Supplementary Material for: Indicated Stress Prevention addressing adolescents with high stress levels based on principles of Acceptance and Commitment Therapy – a Randomized Controlled Trial

Introduction: Stress affects many adolescents and is associated with physical and mental health symptoms that can have a negative impact on normative development. However, there are very few evidence-based, specific treatment approaches. The aim of the study was to investigate an eight-session group intervention using components of Acceptance and Commitment Therapy (ACT) enriched with elements of CBT (psychoeducation, problem solving) and art therapy, compared to a waitlist-control-group (WLC), regarding its efficacy in reducing stress and associated symptoms. Methods: We conducted a randomized controlled trial in eight cohorts. Eligible participants were 13 to 18 years old with elevated stress levels. Via block-randomization n = 70 participants were allocated to receive ACT (n = 38) or WLC (n = 32) and subsequent ACT. We used a multimodal assessment (self-reports, interviews, ecological momentary assessment, physiological markers), before treatment (T1), after the training of the ACT-group (T2) and after subsequent training in the WLC-group (T3). Primary outcome was perceived stress at T2 assessed with the Perceived Stress Scale. The trial was preregistered at the German Clinical Trials Register (ID: DRKS00012778). Results: Results showed significantly lower levels of perceived stress in the ACT group at T2, illustrating superiority of ACT compared to WLC with a medium to large effect size (d = .77). Furthermore, the training was effective in the reduction of symptoms of school burnout and physical symptoms associated with stress. Conclusion: Indicated prevention, especially when based on principles of ACT and CBT, seems efficient in significantly decreasing stress in adolescents with increased stress

Supplementary Material for: Machine Learning Facial Emotion Classifiers in Psychotherapy Research. A proof-of-concept study.

Background: New advances in the field of machine learning make it possible to track facial emotional expression with high resolution, including micro-expressions. These advances have promising applications for psychotherapy research, since manual coding (e.g. the Facial Action Coding System [FACS]), is time-consuming. Purpose: We tested whether this technology can reliably identify in-session emotional expression in a naturalistic treatment setting, and how these measures relate to the outcome of psychotherapy. Method: We applied a machine learning emotion classifier to video material from 389 psychotherapy sessions of 23 patients with borderline personality pathology. We validated the findings with human ratings according to the Clients Emotional Arousal Scale (CEAS) and explored associations with treatment outcomes. Results: Overall, machine learning ratings showed significant agreement with human ratings. Machine learning emotion classifiers, particularly the display of positive emotions (smiling and happiness), showed medium effect size on median-split treatment outcome (d=0.3) as well as continuous improvement (r=0.49, p<.05). Patients who dropped out form psychotherapy, showed significantly more neutral expressions, and generally less social smiling, particularly at the beginning of psychotherapeutic sessions. Conclusions: Machine learning classifiers are a highly promising resource for research in psychotherapy. The results highlight differential associations of displayed positive and negative feelings with treatment outcomes. Machine learning emotion recognition may be used for the early identification of drop-out risks and clinically relevant interactions in psychotherapy

Supplementary Material for: Neurochemical Correlates of Cue Reactivity in Individuals with Excessive Smartphone Use

Background: Excessive smartphone use (ESU), that is, a pattern of smartphone use that shows specific features of addictive behavior, has increasingly attracted societal and scientific interest in the past years. On the neurobiological level, ESU has recently been related to structural and functional variation in reward and salience processing networks, as shown by, for example, aberrant patterns of neural activity elicited by specific smartphone cues. Objectives: Expanding on these findings, using cross-modal correlations of magnetic resonance imaging (MRI)-based measures with nuclear imaging-derived estimates, we aimed at identifying neurochemical pathways that are related to ESU. Methods: Cross-modal correlations between functional MRI data derived from a cue-reactivity task administered in persons with and without ESU and specific PET/SPECT receptor probability maps. Results: The endogenous mu-opioid receptor (MOR) system was found to be significantly (FDR-corrected) correlated with fMRI data, and z-transformed correlation coefficients showed an association (albeit nonsignificant after FDR-correction) between MOR and the Smartphone Addiction Inventory “withdrawal” dimension. Conclusions: We could identify the MOR system as a neurochemical pathway associated with ESU. The MOR system is closely linked to the reward system, which has been recognized as a key player in addictive disorders. Together with its potential link to withdrawal, the MOR system hints toward a biologically highly relevant marker, which should be taken into consideration in the ongoing scientific discussion on technology-related addictive behaviors