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Introduction COVID-19 lockdown measures resulted in children and adolescents staying and learning at home. This study investigated health-related quality of life (HRQoL) and its associated factors among youth during the first lockdown. Methods A cross-sectional study was conducted among 8- to 18-year-olds from the French Grand Est region. Sociodemographic data and information on living and learning conditions were collected using an online survey. HRQoL was assessed using the KIDSCREEN-27. Multiple regression analysis was performed to explore factors related to low HRQoL in each dimension. Results In total, 471 children from 341 households were included. Difficulties isolating at home were associated with low HRQoL in the psychological well-being (OR = 2.2, 95% CI: 1.2–4.0) and parent relations and autonomy (OR = 2.1, 95% CI: 1.2–3.8) dimensions. Conflicts with dwelling occupants were related to increased ORs in the psychological well-being (OR = 2.9, 95% CI: 1.9–4.6), parent relations and autonomy (OR = 2.2, 95% CI: 1.4–3.4) and school environment (OR = 2.4, 95% CI: 1.5–3.7) dimensions. Living in an apartment (OR = 1.8, 95% CI: 1.1–3.1), never leaving home (OR = 2.6, 95% CI: 1.2–5.9), having indoor noise at home (OR = 2.3, 95% CI: 1.2–4.6), and having a parent with high anxiety (OR = 1.8, 95% CI: 1.1–3.1) were associated with low HRQoL in the social support and peers dimension. Children working less than 1 h/day on schoolwork had an increased OR of 3.5 (95% CI: 1.4–9.0) in the school environment dimension. Conclusion Living and learning conditions were associated with low HRQoL among children and adolescents during the COVID-19 lockdown. Prevention and intervention programs are needed to support youth by facilitating their interactions and improving their coping and to prepare for future waves.

Background The COVID-19 epidemic has sent students around the world in to lockdown. This study sought to assess the prevalence of impaired self-perceived mental health and identify associated factors among French post-secondary students during the lockdown. Methods A cross-sectional study was conducted among French students living in the Grand Est area in France from May 7 to 17, 2020 during the first lockdown. An online survey was used to collect sociodemographic data, learning and teaching conditions, living conditions, and exposure to COVID-19, and self-perceived mental health was assessed with mental composite score (MCS) of the SF-12. Results Overall, 4018 were analyzed. Most participants were female (70.7%), and the mean age was 21.7 years (SD 4.0). The mean MCS score was 44.5 (SD 17.3). Impaired mental health, defined by a MCS < 1st Quartile, was mainly associated with female sex; decreased time for learning; not having access to the outside with a garden, a terrace or a balcony; difficulties with the living situation and having someone in the home affected by the SARS-COV2 requiring hospitalization or not. Conclusions This study showed that living conditions during lockdown had a clear impact on the mental health of French post-secondary students. There is a need to improve prevention and to access distance education as well as an urgent need for measures to develop healthy coping strategies for students. This is significant challenge and will assist in moderating the risk for the development of further distress and mental health concerns.

In recent years, due to its great economic and social potential, the recognition of facial expressions linked to emotions has become one of the most flourishing applications in the field of artificial intelligence, and has been the subject of many developments. However, despite significant progress, this field is still subject to many theoretical debates and technical challenges. It therefore seems important to make a general inventory of the different lines of research and to present a synthesis of recent results in this field. To this end, we have carried out a systematic review of the literature according to the guidelines of the PRISMA method. A search of 13 documentary databases identified a total of 220 references over the period 2014–2019. After a global presentation of the current systems and their performance, we grouped and analyzed the selected articles in the light of the main problems encountered in the field of automated facial expression recognition. The conclusion of this review highlights the strengths, limitations and main directions for future research in this field.