With the increase in the number of people using digital devices, complaints about eye and vision problems have been increasing, making the problem of computer vision syndrome (CVS) more serious. Accompanying the increase in CVS in occupational settings, new and unobstructive solutions to assess the risk of this syndrome are of paramount importance. This study aims, through an exploratory approach, to determine if blinking data, collected using a computer webcam, can be used as a reliable indicator for predicting CVS on a real-time basis, considering real-life settings. A total of 13 students participated in the data collection. A software that collected and recorded users’ physiological data through the computer’s camera was installed on the participants’ computers. The CVS-Q was applied to determine the subjects with CVS and its severity. The results showed a decrease in the blinking rate to about 9 to 17 per minute, and for each additional blink the CVS score lowered by 1.26. These data suggest that the decrease in blinking rate was directly associated with CVS. These results are important for allowing the development of a CVS real-time detection algorithm and a related recommendation system that provides interventions to promote health, well-being, and improved performance.

Human Digital Twin (HDT) is a powerful tool to create a virtual replica of a human, to be used for example for designing interactions with physical systems, preventing cognitive overload, managing human capital, and maintaining a healthy and motivated workforce. Building human twins is a challenging task due to the need to reliably represent each corresponding human being, and the fact that human beings notably differ from each other. Therefore, relying solely on expert knowledge is insufficient, and human twins must learn the specifics of each individual in order to accurately represent them. This paper focuses on AI methods for modelling the mental wellbeing of knowledge workers because the mounting cognitive demands of both white-collar and blue-collar work lead to employees’ stress, and stress leads to diminished creativity and motivation, increased sick leaves, and in severe cases, accidents, burnouts, and disabilities. This paper describes the main building blocks of AI-based detectors of mental stress and highlights the main challenges and future directions of research., which are expected to be relevant also for HDT learning in other domains because the high degree of individuality is ubiquitous in all human activities.

This study reviews the scientific use of chest-strap wearables, analyzing their advantages and limitations, following PRISMA guidelines. Eligible studies assessed chest-strap devices in adults and reported physiological outcomes such as heart rate, heart rate variability, R–R intervals, or electrocardiographic waveform morphology. Studies involving implanted devices, wrist-worn wearables, or lacking validation against reference standards were excluded. Searches were conducted in PubMed, Scopus, Web of Science, and ScienceDirect for studies published in the last 10 years. The quality of the studies was assessed using the Mixed Methods Appraisal Tool, and results were synthesized narratively. Thirty-two studies were included. The most frequently evaluated devices were the Polar H10 and Zephyr BioHarness 3.0, which showed strong correlations with electrocardiography at rest and during light-to-moderate activity. Reported limitations included motion artefacts, poor strap placement, sweating, and degradation of the skin–electrode interface. None of the devices had CE or FDA approval for clinical use, and most studies were conducted in controlled settings, limiting generalizability. Ergonomic concerns such as discomfort during prolonged wear and restricted mobility were also noted. Overall, chest-strap sensors showed good validity and were widely used in validation studies. However, technical refinements and large-scale field trials are needed for broader clinical and occupational application. This review is registered in PROSPERO and is part of the SIREN project.

Child abuse portrays health and well-being issues that can last for several years, including altering interpersonal behavior. Evidence has shown that these early negative experiences may cause changes in sensory modulation. This study aimed to understand if sensory over-responsivity (SOR) plays an important role in mediating the association between childhood abuse (emotional abuse, emotional neglect, sexual abuse, physical abuse, physical neglect) and attachment-related anxiety or avoidance in adult romantic relationships. An online survey was conducted to examine these associations in a Portuguese community sample (N = 500) aged 18–62 years. The presented mediation models shown a higher mediation percentage for attachment-related anxiety rather than avoidance. All variables predicted the mediator, except sexual abuse which did not predict the overall model when the mediator was included. Our results have implications for individual treatment approaches regarding child abuse victims and can certainly influence intervention strategies.

Pupil size can be used as an important biomarker for occupational risks. In recent years, there has been an increase in the development of open-source tools dedicated to obtaining and measuring pupil diameter. However, it remains undetermined determined whether these tools are suitable for use in occupational settings. This study explores the significance of pupil size variation as a biomarker for occupational risks and evaluates existing open-source methods for potential use in both research and occupational settings, with the goal of to prevent occupational accidents and improve the health and performance of workers. To this end, a two-phase systematic literature review was conducted in the Web of Science™, ScienceDirect®, and Scopus® databases. For the relevance of monitoring pupil size variation in occupational settings, 15 articles were included. The articles were divided into three groups: mental workload, occupational stress, and mental fatigue. In most cases, pupil dilation increased with workload enhancement and with higher levels of stress. Regarding fatigue, it was noted that an increase in this condition corresponded with a decrease in pupil size. With respect to the open-source methodologies, 16 articles were identified, which were categorized into two groups: algorithms and software. Convolutional neural networks (CNN) ¹ have exhibited superior performance among the various algorithmic approaches studied. Building on this insight, and considering the evaluations of software options, MEYE emerges as the premier open-source system for deployment in occupational settings due to its compatibility with a standard computer webcam. This feature positions MEYE as a particularly practical tool for workers in stable environments, like those of developers and administrators.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: In modern, high-speed work settings, the significance of mental health disorders is increasingly acknowledged as a pressing health issue, with potential adverse consequences for organizations, including reduced productivity and increased absenteeism. Over the past few years, various mental health management solutions, such as biofeedback applications, have surfaced as promising avenues to improve employees' mental well-being. Objective: To gain deeper insights into the suitability and effectiveness of employing biofeedback-based mental health interventions in real-world workplace settings, given that most research has predominantly been conducted within controlled laboratory conditions. Methods: A systematic review was conducted to identify studies that used biofeedback interventions in workplace settings. The review focused on traditional biofeedback, mindfulness, app-directed interventions, immersive scenarios, and in-depth physiological data presentation. Results: The review identified nine studies employing biofeedback interventions in the workplace. Breathing techniques showed great promise in decreasing stress and physiological parameters, especially when coupled with visual and/or auditory cues. Conclusions: Future research should focus on developing and implementing interventions to improve well-being and mental health in the workplace, with the goal of creating safer and healthier work environments and contributing to the sustainability of organizations.