Hasil untuk "Mental healing"

Kaidong Feng, Zhu Sun, Roy Ka-Wei Lee et al.

Smartphone sensing offers an unobtrusive and scalable way to track daily behaviors linked to mental health, capturing changes in sleep, mobility, and phone use that often precede symptoms of stress, anxiety, or depression. While most prior studies focus on detection that responds to existing conditions, forecasting mental health enables proactive support through Just-in-Time Adaptive Interventions. In this paper, we present the first comprehensive benchmarking study comparing traditional machine learning (ML), deep learning (DL), and large language model (LLM) approaches for mental health forecasting using the College Experience Sensing (CES) dataset, the most extensive longitudinal dataset of college student mental health to date. We systematically evaluate models across temporal windows, feature granularities, personalization strategies, and class imbalance handling. Our results show that DL models, particularly Transformer (Macro-F1 = 0.58), achieve the best overall performance, while LLMs show strength in contextual reasoning but weaker temporal modeling. Personalization substantially improves forecasts of severe mental health states. By revealing how different modeling approaches interpret phone sensing behavioral data over time, this work lays the groundwork for next-generation, adaptive, and human-centered mental health technologies that can advance both research and real-world well-being.

Shuanglin Li, Siyang Song, Rajesh Nair et al.

Depression and Attention Deficit Hyperactivity Disorder (ADHD) stand out as the common mental health challenges today. In affective computing, speech signals serve as effective biomarkers for mental disorder assessment. Current research, relying on labor-intensive hand-crafted features or simplistic time-frequency representations, often overlooks critical details by not accounting for the differential impacts of various frequency bands and temporal fluctuations. Therefore, we propose a frequency-aware augmentation network with dynamic convolution for depression and ADHD assessment. In the proposed method, the spectrogram is used as the input feature and adopts a multi-scale convolution to help the network focus on discriminative frequency bands related to mental disorders. A dynamic convolution is also designed to aggregate multiple convolution kernels dynamically based upon their attentions which are input-independent to capture dynamic information. Finally, a feature augmentation block is proposed to enhance the feature representation ability and make full use of the captured information. Experimental results on AVEC 2014 and self-recorded ADHD dataset prove the robustness of our method, an RMSE of 9.23 was attained for estimating depression severity, along with an accuracy of 89.8\% in detecting ADHD.

Kazutoshi Shinoda, Nobukatsu Hojo, Kyosuke Nishida et al.

Existing Theory of Mind (ToM) benchmarks diverge from real-world scenarios in three aspects: 1) they assess a limited range of mental states such as beliefs, 2) false beliefs are not comprehensively explored, and 3) the diverse personality traits of characters are overlooked. To address these challenges, we introduce ToMATO, a new ToM benchmark formulated as multiple-choice QA over conversations. ToMATO is generated via LLM-LLM conversations featuring information asymmetry. By employing a prompting method that requires role-playing LLMs to verbalize their thoughts before each utterance, we capture both first- and second-order mental states across five categories: belief, intention, desire, emotion, and knowledge. These verbalized thoughts serve as answers to questions designed to assess the mental states of characters within conversations. Furthermore, the information asymmetry introduced by hiding thoughts from others induces the generation of false beliefs about various mental states. Assigning distinct personality traits to LLMs further diversifies both utterances and thoughts. ToMATO consists of 5.4k questions, 753 conversations, and 15 personality trait patterns. Our analysis shows that this dataset construction approach frequently generates false beliefs due to the information asymmetry between role-playing LLMs, and effectively reflects diverse personalities. We evaluate nine LLMs on ToMATO and find that even GPT-4o mini lags behind human performance, especially in understanding false beliefs, and lacks robustness to various personality traits.

Gökmen Arslan

This editorial explores the evolving landscape of positive psychology by tracing its development through three major waves and outlining emerging discussions toward a potential fourth. The first wave, launched in the late 1990s, emphasized individual strengths, positive emotions, and subjective wellbeing—largely shaped by Western epistemologies and dominated by quantitative, empirical methodologies. While foundational to the field, this wave has been critiqued for neglecting complexity, cultural diversity, and ethical concerns. In response, the second wave introduced a more dialectical understanding of wellbeing, integrating both positive and negative experiences and recognizing the transformative potential of adversity. This phase embraced contextual sensitivity, methodological pluralism, and cross-cultural considerations, fostering a more nuanced view of human flourishing. Building on these earlier developments, the third wave of positive psychology adopts a systems-level and interdisciplinary approach. It emphasizes interconnectedness, ecological and cultural contexts, spiritual dimensions, and social justice. This wave views wellbeing as a dynamic, relational, and ethically grounded phenomenon that transcends individual psychological states. Researchers increasingly engage in post-disciplinary collaborations, employing diverse methods to understand how flourishing unfolds across individuals, communities, and ecosystems. The editorial also highlights initial proposals for a fourth wave that aligns wellbeing science with global challenges such as sustainability, equity, and public health—framing flourishing as a shared responsibility in an interconnected world. Ultimately, this editorial calls for a reimagining of positive psychology as a science not just of personal happiness but of collective and sustainable wellbeing. By embracing complexity, humility, and global perspectives, future directions in the field can better serve the diverse needs of humanity and foster flourishing at both individual and societal levels.

Haoxiang Guan, Jiyan He, Shuxin Zheng et al.

Large language models (LLMs) have demonstrated impressive performance on many tasks. However, to achieve optimal performance, specially designed prompting methods are still needed. These methods either rely on task-specific few-shot examples that require a certain level of domain knowledge, or are designed to be simple but only perform well on a few types of tasks. In this work, we attempt to introduce the concept of generalist prompting, which operates on the design principle of achieving optimal or near-optimal performance on a wide range of tasks while eliminating the need for manual selection and customization of prompts tailored to specific problems. Furthermore, we propose MeMo (Mental Models), an innovative prompting method that is simple-designed yet effectively fulfills the criteria of generalist prompting. MeMo distills the cores of various prompting methods into individual mental models and allows LLMs to autonomously select the most suitable mental models for the problem, achieving or being near to the state-of-the-art results on diverse tasks such as STEM, logical reasoning, and commonsense reasoning in zero-shot settings. We hope that the insights presented herein will stimulate further exploration of generalist prompting methods for LLMs.

Rina Carines Cabral, Siwen Luo, Josiah Poon et al.

The significance of mental health classification is paramount in contemporary society, where digital platforms serve as crucial sources for monitoring individuals' well-being. However, existing social media mental health datasets primarily consist of text-only samples, potentially limiting the efficacy of models trained on such data. Recognising that humans utilise cross-modal information to comprehend complex situations or issues, we present a novel approach to address the limitations of current methodologies. In this work, we introduce a Multimodal and Multi-Teacher Knowledge Distillation model for Mental Health Classification, leveraging insights from cross-modal human understanding. Unlike conventional approaches that often rely on simple concatenation to integrate diverse features, our model addresses the challenge of appropriately representing inputs of varying natures (e.g., texts and sounds). To mitigate the computational complexity associated with integrating all features into a single model, we employ a multimodal and multi-teacher architecture. By distributing the learning process across multiple teachers, each specialising in a particular feature extraction aspect, we enhance the overall mental health classification performance. Through experimental validation, we demonstrate the efficacy of our model in achieving improved performance.

Jung In Park, Mahyar Abbasian, Iman Azimi et al.

Objective: This study aims to develop and validate an evaluation framework to ensure the safety and reliability of mental health chatbots, which are increasingly popular due to their accessibility, human-like interactions, and context-aware support. Materials and Methods: We created an evaluation framework with 100 benchmark questions and ideal responses, and five guideline questions for chatbot responses. This framework, validated by mental health experts, was tested on a GPT-3.5-turbo-based chatbot. Automated evaluation methods explored included large language model (LLM)-based scoring, an agentic approach using real-time data, and embedding models to compare chatbot responses against ground truth standards. Results: The results highlight the importance of guidelines and ground truth for improving LLM evaluation accuracy. The agentic method, dynamically accessing reliable information, demonstrated the best alignment with human assessments. Adherence to a standardized, expert-validated framework significantly enhanced chatbot response safety and reliability. Discussion: Our findings emphasize the need for comprehensive, expert-tailored safety evaluation metrics for mental health chatbots. While LLMs have significant potential, careful implementation is necessary to mitigate risks. The superior performance of the agentic approach underscores the importance of real-time data access in enhancing chatbot reliability. Conclusion: The study validated an evaluation framework for mental health chatbots, proving its effectiveness in improving safety and reliability. Future work should extend evaluations to accuracy, bias, empathy, and privacy to ensure holistic assessment and responsible integration into healthcare. Standardized evaluations will build trust among users and professionals, facilitating broader adoption and improved mental health support through technology.

David W. Vinson, Mihael Arcan, David-Paul Niland et al.

Employee well-being is a critical concern in the contemporary workplace, as highlighted by the American Psychological Association's 2021 report, indicating that 71% of employees experience stress or tension. This stress contributes significantly to workplace attrition and absenteeism, with 61% of attrition and 16% of sick days attributed to poor mental health. A major challenge for employers is that employees often remain unaware of their mental health issues until they reach a crisis point, resulting in limited utilization of corporate well-being benefits. This research addresses this challenge by presenting a groundbreaking stress detection algorithm that provides real-time support preemptively. Leveraging automated chatbot technology, the algorithm objectively measures mental health levels by analyzing chat conversations, offering personalized treatment suggestions in real-time based on linguistic biomarkers. The study explores the feasibility of integrating these innovations into practical learning applications within real-world contexts and introduces a chatbot-style system integrated into the broader employee experience platform. This platform, encompassing various features, aims to enhance overall employee well-being, detect stress in real time, and proactively engage with individuals to improve support effectiveness, demonstrating a 22% increase when assistance is provided early. Overall, the study emphasizes the importance of fostering a supportive workplace environment for employees' mental health.

Frederik Riedmann, Bernhard Egger, Tim Rohe

Previous studies have shown that faces are rated as more attractive when they are partially occluded. The cause of this observation remains unclear. One explanation is a mental reconstruction of the occluded face parts which is biased towards a more attractive percept as shown in face-attractiveness rating tasks. We aimed to test for this hypothesis by using a delayed matching-to-sample task, which directly requires mental reconstruction. In two online experiments, we presented observers with unattractive, neutral or attractive synthetic reconstructions of the occluded face parts using a state-of-the-art diffusion-based image generator. Our experiments do not support the initial hypothesis and reveal an unattractiveness bias for occluded faces instead. This suggests that facial attractiveness rating tasks do not prompt reconstructions. Rather, the attractivity bias may arise from global image features, and faces may actually be reconstructed with unattractive properties when mental reconstruction is applied.

T. S. Sathyanarayana Rao, Chittaranjan Andrade

Anjali Yadav, Prabhakar Tiwari, Rima Dada

Infertility presents multifaceted challenges that encompass both physical and emotional burdens. Yoga, as a comprehensive system of mind–body medicine, serves as an effective intervention for managing male factor infertility, a complex lifestyle disorder with significant psychosomatic elements. This review explores the transformative role of yoga in addressing both the emotional and physical dimensions of infertility. By incorporating physical postures, breath control and meditation, yoga promotes emotional well-being and enhances reproductive health by improving the integrity of nuclear and mitochondrial genomes as well as the epigenome. In addition, yoga contributes to maintaining sperm telomere length through the regulation of seminal free radical levels and increased telomerase activity, which are crucial for optimal embryo cleavage and the development of high-quality blastocysts. Integrating yoga as an adjunctive therapeutic approach fosters a supportive intrauterine environment and facilitates physiological homoeostasis, thereby increasing the likelihood of successful fertilisation and implantation. Gentle asanas and flowing sequences promote relaxation, alleviate tension and cultivate emotional stability, while meditation aids in emotional healing and resilience during the infertility journey. Specific asanas, such as Baddha Konasana (bound angle pose), Bhujangasana (cobra pose) and Sarvangasana (shoulder stand), stimulate reproductive organs, enhance blood circulation and regulate hormone production. Pranayama techniques further support endocrine balance and overall vitality. Moreover, yoga provides a non-invasive strategy for managing fertility-related conditions leading to improved reproductive health and overall well-being. This review aims to elucidate the comprehensive role of yoga in improving male infertility, focusing on its impact on sperm nuclear and mitochondrial genomes, the epigenome and telomere health. In addition, it underscores the importance of self-care, open communication and shared experiences with partners. Practicing yoga regularly supports psychosocial well-being, promotes holistic healing, enhances physical and mental health and probably helps in improving reproductive health, thereby fostering resilience and self-efficacy throughout the journey of fertility and reproduction.

Sean Kim, Samuel Kim

Mental disorders impact the lives of millions of people globally, not only impeding their day-to-day lives but also markedly reducing life expectancy. This paper addresses the persistent challenge of predicting mortality in patients with mental diagnoses using predictive machine-learning models with electronic health records (EHR). Data from patients with mental disease diagnoses were extracted from the well-known clinical MIMIC-III data set utilizing demographic, prescription, and procedural information. Four machine learning algorithms (Logistic Regression, Random Forest, Support Vector Machine, and K-Nearest Neighbors) were used, with results indicating that Random Forest and Support Vector Machine models outperformed others, with AUC scores of 0.911. Feature importance analysis revealed that drug prescriptions, particularly Morphine Sulfate, play a pivotal role in prediction. We applied a variety of machine learning algorithms to predict 30-day mortality followed by feature importance analysis. This study can be used to assist hospital workers in identifying at-risk patients to reduce excess mortality.

Hayrettin Tumani

The limited data available on CSF analysis in patients with COVID-19 prompted us to conduct a large-scale multicenter study on behalf of the German Society for CSF Diagnostics and Clinical Neurochemistry (DGLN), taking into account a wide range of parameters, including white blood cell count (WCC) in CSF and cytology, quantitative and qualitative detection of intrathecal IgG, IgM and IgA synthesis, markers of blood CSF barrier (BCB) dysfunction, total protein, lactate, glucose and SARS-CoV-2 CSF polymerase chain reaction (PCR), antibody indices (AI), autoantibody findings and cytokine levels. CSF findings from 150 lumbar punctures in 127 patients with PCR-proven COVID-19 and neurologic symptoms showed that direct CNS infection with SARS-CoV-2 appears to be rare. The major findings were BCB dysfunction in the absence of intrathecal inflammation consistent with cerebrospinal endotheliopathy and a decrease in CSF flow rate. Cytokine levels were frequently elevated in CSF (often in association with BCB dysfunction) and serum. Persistent BCB dysfunction and elevated cytokine levels may contribute to both acute symptoms and ''long COVID.'' Further analysis of COVID-19 patients with longitudinal data is warranted.

Desiree Armas, Israel Juarez, Jennifer Ayala et al.

This paper describes how a collective of Latine youth and adult allies used art-based approaches in a participatory action research project to better understand the ways in which young U.S. Latines make meaning of wellbeing. In this study, we interviewed 19 individuals who identified as Latino/a/e, ages 19–24, from Colorado, Washington state and New Jersey. Our team intentionally chose art-based approaches, including music and painting, as analytical tools and healing methods to synthesize the responses of the Latine youth we interviewed. We found that Latine youth and young adults initially struggle with defining wellbeing, considering it to be an overly abstract concept or something only achievable through expensive, Western-based medical practices. We also found that many Latine youth often link the root cause of a majority of their mental health issues to numerous systemic terrors such as racism, capitalism and sexism that directly harm their most intimate and supportive relationships: their immediate or extended family and friendships. Young Latine adults have identified these components as pillars of their wellbeing, along with the need for intergenerational conversations, a sense of convivencia, rootedness with freedom of movement and our right to healing and joy.

Bo Kim, Christopher J. Miller, Mona J. Ritchie et al.

Background: Facilitation is an effective strategy to implement evidence-based practices, often involving external facilitators (EFs) bringing content expertise to implementation sites. Estimating time spent on multifaceted EF activities is complex. Furthermore, collecting continuous time–motion data for facilitation tasks is challenging. However, organizations need this information to allocate implementation resources to sites. Thus, our objectives were to conduct a time–motion analysis of external facilitation, and compare continuous versus noncontinuous approaches to collecting time–motion data. Methods: We analyzed EF time–motion data from six VA mental health clinics implementing the evidence-based Collaborative Chronic Care Model (CCM). We documented EF activities during pre-implementation (4–6 weeks) and implementation (12 months) phases. We collected continuous data during the pre-implementation phase, followed by data collection over a 2-week period (henceforth, “a two-week interval”) at each of three time points (beginning/middle/end) during the implementation phase. As a validity check, we assessed how closely interval data represented continuous data collected throughout implementation for two of the sites. Results: EFs spent 21.8 ± 4.5 h/site during pre-implementation off-site, then 27.5 ± 4.6 h/site site-visiting to initiate implementation. Based on the 2-week interval data, EFs spent 2.5 ± 0.8, 1.4 ± 0.6, and 1.2 ± 0.6 h/week toward the implementation’s beginning, middle, and end, respectively. Prevalent activities were preparation/planning, process monitoring, program adaptation, problem identification, and problem-solving. Across all activities, 73.6% of EF time involved email, phone, or video communication. For the two continuous data sites, computed weekly time averages toward the implementation’s beginning, middle, and end differed from the interval data’s averages by 1.0, 0.1, and 0.2 h, respectively. Activities inconsistently captured in the interval data included irregular assessment, stakeholder engagement, and network development. Conclusions: Time–motion analysis of CCM implementation showed initial higher-intensity EF involvement that tapered. The 2-week interval data collection approach, if accounting for its potential underestimation of irregular activities, may be promising/efficient for implementation studies collecting time–motion data.

Julia Barbara Krakowczyk, Jari Planert, Eva-Maria Skoda et al.

Kariena Permanasari, Dian Kartika Amelia Arbi

Gangguan makan merupakan salah satu gangguan dengan prevalensi tinggi, terutama pada remaja sebagai usia rentan. Gangguan tersebut berbahaya bagi remaja, karena dampaknya bahkan dapat menyebabkan kegagalan remaja dalam mencapai tugas perkembangan. Remaja mengalami pubertas yang menyebabkan berbagai perubahan fisik dan kognitif, yang membuat remaja terus memperhatikan tubuhnya. Peneliti menduga bahwa body dissatisfaction dapat mempengaruhi kecenderungan gangguan makan. Hasil penelitian terdahulu masih memiliki hasil yang bertentangan pula, sehingga penelitian dilakukan dengan tujuan membuktikan secara empiris pengaruh body dissatisfaction terhadap kecenderungan eating disorder pada remaja. Penelitian dilakukan dengan metode survei cross-sectional dengan total 117 partisipan. Hasil penelitian menunjukkan bahwa terdapat pengaruh signifikan dari body dissatisfaction terhadap kecenderungan eating disorder sehingga H0 ditolak (X2(1,115)=79,4, p<0,001; R2McF=0,506; R2CS=0,493; R2N=0,667). Body dissatisfaction terbukti secara empiris mampu menjelaskan 66,7% varians dari kecenderungan gangguan makan.

Harsh Sakhrani, Saloni Parekh, Shubham Mahajan

It may be difficult for some individuals to open up and share their thoughts and feelings in front of a mental health expert. For those who are more at ease with a virtual agent, conversational agents can serve as an intermediate step in the right direction. The conversational agent must therefore be empathetic and able to conduct free-flowing conversations. To this effect, we present an approach for creating a generative empathetic open-domain chatbot that can be used for mental health applications. We leverage large scale pre-training and empathetic conversational data to make the responses more empathetic in nature and a multi-turn dialogue arrangement to maintain context. Our models achieve state-of-the-art results on the Empathetic Dialogues test set.

Ananya Bhattacharjee, S M Taiabul Haque, Abdul Hady et al.

Objective: This study aims to identify the social determinants of mental health among undergraduate students in Bangladesh, a developing nation in South Asia. Our goal is to identify the broader social determinants of mental health among this population, study the manifestation of these determinants in their day-to-day life, and explore the feasibility of self-monitoring tools in helping them identify the specific factors or relationships that impact their mental health. Methods: We conducted a 21-day study with 38 undergraduate students from seven universities in Bangladesh. We conducted two semi-structured interviews: one pre-study and one post-study. During the 21-day study, participants used an Android application to self-report and self-monitor their mood after each phone conversation. The app prompted participants to report their mood after each phone conversation and provided graphs and charts so that participants could independently review their mood and conversation patterns. Results: Our results show that academics, family, job and economic condition, romantic relationships, and religion are the major social determinants of mental health among undergraduate students in Bangladesh. Our app helped the participants pinpoint the specific issues related to these factors as participants could review the pattern of their moods and emotions from past conversation history. Although our app does not provide any explicit recommendation, participants took certain steps on their own to improve their mental health (e.g., reduced the frequency of communication with certain persons). Conclusions: Overall, the findings from this study would provide better insights for the researchers to design better solutions to help the younger population from this part of the world.