Emotion regulation abilities as a predictor of anxiety, positive emotions, heart rate, and alpha asymmetry during an impromptu speech task

Effectiveness of a Biofeedback Intervention Targeting Mental and Physical Health Among College Students Through Speech and Physiology as Biomarkers Using Machine Learning: A Randomized Controlled Trial

Biofeedback therapy is mainly based on the analysis of physiological features to improve an individual's affective state. There are insufficient objective indicators to assess symptom improvement after biofeedback. In addition to psychological and physiological features, speech features can precisely convey information about emotions. The use of speech features can improve the objectivity of psychiatric assessments. Therefore, biofeedback based on subjective symptom scales, objective speech, and physiological features to evaluate efficacy provides a new approach for early screening and treatment of emotional problems in college students. A 4-week, randomized, controlled, parallel biofeedback therapy study was conducted with college students with symptoms of anxiety or depression. Speech samples, physiological samples, and clinical symptoms were collected at baseline and at the end of treatment, and the extracted speech features and physiological features were used for between-group comparisons and correlation analyses between the biofeedback and wait-list groups. Based on the speech features with differences between the biofeedback intervention and wait-list groups, an artificial neural network was used to predict the therapeutic effect and response after biofeedback therapy. Through biofeedback therapy, improvements in depression (p = 0.001), anxiety (p = 0.001), insomnia (p = 0.013), and stress (p = 0.004) severity were observed in college-going students (n = 52). The speech and physiological features in the biofeedback group also changed significantly compared to the waitlist group (n = 52) and were related to the change in symptoms. The energy parameters and Mel-Frequency Cepstral Coefficients (MFCC) of speech features can predict whether biofeedback intervention effectively improves anxiety and insomnia symptoms and treatment response. The accuracy of the classification model built using the artificial neural network (ANN) for treatment response and non-response was approximately 60%. The results of this study provide valuable information about biofeedback in improving the mental health of college-going students. The study identified speech features, such as the energy parameters, and MFCC as more accurate and objective indicators for tracking biofeedback therapy response and predicting efficacy. Trial Registration ClinicalTrials.gov ChiCTR2100045542.

Effectiveness of the REThink therapeutic online video game in promoting mental health in children and adolescents

Although evidence-based interventions exist, estimates suggest that about 60% percent of children and adolescents with mental health disorders do not receive the treatment they need. In this context, one expanding strategy for increasing access to mental health care for children and adolescents is the use therapeutic, or serious, games. REThink is one such therapeutic game, developed to offer a CBT-based prevention that was documented in a controlled trial to develop psychological resilience in children and adolescents, aged between 10 and 16, helping them learn healthy strategies for coping with dysfunctional negative emotions such as anxiety, anger and depression. This study aims to test the effectiveness of the REThink therapeutic online video game in promoting emotional health in children and adolescents in a pilot study. Participants (N = 31), aged between 10 and 16 years, were recruited on a volunteer basis from a school. Emotional problems, irrational beliefs, negative automatic thoughts, rational beliefs, and problem solving abilities were assessed pre- and post-using the therapeutic game. We also measured participants' satisfaction with the game. Results obtained show improvements in terms of emotional problems of the youths, their irrational beliefs, negative automatic thoughts and high levels of intervention satisfaction. of this study are in support of the previous findings suggesting that the REThink online game can be a valuable tool for large-scale mental health efforts aimed at the prevention of emotional disorders in children and adolescents, in accordance with evidence-based prevention protocols.