Background: Transcutaneous auricular Vagus Nerve Stimulation (taVNS) is a noninvasive technique that activates vagal projections in the brain and brainstem by stimulating the auricular branch of the vagus nerve. taVNS may be a safer alternative to invasive vagal nerve stimulation for treating treatment-resistant mood disorders, chronic pain, inflammation, cardiovascular dysfunction, inflammatory bowel disease, and Crohn’s disease. Objective: This study aims to systematically review the current evidence on the efficacy and safety of taVNS in treating depressive disorders and its modulatory effects on the autonomic nervous system. Methods: Relevant primary and secondary sources were identified through a systematic search of the PubMed and Google Scholar databases from 2008 to 2023. The review used the Scale for Assessment of Narrative Review Articles, resulting in a 0.77% intraclass correlation coefficient, 95% CI, and 0.88 inter-rater reliability. Results: taVNS, a new noninvasive neuromodulation method, stimulates the auricular branch of the vagus nerve and regulates the sympathetic and parasympathetic branches of the ANS, increasing norepinephrine secretion, vagus nerve stimulation adaptability, and heart rate variability. Future studies should clarify the mechanisms and address inconsistencies in taVNS parameters. Standardizing treatment regimens can establish taVNS as a viable, noninvasive treatment. Conclusion: Our findings suggest that taVNS may be a safer alternative to invasive vagal nerve stimulation for treatment-resistant mood disorders, chronic pain, inflammation, cardiovascular dysfunction, inflammatory bowel disease, and Crohn’s disease. However, further empirical research is needed to elucidate the mechanisms and resolve inconsistencies in the stimulation parameters, and larger studies are required to confirm taVNS as an effective noninvasive treatment.

This narrative review examines the theoretical foundations of mental workload, evaluates biometric monitoring methods, addresses ethical and privacy issues, and highlights future directions for longitudinal research. We focus on the integration of wearable sensors, multimodal data, artificial intelligence (AI), and machine learning (ML) frameworks to enhance adaptive task scheduling and safety in cognitively demanding professions. Continuous, real-time monitoring through wearable devices and multidimensional data analysis show promise for identifying and managing cognitive overload before it degrades performance. Despite this potential, significant challenges exist, including data protection, sensor reliability, calibration consistency, information processing, network capabilities, and variability in individuals' responses. Physiological and behavioral measures as well as subjective and performance indicators offer valuable insights into the early signs of cognitive strain, suggesting that biometric monitoring could help organizations detect performance decline sooner. Evidence shows that these technologies are feasible in professions that require high precision, rapid decision-making, and sustained attention. However, only sparse longitudinal comparisons exist regarding the effectiveness of different biometric tools in real-world operational contexts, particularly with respect to data security and standardization. Integrating physiological and behavioral data with subjective assessments analyzed through AI and ML may enable early warning signs for overload in both individuals and teams working in high-stress, time-critical settings. Such approaches could inform work-recovery cycles, reduce error rates, and sustain cognitive performance. Further empirical research is necessary to confirm sensor accuracy in applied environments and to validate AI and ML predictions before large-scale deployment in sectors such as air traffic control, public safety, healthcare, and industrial operations.

Background/Objectives: Parkinson’s disease with mild cognitive impairment (PD-MCI) represents a critical early-intervention window before progression to dementia. However, non-pharmacological cognitive treatment options are poorly characterized. This scoping review maps noninvasive neuromodulation interventions evaluated for cognitive outcomes in PD-MCI and in broader PD populations. Methods: Following JBI scoping review methodology and PRISMA-ScR guidelines, PubMed, Scopus and Google Scholar were searched for empirical studies published between May 2006 and September 2026 that applied noninvasive neuromodulation in adults with PD-MCI or broader PD populations and reported cognitive outcomes. Studies were classified as direct PD-MCI evidence (diagnosis confirmed using Movement Disorder Society Task Force criteria) or adjacent PD cognitive evidence. Results: Eleven studies across five modalities were included: tDCS (n = 6), iTBS (n = 2), TPS (n = 1), tcVNS (n = 1), and taVNS (n = 1). Only four studies (two tDCS and two iTBS) enrolled participants with confirmed PD-MCI; the remaining seven enrolled broader PD populations. Favorable findings came mainly from small samples, and protocols, outcome measures, and populations were heterogeneous. Conclusions: Evidence specific to PD-MCI is limited and mixed. Several studies pairing stimulation with cognitive training reported favorable outcomes, but this pattern was inconsistent and does not establish superiority over stimulation alone. No VNS trial enrolled participants with confirmed PD-MCI or used cognition as a primary endpoint.