Background: Attention-Deficit/Hyperactivity Disorder (ADHD) is a multifaceted neurodevelopmental psychiatric condition that typically emerges during childhood but often persists into adulthood, significantly impacting individuals’ functioning, relationships, productivity, and overall quality of life. However, the current diagnostic process exhibits limitations that can significantly affect its overall effectiveness. Notably, its face-to-face and time-consuming nature, coupled with the reliance on subjective recall of historical information and clinician subjectivity, stand out as key challenges. To address these limitations, objective measures such as neuropsychological evaluations, imaging techniques and physiological monitoring of the Autonomic Nervous System functioning, have been explored. Methods: The main aim of this study was to investigate whether physiological data (i.e., Electrodermal Activity, Heart Rate Variability, and Skin Temperature) can serve as meaningful indicators of ADHD, evaluating its utility in distinguishing adult ADHD patients. This observational, case-control study included a total of 76 adult participants (32 ADHD patients and 44 healthy controls) who underwent a series of Stroop tests, while their physiological data was passively collected using a multi-sensor wearable device. Univariate feature analysis was employed to identify the tests that triggered significant signal responses, while the Informative k-Nearest Neighbors (KNN) algorithm was used to filter out less informative data points. Finally, a machine-learning decision pipeline incorporating various classification algorithms, including Logistic Regression, KNN, Random Forests, and Support Vector Machines (SVM), was utilized for ADHD patient detection. Results: Results indicate that the SVM-based model yielded the optimal performance, achieving 81.6% accuracy, maintaining a balance between the experimental and control groups, with sensitivity and specificity of 81.4% and 81.9%, respectively. Additionally, integration of data from all physiological signals yielded the best results, suggesting that each modality captures unique aspects of ADHD. Conclusions: This study underscores the potential of physiological signals as valuable diagnostic indicators of adult ADHD. For the first time, to the best of our knowledge, our findings demonstrate that multimodal physiological data collected via wearable devices can complement traditional diagnostic approaches. Further research is warranted to explore the clinical applications and long-term implications of utilizing physiological markers in ADHD diagnosis and management.

As mental health disorders like Major Depressive Disorder and Generalized Anxiety Disorder rise globally, effective, scalable, and personalized treatments are urgently needed. This 16-week prospective, decentralized, randomized, waitlist-controlled study investigated the effectiveness of a digital data-driven therapeutic integrating wearable sensor data with a mobile app to deliver personalized CBT-based interventions for individuals with depressive and generalized anxiety symptoms. 200 adults were randomized to intervention or control groups, with 164 completing the study. The intervention group demonstrated significant reductions in depressive (mean change = −5.61, CI = −7.14, −4.08) and anxiety symptoms (mean change = −5.21, CI = −6.66, −3.76), compared to the control group, with medium-to-large effect sizes (r = 0.64 and r = 0.62, P < 0.001). Notably, these improvements were also observed in participants with clinically significant depression and anxiety, further reinforcing the potential of digital therapeutics in targeting more severe cases. These findings, combined with high engagement levels, suggest that data-driven digital health interventions could complement traditional treatments, though further research is needed to assess their long-term impact.

The interaction between the surface of a 3D topological insulator and a skyrmion/anti-skyrmion structure is studied in order to investigate the possibility of electron confinement due to the skyrmion presence. Both hedgehog (Néel) and vortex (Bloch) skyrmions are considered. For the hedgehog skyrmion, the in-plane components cannot be disregarded and their interaction with the surface state of the topological insulator (TI) has to be taken into account. A semi-classical description of the skyrmion chiral angle is obtained using the variational principle. It is shown that both the hedgehog and the vortex skyrmion can induce bound states on the surface of the TI. However, the number and the properties of these states depend strongly on the skyrmion type and the skyrmion topological number NSk. The probability densities of the bound electrons are also derived where it is shown that they are localized within the skyrmion region.

Background: A total of 1 in 2 adults with attention-deficit/hyperactivity disorder (ADHD) struggles with major depressive or anxiety disorders. The co-occurrence of these disorders adds to the complexity of finding utility in as well as adherence to a treatment option. Digital therapeutic solutions may present a promising alternative treatment option that could mitigate these challenges and alleviate symptoms. Objective: This study aims to investigate (1) the feasibility and acceptance of a digital mental health intervention, (2) participants’ engagement and retention levels, and (3) the potential efficacy with respect to anxiety and depression symptoms in a population with ADHD. Our main hypothesis was that a digital, data-driven, and personalized intervention for adults with coexisting ADHD and depressive or anxiety symptoms would show high engagement and adherence, which would be accompanied by a decrease in depressive and anxiety symptoms along with an increase in quality of life and life satisfaction levels. Methods: This real-world data, single-arm study included 30 adult participants with ADHD symptomatology and coexisting depressive or anxiety symptoms who joined a 16-week digital, data-driven mental health support program. This intervention is based on a combination of evidence-based approaches such as cognitive behavioral therapy, mindfulness, and positive psychology techniques. The targeted symptomatology was evaluated using the Patient Health Questionnaire–9, Generalized Anxiety Disorder–7, and Barkley Adult ADHD Rating Scale–IV. Quality of life aspects were evaluated using the Satisfaction With Life Scale and the Life Satisfaction Questionnaire, and user feedback surveys were used to assess user experience and acceptability. Results: The study retention rate was 97% (29/30), and high engagement levels were observed, as depicted by the 69 minutes spent on the app per week, 5 emotion logs per week, and 11.5 mental health actions per week. An average decrease of 46.2% (P<.001; r=0.89) in depressive symptoms and 46.4% (P<.001; r=0.86) in anxiety symptoms was observed, with clinically significant improvement for more than half (17/30, 57% and 18/30, 60%, respectively) of the participants. This was followed by an average increase of 23% (P<.001; r=0.78) and 20% (P=.003; r=0.8) in Satisfaction With Life Scale and Life Satisfaction Questionnaire scores, respectively. The overall participant satisfaction level was 4.3 out of 5. Conclusions: The findings support the feasibility, acceptability, and value of the examined digital program for adults with ADHD symptomatology to address the coexisting depressive or anxiety symptoms. However, controlled trials with larger sample sizes and more diverse participant profiles are required to provide further evidence of clinical efficacy.

The effect of the magnetic skyrmion texture on the electronic transport properties of the TI surface state coupled to a thin-film FM is numerically investigated. It is shown that both Bloch (vortex) and Néel (hedgehog) skyrmion textures induce additional scattering on top of a homogeneous background FM texture which can modify the conductance of the system. The change in conductance depends on several factors including the skyrmion size, the dimensions of the FM and the exchange interaction strength. For the Néel skyrmion, the result of the interaction strongly depends on the skyrmion number N _sk and the skyrmion helicity h. For both skyrmion types, significant change of the resistance can be achieved, which is in the order of kΩ.

Decomposing Electrodermal Activity (EDA) into phasic (short-term, stimulus-linked responses) and tonic (longer-term baseline) components is essential for extracting meaningful emotional and physiological biomarkers. This study presents a comparative analysis of knowledge-driven, statistical, and deep learning-based methods for EDA signal decomposition, with a focus on in-the-wild data collected from wearable devices. In particular, the authors introduce the Feel Transformer, a novel Transformer-based model adapted from the Autoformer architecture, designed to separate phasic and tonic components without explicit supervision. The model leverages pooling and trend-removal mechanisms to enforce physiologically meaningful decompositions. Comparative experiments against methods such as Ledalab, cvxEDA, and conventional detrending show that the Feel Transformer achieves a balance between feature fidelity (SCR frequency, amplitude, and tonic slope) and robustness to noisy, real-world data. The model demonstrates potential for real-time biosignal analysis and future applications in stress prediction, digital mental health interventions, and physiological forecasting.