While the cross-sectional relationship between internet gaming disorder (IGD) and depression is well-established, whether IGD predicts future depression remains debated, and the underlying mechanisms are not fully understood. This large-scale, three-wave longitudinal study aimed to clarify the predictive role of IGD in depression and explore the mediating effects of resilience and sleep distress.

A cohort of 41,215 middle school students from Zigong City was assessed at three time points: November 2021 (T1), November 2022 (T2) and November 2023 (T3). IGD, depression, sleep distress and resilience were measured using standardized questionnaires. Multiple logistic regression was used to examine the associations between baseline IGD and both concurrent and subsequent depression. Mediation analyses were conducted with T1 IGD as the predictor, T2 sleep distress and resilience as serial mediators and T3 depression as the outcome. To test the robustness of the findings, a series of sensitivity analyses were performed. Additionally, sex differences in the mediation pathways were explored.

(1) IGD was independently associated with depression at baseline (T1: adjusted odds ratio [AOR] = 4.76, 95% confidence interval [CI]: 3.79–5.98, p < 0.001), 1 year later (T2: AOR = 1.42, 95% CI: 1.16–1.74, p < 0.001) and 2 years later (T3: AOR = 1.24, 95% CI: 1.01–1.53, p = 0.042); (2) A serial multiple mediation effect of sleep distress and resilience was identified in the relationship between IGD and depression. The mediation ratio was 60.7% in the unadjusted model and 33.3% in the fully adjusted model, accounting for baseline depression, sleep distress, resilience and other covariates. The robustness of our findings was supported by various sensitivity analyses; and (3) Sex differences were observed in the mediating roles of sleep distress and resilience, with the mediation ratio being higher in boys compared to girls.

IGD is a significant predictor of depression in adolescents, with resilience and sleep distress serving as key mediators. Early identification and targeted interventions for IGD may help prevent depression. Intervention strategies should prioritize enhancing resilience and improving sleep quality, particularly among boys at risk.

The concept of Mental Health Literacy (MHL) is inherently multidimensional. However, the interrelationships among its various dimensions remain insufficiently elucidated. In recent years, the textual analysis of social media posts has emerged as a promising methodological approach for longitudinal research in this domain.

This study aimed to investigate whether temporal causal associations exist between recognition of mental illness (R), mental illness stigma (S), help-seeking efficacy (HE), maintenance of positive mental health (M), and help-seeking attitude (HA).

Tweets were collocted at three distinct time points: T1, T2, and T3, spanning the period from November 1, 2021, to December 31, 2022. We employed a machine-learning approach to categorize the posts into five MHL facets. Using these facets, we trained a machine learning model, specifically Bidirectional Encoder Representations from Transformers (BERT), to determine the MHL scores. To be eligible, an account must have an R facet score at T1, and M, S, HE facet scores at T2, as well as an HA facet score at T3. In total, we retrieved 4,471,951 MHL-related tweets from 941 users. We further employed structural equation modeling to validate the causal relationships within the MHL framework.

In the evaluation, BERT achieved average accuracy scores exceeding 89% across the five MHL facets in the validation set, along with F1-scores ranging between 0.75 and 0.89. Among the five MHL facets—maintenance of positive mental health, recognition of mental illness, help-seeking efficacy, and help-seeking attitudes—each demonstrated a statistically significant positive correlation with the others. Conversely, mental illness stigma exhibited a statistically significant negative correlation with the remaining four facets. In the analysis using single-mediation models, each of the individual mediator variables—namely, mental illness stigma, help-seeking efficacy, and maintenance of positive mental health—exhibited significant indirect effects. In the multiple-mediation model, two mediator variables—help-seeking efficacy and maintenance of positive mental health—demonstrated significant indirect effects. These findings suggested that the recognition of mental illness exerted an influence on help-seeking attitudes through one or more of these mediators.

By leveraging machine learning techniques for the textual analysis of social media and employing a longitudinal research design with panel data, this study elucidates the potential mechanisms through which the MHL framework influences attitudes toward seeking mental health services. These insights hold significant implications for the design of future interventions and the development of targeted policies aimed at promoting help-seeking behaviors.

None Declared

Globally, there is a mental health crisis, and anxiety is the most prevalent mental health condition. However, the impact of the COVID-19 pandemic (COVID) on generalized anxiety disorder (GAD) prevalence has not been quantified across European countries, and such impact could establish a new baseline of GAD estimates in European countries.

To assess GAD by severity level before and during COVID in 5 European countries, using the 7-Item GAD Questionnaire (GAD-7).

Adults (age 18+) in France, Germany, UK, Italy, and Spain completed a short survey in May 2020 to assess the impact of COVID on their mental health. All respondents had previously participated in the National Health and Wellness Survey, a nationally representative survey of the adult general population in each country, before COVID (December 2019–March 2020). In both surveys, respondents completed the GAD-7. GAD symptoms were defined by GAD-7 score as mild (5-9), moderate (10-14), and severe GAD (≥15). Positive screen was defined as GAD-7 score ≥10. Positive screen and GAD symptom severity prevalence were reported for the pooled European sample and by country, both before and during COVID. Chi-square and McNemar’s tests were used to evaluate the difference in GAD severity across countries and changes over baseline in GAD positive screen during COVID. P-values were reported for both tests.

In total, 2401 adults were included in analysis (France, n=482; Germany, n=487; UK, n=487; Italy, n=474; Spain, n=471). Prior to COVID, 311 (13%) screened positive for GAD, with 208 (9%) moderate and 103 (4%) severe in the pooled European sample. During COVID, the distribution of GAD symptoms almost doubled, as 576 (24%) screened positive for GAD, and shifted towards greater severity with 337 (14%) moderate and 239 (10%) severe in the pooled European sample (Figure 1). Before COVID, the prevalence of positive screen ranged from 11% (France, Germany, Spain) to 16% (UK). Statistically significant increases in positive screen over baseline levels were observed across all countries (p<0.01), except Germany. Spain was the most impacted by COVID (increase: 16%), followed by Italy, France, and UK (increase: 14%, 12%, and 9%, respectively). Germany was the least affected, overall (increase: 4%) (Figure 2).

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During COVID, estimates of positive screen for GAD increased substantially to 24% across 5 European countries. Surges in positive screen and GAD symptom severity were observed in all 5 countries, with more profound impact in Spain, Italy, France, and UK. With new baseline GAD estimates, the country-specific data of COVID impact on GAD could help to inform appropriate allocation of mental health resources.

D. Karlin Employee of: MindMed, S. Suponcic Shareolder of: Eli Lilly, Stryker, Abbott, Amgen, Consultant of: MindMed, Becton Dickinson Company, CSL Behring, N. Chen Consultant of: MindMed, C. Steinhart Employee of: MindMed, P. Duong Employee of: MindMed

Digital interventions have been found to be successful in preventing occupational mental health concerns, however, they seem to be affected by attrition bias through high attrition rates and differential attrition. Differential attrition arises when the rates of participant dropouts differ across different treatment conditions and is considered a significant challenge to internal validity.

We aimed at systematically review and meta-analyse differential attrition of digital mental health interventions in the workplace setting.

On January 2, 2022, we performed a search in the following electronic databases: PubMed, Scopus, and Web of Science Core. We utilized a combination of terms from five distinct areas, namely mental health, intervention, workplace, implementation, and study design. The study encompassed adult employees who took part in a randomized control trial aimed at preventing mental health issues in the workplace through an online intervention. A team of six reviewers collaborated on the study selection process, while two independent researchers conducted the data extraction for the selected studies. We performed a meta-analysis of the log-transformed relative attrition rates of the included studies using a random-effects model with limited maximum-likelihood (REML) estimation to account for the degree of heterogeneity.

A total of 19 studies were included in the meta-analysis. For baseline to post-intervention, the average total attrition was 26.27% (SD = 21.16%, range = 0 – 66.3%) and the random effects model revealed a higher attrition rate in the intervention group compared to the control group, with a pooled risk ratio of 1.05 (95% CI: 1.01 - 1.10, p = .014). For baseline to follow-up measurement the average total attrition was 27.71% (SD = 20.80%, range = 0 – 67.78%), however, in this case the random effects model did not indicate a higher attrition in the intervention group when compared to the control group (pooled risk ratio = 1.05, 95% CI: 0.98 – 1.12, p = .183).

There is an indication of higher attrition in the intervention group as compared to the control group in occupational e-mental health interventions from baseline to post-intervention, however this does not seem to be the case for baseline to follow-up attrition. These results should be taken into account in the design process of studies and statistical analyses should be adapted to counteract the bias that could result from differential attrition.

None Declared

Social support may protect against Alzheimer’s disease and related dementias (ADRD), potentially through emotional or instrumental support elements. Black and Hispanic/Latinx older adults bear a disproportionate burden of ADRD. However, independent effects of emotional and instrumental support on cognition, a primary indicator of ADRD risk, are largely understudied in these groups. Guided by the differential vulnerability hypothesis – the theoretical framework which posits that systemic racism disadvantages Black and Hispanic/Latinx individuals’ health – we hypothesize that emotional and instrumental support may be particularly important to protect against worse cognition for Black and Hispanic/Latinx older adults, who often have fewer resources due to these inequalities (e.g., wealth, educational opportunities) to otherwise maintain health. Using the NIH Toolbox Emotion Module measures of emotional (e.g., the extent to which individuals can rely on others in challenging times) and instrumental support (e.g., the extent to which individuals can rely on others for assistance in daily activities), we aimed to identify positive social support factors (i.e., emotional and instrumental support) that may protect against ADRD risk (i.e., longitudinal executive function and memory performance) among Black and Hispanic/Latinx older adults.

Participants were 362 Black and 265 Hispanic/Latinx adults aged 65-89 (63% female, average age=75) from the Kaiser Healthy Aging and Diverse Life Experiences (KHANDLE) Study who completed baseline and up to two additional waves of assessments (every 1.5 years), including questionnaires, neuropsychological evaluations, and the NIH toolbox. Predictors included baseline covariates (i.e., age, language of test administration, gender, education, income, self-rated health) and NIH toolbox emotional and instrumental support variables. Outcomes were baseline and longitudinal memory (visual and verbal episodic memory) and executive functioning (verbal fluency and working memory) composites from the Spanish and English Neuropsychological Assessment Scales (SENAS). Latent growth curve models were conducted separately in Black and Hispanic/Latinx participants to estimate effects of emotional and instrumental support on baseline cognition and subsequent change in each domain.

Black participants reported greater emotional support. There were no group differences in levels of instrumental support. Greater instrumental support was associated with better initial memory (standardized β= .194, 95%CI: [.063, .325]) among Black participants but not among Hispanic/Latinx participants. In Hispanic/Latinx participants, greater emotional support was associated with better initial executive functioning (standardized β= .215, 95%CI: [.079, .350]. Emotional support was not associated with either cognitive domain in Black participants. There were no associations between emotional or instrumental support on cognitive change in either group.

Results point to differences between Black and Hispanic/Latinx older adults in the impact of specific aspects of social support on different cognitive domains. Positive associations between instrumental support and baseline memory in Black participants and between emotional support and executive functioning in Hispanic/Latinx participants suggest unique cognitive consequences of social support across groups. Differences in the role of specific types of social supports may be useful in identifying intervention targets specifically for Black and Hispanic/Latinx older adults, who are disproportionately affected by ADRD. Future research will examine these constructs using multiple group models to test these associations more rigorously.

We identify a set of essential recent advances in climate change research with high policy relevance, across natural and social sciences: (1) looming inevitability and implications of overshooting the 1.5°C warming limit, (2) urgent need for a rapid and managed fossil fuel phase-out, (3) challenges for scaling carbon dioxide removal, (4) uncertainties regarding the future contribution of natural carbon sinks, (5) intertwinedness of the crises of biodiversity loss and climate change, (6) compound events, (7) mountain glacier loss, (8) human immobility in the face of climate risks, (9) adaptation justice, and (10) just transitions in food systems.

The Intergovernmental Panel on Climate Change Assessment Reports provides the scientific foundation for international climate negotiations and constitutes an unmatched resource for researchers. However, the assessment cycles take multiple years. As a contribution to cross- and interdisciplinary understanding of climate change across diverse research communities, we have streamlined an annual process to identify and synthesize significant research advances. We collected input from experts on various fields using an online questionnaire and prioritized a set of 10 key research insights with high policy relevance. This year, we focus on: (1) the looming overshoot of the 1.5°C warming limit, (2) the urgency of fossil fuel phase-out, (3) challenges to scale-up carbon dioxide removal, (4) uncertainties regarding future natural carbon sinks, (5) the need for joint governance of biodiversity loss and climate change, (6) advances in understanding compound events, (7) accelerated mountain glacier loss, (8) human immobility amidst climate risks, (9) adaptation justice, and (10) just transitions in food systems. We present a succinct account of these insights, reflect on their policy implications, and offer an integrated set of policy-relevant messages. This science synthesis and science communication effort is also the basis for a policy report contributing to elevate climate science every year in time for the United Nations Climate Change Conference.

We highlight recent and policy-relevant advances in climate change research – with input from more than 200 experts.

Clinical trials provide the “gold standard” evidence for advancing the practice of medicine, even as they evolve to integrate real-world data sources. Modern clinical trials are increasingly incorporating real-world data sources – data not intended for research and often collected in free-living contexts. We refer to trials that incorporate real-world data sources as real-world trials. Such trials may have the potential to enhance the generalizability of findings, facilitate pragmatic study designs, and evaluate real-world effectiveness. However, key differences in the design, conduct, and implementation of real-world vs traditional trials have ramifications in data management that can threaten their desired rigor.

Three examples of real-world trials that leverage different types of data sources – wearables, medical devices, and electronic health records are described. Key insights applicable to all three trials in their relationship to Data and Safety Monitoring Boards (DSMBs) are derived.

Insight and recommendations are given on four topic areas: A. Charge of the DSMB; B. Composition of the DSMB; C. Pre-launch Activities; and D. Post-launch Activities. We recommend stronger and additional focus on data integrity.

Clinical trials can benefit from incorporating real-world data sources, potentially increasing the generalizability of findings and overall trial scale and efficiency. The data, however, present a level of informatic complexity that relies heavily on a robust data science infrastructure. The nature of monitoring the data and safety must evolve to adapt to new trial scenarios to protect the rigor of clinical trials.

This paper used data from the Apathy in Dementia Methylphenidate Trial 2 (NCT02346201) to conduct a planned cost consequence analysis to investigate whether treatment of apathy with methylphenidate is economically attractive.

A total of 167 patients with clinically significant apathy randomized to either methylphenidate or placebo were included. The Resource Utilization in Dementia Lite instrument assessed resource utilization for the past 30 days and the EuroQol five dimension five level questionnaire assessed health utility at baseline, 3 months, and 6 months. Resources were converted to costs using standard sources and reported in 2021 USD. A repeated measures analysis of variance compared change in costs and utility over time between the treatment and placebo groups. A binary logistic regression was used to assess cost predictors.

Costs were not significantly different between groups whether the cost of methylphenidate was excluded (F(2,330) = 0.626, ηp2 = 0.004, p = 0.535) or included (F(2,330) = 0.629, ηp2 = 0.004, p = 0.534). Utility improved with methylphenidate treatment as there was a group by time interaction (F(2,330) = 7.525, ηp2 = 0.044, p < 0.001).

Results from this study indicated that there was no evidence for a difference in resource utilization costs between methylphenidate and placebo treatment. However, utility improved significantly over the 6-month follow-up period. These results can aid in decision-making to improve quality of life in patients with Alzheimer’s disease while considering the burden on the healthcare system.

In adults with Clostridioides difficile infection (CDI), higher stool concentrations of toxins A and B are associated with severe baseline disease, CDI-attributable severe outcomes, and recurrence. We evaluated whether toxin concentration predicts these presentations in children with CDI.

We conducted a prospective cohort study of inpatients aged 2–17 years with CDI who received treatment. Patients were followed for 40 days after diagnosis for severe outcomes (intensive care unit admission, colectomy, or death, categorized as CDI primarily attributable, CDI contributed, or CDI not contributing) and recurrence. Baseline stool toxin A and B concentrations were measured using ultrasensitive single-molecule array assay, and 12 plasma cytokines were measured when blood was available.

We enrolled 187 pediatric patients (median age, 9.6 years). Patients with severe baseline disease by IDSA-SHEA criteria (n = 34) had nonsignificantly higher median stool toxin A+B concentration than those without severe disease (n = 122; 3,217.2 vs 473.3 pg/mL; P = .08). Median toxin A+B concentration was nonsignificantly higher in children with a primarily attributed severe outcome (n = 4) versus no severe outcome (n = 148; 19,472.6 vs 429.1 pg/mL; P = .301). Recurrence occurred in 17 (9.4%) of 180 patients. Baseline toxin A+B concentration was significantly higher in patients with versus without recurrence: 4,398.8 versus 280.8 pg/mL (P = .024). Plasma granulocyte colony-stimulating factor concentration was significantly higher in CDI patients versus non-CDI diarrhea controls: 165.5 versus 28.5 pg/mL (P < .001).

Higher baseline stool toxin concentrations are present in children with CDI recurrence. Toxin quantification should be included in CDI treatment trials to evaluate its use in severity assessment and outcome prediction.

Lower parental education has been linked to adverse youth mental health outcomes. However, the relationship between parental education and youth suicidal behaviours remains unclear. We explored the association between parental education and youth suicidal ideation and attempts, and examined whether sociocultural contexts moderate such associations.

We conducted a systematic review and meta-analysis with a systematic literature search in PubMed, PsycINFO, Medline and Embase from 1900 to December 2020 for studies with participants aged 0–18, and provided quantitative data on the association between parental education and youth suicidal ideation and attempts (death included). Only articles published in English in peer-reviewed journals were considered. Two authors independently assessed eligibility of the articles. One author extracted data [e.g. number of cases and non-cases in each parental education level, effect sizes in forms of odds ratios (ORs) or beta coefficients]. We then calculated pooled ORs using a random-effects model and used moderator analysis to investigate heterogeneity.

We included a total of 59 articles (63 study samples, totalling 2 738 374 subjects) in the meta-analysis. Lower parental education was associated with youth suicidal attempts [OR = 1.12, 95% Confidence Interval (CI) = 1.04–1.21] but not with suicidal ideation (OR = 1.05, 95% CI = 0.98–1.12). Geographical region and country income level moderated the associations. Lower parental education was associated with an increased risk of youth suicidal attempts in Northern America (OR = 1.26, 95% CI = 1.10–1.45), but with a decreased risk in Eastern and South-Eastern Asia (OR = 0.72, 95% CI = 0.54–0.96). An association of lower parental education and increased risk of youth suicidal ideation was present in high- income countries (HICs) (OR = 1.14, 95% CI = 1.05–1.25), and absent in low- and middle-income countries (LMICs) (OR = 0.91, 95% CI = 0.77–1.08).

The association between youth suicidal behaviours and parental education seems to differ across geographical and economical contexts, suggesting that cultural, psychosocial or biological factors may play a role in explaining this association. Although there was high heterogeneity in the studies reviewed, this evidence suggests that the role of familial sociodemographic characteristics in youth suicidality may not be universal. This highlights the need to consider cultural, as well as familial factors in the clinical assessment and management of youth's suicidal behaviours in our increasingly multicultural societies, as well as in developing prevention and intervention strategies for youth suicide.

Response to lithium in patients with bipolar disorder is associated with clinical and transdiagnostic genetic factors. The predictive combination of these variables might help clinicians better predict which patients will respond to lithium treatment.

To use a combination of transdiagnostic genetic and clinical factors to predict lithium response in patients with bipolar disorder.

This study utilised genetic and clinical data (n = 1034) collected as part of the International Consortium on Lithium Genetics (ConLi+Gen) project. Polygenic risk scores (PRS) were computed for schizophrenia and major depressive disorder, and then combined with clinical variables using a cross-validated machine-learning regression approach. Unimodal, multimodal and genetically stratified models were trained and validated using ridge, elastic net and random forest regression on 692 patients with bipolar disorder from ten study sites using leave-site-out cross-validation. All models were then tested on an independent test set of 342 patients. The best performing models were then tested in a classification framework.

The best performing linear model explained 5.1% (P = 0.0001) of variance in lithium response and was composed of clinical variables, PRS variables and interaction terms between them. The best performing non-linear model used only clinical variables and explained 8.1% (P = 0.0001) of variance in lithium response. A priori genomic stratification improved non-linear model performance to 13.7% (P = 0.0001) and improved the binary classification of lithium response. This model stratified patients based on their meta-polygenic loadings for major depressive disorder and schizophrenia and was then trained using clinical data.

Using PRS to first stratify patients genetically and then train machine-learning models with clinical predictors led to large improvements in lithium response prediction. When used with other PRS and biological markers in the future this approach may help inform which patients are most likely to respond to lithium treatment.