Corentin J. Gosling’s research while affiliated with University of Southampton and other places

La flexibilité cognitive est une fonction exécutive fondamentale qui permet de s’adapter aux changements environnementaux, et constitue un marqueur transdiagnostique prometteur dans plusieurs troubles psychiatriques. Cette revue examine le rôle spécifique de la flexibilité cognitive parmi les autres fonctions cognitives. Elle explore les approches thérapeutiques potentielles, et présente son évaluation dans le cadre du PEPR PROPSY. À travers ses différentes sections, le lecteur peut ainsi approfondir ses connaissances sur la flexibilité cognitive, et découvrir des pistes encourageantes, tant pour son évaluation que pour son ciblage dans l’optique d’approches interventionnelles.

Recent research has shown mixed evidence for the morning morality effect (MME; i.e., the observation that individuals are less immoral in the morning than in the afternoon). In the present research, we target the morning morality effect in the context of moral utilitarianism, for which this effect has never been explored. We first reanalyzed observational data from six studies previously conducted by our lab, which included different tasks capturing moral utilitarianism. A meta-analytic model showed that participants become less utilitarian as the day goes on, but with a small effect size (r = −0.14, 95% CI = [−0.25, −0.02]) and large heterogeneity. Exploration of this heterogeneity showed that this association was statistically significant for classic sacrificial dilemmas only. We next conducted an experimental study of the morning morality effect, which aimed to experimentally support the results previously observed in the meta-analysis, as well as to explore, in addition, a possible moderating effect of chronotype. These experimental results showed no reliable overall effect of time of day on moral utilitarianism (SMD = 0.04, 95% CI = [−0.21, 0.28]). A potential moderating effect of chronotype was detected in secondary analyses, but that needs to be replicated. The implications and limitations are discussed.

A fundamental pillar of science is the estimation of the effect size of associations. However, this task is sometimes difficult and error-prone. To facilitate this process, the R package metaConvert automatically calculates and flexibly converts multiple effect size measures. It applies more than 120 formulas to convert any relevant input data into Cohen’s d , Hedges’ g , mean difference, odds ratio, risk ratio, incidence rate ratio, correlation coefficient, Fisher’s r-to-z transformed correlation coefficient, variability ratio, coefficient of variation ratio, or number needed to treat. Researchers unfamiliar with R can use this software through a browser-based graphical interface ( https://metaconvert.org/ ). We hope this suite will help researchers in the life sciences and other disciplines estimate and convert effect sizes more easily and accurately.

The diagnosis of autism is currently based on the developmental history, direct observation of behavior, and reported symptoms, supplemented by rating scales/interviews/structured observational evaluations—which is influenced by the clinician’s knowledge and experience—with no established diagnostic biomarkers. A growing body of research has been conducted over the past decades to improve diagnostic accuracy. Here, we provide an overview of the current diagnostic assessment process as well as of recent and ongoing developments to support diagnosis in terms of genetic evaluation, telemedicine, digital technologies, use of machine learning/artificial intelligence, and research on candidate diagnostic biomarkers. Genetic testing can meaningfully contribute to the assessment process, but caution is required when interpreting negative results, and more work is needed to strengthen the transferability of genetic information into clinical practice. Digital diagnostic and machine-learning-based analyses are emerging as promising approaches, but larger and more robust studies are needed. To date, there are no available diagnostic biomarkers. Moving forward, international collaborations may help develop multimodal datasets to identify biomarkers, ensure reproducibility, and support clinical translation.

Importance Amid escalating mental health challenges among young individuals, intensified by the COVID-19 pandemic, analyzing postpandemic trends is critical. Objective To examine mental health care utilization and prescription rates for children, adolescents, and young adults before and after the COVID-19 pandemic. Design, Setting, and Participants This population-based time trend study used an interrupted time series analysis to examine mental health care and prescription patterns among the French population 25 years and younger. Aggregated data from the French national health insurance database from January 2016 to June 2023. Data were analyzed from September 2023 to February 2024. Main Outcomes and Measures The number of individuals with at least 1 outpatient psychiatric consultation, those admitted for full-time psychiatric hospitalization, those with a suicide attempt, and those receiving psychotropic medication was computed. Data were stratified by age groups and sex. Quasi-Poisson regression modeled deseasonalized data, estimating the relative risk (RR) and 95% CI for differences in slopes before and after the pandemic. Results This study included approximately 20 million individuals 25 years and younger (20 829 566 individuals in 2016 and 20 697 169 individuals in 2022). In 2016, the population consisted of 10 208 277 of 20 829 566 female participants (49.0%) and 6 091 959 (29.2%) aged 18 to 25 years. Proportions were similar in 2022. Significant increases in mental health care utilization were observed postpandemic compared with the prepandemic period, especially among females and young people aged 13 years and older. Outpatient psychiatric consultations increased among women (RR, 1.13; 95% CI, 1.07-1.20), individuals aged 13 to 17 years (RR, 1.15; 95% CI, 1.06-1.23), and individuals aged 18 to 25 years (RR, 1.08; 95% CI, 1.03-1.14). Hospitalizations for suicide attempt increased among women (RR, 1.14; 95% CI, 1.02-1.27) and individuals aged 18 to 25 years (RR, 1.07; 95% CI, 1.03-1.12). Regarding psychotropic medications, almost all classes, except hypnotics, increased in prescriptions between 2016 and 2022 for females, with a particularly marked rise in the postpandemic period. For men, only increases in the prescriptions of antidepressants (RR, 1.03; 95% CI, 1.01-1.06), methylphenidate (RR, 1.09; 95% CI, 1.06-1.12), and medications prescribed for alcohol use disorders (RR, 1.08; 95% CI, 1.04-1.13) were observed, and these increases were less pronounced than for women (antidepressant: RR, 1.13, 95% CI, 1.09-1.16; methylphenidate: RR, 1.15; 95% CI, 1.13-1.18; alcohol use dependence: RR, 1.12; 95% CI, 1.08-1.16). Medications reserved for severe mental health situations, such as lithium or clozapine, were prescribed more frequently starting at the age of 6 years. Conclusions and Relevance In this study, an interrupted time-series analysis found a marked deterioration in the mental health of young women in France in the after the COVID-19 pandemic, accentuating a trend of deterioration that was already observed in the prepandemic period.

Systematic reviews and meta-analyses have become crucial for evidence-based decision-making in recent decades. However, it is common for the results of multiple reviews on the same topic to be inconsistent, and it is widely recognised that the results of the reviews are not always effectively communicated to healthcare professionals and the lay public. This manuscript proposes a strategy to summarise and communicate the findings of previous systematic reviews and meta-analyses to wider audiences. The proposed approach couples the findings of umbrella reviews with the creation of open-access online platforms that present the results of these umbrella reviews in an accessible way to various stakeholders. The key potential methodological avenues of this approach are presented, and specific examples from the author’s own works and those from other teams are provided. An accompanying website ( https://u-reach.org/ ) has been designed to present this Umbrella-Review, Evaluation, Analysis, and Communication Hub (U-REACH) approach and to overcome the technical challenges associated with this type of project (by sharing the code used to build existing U-REACH projects). The present document is intended to serve as a methodological and technical guide for the creation of large-scale projects designed to synthesise and disseminate scientific information to a broad audience.

Background Despite the growing interest in the prevalence and consequences of loneliness, the way it is measured still raises a number of questions. In particular, few studies have directly compared the psychometric properties of very short measures of loneliness to standard measures. Methods We conducted a large epidemiological study of midwife students ( n = 1742) and performed a head-to-head comparison of the psychometric properties of the standard (20 items) and short version (3 items) of the UCLA Loneliness Scales (UCLA-LS). All participants completed the UCLA-LS-20, UCLA-LS-3, as well as other measures of mental health, including anxiety and depression. Results First, as predicted, we found that the two loneliness scales were strongly associated with each other. Second, when using the dimensional scores of the scales, we showed that the internal reliability, convergent-, discriminant-, and known-groups validities were high and of similar magnitude between the UCLA-LS-20 and the UCLA-LS-3. Third, when the scales were dichotomized, the results were more mixed. The sensitivity and/or specificity of the UCLA-LS-3 against the UCLA-LS-20 were systematically below acceptable thresholds, regardless of the dichotomizing process used. In addition, the prevalence of loneliness was strikingly variable as a function of the cut-offs used. Conclusions Overall, we showed that the UCLA-LS-3 provided an adequate dimensional measure of loneliness that is very similar to the UCLA-LS-20. On the other hand, we were able to highlight more marked differences between the scales when their scores were dichotomized, which has important consequences for studies estimating, for example, the prevalence of loneliness.