A database of videos of physical exercise and sedentary behaviours to investigate automatic processes and perceptions related to physical exercise.

OBJECTIVE: The Videos of Physical Exercise and Sedentary Behaviours (VPESB) database is a novel database designed to experimentally investigate neural reactivity to physical exercise. The aim of this database is to provide a variety of dynamic images with a minimum of confounding factors. METHODS: A total of 196 healthy participants were recruited to evaluate 10 clips of sedentary activities and 10 clips of physical exercise. Each activity was performed by a male and female performer, resulting in a total of 40 video clips of 10 s each. RESULTS: The validation procedure confirmed the ability of these videoclips to accurately represent both sedentary and physical activity. In addition, video clips of physical activity were associated with higher effort ratings than sedentary activities (p < 0.01). CONCLUSIONS: The VPESB is a versatile, rapid and easy-to-use tool that can be used to understand emotional and behavioural approaches to physical activity and to better disentangle some clinical conditions in which physical activity plays a central role.

The capacity of cognitive tests to detect generalized anxiety disorder (GAD): A pilot study.

Cognitive impairment remains understudied in generalized anxiety disorder (GAD), despite the high prevalence and substantial burden associated with this disorder. We aimed to assess cognitive impairment in patients with GAD and evaluate the ability of cognitive tests to detect this disorder. Because of its high rate of comorbidity, we also examined how other anxiety disorders and current major depressive episodes affected our results. We tested 263 consecutive general practice outpatients. We used the GAD-7 and the Mini International Neuropsychiatric Interview (MINI) to detect anxiety and mood disorders. We assessed cognitive performance with the Stroop test, a facial emotion recognition test, and the trail-making test (TMT). Compared to patients without GAD, patients with GAD were significantly slower to complete the TMT(B-A) and faster to recognize emotions, especially negative ones such as disgust and anger. When controlling for other anxiety disorders and current major depressive episode, GAD retained a significant effect on the TMT(B-A), but not on the emotion recognition test. The TMT(B-A) could detect GAD with good accuracy (area under the curve (AUC) = 0.83, maximal Youden's index = 0.56), which was by no means comparable to the GAD-7 (AUC = 0.97, Youden's index = 0.81). While it is not efficient enough to replace the GAD-7 as a diagnostic tool, the capacity of the TMT(B-A) to detect GAD emphasizes the importance of cognitive flexibility impairment in GAD.

[Addictions, complex pathologies in constant evolution]. / Les addictions, des pathologies complexes en constante évolution.

Addiction is a dynamic field. Its ongoing changes reflect a persistent but evolving public health problem. Its evolution is reflected in subsequent classifications of mental illness. Numerous theories, inspired by psychoanalysis and neurobiology, address the development of the addictive process an many factors explain differences in susceptibility between individuals. In the last decade, behavioural addictions have been the subject of intense debate, leading to the inclusion of gambling disorder in the latest classification. The treatment of addictions is multifactorial and should take into account the specificities of each individual.

Trajectories and predictive factors of weight recovery in patients with anorexia nervosa completing treatment. A latent class mixed model approach.

BACKGROUND: Treatment of anorexia nervosa (AN) sometimes requires hospitalisation, which is often lengthy, with little ability to predict individual trajectory. Depicting specific profiles of treatment response and their clinical predictors could be beneficial to tailor inpatient management. The aim of this research was to identify clusters of weight recovery during inpatient treatment, and their clinical predictors. METHODS: A sample of 181 inpatients who completed a treatment programme for AN was included in a retrospective study. A latent class mixed model approach was used to identify distinct weight-gain trajectories. Clinical variables were introduced in a multinomial logistic regression model as predictors of the different classes. RESULTS: A four-class quadratic model was retained, able to correctly classify 63.7% of the cohort. It encompassed a late-rising, flattening, moderate trajectory of body mass index (BMI) increase (class 1), a late-rising, steady, high trajectory (class 2), an early-rising, flattening, high trajectory (class 3) and an early-rising, steady, high trajectory (class 4). Significant predictors of belonging to a class were baseline BMI (all classes), illness duration (class 2), and benzodiazepine prescription (class 3). CONCLUSION: Predicting different kinetics of weight recovery based on routinely collected clinical indicators could improve clinician awareness and patient engagement by enabling shared expectations of treatment response.