Using ecological momentary assessment and machine learning techniques to predict depressive symptoms in emerging adults.

The objective of this study was to predict the level of depressive symptoms in emerging adults by analyzing sociodemographic variables, affect, and emotion regulation strategies. Participants were 33 emerging adults (M = 24.43; SD = 2.80; 56.3 % women). They were asked to assess their current emotional state (positive or negative affect), recent events that may relate to that state, and emotion regulation strategies through ecological momentary assessment. Participants were prompted randomly by an app 6 times per day between 10 am and 10 pm for a seven-day period. They answered 1233 of the 2058 surveys (beeps), collectively. The analysis of observations, using Machine Learning (ML) techniques, showed that the Random Forest algorithm yields significantly better predictions than other models. The algorithm used 13 out of the 36 variables adopted in the study. Furthermore, the study revealed that age, emotion of worried and a specific emotion regulation strategy related to social exchange were the most accurate predictors of severe depressive symptoms. By carefully selecting predictors and utilizing appropriate sorting techniques, these findings may provide valuable supplementary information to traditional diagnostic methods and psychological assessments.

EmoTIC: Impact of a game-based social-emotional programme on adolescents.

INTRODUCTION: Technologies provide a brilliant opportunity to promote social-emotional competences, well-being and adjustment in adolescence. Game-based programmes and serious games are digital tools that pursue an educational goal in an attractive environment for adolescents. The purpose of this study was therefore to determine the effectiveness of emoTIC, a game-based social-emotional programme designed according to Mayer, Caruso, and Salovey's model of emotional intelligence. MATERIALS AND METHODS: The participants were 119 adolescents between 11 and 15 years, randomly assigned to the experimental group and the control group. The adolescents completed questionnaires to assess their emotional intelligence, self-esteem, affect balance, difficulties, prosocial behaviour, depression, anxiety and stress. RESULTS: The MANCOVA results showed that adolescents who completed the game-based programme had improved self-esteem, affect balance, emotional symptoms, behavioural problems, and hyperactivity (Wilks' λ = .77; F = 2.10; p = .035). Hierarchical multiple regression indicated that adolescents in the experimental group had a greater change in self-esteem and affect balance (positive ß), while their emotional problems and hyperactivity decreased (negative ß). Anxiety moderated the influence of the intervention on self-esteem (b = .04; t = -2.55; p ≤ .05; LLCI = -0.43, ULCI = -0.05). Adolescents with low or medium anxiety improved their self-esteem with the intervention, while those with high anxiety did not develop it. CONCLUSIONS: The use of technology in social-emotional programmes could be the first step in increasing adolescents' interest in emotions and emoTIC could be considered a useful programme which influences their personal, emotional and social factors. TRIAL REGISTRATION: Clinical Trial identifier: NCT04414449.