The prevalence of mental disorders continues to increase, especially with the advent of the COVID-19 pandemic. Although we have evidence-based psychological treatments to address these conditions, most people encounter some barriers to receiving this help (e.g., stigma, geographical or time limitations). Digital mental health interventions (e.g., Internet-based interventions, smartphone apps, mixed realities -virtual and augmented reality) provide an opportunity to improve accessibility to these treatments. This article summarizes the main contributions of the different types of digital mental health solutions. It analyzes their limitations (e.g., drop-out rates, lack of engagement, lack of personalization, lack of cultural adaptations) and showcases the latest sophisticated and innovative technological advances under the umbrella of precision medicine (e.g., digital phenotyping, chatbots, or conversational agents). Finally, future challenges related to the need for real world implementation of these interventions, the use of predictive methodology, and hybrid models of care in clinical practice, among others, are discussed.

Background: Therapeutic alliance (TA) between the patient and therapist has been related to positive therapeutic outcomes. Because Internet-based interventions are increasingly being implemented, a tool is needed to measure the TA with Internet-based self-guided programs. The Working Alliance Inventory for online interventions (WAI-TECH-SF) was adapted based on the WAI Short Form (Hatcher & Gillaspy, 2006). The objectives of this study were: (1) to analyse the psychometric properties of the WAI-TECH-SF; (2) to explore the differences in the WAI-TECH-SF scores according to different categories of the sample; and (3) to analyse whether the WAI-TECH-SF can predict therapeutic outcomes and satisfaction with the treatment. Methods: 193 patients diagnosed with depression were included and received blended Cognitive-Behavioural Therapy. Measures of preferences, satisfaction, and credibility about the treatment, TA with the online program, depressive symptoms, and satisfaction with the treatment were administered. Results: An exploratory factor analysis revealed a one-dimensional structure with adequate internal consistency. Linear regression analyses showed that the WAI-TECH-SF predicted changes in depressive symptoms and satisfaction with the treatment. Conclusions: WAI-TECH-SF is a reliable questionnaire to assess the TA between the patient and the online program, which is associated with positive therapeutic outcomes and satisfaction with the treatment.

People all use more or less adapted strategies to confront adverse emotional situations in their lives without being psychologically affected. The emotional regulation (ER) strategies that we use determine the way in which we feel, express, and behave. Moreover, ER strategies are particularly important in adolescents, a population for which ER strategy deficits can be linked to the appearance of numerous mental health disorders, such as depression or anxiety, or disruptive behaviors. Thus, the early detection of dysfunctional ER strategies and training in adaptive ER strategies can help prevent future occurrences of possible behavioral and psychosocial disorders. In this article, the authors present the GameTeen System (GT-System), a novel instrument based on virtual reality and serious games for the assessment and training of ER strategies in adolescents. The results of their preliminary evaluation suggest that this system can effectively train and evaluate emotional regulation strategies in adolescents.

: COVID-19 pandemic-related confinement may be a fruitful opportunity to use individual resources to deal with it or experience psychological functioning changes. This study aimed to analyze the evolution of different psychological variables during the first coronavirus wave to identify the different psychological response clusters, as well as to keep a follow-up on the changes among these clusters. The sample included 459 Spanish residents (77.8% female, M_age = 35.21 years, SD_age = 13.00). Participants completed several online self-reported questionnaires to assess positive functioning variables (MLQ, Steger et al. in J Loss Trauma 13(6):511–527, 2006. 10.1080/15325020802173660; GQ-6, McCullough et al. in J Person Soc Psychol 82:112–127, 2002. 10.1037/0022-3514.82.1.112; CD-RISC, Campbell-Sills and Stein in J Traum Stress 20(6):1019–1028, 2007. 10.1002/jts.20271; CLS-H, Chiesi et al. in BMC Psychol 8(1):1–9, 2020. 10.1186/s40359-020-0386-9; SWLS; Diener et al. in J Person Assess, 49(1), 71–75, 1985), emotional distress (PHQ-2, Kroenke et al. in Med Care 41(11):1284–1292, 2003. 10.1097/01.MLR.0000093487.78664.3C; GAD-2, Kroenke et al. in Ann Internal Med 146(5):317–325, 2007. 10.7326/0003-4819-146-5-200703060-00004; PANAS, Watson et al. in J Person Soc Psychol 47:1063–1070, 1988; Perceived Stress, ad hoc), and post-traumatic growth (PTGI-SF; Cann et al. in Anxiety Stress Coping 23(2):127–137, 2010. 10.1080/10615800903094273), four times throughout the 3 months of the confinement. Linear mixed models showed that the scores on positive functioning variables worsened from the beginning of the confinement, while emotional distress and personal strength improved by the end of the state of alarm. Clustering analyses revealed four different patterns of psychological response: “Survival”, “Resurgent”, “Resilient”, and “Thriving” individuals. Four different profiles were identified during mandatory confinement and most participants remained in the same cluster. The “Resilient” cluster gathered the largest number of individuals (30–37%). We conclude that both the heterogeneity of psychological profiles and analysis of positive functioning variables, emotional distress, and post-traumatic growth must be considered to better understand the response to prolonged adverse situations.