The global spread of COVID-19 led the World Health Organization to declare a pandemic on 11 March 2020. To decelerate this spread, countries have taken strict measures that have affected the lifestyles and economies. Various studies have focused on the identification of COVID-19’s impact on the mental health of children and adolescents via traditional statistical approaches. However, a machine learning methodology must be developed to explain the main factors that contribute to the changes in the mood state of children and adolescents during the first lockdown. Therefore, in this study an explainable machine learning pipeline is presented focusing on children and adolescents in Greece, where a strict lockdown was imposed. The target group consists of children and adolescents, recruited from children and adolescent mental health services, who present mental health problems diagnosed before the pandemic. The proposed methodology imposes: (i) data collection via questionnaires; (ii) a clustering process to identify the groups of subjects with amelioration, deterioration and stability to their mood state; (iii) a feature selection process to identify the most informative features that contribute to mood state prediction; (iv) a decision-making process based on an experimental evaluation among classifiers; (v) calibration of the best-performing model; and (vi) a post hoc interpretation of the features’ impact on the best-performing model. The results showed that a blend of heterogeneous features from almost all feature categories is necessary to increase our understanding regarding the effect of the COVID-19 pandemic on the mood state of children and adolescents.

Social anxiety disorder (SAD) is a debilitating condition that usually begins in adolescence. We recently demonstrated preliminary efficacy of an internet-delivered therapist-assisted version of Cognitive Therapy for Social Anxiety Disorder (SAD) for adolescents called Online Social anxiety Cognitive therapy for Adolescents (OSCA). Here we report on the helpfulness, support, and overall acceptability of OSCA from the perspective of trial participants. Participants were 17 young people aged 15–18 years who had participated in a trial of OSCA. Post-treatment, participants completed an online treatment acceptability questionnaire and took part in a semi-structured interview to gain an understanding of their experience of OSCA. Overall, there was a very high rate of treatment satisfaction. Core aspects of the treatment were viewed as most helpful, including behavioural experiments even though participants found them challenging. Participants found the online nature of the treatment helpful, allowing for easier communication with the therapist, regular encouragement from the therapist, and the ability to go back to their treatment and view their progress. Challenges were, for some, the quantity of content and practical issues around scheduling the short weekly calls with their therapist. This study suggests that young people felt helped and supported by OSCA.

The COVID-19 pandemic and its accompanying restrictions have significantly impacted people’s lives globally. There is an increasing interest in examining the influence of this unprecedented situation on our mental well-being, with less attention towards the impact of the elongation of COVID-19-related measures on youth with a pre-existing psychiatric/developmental disorder. The majority of studies focus on individuals, such as students, adults, and youths, among others, with little attention being given to the elongation of COVID-19-related measures and their impact on a special group of individuals, such as children and adolescents with diagnosed developmental and psychiatric disorders. In addition, most of these studies adopt statistical methodologies to identify pair-wise relationships among factors, an approach that limits the ability to understand and interpret the impact of various factors. In response, this study aims to adopt an explainable machine learning approach to identify factors that explain the deterioration or amelioration of mood state in a youth clinical sample. The purpose of this study is to identify and interpret the impact of the greatest contributing features of mood state changes on the prediction output via an explainable machine learning pipeline. Among all the machine learning classifiers, the Random Forest model achieved the highest effectiveness, with 76% best AUC-ROC Score and 13 features. The explainability analysis showed that stress or positive changes derived from the imposing restrictions and COVID-19 pandemic are the top two factors that could affect mood state.

Argyris Stringaris was initially included as an author in the original publication