Suicidal thoughts and behaviors (STBs) emerge during adolescence and often persist or escalate into adulthood. Although depression is a well-established risk factor, it remains unclear how specific depressive symptoms distinctly predict the escalation of STBs over time. Using data from the National Longitudinal Study of Adolescent to Adult Health, we applied adjacent-category logit models, an ordinal regression approach that estimates the odds of progression between levels of suicidality, to examine how individual depressive symptoms predicted STBs cross-sectionally, longitudinally, and in terms of escalation. In cross-sectional models, poor appetite, sadness, feelings of worthlessness, and sleep disturbance were significantly associated with suicidal ideation (SI), while psychomotor retardation and identifying as White were associated with lower odds. Longitudinally, sadness, being female, and prior ideation predicted later SI, while poor appetite was inversely associated. Feelings of worthlessness, poor appetite, and prior suicide attempts predicted progression from ideation to attempt. No depressive symptoms significantly predicted progression to serious suicide attempts. In transitional models, sadness, being female, younger age, and identifying as a racial/ethnic minority predicted escalation in suicidal severity over time. The findings suggest that targeting specific symptoms, rather than overall depression severity, may improve early identification and intervention for adolescents at risk of escalating suicide risk.
Depressive, anxiety, and fatigue symptoms are highly prevalent in people with multiple sclerosis (pwMS) and have been found to co-occur. Together, these symptoms result in poorer outcomes for pwMS. However, the network topology of comorbid depression, anxiety, and fatigue in pwMS has been to be investigated. We estimated depressive, anxiety, and fatigue symptom networks using data from the same people with multiple sclerosis at two time points: at baseline (N = 272) and at 6-months follow-up (N = 141). Expected influence (EI) centrality analyses were performed to estimate the relative influence of each symptom within the two networks. Bridge EI and community analyses were performed to identify potential bridge symptoms and densely connected symptom groups. 'Worthlessness' and 'anhedonia' emerged with the highest EI at baseline and follow-up, respectively. In terms of bridging symptoms, 'worthlessness', 'afraid something awful would happen', and 'fatigue severity' emerged as potential bridging symptoms that clustered depressive, anxiety, and fatigue symptoms in pwMS. This changed to 'restlessness', 'uncontrollable worry', and 'suicidal ideation' at follow-up. Further analyses indicated that the two networks remained similar with respect to global strength (p = .97) CONCLUSIONS: Our findings demonstrate that depressive, anxiety, and fatigue symptoms are highly interconnected in MS. Identifying bridging symptoms may allow for a renewed therapeutic focus and avenue for symptomatic improvement across board areas of psychopathology in MS.
Depression is a prevalent and heterogeneous symptom among cancer survivors, yet gender-specific differences in symptom patterns remain underexplored. This study aims to characterize gender-specific depressive-symptom networks in cancer survivors and identify optimal intervention targets via computer-simulated interventions. In this cross-sectional study of 2141 cancer survivors from seven National Health and Nutrition Examination Survey (NHANES) cycles, we compared Patient Health Questionnaire-9 (PHQ-9) scores by gender. We used Gaussian graphical models to identify core symptoms, applied Bayesian network analysis to map symptom activation pathways, and conducted computer-simulated interventions to pinpoint gender-specific treatment targets. Overall, the average PHQ-9 scores were relatively low, suggesting very mild reported depressive symptoms. In men's networks, three central nodes, specifically "depressed mood", "anhedonia", and "psychomotor changes" emerged. "Worthlessness" was identified as the primary driver of "suicidal ideation", with "worthlessness" and "fatigue" as the potential intervention targets. In women's networks, "depressed mood" served as the only core symptom driving "concentration difficulties", with "depressed mood" and "psychomotor changes" as the potential intervention targets. Distinct gender-specific differences in core depressive emerged in cancer survivors' symptom networks. These findings provide direction for targeted psychosocial interventions in oncology care.
Suicidal ideation is often assessed using a single self-report item in routine screening. We developed a model that combines machine learning with symptom-network analytics to infer an auxiliary signal relevant to suicidal ideation from routine depressive-symptom data. Adults from the National Health and Nutrition Examination Survey (N = 44,922) were used to predict ideation (PHQ-9 item 9 ≥1) under three specifications: (1) PHQ-8 total score; (2) eight PHQ-8 items; and (3) those items plus 37 network features (8 centrality measures, 28 edges, and 1 density). Data was split 70/30 and trained using 10-fold cross-validation with fold-internal class balancing. Precision-recall area under the curve (PR AUC) was the primary metric. External validation used five independent datasets (total N = 808,023) with normalized PR AUC for comparison. Item-level models outperformed the PHQ-8 total-score baseline. With network features, XGBoost yielded the strongest performance. The optimized network-augmented XGBoost met the prespecified screening criterion (recall/sensitivity ≥0.80; specificity ≥0.50) and achieved PR AUC 0.37, improving on the total-score baseline (0.32). The analysis highlighted the importance of the centrality and severity of depressed mood and worthlessness/guilt, the overall density, and the edges linking depressed mood with worthlessness/guilt and sleep disturbance with psychomotor change. Across five external datasets, normalized PR AUCs ranged 0.32-0.51. Cross-sectional data limit causality. Thresholds prioritize first-line screening over confirmation. Integrating symptom-network features with machine learning enhanced interpretability while maintaining discrimination over item-only models and outperforming the PHQ-8 total-score baseline. The optimized model satisfies pragmatic screening criteria and is suited for first-line case finding.
Depression and anxiety are highly comorbid during pregnancy, yet their symptom patterns may vary across gestational stages. Most existing studies rely on cross-sectional designs and overlook the dynamic interplay of symptoms over time. This study employs both cross-sectional and longitudinal network analyses to identify core and bridge symptoms and explore their temporal evolution throughout pregnancy. A total of 41,140 pregnant women in Shenzhen, China, were assessed between 2020 and 2022 during early, mid, and late pregnancy. Depressive and anxiety symptoms were measured using the nine-item Patient Health Questionnaire (PHQ-9) and the seven-item Generalized Anxiety Disorder Scale (GAD-7), respectively. Cross-sectional networks were constructed using graphical Gaussian models with regularization, while longitudinal relationships were analyzed via cross-lagged panel network models. Symptom centrality and bridge roles were examined, and network accuracy and stability were assessed through bootstrapping. Across trimesters, core symptoms included excessive worry (GAD-A3), low energy (PHQ-D4), and anhedonia (PHQ-D1), with their prominence shifting over time. From early to mid-pregnancy, worthlessness (PHQ-D6) predicted anhedonia, while from mid to late pregnancy, anhedonia, worthlessness, and excessive worry had the strongest predictive effects. Cognitive rumination may underlie persistent worry and emotional distress, particularly in the context of unmet family support. Antenatal depression and anxiety are marked by dynamic shifts in worry, fatigue, and loss of pleasure. Findings underscore the need for trimester-specific screening and culturally adaptive intervention frameworks that account for dynamic symptom evolution.
Previous fMRI studies have documented links between internalizing problems in youth and brain functional connectivity of the default (DN), frontoparietal (FP), and salience (SA) networks. Characterized by a large symptom heterogeneity and comorbidity, it remains elusive how individual internalizing symptoms relate to DN, FP, and SA connectivity. Leveraging a large population-based sample of adolescents (N = 2426; mean age = 14.1 years) and an integrated network modelling approach, we identified symptom-specific associations between internalizing problems and functional connectivity and explored sex and timing-specific differences in these links. Our findings revealed small negative associations between self-reported feelings of worthlessness and guilt and DN within-network connectivity and positive associations between fearfulness and FP within-network connectivity. Moreover, sadness and fearfulness were positively associated with DN-SA between-network connectivity. Exploratory analyses revealed no significant sex differences but indicated that DN within-network connectivity around age 10 was negatively associated with self-reported worthlessness at age 14. Our findings show symptom-specific associations between internalizing problems and brain functional circuitry in youth and highlight the complex interplay of symptoms and brain networks.
Major Depressive Disorder (MDD) is clinically and biologically heterogeneous. Here, we leveraged the genetics of individual depressive symptoms to dissect the disorder's underlying heterogeneity. We utilized the BIObanks Netherlands Internet Collaboration (BIONIC). A series of genome-wide association studies (effective-N range: 14,407 - 47,110) compared controls (N=48,286) with partially different subsets of lifetime MDD cases (range: 3,892-15,577), each endorsing one of 12 individual DSM-based depressive symptoms. Results were combined in genetic correlations that informed factor analyses with Genomic Structural Equation Modeling, decomposing underlying MDD liability dimensions. The identified factors were assessed and further characterized using multivariate regression of neurodevelopmental/psychiatric and cardiometabolic traits. All symptoms demonstrated substantial SNP-based heritability (h 2 SNP : 0.088 - 0.127). Despite high between-symptom genetic correlations, factor analyses yielded two highly correlated (rg=0.85) but still distinct latent factors: factor 1 (F1), capturing appetite/weight loss, insomnia, guilt/worthlessness, psychomotor slowing and suicidality, and factor 2 (F2), reflecting concentration problems, anhedonia, depressed mood, appetite/weight gain and fatigue. Overall, F1 had a stronger genetic overlap with neurodevelopmental/psychiatric phenotypes (e.g., autism: standardized estimate β=0.45, p=4.49×10-4; schizophrenia: β=0.40, p=1.73×10-4), while F2 significantly overlapped with cardiometabolic traits (e.g., metabolic syndrome: β=0.44, p=8.69×10-4; coronary artery disease: β=0.31, p=0.009). We identified two genetic dimensions of MDD, each linked to partially distinct clinical manifestations and underlying biology, with one reflecting neurodevelopmental/psychiatric liabilities and the other capturing a strong cardiometabolic vulnerability. Disentangling such distinct dimensions may help guide patient stratification and targeted treatment, thereby advancing precision psychiatry.
This study aims to develop and validate a risk estimation model for identifying suicidal tendencies among middle school students. The effectiveness of the model is evaluated, offering insights for preventing and managing student suicides in educational institutions. This study employed a cross-sectional design. From December 2018 to January 2019, a total of 12,798 middle school students from all 18 public schools in an urban district of Suzhou were surveyed. After data cleaning, 12,063 valid questionnaires were included and randomly divided into a training set (n=8,444) and a validation set (n=3,619) in a 7:3 ratio for model development and internal validation, respectively. Predictors were selected through univariate analysis and LASSO regression, with independent associated factors subsequently identified by multivariable logistic regression. Based on these factors, a nomogram risk estimation model was constructed using R software. To assess generalizability, external validation was performed using data from 6,262 valid questionnaires collected from 11 public middle schools in Changshu, Suzhou, in 2023. The nomogram incorporated nine selected factors: trouble asking for help, parents' marital relationship, gender, school bullying, nightmares, depressive mood (PHQ02), sleep disturbance (PHQ03), feelings of worthlessness (PHQ06), and psychomotor changes (PHQ08). The model demonstrated good discrimination in the internal validation set area under the curve (AUC) 0.807 (95% CI [0.790, 0.824]) and in a temporal external validation cohort AUC 0.764 (95% CI [0.751, 0.778]). Calibration was satisfactory internally but required adjustment in the external cohort. This study developed and validated a multidimensional nomogram that effectively discriminates middle school students at risk of suicide, providing a framework for initial risk stratification. For application in new settings, local calibration of the model's risk estimates is mandatory. This tool holds potential to aid early identification in school and primary care contexts.
Depressive symptoms have been associated with shorter disability-free survival in older adults; however, whether this association differs according to the structure of depressive symptoms remains unclear. We examined the association between the structure of depressive symptoms and risk of disability or death among older men and women in Japan. We analyzed 585 individuals who underwent a comprehensive geriatric assessment and agreed to provide information on long-term care insurance. Factor analysis was performed using items from the Geriatric Depression Scale (GDS-15) to extract factors of depressive symptoms. The endpoint was the composite outcome of disability or death, defined as the first certification of any level of care requirement. Associations between the extracted depressive symptom factors and outcomes were examined using a Cox proportional hazards models. During 18 years of follow-up, 497 incident cases of disability or death occurred. In men, "worthlessness" was positively associated with the incidence of disability or death (hazard ratio [95% confidence intervals], 1.85 [0.98-3.49], P for trend = 0.04) after adjusting for potential covariates. In women, "anxiety" was positively associated with the incidence of disability or death (1.88 [1.15-3.07], P for trend = 0.02), whereas "unhappiness" showed an inverse association with the incidence of disability or death (0.51 [0.30-0.87], P for trend = 0.01). The association between depressive symptoms and the risk of disability or death in older adults varied according to the structure of depressive symptoms and sex.
South Africa has been experiencing a persistently high unemployment rate among young people. This high youth unemployment is a stressor to young people, which may culminate in mental health issues. The study aimed to describe the self-reported mental health effects of unemployment among young people in Mdantsane township, Eastern Cape province. The study was conducted in Mdantsane township, at the Lingomso Youth Centre in the Eastern Cape province, South Africa. A descriptive qualitative approach was used. Participants were recruited purposively until data saturation. Data were collected through individual face-to-face interviews. Thematic analysis was conducted using a six-step approach. The sample comprised 19 young people aged between 19 years and 29 years, and most were men. Two themes emerged: theme one was negative effects, which were characterised by substance use, feelings of worthlessness, stress and anxiety, masking of personality, and social isolation; theme two was that social support buffered the potential adverse effects of unemployment, and this was supported by interdependent role relationships that protected individuals from the effects of unemployment. There is a need to recognise mental health issues emanating from unemployment, like social isolation, to enable comprehensive, appropriate interventions for young people. This study contributes to understanding mental health issues experienced by unemployed young people. Findings can be used to develop targeted interventions in a large township, such as Mdantsane, in South Africa.
Despite extensive attention on psychological distress and socioeconomic disadvantage, no study has mapped conditional associations between specific distress symptoms and disadvantage across both household and neighborhood levels. Here, we estimated a preregistered network analysis to examine the conditional associations between eight specific aspects of psychological distress on the one hand and 15 household- (e.g., household crowding, income, financial ability to keep house warm in winter) and neighborhood-level (e.g., area-level deprivation, perceptions of pollution, vandalism) disadvantage variables on the other, using the U.K. Household Longitudinal Study (N = 15,851). Limitations on social activities and daily roles as a result of emotional and physical health problems were most strongly interconnected with socioeconomic disadvantage while feeling depressed showed no conditional associations with disadvantage. Being unable to afford replacement of large electrical items was the disadvantage variable most associated with distress, including sleep loss and worthlessness. Distress variables were associated with aspects of disadvantage across both the neighborhood and household levels, although the latter associations were more frequent and stronger. Our findings highlight a core role for functional impairments due to emotional problems and underline the need to assess and address the psychological consequences of socioeconomic circumstances across multiple levels. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Depression and anxiety are prevalent among hospice patients. A detailed understanding of the symptom comorbidity and key symptoms of depression and anxiety among Chinese hospice patients can promote targeted interventions. This study investigates the depression and anxiety symptom network and compares networks in different symptom groups in 388 Chinese hospice patients. Patient Health Questionnaire-9 and the Seven-Item Generalized Anxiety Disorder Scale were used to measure depression and anxiety. Psychometric network analysis and latent class analysis were conducted using R and MPLUS. Hopelessness and anhedonia in depression and excessive worry and nervousness in anxiety symptoms were identified as the most central symptoms. Hopelessness, nervousness, and irritability were identified as the bridging symptoms. Latent class analysis identified two groups based on sixteen symptoms: "mild-symptom" and "moderately-severe-symptom." Significant global strength differences were found between the networks of the mild-symptom group and the moderately-severe-symptom group. In the mild-symptom network, hopelessness, excessive worry, uncontrollable worry, and anhedonia were the central symptoms, while hopelessness, worthlessness, and uncontrollable worry were the bridging symptoms. In the moderately-severe-symptom network, nervousness, difficulty relaxing, fatigue, impending doom, and uncontrollable worry were the central symptoms, while hopelessness and nervousness were the bridging symptoms. These findings suggest that hopelessness should be a primary intervention target to reduce overall depression and anxiety symptoms. Additional focus should be placed on anhedonia, excessive worry, and nervousness. Intervening in hopelessness, nervousness, and irritability helps reduce the concurrence between depression and anxiety. Nuanced intervention strategies should be implemented based on the severity of symptoms among hospice patients.
Despite extensive research on depressive symptom networks, studies focusing on clinical outpatients with major depressive disorder (MDD), particularly from an intervention perspective, as well as consistent comparisons with subclinical groups, remain limited, thereby restricting the generalizability of central symptom findings. Clinically diagnosed MDD outpatients (N = 3428), based on ICD-10 criteria, and subclinical individuals identified using the Patient Health Questionnaire-9 screening cutoff score of 8 (N = 1104), were included in the analysis. All participants completed the depression subscale of the Symptom Checklist-90. This study estimated the Ising network based on binary data and applied the NodeIdentifyR algorithm to perform simulation-based analyses, modeling the potential impact of symptom-level changes (alleviation or aggravation) on the overall network structure. The symptom "feeling blue" plays an essential role within the depressive symptom network both in clinical outpatients and subclinical populations. Simulation-based alleviation interventions on "feeling blue" and "worrying too much about things" may reduce the overall MDD severity among subclinical populations, while simulated alleviation interventions targeting "feelings of worthlessness" and "feeling that everything is an effort" may reduce the overall MDD severity among clinical outpatients. Additionally, the "feeling of being trapped or caught" and "thoughts of ending your life" were found to be risk symptoms that may aggravate the overall MDD severity of clinical outpatients and subclinical populations, respectively. The present findings highlight the importance of targeting specific symptoms to optimize intervention strategies for reducing the overall severity of MDD in both clinical outpatient and subclinical populations.
Communication between patients and staff during emergency caesarean birth is important for ensuring positive outcomes and reducing the negative psychological impact of the procedure. Communication failures have been linked to obstetric violence, mistrust, and post-traumatic stress disorder. This study aimed to explore patient-healthcare provider communication before and after emergency caesarean birth at the University Teaching Hospitals, Women and Newborn Hospital in Lusaka, Zambia. This study employed a qualitative phenomenological design to explore the lived experiences of women who had undergone emergency caesarean birth. Interviews were conducted with 30 women who were purposively sampled from the hospital's wards. An inductive thematic analysis, which involved transcribing interviews, reading and rereading transcripts, coding, categorising similar codes and developing themes, was used for data analysis. Thematic analysis yielded four primary themes: mode of communication, emergency caesarean birth communication experience, consequences of inadequate communication and information, and barriers to effective communication. Communication between healthcare providers and women who underwent emergency caesarean birth was inadequate, untimely and lacked detail about the surgical procedure. As a result, women felt afraid, angry, and anxious, resulting in a sense of worthlessness and helplessness. The use of medical jargon by healthcare providers, misconceptions about caesarean birth, the presence of pain and poor staff attitudes towards mothers were identified as some of the communication barriers. The findings highlight systemic gaps in provider-patient communication during emergency caesarean birth, influenced by workload pressures, staff shortages and power dynamics. Interventions are needed to promote respectful maternity care through training in patient centred communication, use of simple language, and addressing structural barriers at the University Teaching Hospitals Women and Newborn Hospital. Clear communication can help to improve the overall experience of caesarean birth.
In this study, we aimed to elucidate the underlying structural mechanisms that generate a desire for hastened death (DHD) in patients with terminal cancer from a whole-person perspective based on insights from palliative-care professionals (PCPs). We conducted semi-structured interviews with 36 PCPs experienced in caring for patients with terminal cancer and DHD, followed by a thematic analysis based on Boyatzis' hybrid approach. We identified 6 themes that characterize the underlying structural mechanisms of DHD. DHD arises from feelings such as loss of self-control, inability to escape adverse circumstances, confronting death and letting go of life, pain of loneliness, being unable to accept living life as it is, and feeling unable to live with the thought of being an inconvenience to others, in addition to physical and psychological pain. In contrast, certain patients who had built good relationships with family members and/or PCPs found new meaning and value in their current lives, expressing a desire to live in the moment and choosing to continue living until the end. This study provides the first comprehensive analysis of the underlying structural mechanisms of DHD in patients with terminal cancer from a whole-person perspective. DHD with spiritual pain is linked to the loss of future orientation, autonomy, and meaningful relationships through interconnected structural pathways, leading to feelings of worthlessness and existential meaninglessness. The identified framework demonstrates that these underlying mechanisms operate through an interplay of existential, relational, and autonomy-related factors extending beyond physical and psychological symptoms, reflecting an interconnected human experience across physical, psychological, social, and spiritual dimensions. This study established an evidence-based framework enabling healthcare professionals to implement whole-person approaches to recognize the multidimensional nature of DHD and address existential distress across all dimensions of human experience in end-of-life care.
Heart rate variability biofeedback (HRVB) has been confirmed to enhance cardiovagal activation and alleviate depressive symptoms in patients with major depressive disorder (MDD). However, it remains unknown which dimensions of depression predict better treatment outcomes following HRVB. This study utilized a randomized controlled trial design. A total of 59 patients with depressive disorder were enrolled and randomly assigned to either the HRVB group or the relaxation training (RT) group. Both groups receive 60-min training sessions twice weekly for 10 sessions over five weeks. Psychological variables (depression and anxiety) and lead II electrocardiogram (ECG) were collected at pre-test and post-test. ECG data were converted into HRV indices. Two-way mixed-design analyses of variances were conducted to examine the Group (HRVB vs. RT) × Time (pre-test vs. post-test transfer) interaction effects on psychological outcomes and HRV indices. In addition, participants in the HRVB group were classified as responders (n = 10) or non-responders (n = 14) based on change in the root mean square of successive differences between normal heartbeats (RMSSD) following HRVB. Differences in demographic and psychological variables between these subgroups were further examined. (1) Both groups showed significant reductions in depression and anxiety over time; however, no significant differences between groups were observed. (2) At the transfer stage, the HRVB group demonstrated significant increases in HRV indices from pre-test rest stage to post-test transfer stage. (3) Within the HRVB group, responders exhibited significantly lower levels of depressive symptoms (including loss of pleasure, loss of interest, worthlessness, and loss of energy) and lower parasympathetic activity compared with non-responders. HRVB not only alleviated depression and anxiety symptoms but also enhanced autonomic nervous system activity. Moreover, patients who derived the greatest benefit from HRVB tended to exhibit more favorable psychological features at pre-test. These findings may inform the development of personalized and evidence-based psychological interventions for patients with depressive disorders.
Pregnancy is accompanied by numerous physical and psychological changes that can affect sexual quality of life and increase sexual distress in women. Identifying effective interventions to address these issues is essential. This study aimed to evaluate the effect of mindfulness-based cognitive-behavioral therapy on sexual distress and sexual quality of life in pregnant women. In this single-blind randomized clinical trial, 84 pregnant women (20-35 weeks of gestation) were randomly assigned to an intervention group (n = 40) or a control group (n = 41) using a block design. The intervention group participated in seven weekly sessions of mindfulness-based cognitive therapy. Data were collected using a demographic questionnaire, the Sexual Quality of Life-Female (SQOL-F) questionnaire, and the Female Sexual Distress Scale (FSDS). At baseline, there were no statistically significant differences between the two groups in demographic characteristics, sexual distress, or sexual quality of life. Repeated measures analysis showed significant improvements in the intervention group compared with the control group in overall sexual quality of life and its dimensions (psychosexual feelings, sexual and relationship satisfaction, self-worthlessness, and sexual repression). Sexual distress scores were also significantly reduced in the intervention group after the intervention and at follow-up. Mindfulness-based cognitive-behavioral therapy appears to be an effective approach for enhancing sexual quality of life and reducing sexual distress in pregnant women. Integrating this intervention into prenatal care services may help promote the overall well-being of expectant mothers. This study was approved by the Ethics Committee of Ahvaz Jundishapur University of Medical Sciences (IR.AJUMS.REC.1399.135) and registered in the Iranian Registry of Clinical Trials (IRCT20200901048581N1) on September 21, 2020.
In view of the increase in suicides over recent years, this review set out to explore the mechanisms proposed to underlie the association between the use of digital social networks and suicidal behavior in adolescents and young adults. Understanding these mechanisms will help improve the actions aimed at reducing youth suicides. This systematic review of the literature was carried out following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) guidelines. The search focused on studies published over the past seven years in English or Spanish that are stored in databases like APA PsycINFO, APA PsycArticles, Psychology and Behavioral Sciences Collection, PSICODOC, MEDLINE and the Web of Science (WOS). The search yielded 16 articles for analysis, 13 descriptive empirical studies and 3 review papers identifying various mechanisms of association, including: emotional dysregulation, social contagion, behavioral addiction (to social networks and/or online video games), cyberbullying, exposure to self-harm, negative feelings like helplessness or worthlessness, social disconnection, and sleep disorders. There is increasing evidence of the effects of digital social network use, and of the variables that moderate or mediate its association with suicidal behavior in adolescents and young adults, providing valuable information to design prevention strategies.
Chronic orofacial pain (COP) is a complex condition often resistant to treatment and associated with psychological comorbidities. Yet, its neuropsychological profile remains under-investigated. This case-control study aims to identify the cognitive, behavioral, and psychological profiles of COP and their associations with clinical symptoms, with a focus on persistent idiopathic facial pain (PIFP), a condition particularly underexplored. A cohort of 42 patients (COPc), including 23 with PIFP, and 42 healthy controls (HCs) underwent a comprehensive assessment of mood, coping strategies, personality traits, cognitive functioning, and social well-being. Between-group and correlation analyses were performed, and Bonferroni correction was applied to account for multiple comparisons. The psychological framework of COPc was marked by depressive symptoms, loneliness, alexithymia, poor quality of life, and low physical and mental well-being. Personality assessment indicated worthlessness. Catastrophizing was a dominant coping strategy, characterized by helplessness and rumination. Cognitive assessments revealed deficits in attention and executive functions. PIFP patients exhibited particularly psychological vulnerabilities, namely, catastrophizing thinking and difficulties in describing their own feelings. Correlation analyses showed complex relationships between cognitive, behavioral, and psychological impairments in COPc, and a strong association between the negative impact of pain symptoms on social life and psychological, catastrophizing, and cognitive functioning. This is the first study to characterize the neuropsychological profile of PIFP and COP conditions, revealing a complex interplay of cognitive, behavioral, and psychological vulnerabilities. These findings underscore the importance of addressing both neuropsychological and social functioning in the management of chronic pain to improve patient well-being.
People's eating habits are influenced by psychological, social, cultural, and behavioral factors. Research shows that certain personality types expose people to risky eating behaviors. Given the complexity of nutrition-related factors and the limitations of traditional statistical methods, the use of new approaches such as artificial intelligence and machine learning can play an effective role in analyzing multidimensional data and identifying complex patterns. This cross-sectional pilot study aimed to predict food addiction among university students by integrating demographic, anthropometric and personality data with machine learning methods. The data consisted of 210 samples, which were first preprocessed to ensure data quality and integrity. Tomek Links and SMOTE techniques were used to remove class imbalance. Feature selection was performed using the twelve different algorithms to identify the most important features related to food addiction prediction. Then, ten different machine learning models were implemented, including Logistic Regression (LR), K-Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Support Vector Classifier (SVC) with probability estimation, Decision Tree (DT), Random Forest (RF), AdaBoost, Gradient Boosting Classifier (GBC), CatBoost and LightGBM. The models were trained on the training dataset and their performance was evaluated using the accuracy, precision, recall, F1-Score and AUC metrics on the test dataset. In addition, the SHAP (SHapley Additive exPlanations) method was used to analyze the importance of features and interpret the advanced models to determine the impact of each psychological and behavioral feature on the prediction of food addiction. The results showed that more advanced models, especially ensemble methods such as Random Forest and CatBoost, have high power in identifying complex patterns and accurately predicting food addiction behaviors. SHAP analysis also showed that psychological characteristics such as feelings of worthlessness, impulsivity, anger, psychological distress, rigid cognitive styles, weight and height, body mass index (BMI) were related the most important factors affecting prediction. Although limitations such as small sample size, focusing on a specific student population, and the use of self-report instruments reduce the generalizability of the results, the innovation of this study in combining psychological and artificial intelligence approaches for early identification of high-risk individuals is remarkable. Overall, the integration of personality profiles with advanced computational models can form the basis for the development of artificial intelligence-based screening tools and targeted interventions to improve nutritional behaviors in young populations.