Niclas Braun

The heterogeneity of attention-deficit/hyperactivity disorder (ADHD) and its symptom overlap with other mental disorders limit the clinical utility of isolated diagnostic markers. Virtual reality (VR)-based paradigms offer potential to aid individual diagnostic decisions. This study investigated the diagnostic value of classification models based on multimodal data derived from a virtual email sorting task (VEST). Data were pooled from two studies and used to distinguish unmedicated participants with ADHD ( n = 43) from medicated participants with ADHD ( n = 23), participants with major depressive disorder (MDD, n = 16), and control participants ( n = 21). During the VEST, objective measures of task performance, head actigraphy, and eye tracking were recorded. Subjective data were obtained by assessing task-related ADHD symptoms. Using nested cross-validation, support vector machines were trained and tested, comparing model performance between objective, subjective and combined parameters. The highest balanced accuracy for the classification of participants with ADHD versus control participants (0.85) was achieved by a single-feature model using self-reported inattentiveness. Participants with ADHD and MDD were differentiated with 0.75 balanced accuracy using a 13-feature model combining subjective and objective measures. A 13-feature model derived exclusively from objective measures yielded 0.64 balanced accuracy for the differentiation of medicated versus unmedicated participants with ADHD yet did not perform significantly above chance level. These findings underscore the potential of VR-based multimodal assessments and machine learning to advance precision diagnostics and highlight the utility of self-report measures to detect ADHD while demonstrating the diagnostic potential of objective measures to distinguish disorders and capture medication effects.

The heterogeneity of attention-deficit/hyperactivity disorder (ADHD) and its symptom overlap with other mental disorders limit the clinical utility of isolated diagnostic markers. Virtual reality (VR)-based paradigms offer potential to aid individual diagnostic decisions. This study investigated the diagnostic value of classification models based on multimodal data derived from a virtual email sorting task (VEST). Data were pooled from two studies and used to distinguish unmedicated participants with ADHD (n = 43) from medicated participants with ADHD (n = 23), participants with major depressive disorder (MDD, n = 16), and control participants (n = 21). During the VEST, objective measures of task performance, head actigraphy, and eye tracking were recorded. Subjective data were obtained by assessing task-related ADHD symptoms. Using nested cross-validation, support vector machines were trained and tested, comparing model performance between objective, subjective and combined parameters. The highest balanced accuracy for the classification of participants with ADHD versus control participants (0.85) was achieved by a single-feature model using self-reported inattentiveness. Participants with ADHD and MDD were differentiated with 0.75 balanced accuracy using a 13-feature model combining subjective and objective measures. A 13-feature model derived exclusively from objective measures yielded 0.64 balanced accuracy for the differentiation of medicated versus unmedicated participants with ADHD yet did not perform significantly above chance level. These findings underscore the potential of VR-based multimodal assessments and machine learning to advance precision diagnostics and highlight the utility of self-report measures to detect ADHD while demonstrating the diagnostic potential of objective measures to distinguish disorders and capture medication effects.

In recent years, digital health interventions have proven effective for treating depression. However, it remains unclear which therapeutic components drive these effects, and the effectiveness of digital stress reduction interventions is under-researched. The present RCT investigated the effectiveness of a four-week self-guided digital stress regulation intervention in individuals with depression. After confirming the diagnosis of major depression, we randomly assigned N = 62 adults to either a control group receiving treatment as usual or to a digital intervention group receiving an additional digital stress regulation intervention. Self-reported depressive symptoms were our primary outcome. Secondary outcomes included anxiety, perceived stress, quality of life, usability and participant satisfaction. Descriptively, depressive symptoms improved in both groups with larger decreases in the digital intervention group at post-treatment (T0: M = 13.48, SD = 4.90; T1: M = 13.00, SD = 4.80) compared to the control group (T0: M = 14.26, SD = 3.69; T1: M = 14.11, SD = 4.80). However, compared to treatment as usual alone, the stress regulation intervention neither led to a significant reduction in depressive symptoms (F(2, 97.47) = 0.77, p = .464) nor to a significant improvement in any of the secondary outcomes. On average, participants rated the intervention’s usability as good and expressed medium to high user satisfaction. Our findings suggest that digital stress regulation interventions may be a feasible option for individuals with depression. Nevertheless, contrary to previous studies, our results do not provide evidence of superiority over treatment as usual alone.

Background: Over the past 2 decades, virtual reality–based neuropsychological tasks have gained traction as tools for objectively assessing symptoms of attention-deficit/hyperactivity disorder (ADHD), offering enhanced ecological validity by simulating naturalistic environments. To complement realistic settings with an ecologically valid task, we recently developed the virtual email sorting task (VEST), which immerses participants into an office environment where they sort emails while being exposed to distractors. Objective: This study examined for the first time the VEST’s sensitivity to medication effects and its specificity in differentiating ADHD from other psychiatric disorders that share overlapping cognitive symptoms, such as major depressive disorder (MDD). Methods: A total of 23 unmedicated individuals with ADHD, 23 medicated individuals with ADHD, and 16 unmedicated individuals with MDD completed the VEST. During alternating distractor phases (DP) and nondistractor phases (NDP), we recorded the participants’ task performance; head, torso, and leg actigraphy; eye movements; and brain activity using functional near-infrared spectroscopy (fNIRS), and subjective symptom ratings. Correlational analyses of main objective and subjective task-related parameters were computed. Data were analyzed using mixed-design ANOVAs. Results: Processing time variability increased over time in participants with MDD and unmedicated ADHD as indicated by a group × block interaction (P=.04; η2p=0.10), while a group × phase interaction (P=.009; η2p=0.15) revealed that medicated participants with ADHD showed an increase during DP compared to NDP. Moreover, both ADHD groups exhibited increased head movements during DP compared to NDP (trend group × phase interaction: P=.09; η2p=0.08), an effect not observed in the MDD group, and higher rotation during DP in unmedicated individuals with ADHD (P<.001; η2p=0.23). Also, scores in 3 out of 4 subjective symptom intensity ratings of inattention, impulsivity, and emotional dysregulation were higher in at least 1 of the ADHD groups compared to the MDD group. No significant group differences were found in actigraphy measures of the arm and torso, fNIRS brain activity, or eye-tracking data. Regarding correlational analyses, inattention was correlated to off-task gaze (r=0.28; P=.03), hyperactivity with mean processing time (r=0.33; P=.01) and head movement (r=0.35; P=.006), and impulsivity with error rate (r=0.35; P=.006), and various significant correlations between objective parameters were found. Conclusions: Our findings highlight the potential of the VEST to differentiate between ADHD and MDD, as well as to detect medication-related effects within ADHD. The results underscore the value of multimodal and ecological assessment approaches with distractors in the evaluation of attentional and behavioral symptoms. The VEST may offer a standardized way to investigate complex behavior in mental disorders in research settings and, potentially, in clinical practice. However, further studies with greater statistical power are needed to confirm these findings. Trial Registration: German Clinical Trials Register DRKS00031259; https://www.drks.de/search/de/trial/DRKS00031259/details

Zusammenfassung: Einleitung: Mittels Virtueller Realität (VR) kann eine realitätsnahe und komplexe Szenerie erschaffen werden, die dabei zeitgleich vollständig kontrollierbar ist. Daher ermöglicht VR, Symptome und Krankheitsmechanismen psychischer Störungen wie der Aufmerksamkeitsdefizit-Hyperaktivitätsstörung (ADHS) unter realistischen und trotzdem standardisierten Bedingungen zu untersuchen. Methoden: In der vorliegenden Übersichtsarbeit wird die bisherige Forschungsaktivität zur Verwendung von VR zur Untersuchung der ADHS anhand ausgewählter Anwendungsbereiche aufgezeigt. Zu jedem vorgestellten Bereich wird eine Kurzzusammenfassung der Studienlage sowie des erreichten Reifegrads gegeben. Ergebnisse und Diskussion: Die meisten Studien wurden bisher zu virtuellen Lehrraum-Szenarien sowie Straßenverkehrsszenarien durchgeführt. Die Lehrraum-Szenarien liefern gute Belege dafür, dass mit VR neuropsychologische Testumgebungen entwickelt werden können, die eine Unterscheidung von Personen mit und ohne ADHS sowie von medikamentösen und nicht medikamentösen Zuständen ermöglichen. Die vorgestellten Straßenverkehrsszenarien wiederum liefern erste Hinweise, dass sie zur Untersuchung der Fahrleistung und des Straßenüberquerungsverhaltens von Personen mit ADHS gut geeignet erscheinen. Trotz der vielversprechenden Ergebnisse müssen weitere Validierungsstudien durchgeführt und Zulassungsprozesse durchlaufen werden, bevor die VR-basierten Tests auch in der regulären Patientenversorgung genutzt werden können.

Background: Mobile apps that implement disorder-specific psychotherapy for panic disorder (PD) with and without agoraphobia can be used in real-life situations that trigger symptoms and, therefore, are a promising novel therapeutic tool. Objective: This randomized controlled trial aimed to expand the currently limited evidence base for the efficacy of mobile interventions for PD with and without agoraphobia by evaluating a mobile app focusing on interoceptive and in-vivo exposure therapy. Methods: After establishing the diagnosis of PD with and without agoraphobia using a secure video communication platform, we randomized 111 adults to 3 equally sized groups: disorder-specific exposure therapy app, mindfulness meditation app unrelated to the disorder (active control condition), and waiting list (passive control condition). Participants used the apps in a self-guided manner. Additional psychotherapy was not allowed during the study. Self-reported PD with and without agoraphobia symptom severity was our primary outcome parameter and measured by the Panic and Agoraphobia Scale and symptom subset scales. Secondary outcome parameters included depressive symptoms and quality of life. We conducted assessments at baseline, after the intervention (allocation+5 wk), and at follow-up (allocation+13 wk). Results: We observed significantly lower overall PD with and without agoraphobia symptom severity in the exposure app group compared to waiting list post-treatment ( P =.04, Cohen d =0.55) and at follow-up ( P =.04, Cohen d =0.60). At follow-up, the exposure app group demonstrated significantly stronger improvements than the waiting list group in depressive symptoms ( P =.007, Cohen d =0.75) and psychological quality of life ( P =.01, Cohen d =0.63). We observed no significant differences between the exposure and the meditation apps in overall PD with and without agoraphobia symptom severity (post-treatment: P >.99, follow-up: P =.84). In the exposure app group, 35% (n=11) of participants showed reliable improvement in overall PD with and without agoraphobia symptom severity at post-treatment (meditation app: n=2, 6%; waiting list: n=2, 7%; P =.002 ) . The dropout rate after 5 weeks was 14% (5/37) in the exposure app group (meditation app: 3/37, 8%; waiting list: 6/37, 16%; P =.68). No adverse outcomes were reported in the exposure app group. Conclusions: Our findings suggest that app-guided exposure therapy can be a useful further treatment option in addition to established psychological and pharmacological strategies. However, superiority compared to the disorder-unspecific meditation app remains unclear.

BACKGROUND Over the past 2 decades, virtual reality–based neuropsychological tasks have gained traction as tools for objectively assessing symptoms of attention-deficit/hyperactivity disorder (ADHD), offering enhanced ecological validity by simulating naturalistic environments. To complement realistic settings with an ecologically valid task, we recently developed the virtual email sorting task (VEST), which immerses participants into an office environment where they sort emails while being exposed to distractors. OBJECTIVE This study examined for the first time the VEST’s sensitivity to medication effects and its specificity in differentiating ADHD from other psychiatric disorders that share overlapping cognitive symptoms, such as major depressive disorder (MDD). METHODS A total of 23 unmedicated individuals with ADHD, 23 medicated individuals with ADHD, and 16 unmedicated individuals with MDD completed the VEST. During alternating distractor phases (DP) and nondistractor phases (NDP), we recorded the participants’ task performance; head, torso, and leg actigraphy; eye movements; and brain activity using functional near-infrared spectroscopy (fNIRS), and subjective symptom ratings. Correlational analyses of main objective and subjective task-related parameters were computed. Data were analyzed using mixed-design ANOVAs. RESULTS Processing time variability increased over time in participants with MDD and unmedicated ADHD as indicated by a group × block interaction (P=.04; η2p=0.10), while a group × phase interaction (P=.009; η2p=0.15) revealed that medicated participants with ADHD showed an increase during DP compared to NDP. Moreover, both ADHD groups exhibited increased head movements during DP compared to NDP (trend group × phase interaction: P=.09; η2p=0.08), an effect not observed in the MDD group, and higher rotation during DP in unmedicated individuals with ADHD (P<.001; η2p=0.23). Also, scores in 3 out of 4 subjective symptom intensity ratings of inattention, impulsivity, and emotional dysregulation were higher in at least 1 of the ADHD groups compared to the MDD group. No significant group differences were found in actigraphy measures of the arm and torso, fNIRS brain activity, or eye-tracking data. Regarding correlational analyses, inattention was correlated to off-task gaze (r=0.28; P=.03), hyperactivity with mean processing time (r=0.33; P=.01) and head movement (r=0.35; P=.006), and impulsivity with error rate (r=0.35; P=.006), and various significant correlations between objective parameters were found. CONCLUSIONS Our findings highlight the potential of the VEST to differentiate between ADHD and MDD, as well as to detect medication-related effects within ADHD. The results underscore the value of multimodal and ecological assessment approaches with distractors in the evaluation of attentional and behavioral symptoms. The VEST may offer a standardized way to investigate complex behavior in mental disorders in research settings and, potentially, in clinical practice. However, further studies with greater statistical power are needed to confirm these findings. CLINICALTRIAL German Clinical Trials Register DRKS00031259; https://www.drks.de/search/de/trial/DRKS00031259/details

BACKGROUND Mobile apps that implement disorder-specific psychotherapy for panic disorder with and without agoraphobia (PD/A) can be used in real-life situations that trigger symptoms and, therefore, are a promising novel therapeutic tool. OBJECTIVE This RCT aims to expand the currently limited evidence base for the efficacy of mobile interventions for PD/A by evaluating a mobile app focusing on interoceptive and in-vivo exposure therapy. METHODS After establishing the diagnosis of PD/A using a secure video communication platform, we randomized 111 adults to three equally sized groups: PD/A-specific exposure therapy app; mindfulness meditation app unrelated to the disorder (active control condition); and waiting list (passive control condition). Participants used the apps self-guided. Additional psychotherapy was not allowed during the study. Self-reported PD/A symptom severity was our primary outcome parameter and measured by the Panic and Agoraphobia Scale (PAS) and symptom subset scales. Secondary outcome parameters included depressive symptoms and quality of life (QoL). We conducted assessments at baseline, after the intervention (allocation + 5 weeks), and at follow-up (allocation + 13 weeks). RESULTS We observed significantly lower overall PD/A symptom severity in the exposure app group compared to waiting list post-treatment (P = .04, Cohen's d = 0.55) and at follow-up (P = .04, d = 0.60). At follow-up, the exposure app group demonstrated significantly stronger improvements than the waiting list group in depressive symptoms (P =.007, d = 0.75) and psychological QoL (P = .01, d = 0.63). We observed no significant differences between the exposure and the meditation apps. In the exposure app group, 35 % of participants showed reliable improvement in overall PD/A symptom severity at post-treatment (meditation app: 6 %, waiting list 7 %). The dropout rate after five weeks was 14 % in the exposure app group (meditation app: 8 %, waiting list 16 %). No adverse outcomes were reported in the exposure app group. CONCLUSIONS Our findings suggest that app-guided exposure therapy can be a useful further treatment option in addition to established psychological and pharmacological strategies. However, superiority compared to the disorder-unspecific meditation app remains unclear. CLINICALTRIAL German Clinical Trials Register (DRKS00022204)

Given the heterogeneous nature of attention-deficit/hyperactivity disorder (ADHD) and the absence of established biomarkers, accurate diagnosis and effective treatment remain a challenge in clinical practice. This study investigates the predictive utility of multimodal data, including eye tracking, EEG, actigraphy, and behavioral indices, in differentiating adults with ADHD from healthy individuals. Using a support vector machine model, we analyzed independent training (n = 50) and test (n = 36) samples from two clinically controlled studies. In both studies, participants performed an attention task (continuous performance task) in a virtual reality seminar room while encountering virtual distractions. Task performance, head movements, gaze behavior, EEG, and current self-reported inattention, hyperactivity, and impulsivity were simultaneously recorded and used for model training. Our final model based on the optimal number of features (maximal relevance minimal redundancy criterion) achieved a promising classification accuracy of 81% in the independent test set. Notably, the extracted EEG-based features had no significant contribution to this prediction and therefore were not included in the final model. Our results suggest the potential of applying ecologically valid virtual reality environments and integrating different data modalities for enhancing robustness of ADHD diagnosis.