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.

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. 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. 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 depression. The study was preregistered in the Open Science Framework on August 12, 2022 ( https://osf.io/mswfg/ ) and retrospectively registered in the German Clinical Trials Register on November 25, 2024. Clinical Trial Number: DRKS00035577.

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.