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Sleep and rest-activity-rhythm (RAR) abnormalities are commonly reported in schizophrenia spectrum disorder (SSD) patients. However, an in-depth characterization of sleep/RAR alterations in SSD, including patients in different treatment settings, and the relationship between these alterations and SSD clinical features (e.g., negative symptoms) is lacking. SSD (N = 137 altogether, N = 79 residential and N = 58 outpatients) and healthy control (HC) subjects (N = 113) were recruited for the DiAPAson project. Participants wore an ActiGraph for seven consecutive days to monitor habitual sleep-RAR patterns. Sleep/rest duration, activity (i.e., M10, calculated on the 10 most active hours), rhythm fragmentation within days (i.e., intra-daily variability, IV; beta, steepness of rest-active changes), and rhythm regularity across days (i.e., inter-daily stability, IS) were computed in each study participant. Negative symptoms were assessed in SSD patients with the Brief Negative Symptom Scale (BNSS). Both SSD groups showed lower M10 and longer sleep/rest duration vs. HC, while only residential patients had more fragmented and irregular rhythms than HC. Compared to outpatients, residential patients had lower M10 and higher beta, IV and IS. Furthermore, residential patients had worse BNSS scores relative to outpatients, and higher IS contributed to between-group differences in BNSS score severity. Altogether, residentials and outpatients SSD had both shared and unique abnormalities in Sleep/RAR measures vs. HC and relative to one another, which also contributed to the patients' negative symptom severity. Future work will help establish whether improving some of these measures may ameliorate the quality of life and clinical symptoms of SSD patients.

COVID-19 pandemic had a negative impact on the mental health and well-being (WB) of citizens. This cross-sectional study included 4 waves of data collection aimed at identifying profiles of individuals with different levels of WB. The study included a representative stratified sample of 10,013 respondents in Italy. The WHO 5-item well-being scale (WHO-5) was used for the assessment of WB. Different supervised machine learning approaches (multinomial logistic regression, partial least-square discriminant analysis—PLS-DA—, classification tree—CT—) were applied to identify individual characteristics with different WB scores, first in waves 1–2 and, subsequently, in waves 3 and 4. Forty-one percent of participants reported “Good WB”, 30% “Poor WB”, and 28% “Depression”. Findings carried out using multinomial logistic regression show that Resilience was the most important variable able for discriminating the WB across all waves. Through the PLS-DA, Increased Unhealthy Behaviours proved to be the more important feature in the first two waves, while Financial Situation gained most relevance in the last two. COVID-19 Perceived Risk was relevant, but less than the other variables, across all waves. Interestingly, using the CT we were able to establish a cut-off for Resilience (equal to 4.5) that discriminated good WB with a probability of 65% in wave 4. Concluding, we found that COVID-19 had negative implications for WB. Governments should support evidence-based strategies considering factors that influence WB (i.e., Resilience, Perceived Risk, Healthy Behaviours, and Financial Situation).