Currently, the diagnosis of major depressive disorder (MDD) and its subtypes is mainly based on subjective assessments and self-reported measures. However, objective criteria as Electroencephalography (EEG) features would be helpful in detecting depressive states at early stages to prevent the worsening of the symptoms. Scientific community has widely investigated the effectiveness of EEG-based measures to discriminate between depressed and healthy subjects, with the aim to better understand the mechanisms behind the disorder and find biomarkers useful for diagnosis. This work offers a comprehensive review of the extant literature concerning the EEG-based biomarkers for MDD and its subtypes, and identify possible future directions for this line of research. Scopus, PubMed and Web of Science databases were researched following PRISMA’s guidelines. The initial papers’ screening was based on titles and abstracts; then full texts of the identified articles were examined, and a synthesis of findings was developed using tables and thematic analysis. After screening 1871 articles, 76 studies were identified as relevant and included in the systematic review. Reviewed markers include EEG frequency bands power, EEG asymmetry, ERP components, non-linear and functional connectivity measures. Results were discussed in relations to the different EEG measures assessed in the studies. Findings confirmed the effectiveness of those measures in discriminating between healthy and depressed subjects. However, the review highlights that the causal link between EEG measures and depressive subtypes needs to be further investigated and points out that some methodological issues need to be solved to enhance future research in this field.

Given the high prevalence of psychological distress and mental health conditions within higher education contexts, identifying potential factors fostering university students’ well-being may be pertinent. Academic engagement (AE) appears to play a key role in supporting mental health and buffering psychological issues. This study addressed the association between AE, well-being, and mental health among university students, by focusing on a specific vulnerable sub-population: those who seek support at a university counseling facility. The main objectives were to identify distinct patterns of AE, to examine differences among these patterns, and to explore how they are associated with various psychological well-being and mental health outcomes. Data were collected from 650 students (M = 23.9; SD = 3.2) attending the counseling facility. Standardized questionnaires were used to measure AE, anxiety, depression, emotion regulation, and psychological distress. Three distinct patterns of academic engagement were identified through a clustering process: Low AE pattern, Moderate AE pattern, and High AE pattern. Multivariate statistical analyses were conducted to examine the associations between these patterns and various well-being and mental health outcomes. Results indicated significant associations between higher levels of academic engagement and improved psychological well-being. Conversely, lower engagement was associated with more severe psychological symptoms and distress. These findings suggest that students’ AE should be considered as an integral component of counseling interventions. By highlighting the connection between AE and mental health, this study provides insights for designing effective strategies tailored on students’ needs, enhancing mental health and favoring a supportive educational environment.

Based on a previous investigation, a quantitative study aimed to identify user’ preferences towards four synthetic voices of two different quality levels (classified through the sophistication of the synthesizer: low vs. high) is proposed. The voices administered to participants were developed considering two main aspects: the voice quality (high/low) and their gender (male/female). 182 unpaid participants were recruited for the study, divided in four groups according to their age, and therefore classified as adolescents, young adults, middle-aged, and seniors. To collect data regarding each voice, randomly audited by participants, the shortened version of the Virtual Agent Voice Acceptance Questionnaire (VAVAQ) was exploited. Outcomes of the previous study revealed that the voices of high quality, regardless of their gender, received a higher acclaim by all participants examined rather than the corresponding two voices assessed as lower quality. Conversely, findings of the current study suggest that the four new groups of participants involved agreed in showing their strong preference towards the high-quality voice gendered as female compared to all the other considered voices. Regarding the two voices gendered as male, the high-quality one was considered as more original and capable to arouse positive emotional states than the low-quality one. Moreover, the high-quality male voice was judged as more natural than the female low-quality one. Results provide some insights for future directions in the user experience and design field.

University students face a critical developmental transition from adolescence to emerging adulthood, marked by increased vulnerability to psychological distress. Effective and accessible counseling services are essential for supporting their mental health and academic functioning. This pre-post study evaluated changes in psychological well-being and academic engagement following a brief individual counseling intervention at an Italian University Counseling Service (UCS). The intervention consisted of up to six weekly, individual one-hour sessions with a counseling psychologist, delivered in person. A total of 372 students (Mage = 23.9, SD = 2.8; F = 74.7%) completed validated self-report questionnaires assessing anxiety, depressive symptoms, psychological distress, emotion regulation strategies, and at-risk behaviors before and after the intervention. Academic engagement dimensions (persistence, motivation, sense of belonging, and relational aspects) were analyzed in a subsample (n = 280; Mage = 23.9, SD = 2.9; F = 74.6%). Changes were analyzed using mixed models, with moderation by age and gender. Significant improvements were found in anxiety (B = − 0.482, p <.001, f² = 0.26), depressive symptoms (B = − 0.705, p <.001, f² = 0.28), psychological distress (B = − 0.662, p <.001, f² = 0.22), at-risk behaviors (B = − 0.087, p <.001, f² = 0.04), and adaptive emotion regulation (B = 3.791, p <.001, f² = 0.06). Changes in academic engagement emerged mainly in peer relationships (B = 0.144, p =.001, R²c = 0.823, f² = 0.002) and in relationships with professors (B = − 0.115, p =.006, R²c = 0.762, f² = 0.005). Age and gender partially moderated these changes, highlighting the relevance of demographic characteristics. These findings contribute to practice-based research by providing insights into UCS effectiveness and highlighting areas requiring further attention. Future research should explore long-term trajectories and further examine individual differences to better tailor counseling services to students’ diverse needs.