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.
Beyond Theory of Mind: mentalization as a relational and developmental framework for autism
Autistic individuals and those around them often navigate social and emotional situations in which behaviors, intentions, and affects are difficult to interpret. Supporting mentalizing processes within child–caregiver interactions may help address these challenges; however, a broader conceptual shift is needed, moving beyond a narrow deficit-based perspective toward understanding mentalization as a multidimensional, relational, and developmental process. By shifting the focus from individual deficits to child–caregiver meaning-making processes, this framework may help clarify assessment and intervention targets and inform future research on psychopathological vulnerability in autism. This targeted narrative mini-review therefore aimed to summarize preliminary evidence suggests that other-related mentalizing may show greater difficulties than self-related mentalizing, although this hypothesis requires further replication. Findings also highlight caregiver mentalization, particularly parental reflective functioning, as a key relational process shaping how children’s behavior is interpreted, regulated, and responded to over time. In this light, preliminary intervention studies suggest that mentalization-based and mentalization-informed approaches may improve parental reflective functioning, cognitive reappraisal, self-efficacy, and inferential style, with potential indirect benefits for children’s emotional outcomes. We therefore propose a relational-developmental framework in which mentalization is conceptualized as a shared and dynamic process of meaning-making under conditions of social and emotional ambiguity. Adopting an individual, relational and developmentally informed perspective may contribute to the development of more precise assessment models, more targeted interventions, and a deeper understanding of mental health vulnerability in autism.
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.
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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Nonverbal AI-Based Communication Robot for Staff in Disaster-Affected Care Facilities: Exploratory ABAB Intervention Study
Background: Medical and welfare facilities in the Noto region of Japan were severely affected by the 2024 Noto Peninsula earthquake and subsequent torrential rains. Staff working in these facilities were disaster survivors and frontline caregivers with limited psychological support. Nonverbal social robots may provide companionship and emotional comfort; however, their effects on the health-related quality of life (QoL) and well-being of care staff in disaster-affected settings remain unclear. Objective: This study explored whether introducing a nonverbal artificial intelligence communication robot was associated with changes in health-related QoL and well-being among care facility staff working under disaster conditions. Secondary objectives were to evaluate safety, acceptability, and intention to continue use. Methods: This pragmatic, exploratory pilot study used an ABAB design conducted between February 2025 and June 2025. After a 2-week baseline period, staff in dementia care, general care, and short-stay units underwent 2-week intervention, withdrawal, reintervention, and withdrawal phases. Questionnaires were administered at each phase end. The primary outcomes were health-related QoL (EQ-5D-5L), well-being (World Health Organization–5 Well‑Being Index), and positive mental health (Mental Health Continuum–Short Form). Friedman tests compared outcomes across the 5 phases, and effect sizes were expressed as Kendall . Safety, acceptability, and intention to continue use were compared between the first and second intervention phases using Wilcoxon signed rank tests with Bonferroni adjustment and rank-biserial correlations as effect sizes. Results: Of the 58 staff who completed the baseline assessment, 49 (84.5%) were included in the analytic sample (25 in dementia care, 12 in general care, and 12 in short-stay units). Among these participants, 40 (81.6%) were women, and 38 (77.6%) reported disaster-related damage to their homes or families. In the pooled analysis, no phase effect was observed for the EQ-5D-5L (=.10; Kendall =0.032, negligible), the World Health Organization–5 Well‑Being Index (=.70; Kendall =0.016, negligible), or the Mental Health Continuum–Short Form (=.44; Kendall =0.022, negligible). No robot-related adverse events were reported. In the dementia care unit, nominal unadjusted differences were observed for “made me feel calm” (=.045; rank-biserial correlation =0.571, large), “like” (=.03; =0.559, large), and “felt at peace” (=.02; =0.718, large); however, none remained statistically significant after Bonferroni correction. Conclusions: The short-term use of a nonverbal artificial intelligence communication robot did not measurably improve health-related QoL or well-being among staff in disaster-affected care facilities. Deployment appeared feasible and was not associated with reported adverse events, but efficacy as a mental health support intervention remains unproven. Exploratory acceptability and interaction signals may inform future adequately powered studies.
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Active Ingredients in Digital Cognitive Interventions: Integrating Dismantling Designs With Mechanistic Neuroscience
Digital cognitive interventions (DCIs) have emerged as scalable approaches for treating cognitive dysfunction across psychiatric, neurological, and aging populations. Despite growing evidence of efficacy, little is known about which intervention components drive therapeutic effects or through which neurocognitive mechanisms they operate. As a result, null findings are often difficult to interpret, making it unclear whether interventions failed to engage their intended targets, or whether the targets themselves are not causally related to meaningful outcomes. This limits intervention refinement, comparative evaluation, and precision personalization. Here, we argue that DCI research should shift from broad efficacy testing toward mechanistic trials designed to identify active ingredients—the intervention components responsible for engaging prespecified neurocognitive targets and producing clinically meaningful benefits. We propose adapting dismantling design methodology from psychotherapy research in order to integrate Research Domain Criteria constructs, mechanistic neuroscience, and high-resolution digital behavioral data to identify factors driving cognitive and functional outcomes. This approach aligns with the National Institute of Mental Health experimental therapeutics framework by explicitly linking target specification and target engagement with downstream clinical and functional outcomes. Mechanistic dismantling trials can determine whether specific DCI features, including adaptive difficulty, reward schedules, feedback contingencies, task variability, cognitive targets, and human support, are necessary, sufficient, or synergistic for engaging neural circuitry and producing durable and clinically meaningful transfer. Beyond optimizing intervention design, such studies may transform null or negative trials into mechanistically interpretable findings, while clarifying disease mechanisms and supporting the development of personalized, optimized, and usable DCIs.
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A hybrid implementation-effectiveness study of a school-based intervention for promoting health and well-being in low-resource settings: the ISOBAR study protocol
IntroductionSchool-based interventions can improve adolescent health outcomes and tackle the growing burden of non-communicable diseases (NCDs) in low-and-middle-income countries (LMICs). Results to date are variable, partly due to lack of cultural adaptation of Westernised models and limited focus on implementation processes. The aim of the ISOBAR project is to develop, implement, and test a school-based intervention to address the emergence of mental and physical (i.e., nutritional, physical inactivity) health problems in LMICs. The three-stage intervention comprises (1) assessment for mental and physical health problems, (2) universal health literacy, and (3) indicated counselling.Methods and analysisThe ISOBAR multi-site project comprises three phases: (1) pre-intervention (co-development and cultural adaptation of the intervention); (2) implementation of the intervention; and (3) post-intervention evaluation. The intervention will be delivered using a staggered roll-out design to introduce the intervention sequentially to three schools per site (nine in total) in Chennai, Gujarat (India), and Ibadan (Nigeria). Each school will receive the intervention by the end of the study period. Local teams will recruit 200 adolescents per school (Total n=1, 800). All adolescents (in intervention and control conditions) will be assessed for mental health/behavioural problems and nutritional/weight problems at baseline. Adolescents in the intervention school will receive the universal health literacy intervention (main cohort), and those adolescents reaching pre-determined thresholds on mental health and/or nutritional indices (sub-cohort) will be referred to school-based counselling support. A suite of assessments will be conducted throughout the study period including: (1) intervention effectiveness (e.g., impact on help-seeking, weight, and mental health and behavioural outcomes); (2) implementation processes (e.g., facilitators and barriers) and outcomes (e.g., acceptability, appropriateness, sustainability); and (3) cost-effectiveness.Ethics and disseminationThe study was approved by the University of Warwick’s Biomedical and Scientific Research Ethics Committee (BSREC 36/23-24) and the institutional ethics committees of all participating sites. Research findings will be disseminated through peer reviewed scientific publications, public announcements in local communities, policy briefings, print and online media, and institutional and professional social media accounts and websites.
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.
Occupational burnout and risk of suicidality in healthcare professionals: a PRISMA-guided systematic review
BackgroundBurnout, an occupational phenomenon resulting from chronic workplace stress that has not been successfully managed, is increasingly recognized as a critical threat to the mental health of healthcare professionals. Prolonged exposure to work-related stressors may increase the risk of suicidality, including suicidal ideation, suicide attempts, and suicide deaths. This systematic review aimed to synthesize existing evidence on the association between burnout and suicidality in healthcare professionals and to identify vulnerable subgroups and intervention priorities.MethodsWe conducted a systematic review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines (PROSPERO registration: CRD420251037488). PubMed, Scopus, Web of Science, and PsycINFO were searched for studies published between January 2005 and December 2024. Eligible studies included healthcare professionals and, where methodologically relevant, closely related high-stress occupational populations used as comparator cohorts assessed with validated burnout instruments and reporting suicidality or closely related suicide-proximal psychological outcomes. Data were extracted independently by two reviewers, and risk of bias was evaluated using the Newcastle–Ottawa Scale. Where appropriate, findings were synthesized narratively and through meta-analysis.ResultsA total of 29 studies met the inclusion criteria and 10 studies were included in the meta-analysis. Strong associations were consistently observed between burnout and suicidality, with emotional exhaustion and depersonalization emerging as the most robust predictors. Reduced personal accomplishment demonstrated weaker or inverse associations. Nurses and physicians were identified as particularly vulnerable, with pandemic-era studies reporting higher effect sizes compared to pre-pandemic research. Overall methodological quality was moderate to high, and heterogeneity was partly explained by profession, region, and burnout instrument used.ConclusionsBurnout, particularly emotional exhaustion and depersonalization, is consistently associated with increased suicidality among healthcare professionals, with supporting evidence from related high-stress occupational populations. Vulnerable groups include women clinicians, younger professionals, and those engaged in rotating or night-shift work. These findings highlight the need for systematic burnout surveillance, confidential access to mental health support, and organizational reforms such as safe staffing ratios and workload regulation. Integrating suicide-prevention strategies into occupational health frameworks is urgently required to protect clinician wellbeing and sustain healthcare system resilience.
A Web-Based Self-Management Intervention for Return-to-Work Among Persons With Common Mental Disorders on Sick Leave: Case Study of mWorks
Background: mWorks is a co-designed, web-based self-management intervention developed to empower persons with common mental disorders who are on sick leave during the return-to-work process. However, limited knowledge of how mWorks is delivered and engaged with in real-world settings constrains further development and implementation. In line with the Medical Research Council framework for complex intervention evaluation, such an approach is required to examine (1) contextual factors influencing implementation, (2) fidelity and variation in delivery, and (3) how service users and professionals experience and respond to the intervention. Objective: This study aimed to evaluate the process of implementing mWorks, specifically focusing on assessing the intervention’s delivery in relation to the context, implementation process, and mechanisms of impact. Methods: This single-case study was bounded by the delivery period of 10 weeks in a primary and specialist mental health service context. During this period, return-to-work professionals (n=2) and service users (n=6) collaborated to initiate mWorks usage. Both qualitative and quantitative methods were used to triangulate multiple data sources. Results: The pandemic and mental health problems posed contextual barriers, particularly during recruitment. However, perceptions of mWorks as a credible and relevant intervention facilitated its implementation. The delivery was performed according to plan, with minimal adaptations. All users adhered to the intervention, and dialogue meetings were highly valued. mWorks was used flexibly according to users’ needs, both during sick leave and at work. The potential impacts included a transformative process for users, fostering acceptance, self-esteem, self-compassion, and a sense of control. It also had the potential to prevent mental ill health, transform negatives into positives, facilitate disclosure of mental health, and support goal setting. The use of quantitative measures for empowerment, engagement, self-efficacy, depression stigma, and quality of life proved feasible and supported the assumptions and direction of results. Conclusions: The recruitment stage of the implementation program encountered significant contextual barriers. However, once the delivery stage began, the implementation of mWorks proved to be feasible. Despite the limited scope of this study, with its small number of participants, the triangulation of data suggests that both users and professionals benefited from mWorks.
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