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Objectives: In the past months, many countries have adopted varying degrees of lockdown restrictions to control the spread of the COVID-19 virus. According to the existing literature, some consequences of lockdown restrictions on people’s lives are beginning to emerge yet the evolution of such consequences in relation to the time spent in lockdown is understudied. To inform policies involving lockdown restrictions, this study adopted a data-driven Machine Learning approach to uncover the short-term time-related effects of lockdown on people’s physical and mental health. Study design: An online questionnaire was launched on 17 April 2020, distributed through convenience sampling and was self-completed by 2,276 people from 66 different countries. Methods: Focusing on the UK sample (N = 325), 12 aggregated variables representing the participant’s living environment, physical and mental health were used to train a RandomForest model to estimate the week of survey completion. Results: Using an index of importance, Self-Perceived Loneliness was identified as the most influential variable for estimating the time spent in lockdown. A significant U-shaped curve emerged for loneliness levels, with lower scores reported by participants who took part in the study during the 6th lockdown week (p = 0.009). The same pattern was replicated in the Greek sample (N = 137) for week 4 (p = 0.012) and 6 (p = 0.009) of lockdown. Conclusions: From the trained Machine Learning model and the subsequent statistical analysis, Self-Perceived Loneliness varied across time in lockdown in the UK and Greek populations, with lower symptoms reported during the 4th and 6th lockdown weeks. This supports the dissociation between social support and loneliness, and suggests that social support strategies could be effective even in times of social isolation. Nanyang Technological University Published version This research is supported by Nanyang Technological University (Singapore) under the NAP-SUG grant to GE.

Background We validated the Social Mistrust Scale (SMS) and utilized it to examine the structure, prevalence, and heritability of social mistrust in a large sample of Chinese children and adolescents. Methods In Study 1, a large sample of healthy twins (N = 2094) aged 8 to 14 years (M = 10.27 years, SD = 2) completed the SMS. Structural equation modeling (SEM) was conducted to assess the structure of the SMS and to estimate the heritability of social mistrust in a sub-sample of twins (n = 756 pairs). In Study 2, 32 adolescents with childhood-onset schizophrenia were compared with 34 healthy controls on levels of suspiciousness and clinical symptoms to examine the associations between the SMS and the Positive and Negative Syndrome Scale (PANSS). Results We found a three-factor structure for social mistrust (home, school, and general mistrust). Social mistrust was found to be moderately - heritable (19%–40%), with mistrust at home most strongly influenced by genetic factors. Compared with 11.76% of the healthy controls, 56.25% of the adolescents with early-onset schizophrenia exhibited very high levels of social mistrust on all three subscales of the SMS. The SMS exhibited good discriminant validity in distinguishing adolescents with childhood-onset schizophrenia from healthy controls and showed associations with a broad range of symptoms assessed by the PANSS. Conclusions Social mistrust assessed by the SMS may be heritable. The SMS demonstrates good discriminant validity with clinical diagnoses of schizophrenia. However, it seems to be correlated with multiple aspects of psychopathology in the schizophrenia group, rather than being specific to delusional ideation/paranoia.

PURPOSE OF THE REVIEW: This review identifies the early developmental processes that contribute to schizotypy and suspiciousness in adolescence and adulthood. It includes the most recent literature on these phenomena in childhood. RECENT FINDINGS: The early developmental processes that affect schizotypy and paranoia in later life are complex. In contrast to existing studies of psychiatric patients and clinical/nonclinical adult populations, the study of schizotypy and suspiciousness in young children and adolescents is possible due to new child-appropriate dimensional assessments. New assessments and the advancement of technology (e.g., virtual reality in mental health) as well as statistical modeling (e.g., mediation and latent-class analyses) in large data have helped identified the developmental aspects (e.g., psychosocial, neurocognitive and brain factors, nutrition, and childhood correlates) that predict schizotypy and suspiciousness in later life. SUMMARY: Prospective longitudinal designs in community youths can enhance our understanding of the etiology of schizophrenia-spectrum disorders and, in the future, the development of preventive interventions by extending adult theories and interventions to younger populations.