Tag: Depression

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Gone fishin’… for distinct patterns of belief-updating in late-life worry and rumination

IntroductionWorry and rumination are two common forms of repetitive negative thinking encountered as core symptoms of anxiety and depression. They are difficult to treat and increase the risk of relapse for remitted depressive and anxiety disorders, especially in late-life. We propose that dysfunctional belief-updating is a fundamental cognitive mechanism underlying repetitive negative thinking and use a Bayesian model to test whether distinct components of belief updating are differentially associated with worry and rumination.MethodsWe recruited 83 older participants (age≥50) dimensionally for worry and rumination to perform a belief-updating task and undergo neuropsychological testing. We extracted three parameters from a Bayesian model of belief-updating: performance compared to the Bayesian optimal model, emphasis on initial information (prior weight), and relative weighting of new evidence compared to current beliefs (update strength). These parameters were tested for associations with worry and rumination severity, and four neuropsychological domains were tested as moderators (attention, visuospatial, working memory maintenance, and executive function).ResultsWorry severity was uniquely associated with lower prior weight, while rumination was associated with low update strength. Neither worry nor rumination were associated with performance. None of the neuropsychological domains moderated these relationships.ConclusionWorry and rumination are associated with unique alterations in belief-updating but not overall performance. Low prior weight in worry may inflate perceived uncertainty, while low update strength in rumination—specifically reflective pondering—is consistent with slower incorporation of new information. Both biases may contribute to the distinct phenomenological profiles of worry and rumination and inform specific therapeutic targets for these symptoms.
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From promise to practice: artificial intelligence in mental health care in the MENA region

Mental health disorders represent a growing burden across the Middle East and North Africa (MENA) region, where depression and anxiety are highly prevalent amid conflict, displacement, and socioeconomic strain, affecting up to 40 percent of adults, yet treatment gaps remain at 80-95% due to provider shortages, financial strain, and cultural barriers. In this context, artificial intelligence (AI), in the form of large language models (LLMs) and specialized psychotherapy chatbots, may offer a scalable adjunct to help address these gaps through anonymous screening, predictive risk modeling, psychoeducation, and brief interventions. This narrative review examines current evidence of AI-driven conversational tools in mental health with a specific focus on their application, acceptance, and limitations within the MENA region. To do so, A structured search of MEDLINE and Embase (2000–2026) identified studies on conversational AI in mental health, prioritizing evidence from the MENA region and supplemented by relevant global literature. Overall, findings suggest that while these tools offer high accessibility and user engagement, particularly for low-intensity support, their effectiveness is limited by linguistic and cultural mismatches, including Arabic diglossia and poor alignment with locally grounded expressions of distress. At the same time, user acceptance reflects a paradox in which stigma and privacy concerns drive reliance on anonymous AI tools while simultaneously limiting trust in their clinical reliability, reinforcing a preference for hybrid models with human oversight. Taken together, these findings indicate that current systems remain insufficiently adapted to the MENA context, underscoring the need for culturally grounded, dialect-sensitive, and clinically supervised approaches to ensure safe and effective integration.
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High perceived energy: exploring distinct patterns of energetic and cognitive functioning in older adults

BackgroundA hyperthymic temperament and high perceived energy are often framed as positive dispositional traits, particularly in later life. Indeed, these characteristics are generally associated with a greater tendency to preserve autonomy and with more favorable outcomes in terms of health, engagement, and participation in community life-key dimensions of successful aging. However, the relationship between elevated perceived energy and cognitive performance in older adults remains insufficiently explored. Therefore, this exploratory study aimed to compare cognitive performance and well-being in older adults with different self-reported levels of subjective energy, and to explore potential phenotypic heterogeneity within this population.MethodsWe conducted a cross-sectional study on older adults from the general population. Participants were classified as having higher versus lower levels of perceived energy based on item 10 of the Short Form Health Survey (SF-12) questionnaire. Cognitive performance was assessed using the Addenbrooke’s Cognitive Examination-Revised (ACE-R). Depressive symptoms were evaluated with the Patient Health Questionnaire-9 (PHQ-9), biological and behavioral rhythm regulation with the Biological Rhythms Interview of Assessment in Neuropsychiatry (BRIAN), and quality of life with the SF-12 were also evaluated to characterize overall well-being.ResultsStatistically significant difference emerged in global cognitive performance between individuals with higher versus lower perceived energy (ES ACE-R p=0.012). Although subscale scores were within the normative range in both groups, individuals with higher perceived energy consistently showed significantly lower scores in language (p = 0.011) and attention/orientation (p = 0.049) compared to the lower perceived energy group. Interestingly, the higher perceived energy group also reported fewer depressive symptoms (p = 0.013), better social and behavioral rhythm regulation (p = 0.005), and higher quality of life (p < 0.001).Discussion/ConclusionsHyperenergy in older adults with preserved well-being is not associated with global cognitive impairment, but rather with subtle domain-specific performance variations, suggesting a heterogeneous underlying profile. These findings support a high-energy profile that does not appear to be associated with functional impairment, but is instead characterized by preserved or enhanced functioning and overall well-being, despite selective areas of lower performance. Longitudinal studies are needed to clarify its clinical and cognitive implications.
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Construction and validation of multiple machine learning models for influencing factors of postpartum post-traumatic stress disorder in primiparas

ObjectiveTo analyze the multidimensional factors associated with postpartum post-traumatic stress disorder (PP-PTSD) in primiparas based on the Integrated Framework for Population Health Risk Management (IFPHRM), multiple machine learning-based predictive models were constructed and externally validated to identify high-risk individuals and to provide a robust evidence base for targeted preventive interventions.MethodsThis cross-sectional study consecutively enrolled 1, 135 primiparous women from the Department of Obstetrics at Hefei Maternal and Child Health Hospital between June 2024 and May 2025. Participants were divided chronologically into a training cohort and an independent temporal validation cohort. Women recruited from June 2024 to January 2025 were included in the training cohort (n = 794), whereas those recruited from February 2025 to May 2025 were included in the temporal validation cohort (n = 341). At six weeks postpartum, PP-PTSD symptoms were assessed using the Post-traumatic Stress Disorder Checklist-Civilian Version (PCL-C), with a score ≥38 indicating probable PP-PTSD. Multidimensional variables, including physiological and psychological factors, environmental and family-related factors, and social-behavioral factors, were collected. Candidate predictors were first screened using univariate analysis and then selected using least absolute shrinkage and selection operator (LASSO) regression. Multivariable logistic regression was used to identify independent associated factors. Seven machine learning models, including Logistic Regression, Naive Bayes, Support Vector Machine, Decision Tree, Gradient Boosting, AdaBoost, and Linear Discriminant Analysis, were constructed. Model performance was evaluated in the independent temporal validation cohort using receiver operating characteristic curves, calibration curves, decision curve analysis, and the DeLong test. SHAP analysis was used to interpret the optimal model.ResultsAmong the 794 participants in the training cohort, the incidence of PP-PTSD was 25.18%. Five key predictors were selected by LASSO regression: social support, depression, neonatal caregiving style, husband’s participation, and sleep quality. Multivariable logistic regression showed that depression and poor sleep quality were associated with an increased risk of PP-PTSD, whereas higher social support, greater husband’s participation, and parental assistance in neonatal care were associated with a reduced risk. Among the seven models, the Gradient Boosting model achieved the best overall performance in the temporal validation cohort, with an AUC of 0.939, F1 score of 0.700, specificity of 0.943, sensitivity of 0.651, and Youden index of 0.595. The DeLong test showed that Gradient Boosting performed significantly better than Logistic Regression. SHAP analysis further indicated that social support, husband’s participation, sleep quality, and depression were the major contributors to model prediction.ConclusionPostpartum PTSD (PP-PTSD) exhibits a higher incidence among primiparous women and exerts substantial adverse effects on maternal mental health, the mother–infant relationship, and overall family functioning. Guided by the Integrated Framework of Perinatal Health Risk Management (IFPHRM), this study elucidated the multidimensional mechanisms underlying PP-PTSD, encompassing physiological and psychological factors (e.g., sleep quality and depression), environmental and occupational factors (e.g., social support, paternal involvement, and infant caregiving practices), and social behavioral factors. The Gradient Boosting prediction model demonstrated robust performance and high predictive accuracy upon independent external validation, highlighting its potential utility for risk stratification and future clinical translation. Nevertheless, multicentre validation and the development of clinically implementable tools are warranted. Collectively, this study offers a theoretical foundation and methodological framework for the early identification, targeted intervention, and long-term health management of PP-PTSD in primiparous women.
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Modeling Short-Term Symptom Changes and Behavioral Subtypes of Depression and Anxiety in the General Population: Observational Study Using Smartphone Data

Background: Smartphone-based digital phenotyping has emerged as a promising approach for monitoring mental health using passive behavioral data. Prior studies have linked smartphone-derived features to depression and anxiety severity; however, knowledge regarding whether short-term changes in symptoms can be captured using passive smartphone data in general population samples remains limited, as does the understanding of how such findings should be interpreted vis-à-vis behavioral patterns and demographic variability. Objective: This study aimed to model short-term changes in depression and anxiety severity using passive smartphone data, examine model performance across demographic subgroups, and identify behavioral patterns associated with symptom changes. Methods: We collected 2 weeks of smartphone usage data from 95 adults in the general population and assessed depressive and anxiety symptoms using the clinician-rated Hamilton Depression Rating Scale and Hamilton Anxiety Rating Scale, respectively. Behavioral features—including physical activity, app use, and screen usage metrics—were extracted and compressed using an autoencoder and principal component analysis. The resulting features—along with age, sex, and baseline Hamilton scores—were used to train random forest classifiers predicting symptom score changes (increase, decrease, or unchanged). Additionally, we examined whether model performance differed across demographic subgroups and whether models excluding baseline scores retained predictive performance, as baseline severity was expected to be a strong predictor. To add explanatory value beyond prediction, behavioral subtypes associated with symptom changes were identified by applying unsupervised clustering. Results: The model exhibited moderate performance in predicting changes in the Hamilton Depression Rating Scale (mean accuracy=0.70, mean area under the receiver operating characteristic curve=0.74) and Hamilton Anxiety Rating Scale (mean accuracy=0.65, mean area under the receiver operating characteristic curve=0.69) scores. Performance varied according to demographics, with reduced accuracy among younger adults and females, although these differences were not significant in permutation tests. Excluding baseline Hamilton scores diminished performance substantially, suggesting that baseline symptom severity accounted for a substantial proportion of the predictive performance. Clustering revealed 4 distinct behavioral subtypes according to smartphone usage patterns. A cluster characterized by structured, daytime-focused smartphone use and lower temporal entropy demonstrated greater improvement in depressive symptoms, whereas clusters with lower and irregular usage patterns exhibited minimal improvement or worsening. Conclusions: Passive smartphone-derived behavioral data demonstrated moderate ability to model short-term symptom changes in this predominantly nonclinical sample. However, a substantial proportion of the predictive performance was attributable to baseline symptom severity, underscoring that passive smartphone data may provide modest supplementary information rather than robust stand-alone predictive value. Nevertheless, clustering analyses indicated that passive data may still assist in identifying behaviorally distinct subtypes associated with different depressive symptom trajectories. These findings reflect a practical contribution to digital phenotyping research by elucidating both the potential and constraints of passive smartphone data for short-term symptom monitoring in small general population samples.
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Coping styles and mental health outcomes in partners who have experienced a perinatal loss: a longitudinal study

Perinatal loss is common, but little is known about its impact on partners during the grieving process. This study examined psychological outcomes and coping strategies among recently bereaved partners (≤6 months post-loss; N = 73) at baseline (T1) and six-month follow-up (T2) via online survey. Participants were predominantly male (78%) and typically aged 25–44 (90%), and had experienced a range of perinatal losses (<20 weeks’ gestation to 28 days of life). Grief and depression symptoms were assessed using the Perinatal Grief Scale (PGS) and PHQ-9 respectively, and multivariate regression analyses examined the role of coping styles, demographic characteristics, and loss-related factors. Participants reported varying levels of grief and depression symptoms, with 38% displaying moderate-to-severe depression symptoms at follow-up. Across the cohort, participants reported using a range of coping strategies; however, avoidant coping was uniquely associated with higher grief and depression scores at baseline (PHQ-9: β = .30, p = .007; PGS: β = .49, p <.001). Avoidant coping also predicted poorer grief and depression outcomes at follow-up, although these relationships were no longer significant after controlling for baseline symptoms. Additionally, stillbirth, female gender, and younger age were associated with greater psychological distress at baseline across outcomes (β = .21–.32, p ≤.05). Associations between gender, stillbirth, and psychological outcomes remained at follow-up, although these did not reach conventional levels of statistical significance (β = .24–.40, p ≤.073). These findings highlight the psychological impact of perinatal loss on partners and underscore the importance of improving access to support for this group. Avoidant coping may represent a key target for intervention. Future research should further investigate factors influencing grief trajectories and identify effective forms of support for bereaved partners.
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