Digital technologies are reshaping human behavior, health care delivery, and population health; however, their cumulative effects across the lifespan remain underexplored. This viewpoint argues that exposures arising from interactions with digital technologies should be formally integrated into exposome science as a distinct, measurable component of the human environment. Our aims are to (1) redefine the digital component of the exposome (the digital exposome) within the broader exposome framework, (2) examine its life course implications for health and equity, and (3) outline a research and policy agenda to enable its systematic measurement and integration into clinical and public health practice. Digital technology-related exposures can confer benefits such as enhanced health monitoring, personalized interventions, improved access to care, and the promotion of healthy behaviors. However, they may also introduce potential risks, including mental health challenges, cognitive and circadian disruptions, sedentary lifestyles, exposure to misinformation, and widening inequities among vulnerable populations. Despite their ubiquity, digital technology-related exposures remain poorly integrated into clinical medicine, epidemiology, or public and global health policies. Drawing on interdisciplinary evidence from exposure science, epidemiology, and digital phenotyping research, we propose a refined conceptual definition of the digital exposome grounded in the classical exposome domains. We propose redefining the digital exposome as the full spectrum of exposures resulting from interactions or proximities with digital technologies and their combined influence on health across the lifespan. This framework conceptualizes digital technology-related exposures as a dynamic set of environmental influences operating through sociotechnical, behavioral, and biological pathways over the life course. To operationalize this framework, we discuss practical approaches using validated behavioral instruments, objective device use logs, ecological momentary assessments, smartphone-based digital phenotyping, and wearable sensing technologies. Systematic measurement, large-scale longitudinal studies, and harmonized exposure metrics are needed to characterize the cumulative health impacts of digital environments more accurately. Emerging tools such as digital markers or biomarkers and digital phenotypes offer promising opportunities to link real-world technology use with physiological and biological outcomes, thereby supporting precision medicine and population health strategies. Ethical governance, privacy safeguards, and equity considerations must be embedded from the start, drawing on emerging exposomethics frameworks. Recognizing the digital exposome as a modifiable determinant of health offers a foundation for evidence-based guidance, prevention strategies, and policy interventions suited to increasingly digital societies. By integrating digital technology-related exposures into exposome science, clinical practice, and public health research, this viewpoint seeks to foster interdisciplinary dialogue, guide future empirical work, and support the development of safer and more equitable digital environments across the lifespan.

Environmental exposures such as heat and air pollution are critical determinants of health, yet traditional assessment methods rely on stationary monitors or residential address proxies that fail to capture the exposures that individuals experience throughout the day.

This pilot study aimed to assess the feasibility of integrating ecological momentary assessment (EMA), wearable devices, and continuous GPS tracking to capture real-time environmental exposures and to explore associations with concurrent health outcomes.

In total, 7 young adults (aged approximately 16 to 24 years; 5/7, 71% female) wore Fitbit Charge 6 watches from July 2025 to August 2025 (mean 28.1, SD 1.1 days), recording sleep quality and duration, resting heart rate, breathing rate, heart rate variability, and physical activity. GPS location measured at up to 5-minute intervals (mean 19.7, SD 25.8 measurements per day) was linked to ambient temperature, humidity, and air pollution data (particulate matter <2.5 um or <10 um in diameter, nitrogen dioxide, sulfur dioxide, ozone, and carbon monoxide) derived from monitoring stations, satellites, and climate models using data-integration algorithms accessed via an application programming interface. EMA surveys administered 3 times per day captured participants' emotional states and location (inside or outside). Feasibility targets were ≥3 GPS measurements per day, ≥1 survey completed per day, and complete sleep data on ≥50% of days. We examined exploratory bivariate correlations between environmental exposures, physiological measures, and self-reported mood, adjusting for multiple comparisons using false discovery rate correction.

Of the 7 participants, 5 (71%) met predefined feasibility targets. Mean compliance included 565 (SD 457) GPS coordinates per participant, 1.4 (SD 0.2) EMA surveys per day, and complete Fitbit sleep data on 64% (SD 27%) of days. Surveys identified barriers to compliance, including perceived complexity of the system and forgetting to put the Fitbit watch back on after removing it. Exploratory correlations (6/7, 86% of participants with complete Fitbit data) revealed associations between nitrogen dioxide and heat exposure and reduced heart rate variability (a marker of parasympathetic tone), and between air pollutants (sulfur dioxide) and increased negative emotions. Heat exposure showed a paradoxical pattern of lower self-reported sadness but reduced heart rate variability with higher levels of heat exposure. Given the small sample size, these correlations should be considered preliminary and hypothesis generating rather than definitive findings.

This study demonstrates that the multimodal integration of wearable devices, GPS tracking, and EMA is feasible for capturing real-time environmental exposures and concurrent health outcomes in young adults. This approach addresses critical exposure misclassification issues in environmental health research that relies on residential addresses as proxies. Preliminary patterns suggest complex relationships between environmental exposures and both physiological and emotional outcomes, warranting further investigation in larger, more diverse samples. This approach could inform future personalized environmental health interventions.

Dashboards that integrate patient research data for clinical use can streamline information sharing and support clinical care, yet their application in mental health care is underexplored. This study evaluated the clinician-facing dashboards developed from the Toronto Adolescent & Youth (TAY) Cohort Study at the Centre for Addiction and Mental Health (CAMH), guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. Dashboard reach was assessed through quantitative analyses, while semi-structured interviews with seven clinicians were conducted to explore perceptions of effectiveness, adoption, implementation, and maintenance. Quantitative analyses demonstrate that dashboards are completed for 69% of participants. Following the initial implementation phase, the average time from participant consent to dashboard completion decreases to six months. Qualitative findings suggest that dashboards can serve as a supplementary information source that may aid in supporting clinical decision-making and the integration of patient-reported research data into care. Clinicians also identified areas for improvement, including delays in dashboard completion, difficulty locating dashboards, and inefficient dashboard completion notifications. Clinicians suggested that addressing these areas through improved accessibility, timely data availability, and aligned communication strategies can increase uptake and sustainability. These findings emphasize the value of engaging end users and conducting ongoing evaluation to optimize the integration of research data into clinical workflows using dashboards to enhance mental health care. Future research should further examine factors influencing dashboard use across mental health care settings.

Depression and anxiety are prevalent worldwide, yet fewer than 20% of individuals in low- and middle-income countries receive appropriate care. Cultural norms play a significant role in how symptoms are expressed and how individuals seek help. In Nepal, traditional healers are often preferred over mental health specialists. This qualitative study explored how symptom presentation and perceived causes impact treatment-seeking behaviour among adults receiving care for depression or anxiety. Twenty-four participants (13 with depression, 9 with anxiety, and 2 with both conditions) were recruited from Jhapa, Chitwan, and Kailali districts through primary healthcare providers, psychosocial counselors, and mental health specialists. Individual interviews were conducted using the adapted McGill Illness Narrative Interview (MINI) to explore symptom experiences, illness narratives, perceived causes, and help-seeking patterns. Thematic analysis of the data was done using NVIVO software. Participants reported a range of emotional, cognitive, and physical symptoms including fear, anxiety, restlessness, irritability, sadness, and hopelessness. Somatic complaints like headaches, fatigue, and gastrointestinal issues were often interpreted as consequences of psychological stress. Some participants described dissociative experiences, such as detachment, amnesia, or perceptual distortions, leading to panic or self-harm. Stressors mentioned included financial hardships, bereavement, family conflicts, trauma, and culturally ingrained fears. Most participants initially sought help from biomedical providers like private clinics, hospitals, health centers, or health camps with some also consulting traditional healers. Education and caste played a significant role in treatment choices, with individuals with higher education and from higher castes more likely to seek biomedical care first. Gender and age had minimal impact. The study underscores the importance of culturally sensitive, community-based mental health programs to reduce stigma and ensure equitable access to care for depression and anxiety in Nepal.

Loneliness is an emerging public health concern linked to adverse mental and physical outcomes. It may play a key role in cognitive aging, yet its population-level association with subjective cognitive decline (SCD) across demographic groups is not well characterized. We evaluated how the frequency of loneliness relates to SCD in U.S. adults and whether associations differ by sex, age and race/ethnicity.

We performed a cross‑sectional analysis of adults aged ≥18 years using nationally representative 2016-2023 Behavioral Risk Factor Surveillance System data (BFRSS). Loneliness was categorized as never, rarely, sometimes, usually or always. The primary outcome was self‑reported SCD in the past year. Survey‑weighted logistic regression models adjusted for sociodemographic factors, health insurance, metropolitan status and survey year were used to estimate adjusted marginal probabilities of SCD across loneliness categories. Interaction terms and stratified margins evaluated effect modification by sex, age group (18-44, 45-64 and ≥65 years) and race/ethnicity (non‑Hispanic White, non‑Hispanic Black and Hispanic).

Among 85,969 adults who reported loneliness, 13,879 (16.2%) experienced subjective cognitive decline (SCD), with a mean age of 65.7 ± 10.6 years. Loneliness showed a strong dose-response relationship with SCD. Predicted probabilities of SCD increased from 9.9% (95% CI, 9.3-10.5%) among respondents who never felt lonely to 15.0% (14.1-15.9%) for rarely, 24.9% (23.6-26.1%) for sometimes, 38.4% (34.4-42.5%) for usually and 45.7% (41.0-50.4%) for always lonely adults (p < 0.001). Women who were always lonely had an adjusted probability of SCD that was 10.7 percentage points higher than men; sex differences were negligible at lower loneliness levels. Age differences were minimal across most loneliness categories; however, among adults who were always lonely, those aged >64 years had significantly lower predicted cognitive function compared with adults aged 18-64 years (p < 0.001). Racial and ethnic differences were modest; the only significant contrast was a 1.7 percentage‑point lower probability of SCD for non‑Hispanic Black adults compared with Whites among those who never felt lonely. Other subgroup differences were not statistically significant.

Loneliness is independently and strongly associated with higher likelihood of subjective cognitive decline among U.S. adults, and this relationship is most pronounced for chronic loneliness. While sex and age modified the effect of loneliness, racial/ethnic disparities were minimal. These findings identify loneliness as a modifiable social determinant of cognitive health, supporting the need for broad social connection initiatives and targeted efforts for women and mid-life adults with chronic loneliness.

The Child Eating Behaviour Questionnaire (CEBQ) is an internationally applied, parent-report questionnaire on children's eating behaviors, but has mainly been validated in population-based samples or children with obesity. This study presents a first comprehensive validation of the German version of the CEBQ in a treatment-seeking and community-based sample including anorexia nervosa (AN), avoidant-restrictive food intake disorder (ARFID), loss of control (LOC) eating, attention-deficit/hyperactivity disorder (ADHD), and a healthy control group.

The German version of the CEBQ was completed by 226 parents of children and adolescents (9 months to 17 years) in Germany and Switzerland. Factorial, convergent, and discriminant validity, internal consistency, as well as sociodemographic correlates were assessed, using objectively measured anthropometrics and well-established clinical interviews and questionnaires on eating disorders and associated psychopathology in parent- and self-report.

The original 8-factor structure showed acceptable model fit and acceptable to excellent internal consistency. Convergent validity was mostly supported by weight status and interview- and questionnaire-measured eating behaviors. The CEBQ subscales differentiated between groups associated with overeating (ADHD, LOC eating) versus restrictive eating (ARFID and AN).

The results support the German version of the CEBQ as a valid and reliable tool for assessing eating behavior in youth with eating disorders and ADHD. Future research using larger and age-specific samples should examine psychometric comparability across developmental stages and may provide norms for the CEBQ to enhance its utility as a screening tool.

Youth (i.e., child and adolescent) mental health difficulties are a prevalent concern, with anxiety, depression, and disruptive behavior disorders being the most common presentations. Even though psychotherapy is often recommended to help youth and families manage mental health difficulties, recent meta-analyses suggest that youth psychotherapy is only moderately effective, highlighting a need for further improvement and innovation. Emotion dysregulation is a transdiagnostic risk factor across childhood emotional and behavioral disorders, yet despite the important connection between emotion regulation and psychopathology, little research has been conducted on emotion regulation as a potential mechanism of change during psychotherapy. This study will test the biobehavioral regulation of negative emotion as a transdiagnostic mechanism of change in youth psychotherapy using the Modular Approach to Therapy for Children with Anxiety, Depression, Trauma, or Conduct Problems (MATCH). MATCH is a well-researched therapy program for youth that is suitable for testing transdiagnostic mechanisms of treatment response.

This protocol describes a two-site randomized controlled trial that aims to recruit 202 youth between the ages of 8 to 15 years with anxiety, depression, and/or disruptive behavior. Participants are randomized to the MATCH intervention condition or a waitlist control condition. Youth and their parent(s) in both conditions complete in-lab assessments and online questionnaires at the start of the study, every 3 months (i.e., quarterly), and at post-test (i.e., following the intervention/waitlist period). Physiological measures of emotion regulation such as heart rate variability and skin conductance are acquired during lab-based tasks. Youth symptoms and emotion regulation are monitored weekly for both conditions. The primary outcome is change in youth symptoms of psychopathology at post-treatment, and whether this change is mediated by change in behavioral and physiological emotion regulation. Secondary outcomes include parental functioning, parenting, family functioning, impairment, and additional measures of youth psychopathology.

Findings from the study are expected to enhance the understanding of processes that drive therapeutic change, ultimately leading to better therapy personalization and effectiveness.

ClinicalTrials.gov NCT05637320. Prospectively registered on November 15, 2022. https://clinicaltrials.gov/study/NCT05637320.

Bipolar disorder (BD) exhibits significant sex differences in its frequency, symptom presentation, and treatment response, suggesting distinct underlying neurobiological mechanisms. However, transcriptomic studies investigating these sex-specific pathways have been fragmented and underpowered.

We conducted the first meta-analysis of post-mortem brain RNA-seq data to delineate sex-related transcriptomic landscapes in BD. We integrated data from four public datasets (GSE80336, GSE80655, GSE202537, GSE42546) from GEO and Array Express, comprising an aggregate of 173 individuals (66 BD cases and 117 controls). After preprocessing and correcting for batch effects, sex-stratified expression analysis was performed using DESeq2. A meta-analysis was conducted with the metafor package to identify differentially expressed genes (DEGs) at an FDR < 0.05. We also performed functional enrichment, protein-protein interaction (PPI) network analysis, hub gene identification, regulatory network reconstruction, and supplementary quantitative analyses of sex-specific interaction effects.

Our results reveal striking differences in transcriptomic signatures between men and women with bipolar disorder, with the most pronounced changes occurring in the brain. A meta-analysis across brain regions identified 34 significantly dysregulated genes. In females, upregulated genes were enriched for hormonal signaling (FSHR pathway, G-protein signaling) and transcriptional/epigenetic regulation (GLIS1, neural plasticity). In males, upregulated genes were involved in synaptic calcium signaling (PDLIM5, dendritic spine regulation) and DNA mismatch repair pathways (PMS1). Analysis of the striatum identified 289 differentially expressed genes. The most significantly upregulated genes in females were implicated in immunity and synaptic plasticity, while the male-specific pattern pointed to alterations in basic cellular functions like structure, internal communication, and genetic regulation. A quantitative interaction analysis revealed a negligible correlation (r = -0.122) between disease effect sizes in females and males and identified one gene with opposing, sex-dependent dysregulation (MEF2C).

This study provides robust evidence that bipolar disorder engages fundamentally distinct molecular pathways in males and females, underscoring the necessity of integrating sex as a biological variable in psychiatric research and advancing toward personalized therapeutic strategies.

Virtual interventions for patients with substance use disorders (SUDs), including intensive outpatient treatment, were developed during the COVID-19 pandemic and later maintained in some clinical settings. However, the effectiveness of this type of intervention in healthcare professionals (HPs) has not been studied so far.

This is a quasi-experimental cohort study with both retrospective and prospective data comparing the main treatment outcomes of HPs in treatment for SUDs: (1) 29 patients following a 40-hour synchronous virtual group intervention; and, (2) 31 patients following a an 80-hour in-person group psychotherapy. They both underwent in-person psychiatric and psychological individual treatment as well as addictive drug use monitoring. Logistic regression analyses were performed to find predictors of abstinence from addictive substances and of working status. A Cox proportional hazards regression was used to compare time to first lapse (positive addictive drug use monitoring). Satisfaction rates at the end of each intervention were also compared in both groups using a non-parametric test.

The sample consisted of 60 HPs, with a mean age of 49.5 years (range: 30-67). Of these, 53.3% (n = 32) were women. Physicians represented 53.3% of all patients. Patients in the virtual group were more likely to be working during the intervention compared with those in the in-person group. At one-year follow-up, 55% of the sample remained abstinent from addictive substances. After multivariate analysis, the type of intervention did not predict abstinence when controlling for other variables, although having dual diagnosis was inversely correlated with remaining abstinent (OR: 0.24; 95% CI: 0.07-0.85). HPs receiving in-person intervention were more likely to be working one year after the treatment (OR = 8.3; 95% CI: 2.1-33.3). Time to first lapse was similar between groups, although the in-person sample showed a more heterogeneous distribution. Satisfaction rates were similar in both groups.

Virtual interventions may be an effective alternative to in-person interventions for HPs with SUDs. More studies are needed to more deeply analyze these preliminary findings.

Operating room nurses (ORNs) are at high risk for compassion fatigue (CF), which significantly impairs individuals' well-being, undermines the stability of the nursing workforce, and jeopardizes patients' safety. The study aimed to analyze the prevalence and symptom characteristics of CF among ORNs, construct and compare predictive models using machine learning, and determine the relative contribution of distinct features to the models.

This is a multi-center cross-sectional study. The questionnaires used in the study included a sociodemographic questionnaire, the Professional Quality of Life Scale (ProQoL), the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder 7-item Scale (GAD-7), and the Pittsburgh Sleep Quality Index (PSQI). LASSO regression was used to select critical variables, and predictive models such as decision tree, logistic regression, random forest, SVM, and XGBoost were constructed and compared. SHapley Additive exPlanation (SHAP) were drawn to show the contribution of each feature to the models. SPSS version 26.0 and R software version 4.4.0 were used for statistical analyses.

In this study, a total of 1024 ORNs from 20 cities across China were recruited. According to ProQoL, 326 (31.8%) reported severe CF, 311 (30.4%) moderate CF, and the remaining 387 (37.8%) no or mild CF. Among the three dimensions, the incidence of secondary traumatic stress was most common (95.4%), followed by low compassion satisfaction (61.3%) and burnout (35.0%). In five machine learning-based predictive models, the RF model stood out with the highest AUC at 0.851 (95%CI: 0.795-0.907) in testing set. Following closely, the XGBoost model showed favorable efficacy with the AUC at 0.824 (95%CI: 0.769-0.879) in testing set, outperforming the remaining algorithms. The results of the two SHAP plots (RF and XGBoost) were consistent: depression, anxiety, self-mental health training, sleep quality, and length of service emerged as the five most significant contributors to the models.

This study identified severe CF among ORNs, and the most serious symptom was secondary traumatic stress. The RF model exhibited the best performance in identifying high-level CF among ORNs, and SHAP improved the interpretability of the model. The findings of this study could help medical managers and researchers better understand CF and provide timely interventions for ORNs.

Not applicable.