Cognitive reappraisal and self-compassion as emotion regulation strategies for parents during COVID-19: An online randomized controlled trial - Preuss, Hanna; Capito, Klara; Van Eickels, Rahel Lea; Zemp, Martina; Kolar, David Raphael (Internet Interventions, 2021)
Objective: Parenting during pandemic restrictions places extreme demands on everyday family life, leading to increased stress levels for parents and distressed parent-child interactions. This RCT aimed to investigate whether cognitive reappraisal and self-compassion are helpful emotion regulation (ER) strategies to reduce individual and parental stress during the COVID-19 pandemic. Method: An online intervention for parents was developed focusing on the application of ER strategies to pandemic requirements of families. A sample of 265 parents were randomly assigned to either cognitive reappraisal (CR; n = 88), self-compassion (SC; n = 90) or wait-list control (WLC; n = 87) group. Interventions included two video sessions (day 1 and day 3) and three email reminders to transfer the application of ER strategies to daily family life (days 2, 4, 5). Parents' perceived individual stress and parental stress were assessed at baseline (T0), at T1 prior to the booster session on day 3, and at T2 (7 days after baseline). Results: Significant decreases from T0 to T2 emerged for both primary stress outcomes in both intervention groups. Individual stress significantly decreased in CR compared to WLC at T2, but not compared to SC. No time × group interactions for parental stress were found. However, mediation analyses suggested that parental stress was indirectly decreased via reductions in individual stress for CR compared to WLC at both time points. Conclusions: COVID-19 will not be the last pandemic to affect family life. Cognitive reappraisal as a brief online intervention can ease acute stress and strengthen the mental health of parents in acute crises.
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Keywords: Emotion regulation; Lockdown; Pandemic; Parenting; Stress reduction

Detecting Depression using Machine Learning Approaches through Digital Biomarkers - Sadeghi, Misha; Rahimi, Farnaz; Biradar, Pragati Uttamkumar; Richer, Robert; Rupp, Lydia Helene; Schindler-Gmelch, Lena; Keinert, Marie; Hager, Malin; Capito, Klara; Berking, Matthias; Eskofier, Björn (Bavarian Conference on AI in Medicine 2025, 2025)
Depression is a prevalent mental health disorder characterized by persistent sadness and loss of interest in enjoyable activities. Traditional diagnostic methods are subjective, highlighting a need for more objective and accurate approaches. This study investigates the application of classical machine learning (ML) algorithms to detect depression and predict its severity using facial digital biomarkers extracted from video data within the EmpkinS D02 dataset. Facial features, including Action Units (AUs), emotional cues, head orientation, and 2D landmarks, were extracted frame-by-frame using FaceReader 9. Data preprocessing involved handling missing values, feature standardization, and statistical aggregation. Feature selection was performed with SelectKBest using ANOVA F-statistics to identify the most informative features. Binary classification (depressed vs. healthy) and regression models (predicting PHQ-8 severity scores) were evaluated across three distinct experimental conditions: a Depressed Mood Induction phase using negative self-statements, a Coping phase (where participants actively invalidated depressogenic self-statements with disapproving facial expressions or validated antidepressive self-statements with affirming facial expressions), and a Comprehensive phase (including data from all experimental conditions). XGBoost achieved the highest classification performance in the Comprehensive phase with an accuracy of 81.82% and an F1-score of 80.36%, surpassing the targeted benchmark of 80%. For severity prediction, the LightGBM regression model performed best during the Comprehensive phase, attaining a Mean Absolute Error (MAE) of 2.02 and Root Mean Squared Error (RMSE) of 2.52. Statistical analyses identified significant facial biomarkers differentiating depressed and healthy participants, notably eyebrow movements (AU 04) and lip movements (AU 25 and AU 20). Performance variability across experimental conditions emphasized the sensitivity of ML models to data context and distribution. The results demonstrate the feasibility and effectiveness of using ML-based facial feature analysis as a robust tool for enhancing depression diagnostics, suggesting promising integration potential in remote and resource-limited clinical setting

Efficacy of a smartphone-based reappraisal training against depression and benefits of enhancing it with facial expression - Rupp, Lydia; Capito, Klara; Gmelch, Lena Marie; Böhme, Stephanie; Richer, Robert; Sadeghi, Misha; Rahimi, Farnaz; Abel, Luca; Eskofier, Björn; Berking, Matthias (Society for Psychotherapie Research 54th International Annual Meeting, 2023)

Efficacy of a smartphone-based reappraisal training against depressed mood and benefits of enhancing it with facial expression–a feasibility study - Capito, Klara; Gmelch, Lena Marie; Rupp, Lydia; Sadeghi, Misha; Eskofier, Björn; Berking, Matthias (Deutscher Psychotherapiekongress, 2022)

Eine emotionsbasierte Variante des Annäherungs-Vermeidungs-Modifikationstrainings als Maßnahme zur Reduktion erhöhten Stresserlebens: eine Pilotstudie - Keinert, Marie; Streit, Hannah; Böhme, Stephanie; Rupp, Lydia; Schindler-Gmelch, Lena; Capito, Klara; Eskofier, Björn; Schuller, Björn W.; Berking, Matthias (1. Deutscher Psychotherapiekongress, 2022)

EmpkinS-EKSpression-BioFACEBack: Ergebnisse eines randomisiert-kontrollierten Trials zu gesichtsmuskelfokussiertem Biofeedback bei depressiven Erkrankungen - Schindler-Gmelch, Lena; Rupp, Lydia; Rahimi, Farnaz; Sadeghi, Misha; Keinert, Marie; Hager, Malin; Capito, Klara; Richer, Robert; Eskofier, Björn; Berking, Matthias (4. Deutscher Psychotherapie Kongress, 2025)

EmpkinS-EKSpression: Ergebnisse einer Randomisiert-kontrollierten klinischen Studie eines Emotions-gestützten Trainings zur Kognitiven Umstrukturierung - Rupp, Lydia; Schindler-Gmelch, Lena; Sadeghi, Misha; Rahimi, Farnaz; Keinert, Marie; Hager, Malin; Capito, Klara; Richer, Robert; Eskofier, Björn; Berking, Matthias (4. Deutscher Psychotherapie Kongress, 2025)

EmpkinS-EKSpression: Results from a Randomized-Controlled Clinical Trial on the Effects of a Facial Expression-based Training for Cognitive Restructuring - Rupp, Lydia; Schindler-Gmelch, Lena; Sadeghi, Misha; Rahimi, Farnaz; Keinert, Marie; Hager, Malin; Capito, Klara; Richer, Robert; Eskofier, Björn; Berking, Matthias (EACLIPT, 2025)

EmpkinS - Empathokinästhetische Sensorik für Biofeedback bei Depression - Gmelch, Lena Marie; Böhme, Stephanie; Capito, Klara; Rupp, Lydia; Richer, Robert; Sadeghi, Misha; Eskofier, Björn; Berking, Matthias (Deutscher Psychotherapiekongress (DPK), 2022)

EmpkinS: empatho-kinesthetic sensory systems for biofeedback in depression - Gmelch, Lena Marie; Capito, Klara; Rupp, Lydia; Richer, Robert; Sadeghi, Misha; Eskofier, Björn; Berking, Matthias; Böhme, Stephanie (EmpkinS: empatho-kinesthetic sensory systems for biofeedback in depression, 2022)

Evaluating OpenDBM for Emotion Intensity Prediction: A Comparative Study Using Facial and Physiological Data - Sadeghi, Misha; Rahimi, Farnaz; Kumar, Akash; Richer, Robert; Rupp, Lydia Helene; Schindler-Gmelch, Lena; Keinert, Marie; Hager, Malin; Capito, Klara; Berking, Matthias; Eskofier, Björn (EAI PervasiveHealth, 2025)
Accurate emotion recognition is crucial for applications in mental health and affective computing. While commercial tools such as FaceReader are common for video analysis, their proprietary nature limits accessibility and integration. This study evaluates the open-source toolkit OpenDBM for predicting continuous emotion intensities, comparing a video-only approach to a multimodal model that fuses visual features with electrocardiogram (ECG) and respiration (RSP) signals. Using FaceReader-annotated video and biosignal data from 113 participants, we trained Random Forest (RF) and Gated Recurrent Unit (GRU) models. The video-only RF model achieved the best performance (MAE = 0.040), outperforming the multimodal model (MAE = 0.070). These results demonstrate the value of OpenDBM as an accessible alternative to commercial tools and suggest that visually derived labels may not fully capture emotion-related patterns present in physiological signal
Keywords: Emotion recognition; Facial expressions; Machine Learning; Multimodal fusion; Physiological signals; OpenDBM; FaceReader

Exploring the Capabilities of a Language Model-Only Approach for Depression Detection in Text Data - Sadeghi, Misha; Egger, Bernhard; Agahi, Reza; Richer, Robert; Capito, Klara; Rupp, Lydia; Gmelch, Lena Marie; Berking, Matthias; Eskofier, Björn (IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), 2023)
Depression is a prevalent and debilitating mental health condition that requires accurate and efficient detection for timely and effective treatment. In this study, we utilized the E-DAIC (Extended Distress Analysis Interview Corpus-Wizard-of-Oz) dataset, an extended version of the DAIC-WOZ dataset, which consists of semi-clinical interviews conducted by an animated virtual interviewer called Ellie, controlled by a human interviewer in another room. With 275 participants, the E-DAIC dataset represents a valuable resource for investigating depression detection methods. Our aim is to predict PHQ-8 scores through text analysis. Leveraging state-of-the-art speech processing, LLM-based text summarization, and a specialized depression detection module, we demonstrate the transformative potential of language data analysis in enhancing depression screening. By overcoming the limitations of manual feature extraction methods, our automated techniques provide a more efficient and effective means of evaluating depression. In our evaluation, we achieve robust accuracy on the development set of the E-DAIC dataset, with a Mean Absolute Error (MAE) of 3.65 in estimating PHQ-8 scores from recorded interviews. This remarkable performance highlights the efficacy of our approach in automatically predicting depression severity. Our research contributes to the growing evidence supporting the use of LLMs in mental health assessment, showcasing the role of innovative technologies in advancing patient care for depression.<br
Keywords: Depression detection; mental health; large language models; text analysis; DAIC dataset; GPT-based models; DepRoBERTa

Exploring the Capabilities of a Language Model-Only Approach for Depression Detection in Text Data - Sadeghi, Misha; Egger, Bernhard; Agahi, Reza; Richer, Robert; Capito, Klara; Rupp, Lydia; Gmelch, Lena Marie; Berking, Matthias; Eskofier, Björn (Deutscher Psychotherapiekongress (DPK), 2024)

Facing depression: evaluating the efficacy of the EmpkinS-EKSpression reappraisal training augmented with facial expressions – protocol of a randomized controlled trial - Keinert, Marie; Schindler-Gmelch, Lena; Rupp, Lydia; Sadeghi, Misha; Capito, Klara; Hager, Malin; Rahimi, Farnaz; Richer, Robert; Egger, Bernhard; Eskofier, Björn; Berking, Matthias (BMC Psychiatry, 2024)
Background Dysfunctional depressogenic cognitions are considered a key factor in the etiology and maintenance of depression. In cognitive behavioral therapy (CBT), the current gold-standard psychotherapeutic treatment for depression, cognitive restructuring techniques are employed to address dysfunctional cognitions. However, high drop-out and non-response rates suggest a need to boost the efficacy of CBT for depression. This might be achieved by enhancing the role of emotional and kinesthetic (i.e., body movement perception) features of interventions. Therefore, we aim to evaluate the efficacy of a cognitive restructuring task augmented with the performance of anti-depressive facial expressions in individuals with and without depression. Further, we aim to investigate to what extent kinesthetic markers are intrinsically associated with and, hence, allow for the detection of, depression. Methods In a four-arm, parallel, single-blind, randomized controlled trial (RCT), we will randomize 128 individuals with depression and 128 matched controls without depression to one of four study conditions: (1) a cognitive reappraisal training (CR); (2) CR enhanced with instructions to display anti-depressive facial expressions (CR + AFE); (3) facial muscle training focusing on anti-depressive facial expressions (AFE); and (4) a sham control condition. One week after diagnostic assessment, a single intervention of 90–120-minute duration will be administered, with a subsequent follow-up two weeks later. Depressed mood will serve as primary outcome. Secondary outcomes will include current positive mood, symptoms of depression, current suicidality, dysfunctional attitudes, automatic thoughts, emotional state, kinesthesia (i.e., facial expression, facial muscle activity, body posture), psychophysiological measures (e.g., heart rate (variability), respiration rate (variability), verbal acoustics), as well as feasibility measures (i.e., treatment integrity, compliance, usability, acceptability). Outcomes will be analyzed with multiple methods, such as hierarchical and conventional linear models and machine learning. Discussion If shown to be feasible and effective, the inclusion of kinesthesia into both psychotherapeutic diagnostics and interventions may be a pivotal step towards the more prompt, efficient, and targeted treatment of individuals with depressio

Hair Cortisol Research in Posttraumatic Stress Disorder - 10 Years of Insights and Open Questions. A Systematic Review - Gmelch, Lena Marie; Capito, Klara; Steudte-Schmiedgen, Susann; Kirschbaum, Clemens; Berking, Matthias (Current Neuropharmacology, 2023)
Background: Cortisol is one of the most extensively studied biomarkers in the context of trauma/posttraumatic stress disorder (PTSD). For more than a decade, hair cortisol concentrations (HCC) have been measured in this context, leading to a two-staged dysregulation model. Specifically, an elevated secretion during/immediately after trauma exposure eventually reverts to hyposecretion with increasing time since trauma exposure has been postulated. Objective: The aim of our systematic review was to re-evaluate the two-staged secretion model with regard to the accumulated diagnostic, prognostic, and intervention-related evidence of HCC in lifetime trauma exposure and PTSD. Further, we provide an overview of open questions, particularly with respect to reporting standards and quality criteria. Method: A systematic literature search yielded 5,046 records, of which 31 studies were included. Results: For recent/ongoing (traumatic) stress, the predictions of cortisol hypersecretion could be largely confirmed. However, for the assumed hyposecretion temporally more distal to trauma exposure, the results are more ambiguous. As most studies did not report holistic overviews of trauma history and confounding influences, this may largely be attributable to methodological limitations. Data on the prognostic and intervention-related benefits of HCC remain sparse. Conclusion: Over the last decade, important insights could be gained about long-term cortisol secretion patterns following lifetime trauma exposure and PTSD. This systematic review integrates these insights into an updated secretion model for trauma/PTSD. We conclude with recommendations for improving HCC research in the context of trauma/PTSD in order to answer the remaining open questions. <br
Keywords: Posttraumatic stress disorder; trauma; hair cortisol; diagnostic; prognostic; intervention-related; biomarker

The EmpkinS-EKSpression Reappraisal Training Augmented With Kinesthesia in Depression - Keinert, Marie; Schindler-Gmelch, Lena; Rupp, Lydia; Sadeghi, Misha; Richer, Robert; Capito, Klara; Eskofier, Björn; Berking, Matthias (JMIR Formative Research, 2025)
Background: Harboring dysfunctional depressogenic cognitions contributes to the development and maintenance of depression. A central goal of cognitive behavioral therapy (CBT) for depression is to invalidate such cognitions via cognitive reappraisal (CR). However, relatively low remission rates and high dropout rates in CBT demonstrate the need for further improvement. Potentially, the effects of CBT could be enhanced by addressing not only dysfunctional depressogenic cognitions but also body states associated with depression. This may be done, for example, by systematically pairing the invalidation of depressogenic cognitions with the performance of antidepressive kinesthesia. Objective: This study aimed to examine the feasibility and clinical potential of a smartphone-based cognitive restructuring task that required users to deliberately perform antidepressive kinesthesia in conjunction with the rejection of depressogenic statements and the affirmation of antidepressive statements. This feasibility study was conducted as a precursor to a large-scale randomized controlled trial. Methods: In total, 10 healthy participants engaged in a single 90-120-minute session of smartphone-based CR training. During the training, they completed 2 phases in which they were required to reject 20 depressogenic and affirm 20 antidepressive statements, respectively. Diagnostic assessments were conducted 1 week (T1) before and directly prior (T2) to the training, and again directly posttraining (T3) and at a 2-week follow-up posttraining (T4). Feasibility outcomes assessed at T3 included intervention safety recorded by study therapists, compliance, technical feasibility, usability assessed using the Short Version of the User Experience Questionnaire (UEQ-S), and acceptability assessed using the UEQ-S and self-developed items. Preliminary clinical potential was evaluated via single-item ratings of current depressed and positive mood assessed continuously during the training. Feasibility outcomes were analyzed descriptively, and clinical potential was examined using paired-sample t tests of pre and post ratings of mood at each training phase. Results: Overall, the results indicated that the training was safe, feasible, and usable (UEQ-S pragmatic quality scale: mean 1.45, SD 0.71). However, acceptance was limited (UEQ-S hedonic quality scale: mean 1.05, SD 0.79). While 80% (8/10) of the participants were generally satisfied with the training, 80% (8/10) would recommend it to a friend, 90% (9/10) found it interesting, and 80% (8/10) rated it as “leading edge,” 40% (4/10) to 70% (7/10) did not consider it particularly helpful and 50% (5/10) found it repetitive. Preliminary results regarding clinical potential were promising, with significant increases in positive mood (rejection: Hedges g=0.63; affirmation: Hedges g=0.25), whereas changes in depressed mood were not significant. Conclusions: This study evaluated the feasibility and acceptability of a smartphone-based CR training augmented with validating and invalidating kinesthesia. This provided valuable insights for further optimizing the intervention for the subsequent randomized controlled trial, but also potential similar interventions. If future studies confirm their clinical potential, such interventions offer a promising approach to enhancing CBT for depression. Trial Registration: OSF Registries pw6ma; https://osf.io/pw6m
Keywords: depression; cognitive reappraisal; facial expression; kinesthesia; smartphone-based intervention; mobile phone