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Attention deficit/hyperactivity disorder (ADHD) is associated with poor academic performance also among university students. This relationship may be made more complex by comorbid conditions. The aim of this study was to evaluate the mediating role of anxiety and depressive symptoms in the relationship between ADHD and academic performance. Data were drawn from the CAMPUS study (registration number: 0058642/21), an ongoing survey on university students’ mental health. Using a logit model, mediation analyses were carried out to test whether the relationship between ADHD symptoms (assessed by ASRS-5) and academic performance might be mediated by depressive (assessed by PHQ-9) and anxiety (assessed by GAD-7) symptoms. Our results showed that worse academic performance is associated with ADHD symptoms (p < 0.001). However, about 24% of the overall association between ADHD symptoms and academic performance was mediated by depressive symptoms (indirect effect: 0.065, 95%CI 0.022; 0.100), whereas the contribution of anxiety symptoms to the model was not significant. Along with the association between ADHD symptoms and poor academic performance, our findings highlight the key mediating role of depressive symptoms, which may be targeted with tailored support, ultimately improving both the academic performance and the well-being of university students with ADHD.

Monitoring symptoms of bipolar disorder (BD) is a challenge faced by mental health services. Speech patterns are crucial in assessing the current experiences, emotions, and thought patterns of people with BD. Natural language processing (NLP) and acoustic signal processing may support ongoing BD assessment within a mobile health (mHealth) framework. Using both acoustic and NLP-based features from the speech of people with BD, we built an app-based tool and tested its feasibility and performance to remotely assess the individual clinical status. We carried out a pilot, observational study, sampling adults diagnosed with BD from the caseload of the Nord Milano Mental Health Trust (Italy) to explore the relationship between selected speech features and symptom severity and to test their potential to remotely assess mental health status. Symptom severity assessment was based on clinician ratings, using the Young Mania Rating Scale (YMRS) and Montgomery-Åsberg Depression Rating Scale (MADRS) for manic and depressive symptoms, respectively. Leveraging a digital health tool embedded in a mobile app, which records and processes speech, participants self-administered verbal performance tasks. Both NLP-based and acoustic features were extracted, testing associations with mood states and exploiting machine learning approaches based on random forest models. We included 32 subjects (mean [SD] age 49.6 [14.3] years; 50% [16/32] females) with a MADRS median (IQR) score of 13 (21) and a YMRS median (IQR) score of 5 (16). Participants freely managed the digital environment of the app, without perceiving it as intrusive and reporting an acceptable system usability level (average score 73.5, SD 19.7). Small-to-moderate correlations between speech features and symptom severity were uncovered, with sex-based differences in predictive capability. Higher latency time (ρ=0.152), increased silences (ρ=0.416), and vocal perturbations correlated with depressive symptomatology. Pressure of speech based on the mean intraword time (ρ=-0.343) and lower voice instability based on jitter-related parameters (ρ ranging from -0.19 to -0.27) were detected for manic symptoms. However, a higher contribution of NLP-based and conversational features, rather than acoustic features, was uncovered, especially for predictive models for depressive symptom severity (NLP-based: R2=0.25, mean squared error [MSE]=110.07, mean absolute error [MAE]=8.17; acoustics: R2=0.11, MSE=133.75, MAE=8.86; combined: R2=0.16; MSE=118.53, MAE=8.68). Remotely collected speech patterns, including both linguistic and acoustic features, are associated with symptom severity levels and may help differentiate clinical conditions in individuals with BD during their mood state assessments. In the future, multimodal, smartphone-integrated digital ecological momentary assessments could serve as a powerful tool for clinical purposes, remotely complementing standard, in-person mental health evaluations.

Introduction: COVID-19 restrictions introduced several changes in university academic and social experience. Self-isolation and online teaching have amplified students’ mental health vulnerability. Thus, we aimed to explore feelings and perspectives about the impact of the pandemic on mental health, comparing students from Italy and the UK. Methods: Data were collected from the qualitative portion of “the CAMPUS study”, longitudinally assessing mental health of students at the University of Milano-Bicocca (Italy) and the University of Surrey (UK). We conducted in-depth interviews and thematically analysed the transcripts. Results: The explanatory model was developed from four themes identified across 33 interviews: anxiety exacerbated by COVID-19; putative mechanisms leading to poor mental health; the most vulnerable subgroups; and coping strategies. Generalised and social anxiety resulted from COVID-19 restrictions by being associated with loneliness, excessive time online, unhealthy management of time and space and poor communication with the university. Freshers, international students, and people on the extremes of the introversion/extroversion spectrum, were identified as vulnerable, while effective coping strategies included taking advantage of free time, connection with family and mental health support. The impact of COVID-19 was mostly related to academic issues by students from Italy, whereas to the drastic loss of social connectedness by the UK sample. Conclusions: Mental health support for students has an essential role, and measures that encourage communication and social connectedness are likely to be beneficial.

The diagnostic concept of unipolar mania (UM), i.e. the lifetime occurrence of mania without major depressive episodes, remains a topic of debate despite the evidence accumulated in the last few years. We carried out a systematic review and meta-analysis of observational studies testing factors associated with UM as compared to bipolar disorder with a manic-depressive course (md-BD). Studies indexed up to July 2022 in main electronic databases were searched. Random-effects meta-analyses of the association between UM and relevant correlates yielded odds ratio (OR) or standardized mean difference (SMD), with 95% confidence intervals (CIs). Based on data from 21 studies, factors positively or negatively associated with UM, as compared to md-BD, were: male gender (OR 1.47; 95% CI 1.11-1.94); age at onset (SMD -0.25; 95% CI -0.46 to -0.04); number of hospitalizations (SMD 0.53; 95% CI 0.21-0.84); family history of depression (OR 0.55; 95% CI 0.36-0.85); suicide attempts (OR 0.25; 95% CI 0.19-0.34); comorbid anxiety disorders (OR 0.35; 95% CI 0.26-0.49); psychotic features (OR 2.16; 95% CI 1.55-3.00); hyperthymic temperament (OR 1.99; 95% CI 1.17-3.40). The quality of evidence for the association with previous suicide attempts was high, moderate for anxiety disorders and psychotic features, and low or very low for other correlates. Despite the heterogeneous quality of evidence, this work supports the hypothesis that UM might represent a distinctive diagnostic construct, with peculiar clinical correlates. Additional research is needed to better differentiate UM in the context of affective disorders, favouring personalized care approaches.

Most people with major depressive episodes meet the criteria for the anxious distress (AD) specifier defined by DSM-5 as the presence of symptoms such as feelings of tension, restlessness, difficulty concentrating, and fear that something awful may happen. This cross-sectional study was aimed at identifying clinical correlates of AD in people with unipolar or bipolar depression. Inpatients with a current major depressive episode were included. Data on socio-demographic and clinical variables were collected. The SCID-5 was used to diagnose depressive episodes and relevant specifiers. The Montgomery-Åsberg Depression Rating Scale (MADRS) and Young Mania Rating Scale (YMRS) were used to assess the severity of depressive and manic (mixed) symptoms, respectively. Multiple logistic regression analyses were carried out to identify clinical correlates of AD. We included 206 people (mean age: 48.4 ± 18.6 yrs.; males: 38.8%) admitted for a major depressive episode (155 with major depressive disorder and 51 with bipolar disorder). Around two-thirds of the sample (N = 137; 66.5%) had AD. Multiple logistic regression models showed that AD was associated with mixed features, higher YMRS scores, psychotic features, and a diagnosis of major depressive disorder (  < 0.05). Despite some limitations, including the cross-sectional design and the inpatient setting, our study shows that AD is likely to be associated with mixed and psychotic features, as well as with unipolar depression. The identification of these clinical domains may help clinicians to better contextualize AD in the context of major depressive episodes.