Search Results (50)

Background: Cerebral palsy (CP) is a lifelong neurodevelopmental disorder characterised by motor impairment and commonly associated with comorbidities such as cognitive, communicative, and behavioural difficulties. While the physical and functional aspects of CP have been extensively studied, the mental health needs of this population remain largely underexplored, particularly concerning suicidal ideation and self-injurious behaviours. The purpose of this review is to synthesise the existing literature on suicidality in individuals with CP, explore theoretical and clinical risk factors, and identify key gaps in the current evidence base. Methods: A narrative literature review was conducted focusing on studies addressing suicidal ideation, self-harm, or related psychiatric outcomes in individuals with CP. Additional literature on risks and protective factors was included to support theoretical inferences and clinical interpretations. Results: Only a limited number of studies addressed suicidality directly in CP populations. However, several reports document elevated rates of depression, anxiety, and emotional distress, particularly among adults and individuals with higher levels of functioning. Communication barriers, chronic pain, social exclusion, and lack of accessible mental health services emerged as critical risk factors. Protective elements included strong family support, inclusive environments, and access to augmentative communication. Conclusions: Suicidality in individuals with CP is a neglected yet potentially serious concern. Evidence suggests underdiagnosis due to factors such as communication barriers and diagnostic overshadowing. Future research should prioritise disability-informed methodologies and validated tools for suicidal ideation, while clinicians should incorporate routine, adapted mental health screening in CP care to ensure early detection and person-centred management. Full article

Suicide is a major public health concern, and accurate risk assessment is essential for prevention. Slider-format questions offer a quick, intuitive, and accessible method to evaluate suicide-related dimensions. This study examines the reliability of slider-based items compared to standardized psychometric instruments when delivered via a mobile app. A total of 299 university students completed a digital self-report questionnaire using the AuxiliApp mobile platform. Participants answered validated scales assessing depression, psychological pain, suicidal ideation, anger, impulsivity, loneliness, and reasons for living, each presented in both traditional Likert and novel slider formats. Pearson correlations were used to evaluate the relationship between traditional and slider-based scores. All correlations were statistically significant (p < 0.001). Moderate correlations were found in most domains, including depression, psychological pain, suicidal ideation, loneliness, and key aspects of impulsivity and anger. Lower correlations appeared in subscales related to anger control and protective beliefs against suicide. Slider-based items demonstrated acceptable psychometric equivalence and concurrent validity compared to traditional scales. Their brevity and compatibility with mobile devices support their use in telehealth and digital mental health screening. While not a replacement for clinical evaluation, they may facilitate early detection and ongoing monitoring in at-risk populations. Full article

Background: Unhealthy lifestyles constitute significant risk factors for adolescent suicide, and their detrimental effects may persist from adolescence into adulthood. This research study sought to examine how Body Mass Index (BMI), alongside various lifestyle behaviors among teenagers in Korea, correlates with suicidal thoughts, the formulation of suicide plans, and actual suicide attempts. Methods: The research examined unprocessed information collected during the 2022 Korean Youth Risk Behavior Web-based Survey (KYRBS), which was administered by the Korea Disease Control and Prevention Agency (KDCA). Lifestyle factors associated with suicidal behavior were selected as independent variables. The sample was stratified according to BMI for further analysis. Logistic regression models were applied to assess the association between lifestyle factors and the risk of adolescent suicide. Results: The analysis identified significant correlations between unhealthy dietary patterns, hazardous drinking behavior, smoking, and a sleep duration of less than 5 h, all of which were associated with a heightened suicide risk among adolescents. Notably, underweight adolescents who had a sleep duration of less than 5 h demonstrated a markedly elevated risk of suicidal ideation (OR = 2.391, 95% CI [1.035–5.525]). Among overweight adolescents, frequent coffee consumption was significantly associated with both suicidal planning (OR = 1.850, 95% CI [1.133–3.020]) and suicide attempts (OR = 1.958, 95% CI [1.024–3.742]). Importantly, hazardous drinking behavior was strongly associated with suicide attempts (OR = 2.277, 95% CI [1.132–4.580]). Non-smoking behavior exhibited a significant relationship with a decreased likelihood of suicidal ideation (OR = 0.706, 95% CI [0.507–0.983]) and suicidal planning (OR = 0.528, 95% CI [0.299–0.930]). Furthermore, among obese adolescents, non-smoking behavior significantly decreased the risk of suicidal ideation compared to smoking (OR = 0.514, 95% CI [0.297–0.887]). Conclusions: The study revealed that the combined impact of unhealthy behaviors—smoking, eating an unhealthy breakfast, sleeping for less than 5 h, and hazardous drinking behavior—significantly affect suicide-related behaviors in adolescents. The interaction between BMI and lifestyle factors is a critical determinant of these behaviors. Specifically, sleep health exerts a substantial influence on suicide-related behaviors in underweight adolescents, while smoking strongly correlates with suicidal behaviors in overweight and obese adolescents. Targeted attention to the interplay of smoking, diet, sleep, and alcohol consumption with BMI is crucial for the early detection and prevention of adolescent suicide. Full article

Background: Previous studies have demonstrated that involuntary smoking (e.g., secondhand smoke [SHS] and thirdhand smoke [THS]) is not only associated with an increased risk of several physical health problems, such as cardiovascular disease and cancer, but also impacts mental health, including depression and anxiety. However, the relationships between SHS and THS exposure and non-suicidal self-injury (NSSI), suicidal ideation (SI), and suicide attempts (SAs) remain unclear. Methods: Participants were recruited at a Chinese vocational college via voluntary online surveys conducted on campus. Self-reported SHS exposure was determined by the frequency of contact with smokers or detecting tobacco odors in living environments, while THS was assessed through regular contact with smoker-contaminated surfaces (e.g., clothing, furniture, textiles). Logistic regression analysis was performed to evaluate the associations of SHS and THS exposure with the prevalence of NSSI, SI, and SAs in never-smoking participants. Results: The study included 5716 participants (mean age = 19.3 years; females, 85.4%). The prevalence of SHS and THS exposure was 87.6% and 77.4%, with 8.8% reporting ≥15 min of SHS exposure on at least one day per week. After controlling for potential covariates, exposure to SHS (≥15 min on at least one day per week) was significantly associated with the odds of SAs (OR [95%CI] = 1.85 [1.17–2.91]). Additionally, daily THS exposure was significantly associated with increased past-year NSSI prevalence (2.35 [1.29–4.28]) compared to those without THS exposure, with similar associations observed for SI (2.11 [1.28–3.48]) and SAs (2.40 [1.23–4.69]). Conclusions: Exposure to SHS and THS was significantly associated with increased likelihood of NSSI, SI, and SAs among young adults at a Chinese vocational college. Further studies are needed to validate these associations across more diverse populations. Full article

Mental health disorders represent a substantial global health concern, impacting millions and placing a significant burden on public health systems. Natural Language Processing (NLP) has emerged as a promising tool for analyzing large textual datasets to identify and predict mental health challenges. The aim of this scoping review is to identify the Arabic NLP techniques employed in mental health research, the specific mental health conditions addressed, and the effectiveness of these techniques in detecting and predicting such conditions. This scoping review was conducted according to the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) framework. Studies were included if they focused on the application of NLP techniques, addressed mental health issues (e.g., depression, anxiety, suicidal ideation) within Arabic text data, were published in peer-reviewed journals or conference proceedings, and were written in English or Arabic. The relevant literature was identified through a systematic search of four databases: PubMed, ScienceDirect, IEEE Xplore, and Google Scholar. The results of the included studies revealed a variety of NLP techniques used to address specific mental health issues among Arabic-speaking populations. Commonly utilized techniques included Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), Recurrent Neural Network (RNN), and advanced transformer-based models such as AraBERT and MARBERT. The studies predominantly focused on detecting and predicting symptoms of depression and suicidality from Arabic social media data. The effectiveness of these techniques varied, with trans-former-based models like AraBERT and MARBERT demonstrating superior performance, achieving accuracy rates of up to 99.3% and 98.3%, respectively. Traditional machine learning models and RNNs also showed promise but generally lagged in accuracy and depth of insight compared to transformer models. This scoping review highlights the significant potential of NLP techniques, particularly advanced transformer-based models, in addressing mental health issues among Arabic-speaking populations. Ongoing research is essential to keep pace with the rapidly evolving field and to validate current findings. Full article

Social media has become an essential platform for understanding human behavior, particularly in relation to mental health conditions such as depression and suicidal tendencies. Given the increasing reliance on digital communication, the ability to automatically detect individuals at risk through their social media activity holds significant potential for early intervention and mental health support. This study proposes a machine learning-based framework that integrates pre-trained language models and advanced feature selection techniques to improve the detection of depression and suicidal tendencies from social media data. We utilize six diverse datasets, collected from platforms such as Twitter and Reddit, ensuring a broad evaluation of model robustness. The proposed methodology incorporates Cumulative Weight-based Iterative Neighborhood Component Analysis (CWINCA) for feature selection and Support Vector Machines (SVMs) for classification. The results indicate that the model achieves high accuracy across multiple datasets, ranging from 80.74% to 99.96%, demonstrating its effectiveness in identifying risk factors associated with mental health issues. These findings highlight the potential of social media-based automated detection methods as complementary tools for mental health professionals. Future work will focus on real-time detection capabilities and multilingual adaptation to enhance the practical applicability of the proposed approach. Full article

The timely identification of suicidal ideation on social media is pivotal for global suicide prevention efforts. Addressing the challenges posed by the unstructured nature of social media data, we present a novel Chinese-based dual-channel model, DSI-BTCNN, which leverages deep learning to discern patterns indicative of suicidal ideation. Our model is designed to process Chinese data and capture the nuances of text locality, context, and logical structure through a fine-grained text enhancement approach. It features a complex parallel architecture with multiple convolution kernels, operating on two distinct task channels to mine relevant features. We propose an information gain-based IDFN fusion mechanism. This approach efficiently allocates computational resources to the key features associated with suicide by assessing the change in entropy before and after feature partitioning. Evaluations on a customized dataset reveal that our method achieves an accuracy of 89.64%, a precision of 92.84%, an F1-score of 89.24%, and an AUC of 96.50%, surpassing TextCNN and BiLSTM models by an average of 4.66%, 12.85%, 3.08%, and 1.66%, respectively. Notably, our proposed model has an entropy value of 81.75, which represents a 17.53% increase compared to the original DSI-BTCNN model, indicating a more robust detection capability. This enhanced detection capability is vital for real-time social media monitoring, offering a promising tool for early intervention and potentially life-saving support. Full article

Background: Studies have shown that gay men experience higher levels of depression and are more likely to report suicidal ideation, plans, and attempts over their lifetime compared to heterosexual men. However, most studies have been conducted with adolescents and young adults, while there is a lack of research focusing on older adults. The aims of this study are to assess the level of depressive symptoms among older gay men and examine the associations between five key factors—loneliness, internalized homophobia, self-esteem, ageism, health behavior—and depressive symptoms. Methods: The convenience sample included seventy-nine gay men living in the community. Prospective participants were recruited by facilitators of social and support groups, who either distributed the questionnaire directly to members on-site or forwarded a link to their emails. All study measures used were valid and reliable. Results: Participants’ mean level of depression exceeded the scale’s cutoff point for detecting depression, indicating mild depression. Four variables made a significant contribution to the explanation of depression, with loneliness having the largest contribution, followed by ageism, internalized homophobia, and health behavior. The regression model explained a very high percentage of the depression variance (83%). Conclusions: These four factors are central to understanding depression among older gays. Medical and social professionals should recognize their significance and incorporate them into the treatment provided to those in need. Further studies are needed to gain a deeper understanding of the factors associated with depression in this vulnerable population. Full article

As suicide rates increase globally, there is a growing need for effective, data-driven methods in mental health monitoring. This study leverages advanced artificial intelligence (AI), particularly natural language processing (NLP) and machine learning (ML), to identify suicidal ideation from Twitter data. A predictive model was developed to process social media posts in real time, using NLP and sentiment analysis to detect textual and emotional cues associated with distress. The model aims to identify potential suicide risks accurately, while minimizing false positives, offering a practical tool for targeted mental health interventions. The study achieved notable predictive performance, with an accuracy of 85%, precision of 88%, and recall of 83% in detecting potential suicide posts. Advanced preprocessing techniques, including tokenization, stemming, and feature extraction with term frequency–inverse document frequency (TF-IDF) and count vectorization, ensured high-quality data transformation. A random forest classifier was selected for its ability to handle high-dimensional data and effectively capture linguistic and emotional patterns linked to suicidal ideation. The model’s reliability was supported by a precision–recall AUC score of 0.93, demonstrating its potential for real-time mental health monitoring and intervention. By identifying behavioral patterns and triggers, such as social isolation and bullying, this framework provides a scalable and efficient solution for mental health support, contributing significantly to suicide prevention strategies worldwide. Full article

Background: Fibromyalgia (FM) is a complex condition marked by increased pain sensitivity and central sensitization. Studies often explore the link between FM and depressive anxiety disorders, but few focus on dysthymia or persistent depressive disorder (PDD), which can be more disabling than major depression (MD). Objective: To identify clinical scales and subscales of the Personality Assessment Inventory (PAI) that effectively describe and differentiate the psychological profile of PDD, with or without comorbid MD, in FM patients with PDD previously dimensionally classified by the Millon Clinical Multiaxial Inventory III (MCMI-III). Method: An observational, cross-sectional study was conducted with 66 women (mean age 49.18, SD = 8.09) from Hospital del Mar. The PAI, the MCMI-III, and the Fibromyalgia Impact Questionnaire (FIQ) were used to assess the sample. Results: The PAI showed strong discriminative ability in detecting PDD, characterized by high scores in cognitive and emotional depression and low scores in identity alteration, dominance, and grandeur. High scores in cognitive, emotional, and physiological depression, identity alteration, cognitive anxiety, and suicidal ideation, along with low scores in dominance and grandeur, were needed to detect MD with PDD. Discriminant analysis could differentiate 69.6–73.9% of the PDD group and 84.6% of the PDD+MD group. Group comparisons showed that 72.2% of patients with an affective disorder by PAI were correctly classified in the MCMI-III affective disorder group, and 70% without affective disorder were correctly classified. Conclusions: The PAI effectively identifies PDD in FM patients and detects concurrent MD episodes, aiding in better prognostic and therapeutic guidance. Full article

Suicide is a global public health problem that takes hundreds of thousands of lives each year. The key to effective suicide prevention is early detection of suicidal ideations and timely intervention. However, several factors hinder traditional suicide risk screening methods. Primarily, the social stigma associated with suicide presents a challenge to suicidal ideation detection, as existing methods require patients to explicitly communicate their suicidal propensities. In contrast, progressively more at-risk people choose online platforms—such as Reddit—as their preferred avenues for sharing their suicidal experiences and seeking emotional support. As a result, these online platforms have become an unobtrusive source of user-generated textual data that can be used to detect suicidality with supervised machine learning and natural language processing techniques. In this paper, we proposed a suicidal ideation detection approach that combines textual and psycholinguistic features extracted from the Reddit forum. Subsequently, we selected the most informative features using the Boruta algorithm and employed four classifiers: logistic regression, naïve Bayes, support vector machines, and random forest. The naïve Bayes models trained with the combination of term frequency-inverse document frequency (TF-IDF) and National Research Council (NRC) features demonstrated the highest performance, obtaining a F1 score of 70.99%. Our experimental results illustrate that a combination of textual and psycholinguistic features yields better classification performance compared to using those features separately. Full article

Background: Impulsivity assessment may serve as a valuable clinical tool in the stratification of suicide risk. Acting without forethought is a crucial feature in the psychopathology of many psychiatric disturbances and corresponds with suicidal ideations, behaviors, and attempts. Methods: We present data on biological and psychological correlates of impulsivity among young adults (n = 47). Psychological analysis included both the self-description questionnaire—Barratt Impulsiveness Scale (BIS-11)—and neuropsychological behavioral tests, including the Iowa Gambling Task (IGT), the Simple Response Time task (SRT), and the Continuous Performance Test (CPT). mRNA and micro-RNA were isolated from peripheral blood mononuclear cells (PBMC). Expression levels of Brain-Derived Neurotrophic Factor (BDNF) mRNA and its regulatory micro RNAs, mir-1-3p, mir-15a-5p, mir-26a-5p, mir-26b-5p, and mir-195-5p, were analyzed using the quantitative reverse transcription polymerase chain reaction (RT-qPCR) method. proBDNF and BDNF plasma protein levels were quantified using enzyme-linked immunosorbent assay (ELISA). Results: Significant correlations between BDNF mRNA and mir-15a-5p as well as proBDNF levels and mir-1-3p were detected. proBDNF protein levels correlated with motor and perseverance, while mir-26b correlated with cognitive complexity subdimensions of the BIS-11 scale. Correlations between BDNF, miRNAs, and the results of neuropsychological tests were also detected. Conclusions: The BDNF pathway shows a clinical potential in searching for biomarkers of impulse-control impairment. BDNF-regulatory micro-RNAs are detectable and related to clinical parameters in the studied population, which needs further research. Full article

Background: The early detection of mental health crises is crucial for timely interventions and improved outcomes. This study explores the potential of artificial intelligence (AI) in analyzing social media data to identify early signs of mental health crises. Methods: We developed a multimodal deep learning model integrating natural language processing and temporal analysis techniques. The model was trained on a diverse dataset of 996,452 social media posts in multiple languages (English, Spanish, Mandarin, and Arabic) collected from Twitter, Reddit, and Facebook over 12 months. Its performance was evaluated using standard metrics and validated against expert psychiatric assessments. Results: The AI model demonstrated a high level of accuracy (89.3%) in detecting early signs of mental health crises, with an average lead time of 7.2 days before human expert identification. Performance was consistent across languages (F1 scores: 0.827–0.872) and platforms (F1 scores: 0.839–0.863). Key digital markers included linguistic patterns, behavioral changes, and temporal trends. The model showed varying levels of accuracy for different crisis types: depressive episodes (91.2%), manic episodes (88.7%), suicidal ideation (93.5%), and anxiety crises (87.3%). Conclusions: AI-powered analysis of social media data shows promise for the early detection of mental health crises across diverse linguistic and cultural contexts. However, ethical challenges, including privacy concerns, potential stigmatization, and cultural biases, need careful consideration. Future research should focus on longitudinal outcome studies, ethical integration of the method with existing mental health services, and developing personalized, culturally sensitive models. Full article

Identifying phenotypes at high risk of suicidal behaviour is a relevant objective of clinical and translational research and can facilitate the identification of possible candidate biomarkers. We probed the potential association and eventual stability of neuropsychological profiles and serum BDNF concentrations with lifetime suicide ideation and attempts (LSI and LSA, respectively) in individuals with schizophrenia (SCZ) and schizoaffective disorder (SCA) in a 2-year follow-up study. A secondary analysis was conducted on a convenience sample of previously recruited subjects from a single outpatient clinic. Retrospectively assessed LSI and LSA were recorded by analysing the available longitudinal clinical health records. LSI + LSA subjects consistently exhibited lower PANSS-defined negative symptoms and better performance in the BACS-letter fluency subtask. There was no significant association between BDNF levels and either LSI or LSA. We found a relatively stable pattern of lower negative symptoms over two years among patients with LSI and LSA. No significant difference in serum BDNF concentrations was detected. The translational viability of using neuropsychological profiles as a possible avenue for the identification of populations at risk for suicide behaviours rather than the categorical diagnosis represents a promising option but requires further confirmation. Full article

Linguistic features, particularly the use of first-person singular pronouns (FPSPs), have been identified as potential indicators of suicidal ideation. Machine learning (ML) and natural language processing (NLP) have shown potential in suicide detection, but their clinical applicability remains underexplored. This study aimed to identify linguistic features associated with suicidal ideation and develop ML models for detection. NLP techniques were applied to clinical interview transcripts (n = 319) to extract relevant features, including four cases of FPSP (subjective, objective, dative, and possessive cases) and first-person plural pronouns (FPPPs). Logistic regression analyses were conducted for each linguistic feature, controlling for age, gender, and depression. Gradient boosting, support vector machine, random forest, decision tree, and logistic regression were trained and evaluated. Results indicated that all four cases of FPSPs were associated with depression (p < 0.05) but only the use of objective FPSPs was significantly associated with suicidal ideation (p = 0.02). Logistic regression and support vector machine models successfully detected suicidal ideation, achieving an area under the curve (AUC) of 0.57 (p < 0.05). In conclusion, FPSPs identified during clinical interviews might be a promising indicator of suicidal ideation in Chinese patients. ML algorithms might have the potential to aid clinicians in improving the detection of suicidal ideation in clinical settings. Full article