping our experiences; and yet, individuals differ greatly in how we each identify, categorize, and express emotions. In psychology, variation in the ability of individuals to differentiate between emotion concepts is called emotion granularity (determined through self-reports of one's emotions). High emotion granularity has been linked with better mental and physical health; whereas low emotion granularity has been linked with maladaptive emotion regulation strategies and poor health outcomes. In this work, we propose computational measures of emotion granularity derived from temporally-ordered speaker utterances in social media (in lieu of self-reports that suffer from various biases). We then investigate the effectiveness of such text-derived measures of emotion granularity in functioning as markers of various mental health conditions (MHCs). We establish baseline measures of emotion granularity derived from textual utterances, and show that, at an aggregate level, emotion granularities are significantly lower for people self-reporting as having an MHC than for the control population. This paves the way towards a better understanding of the MHCs, and specifically the role emotions play in our well-being.

Yining Hua, Hongbin Na, Zehan Li et al.

Large language models (LLMs) are emerging as promising tools for mental health care, offering scalable support through their ability to generate human-like responses. However, the effectiveness of these models in clinical settings remains unclear. This scoping review aimed to assess the current generative applications of LLMs in mental health care, focusing on studies where these models were tested with human participants in real-world scenarios. A systematic search across APA PsycNet, Scopus, PubMed, and Web of Science identified 726 unique articles, of which 17 met the inclusion criteria. These studies encompassed applications such as clinical assistance, counseling, therapy, and emotional support. However, the evaluation methods were often non-standardized, with most studies relying on ad hoc scales that limit comparability and robustness. Privacy, safety, and fairness were also frequently underexplored. Moreover, reliance on proprietary models, such as OpenAI's GPT series, raises concerns about transparency and reproducibility. While LLMs show potential in expanding mental health care access, especially in underserved areas, the current evidence does not fully support their use as standalone interventions. More rigorous, standardized evaluations and ethical oversight are needed to ensure these tools can be safely and effectively integrated into clinical practice.

Andreas Triantafyllopoulos, Yannik Terhorst, Iosif Tsangko et al.

Digital technologies have long been explored as a complement to standard procedure in mental health research and practice, ranging from the management of electronic health records to app-based interventions. The recent emergence of large language models (LLMs), both proprietary and open-source ones, represents a major new opportunity on that front. Yet there is still a divide between the community developing LLMs and the one which may benefit from them, thus hindering the beneficial translation of the technology into clinical use. This divide largely stems from the lack of a common language and understanding regarding the technology's inner workings, capabilities, and risks. Our narrative review attempts to bridge this gap by providing intuitive explanations behind the basic concepts related to contemporary LLMs.

Yuchen Cao, Jianglai Dai, Zhongyan Wang et al.

The global increase in mental illness requires innovative detection methods for early intervention. Social media provides a valuable platform to identify mental illness through user-generated content. This systematic review examines machine learning (ML) models for detecting mental illness, with a particular focus on depression, using social media data. It highlights biases and methodological challenges encountered throughout the ML lifecycle. A search of PubMed, IEEE Xplore, and Google Scholar identified 47 relevant studies published after 2010. The Prediction model Risk Of Bias ASsessment Tool (PROBAST) was utilized to assess methodological quality and risk of bias. The review reveals significant biases affecting model reliability and generalizability. A predominant reliance on Twitter (63.8%) and English-language content (over 90%) limits diversity, with most studies focused on users from the United States and Europe. Non-probability sampling (80%) limits representativeness. Only 23% explicitly addressed linguistic nuances like negations, crucial for accurate sentiment analysis. Inconsistent hyperparameter tuning (27.7%) and inadequate data partitioning (17%) risk overfitting. While 74.5% used appropriate evaluation metrics for imbalanced data, others relied on accuracy without addressing class imbalance, potentially skewing results. Reporting transparency varied, often lacking critical methodological details. These findings highlight the need to diversify data sources, standardize preprocessing, ensure consistent model development, address class imbalance, and enhance reporting transparency. By overcoming these challenges, future research can develop more robust and generalizable ML models for depression detection on social media, contributing to improved mental health outcomes globally.

Jingyuan Li, Yansen Wang, Nie Lin et al.

Advancements in non-invasive electroencephalogram (EEG)-based Brain-Computer Interface (BCI) technology have enabled communication through brain activity, offering significant potential for individuals with motor impairments. Existing methods for decoding characters or words from EEG recordings either rely on continuous external stimulation for high decoding accuracy or depend on direct intention imagination, which suffers from reduced discrimination ability. To overcome these limitations, we introduce a novel EEG paradigm based on mental tasks that achieves high discrimination accuracy without external stimulation. Specifically, we propose a codebook in which each letter or number is associated with a unique code that integrates three mental tasks, interleaved with eye-open and eye-closed states. This approach allows individuals to internally reference characters without external stimuli while maintaining reasonable accuracy. For enhanced decoding performance, we apply a Temporal-Spatial-Latent-Dynamics (TSLD) network to capture latent dynamics of spatiotemporal EEG signals. Experimental results demonstrate the effectiveness of our proposed EEG paradigm which achieves five times higher accuracy over direct imagination. Additionally, the TSLD network improves baseline methods by approximately 8.5%. Further more, we observe consistent performance improvement throughout the data collection process, suggesting that the proposed paradigm has potential for further optimization with continued use.

Muskan Garg, Manas Gaur, Raxit Goswami et al.

Low self-esteem and interpersonal needs (i.e., thwarted belongingness (TB) and perceived burdensomeness (PB)) have a major impact on depression and suicide attempts. Individuals seek social connectedness on social media to boost and alleviate their loneliness. Social media platforms allow people to express their thoughts, experiences, beliefs, and emotions. Prior studies on mental health from social media have focused on symptoms, causes, and disorders. Whereas an initial screening of social media content for interpersonal risk factors and low self-esteem may raise early alerts and assign therapists to at-risk users of mental disturbance. Standardized scales measure self-esteem and interpersonal needs from questions created using psychological theories. In the current research, we introduce a psychology-grounded and expertly annotated dataset, LoST: Low Self esTeem, to study and detect low self-esteem on Reddit. Through an annotation approach involving checks on coherence, correctness, consistency, and reliability, we ensure gold-standard for supervised learning. We present results from different deep language models tested using two data augmentation techniques. Our findings suggest developing a class of language models that infuses psychological and clinical knowledge.