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Abstract Background A work accident constitutes a shock to health, likely to alter mental states and affect the use of psychotropic drugs. We focus on the use of benzodiazepines, which are a class of drugs commonly used to treat anxiety and insomnia. Prolonged use can lead to dependence. Our objective is to determine the extent to which work accidents lead to benzodiazepine use and overuse (i.e. exceedance of medical guidelines). Method We use a two-step selection model (the Heckman method) based on data from the French National Health Data System (Système National des Données de Santé, SNDS). Our study sample includes all general plan members who experienced a single work accident in 2016 (and not since 2007). This sample includes 350,000 individuals in the work accident group and more than 1.1 million people randomly drawn from the population without work accidents from 2007 to 2017 (the non-work accident group). Results The occurrence of a work accident leads to an increase in benzodiazepine use and overuse the following year. The selection model shows a clear influence of the accident on the use probability (+ 39%), but a very slight impact on the risk of overuse among users (+ 1.7%), once considered the selection effect. The effect on overuse risk is higher for more severe accidents and among women. Conclusion The increase in the risk of benzodiazepine overuse is due to an increase in the likelihood of using benzodiazepines after a work accident that leads to overuse, rather than an increase in likelihood of overuse among people who use benzodiazepines. Results call for targeting the first-time prescription to limit the risk of overuse after a work accident.

Benzodiazepines (BZDs) are drugs commonly used for treating insomnia and anxiety. Although they are known to induce cognitive and psychomotor impairments, their effect on the risk of causing accidents at work remains understudied. The objective of this study is to estimate this risk by differentiating between the recommended use and overuse of these drugs (i.e., uninterrupted use for four months). The data come from the French National Health Data System, which provide a population composed of French people who had at least one work accident (WA) from 2017 to 2019 (approximately 2.5 million people). A linear probability model with two-way fixed effects is used to deal with time-constant heterogeneity and the time effect independent of individuals. The results show a reduction in the risk of WA after a short period of BZD use (one month) compared with no use at all, but the risk of WA increases when treatment exceeds the recommended duration. The intensity of use results in a greater risk of WAs: a 1% increase in BZD use (expressed as the amount reimbursed) leads to a 4.4% (p

Introduction Docosahexaenoic acid (DHA), an omega-3 fatty acid, is important for brain development with possible implications in neurodevelopmental outcomes. In the two-arm, randomised, double-blind, placebo-controlled Maternal Omega-3 Supplementation to Reduce Bronchopulmonary Dysplasia in Very Preterm Infants trial, very preterm infants (

Interventions to assist individuals in initiating and maintaining regular participation in physical activity are not always effective. Psychological and behavioral theories advocate the importance of both motivation and volition in interventions to change health behavior. Interventions adopting self-regulation strategies that foster motivational and volitional components may, therefore, have utility in promoting regular physical activity participation. We tested the efficacy of an intervention adopting motivational (mental simulation) and volitional (implementation intentions) components to promote a regular physical activity in two studies. Study 1 adopted a cluster randomized design in which participants (n = 92) were allocated to one of three conditions: mental simulation plus implementation intention, implementation intention only, or control. Study 2 adopted a 2 (mental simulation vs. no mental simulation) × 2 (implementation intention vs. no implementation intention) randomized controlled design in which fitness center attendees (n = 184) were randomly allocated one of four conditions: mental simulation only, implementation intention only, combined, or control. Physical activity behavior was measured by self-report (Study 1) or fitness center attendance (Study 2) at 4- (Studies 1 and 2) and 19- (Study 2 only) week follow-up periods. Findings revealed no statistically significant main or interactive effects of the mental simulation and implementation intention conditions on physical activity outcomes in either study. Findings are in contrast to previous research which has found pervasive effects for both intervention strategies. Findings are discussed in light of study limitations including the relatively small sample sizes, particularly for Study 1, deviations in the operationalization of the intervention components from previous research and the lack of a prompt for a goal intention. Future research should focus on ensuring uniformity in the format of the intervention components, test the effects of each component alone and in combination using standardized measures across multiple samples, and systematically explore effects of candidate moderators.

BackgroundWith the advent of digital technology and specifically user-generated contents in social media, new ways emerged for studying possible stigma of people in relation with mental health. Several pieces of work studied the discourse conveyed about psychiatric pathologies on Twitter considering mostly tweets in English and a limited number of psychiatric disorders terms. This paper proposes the first study to analyze the use of a wide range of psychiatric terms in tweets in French. ObjectiveOur aim is to study how generic, nosographic, and therapeutic psychiatric terms are used on Twitter in French. More specifically, our study has 3 complementary goals: (1) to analyze the types of psychiatric word use (medical, misuse, or irrelevant), (2) to analyze the polarity conveyed in the tweets that use these terms (positive, negative, or neural), and (3) to compare the frequency of these terms to those observed in related work (mainly in English). MethodsOur study was conducted on a corpus of tweets in French posted from January 1, 2016, to December 31, 2018, and collected using dedicated keywords. The corpus was manually annotated by clinical psychiatrists following a multilayer annotation scheme that includes the type of word use and the opinion orientation of the tweet. A qualitative analysis was performed to measure the reliability of the produced manual annotation, and then a quantitative analysis was performed considering mainly term frequency in each layer and exploring the interactions between them. ResultsOne of the first results is a resource as an annotated dataset. The initial dataset is composed of 22,579 tweets in French containing at least one of the selected psychiatric terms. From this set, experts in psychiatry randomly annotated 3040 tweets that corresponded to the resource resulting from our work. The second result is the analysis of the annotations showing that terms are misused in 45.33% (1378/3040) of the tweets and that their associated polarity is negative in 86.21% (1188/1378) of the cases. When considering the 3 types of term use, 52.14% (1585/3040) of the tweets are associated with a negative polarity. Misused terms related to psychotic disorders (721/1300, 55.46%) were more frequent to those related to depression (15/280, 5.4%). ConclusionsSome psychiatric terms are misused in the corpora we studied, which is consistent with the results reported in related work in other languages. Thanks to the great diversity of studied terms, this work highlighted a disparity in the representations and ways of using psychiatric terms. Moreover, our study is important to help psychiatrists to be aware of the term use in new communication media such as social networks that are widely used. This study has the huge advantage to be reproducible thanks to the framework and guidelines we produced so that the study could be renewed in order to analyze the evolution of term usage. While the newly build dataset is a valuable resource for other analytical studies, it could also serve to train machine learning algorithms to automatically identify stigma in social media.