Resilience and psychosocial factors in adult with epilepsy: A longitudinal study.

PURPOSE: Resilience is conceptually characterized as a dynamic process encompassing positive adaptation in the context of significant adversity. Our goal was to assess the resilience in people with epilepsy (PWE) and how it impacts longitudinally on psychosocial factors, with a particular focus on the manifestation of stigmatization-related feelings. METHODS: We consecutively enrolled 78 adults PWE (42.5 ± 16.2 years old); among them 36 (46.1 %) were seizure-free. All subjects completed at baseline (T0) the Resilience Scale (RS-14) and questionnaires for the assessment of depressive symptoms, anxiety and quality of life: respectively, Beck Depression Inventory-II (BD-II), Generalized Anxiety Disorder-7 (GAD-7) and QOLIE-31 (Q31). All patients were followed up prospectively and re-evaluated after 6-22 months (T1; mean: 14 ± 8 months; median 14 months); at follow up they also completed the Stigma Scale of Epilepsy (SSE) for the assessment of the stigma associated with epilepsy. We correlated resilience values with all psychosocial scores at T0 and T1. Factors associated with resilient and vulnerable outcomes were identified. Finally, a multiple stepwise regression analysis was applied to identify predictors for resilience and stigma perception. RESULTS: The results showed for the RS-14 score a significant direct correlation with the Q31 (p < 0.001) and an inverse correlation with the depressive and anxiety symptoms at both times (T0 and T1), as evaluated with BDI-II (p < 0.001) and GAD-7 (p < 0.001). Finally, we found a significant inverse correlation between RS-14 at T0 and the levels of stigmatization assessed with SSE at T1 (p =.015). Using a multiple stepwise regression analysis separately for resilience and stigma perception, depressive symptoms turned out as the best predictors for both variables. Finally, considering longitudinal evaluation we did not observe significant changes in depressive and anxious symptoms, despite a significant reduction in the total number of seizures at follow up. CONCLUSIONS: Our study showed that depressive symptoms, anxiety and quality of life were significantly associated with resilience in PwE. Finally, as a novel finding resilience was proved to affect the perception of stigma related to epilepsy more than seizures.

Resilience predicts and modulates anxiety severity in people with epilepsy.

BACKGROUND: Anxiety is one of the most relevant psychiatric comorbidities in people with epilepsy (PwE). The role of resilience (RES) in the development of anxiety is not well understood. We purposed to better characterize RES impact on anxiety severity in PwE. MATERIALS AND METHODS: One hundred and seventy-six PwE underwent online surveys including a collection of socio-demographic, seizure-related, and psychological variables. PwE were grouped according to the data collected; anxiety levels were compared through non-parametric statistics. Hierarchical regression analysis (HRA) and logistic regression were performed to characterize RES contribute in predicting the presence and the severity of anxiety. Mediation/moderation analysis was performed to evaluate causal effects among RES, depression, and anxiety. RESULTS: Anxiety did not differ according to socio-demographic and seizure-related variables, exemption for the presence of drug-related adverse effects. Depression, RES, and sleep quality provided the major contribute on anxiety variance. The addiction of RES level in HRA and logistic regression provided a significant increase of R-squared value (p-value = 0.02) and of area under the curve (p-value = 0.03), respectively. RES modulated depression/anxiety relationship (p-value < 0.001), whereas depression did not mediate RES/anxiety correlation (p-value = 0.68). CONCLUSIONS: We demonstrated that RES is a significant independent predictor of anxiety in PwE and is able to modulate depression impact on anxiety. Moreover, we confirmed the relevance of depression and sleep quality on anxiety severity.

Determinants of medication adherence in people with epilepsy: A multicenter, cross-sectional survey.

OBJECTIVES: Poor medication adherence in people with epilepsy (PwE) increases mortality, hospitalization, and poor quality of life, representing a critical challenge for clinicians. Several demographic, clinical, and neuropsychological factors were singularly found associated with medication adherence in several studies, but the literature lacks a comprehensive study simultaneously assessing all these variables. METHODS: We performed a multicenter and cross-sectional study using online questionnaires with the following clinical scales: Morisky Medication Adherence Scale (MMAS-8), Quality of Life in Epilepsy Inventory 31 (QoLIE-31), Beck Depression Inventory-II (BDI-II), Generalized Anxiety Disorder-7 (GAD-7) and 14-item Resilience scale (RES14) in a population of 200 PwE. We used the ANOVA test and Spearman's correlation to evaluate the relationship between medication adherence and demographic, clinical (seizure frequency, number of anti-seizure medications), and neuropsychological characteristics. We trained separate machine learning models (logistic regression, random forest, support vector machine) to classify patients with medium-high adherence (MMAS-8 ≥ 6) and poor adherence (MMAS-8 < 6) and to identify the main features that influence adherence. RESULTS: Women were more adherent to medication (p-value = 0.035). Morisky Medication Adherence Scale -8 showed a direct correlation with RES14 (p-value = 0.001) and age (p-value = 0.001), while was inversely correlated with BDI-II (p-value = 0.001) and GAD-7 (p-value = 0.001). In our model, the variables mostly predicting treatment adherence were QoLIE-31 subitems, followed by age, resilience, anxiety, years of school, and disease duration. CONCLUSION: Our study confirms that gender, age, and neuropsychological traits are relevant factors in predicting medication adherence to PwE. Furthermore, our data provided the first evidence that machine learning on multidimensional self-report questionnaires could help to develop a decisional support system in outpatient epilepsy clinics.