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In contrast to the classic models in psychopathology, the network model considers that the temporal interactions between symptoms are the causes of their occurrence. This model could also be particularly suitable for understanding the processes involved in post-stroke depression. The aim of this paper is to perform a network analysis in order to describe the temporal dynamic of the links existing between depression symptoms during the acute phase after stroke. Twenty-five patients (64% male, mean age 58.1±14.9 years old) hospitalized for a minor stroke (no neurocognitive or motor impairment) were involved in an Ecological Momentary Assessment methodology-based study. They used a smartphone application in order to complete four brief questionnaires each day during the week after hospital discharge. The questionnaire included 7-point Likert scales to measure the severity of the following depressive symptoms: sadness, anhedonia, fatigue, diminished concentration ability, negative thoughts on oneself, pessimism. We used Multilevel Vector Autoregressive analysis to describe the temporal links between those symptoms. We used the software R 3.6.0 with the mlVAR package. The p-value was set at .05. The results show two independent symptoms networks. The first one involves the anhedonia, fatigue, negative thoughts on oneself and sadness. It shows that: anhedonia predicts the activation of later fatigue (β=0.135, P=0.037) and later negative thoughts (β=0.152, P=0.019); negative thoughts predict later negative thoughts (β=0.143, P=0.028) and later sadness (β=0.171, P=0.021); fatigue predicts later fatigue (β=0.261, P

Resume La litterature met en evidence l’importance des strategies de coping et du sentiment d’efficacite dans les performances academiques et la detresse des etudiants. La perspective temporelle (PT) pourrait influencer le fonctionnement adaptatif des individus ainsi que les croyances en leurs capacites. Afin d’etudier les liens entre PT, sentiment d’efficacite et coping, 362 etudiants en premiere annee ont repondu a des auto-questionnaires evaluant ces trois variables. Les resultats montrent que le passe negatif et le present fataliste sont associes negativement au sentiment d’efficacite et positivement a un coping non-adaptatif. Inversement, le passe positif et le futur sont associes positivement au sentiment d’efficacite et a des strategies de coping adaptatives. Les resultats mettent en evidence un effet de mediation du sentiment d’efficacite dans la relation entre PT et coping. Ces resultats soulignent le role du profil temporel des etudiants dans leur fonctionnement adaptatif. Les perspectives de recherche et de prevention sont discutees.

CNRS 4, FNEGE 4, HCERES C; International audience; This cross-sectional descriptive study aims to (1) describe the current digital technology (DT) use of people with chronic diseases (CD) by identifying different user profiles and (2) determine whether those profiles have specific characteristics regarding health-related variables and patient–doctor relationship quality (RQ). An online questionnaire assessing the uses of multiple types of DT (the Internet, mobile applications and connected devices) and several dimensions related to health and patient–doctor RQ was completed by 954 individuals living with CD. DT user groups were obtained by k-means cluster analysis and then compared using Mann–Whitney tests. The results show three profiles of DT users: (1) hyperconnected (8.9%, regular users of all DTs), (2) biconnected (19.1%, regular users of the Internet and mobile apps) and (3) hypoconnected (72%, casual users of the Internet only). The hyperconnected and biconnected groups are more empowered, more knowledgeable about their treatment and more committed to their doctors than the hypoconnected group. Nonadherence to treatment, health motivations, self-efficacy for health management and the trust dimension of the patient–doctor RQ did not differ between groups. We conclude by discussing the low use of the most recent technologies in the CD population, although these technologies seem to provide access to health information that empowers patients and leads to a better relationship with their doctors.

Few previous investigations have focused on post-stroke anxiety (PSA) predictors in mild stroke patients. The aim of the study was to determine whether anxiety-related psychological symptoms and psychomotor agitation predict PSA. We compared 10 anxious and 10 non-anxious patients at 6 months post-stroke (T2) to their psychological anxiety and psychomotor agitation levels 3 months earlier (T1). Anxious patients had more psychological anxiety symptoms than non-anxious patients. Overall T2 anxiety was strongly correlated with T1 psychological anxiety. Thus, psychological symptoms are a better predictor of PSA than somatic symptoms of anxiety. Those results could improve PSA diagnosis and prognosis by directing clinicians to pay particular attention to psychological anxiety after a stroke, even in early discharged patients with complete functional recovery.