Technostress causes cognitive overload in high-stress people: Eye tracking analysis in a virtual kiosk test

The Impact of Technostress Generated by Artificial Intelligence on the Quality of Life: The Mediating Role of Positive and Negative Affect

In the era of Artificial Intelligence, the magic of achieving results at the "speed of light" for tasks that until recently required a lot of work and effort shocks, arouses enthusiasm and generates fears at the same time. Therefore, starting from this reality of our days, we proposed within the current research to study the relationship between the factors of technostress (techno-overload, techno-invasion, techno-complexity, techno-insecurity, techno-uncertainty) perceived as a result of the implementation of AI at the societal level and the quality of life, filtering the relationship through the "lens" of the positive and negative affect mediators. The mediation analyses, conducted on a sample of 217 adult Romanian citizens (18-62 years old), suggested that although AI-related technostress does not directly influence quality of life, it has a significant indirect impact through affective traits-general tendencies to frequently experience positive or negative emotions. This indicates that technostress contributes to variations in quality of life by influencing emotional experiences, which mediate the relationship. These findings emphasize not only the absence of a direct effect, but also the importance of the indirect pathway in understanding how individuals are affected by AI-related stress. We believe that the results of the current study can be equally useful in raising awareness of the psychological mechanisms responsible for the quality of life and in understanding the importance of implementing official programs, both technically, regarding the development of skills to understand and work with AI, and psychological support programs, considering the management of emotions, with reference to this technology.

Feasibility and safety of interactive virtual reality upper limb rehabilitation in patients with prolonged critical illness

OBJECTIVES

This study investigated the feasibility and safety of interactive virtual reality rehabilitation (VRR) for patients with a critical illness and a long stay in the intensive care unit (ICU), as a motivational tool for rehabilitation.

DESIGN

Single-centre, non-randomised proof-of-concept clinical trial.

PARTICIPANTS

Adult, calm, and alert critically ill patients with a prolonged stay (≥8 days) in the ICU.

METHODS

Patients received interactive VRR therapy for upper limb rehabilitation with a VR-app designed specifically for use in bedridden patients in the supine position. Feasibility was assessed by time registrations, questionnaires for patients and physiotherapists, as well as recording of all perceived barriers. Safety was assessed by recording (changes in) vital clinical parameters, as well as minor and major adverse events.

RESULTS

Twenty patients participated in 79 VRR sessions. Median durations of different session components were 2 minutes (interquartile range [IQR] = 2min, 3min) for set-up and explanation to the patient, 10 minutes (IQR = 10min, 15min) for training time, and 2 minutes (IQR = 2min, 2min) for ending the session and cleaning. The median fun score given by the patients after each session was 9 (IQR = 8, 10) out of 10. Physiotherapists reported no barriers other than a few time-consuming technical problems. Reported problems by patients were all minor and mostly technical. No major and no minor adverse events occurred.

CONCLUSIONS

Interactive upper limb VRR is a feasible, safe, and appreciated tool to use in rehabilitation of critically ill patients during their prolonged ICU stay. Subsequent future studies should focus on the effects of VRR on neuromuscular and cognitive function and the socioeconomic impact of exergaming for rehabilitation purposes of ICU patients.

How Time, Living Situation, and Stress Related to Technology Influence User Acceptance and Usability of a Socialization Service for Older Adults and Their Formal and Informal Caregivers: Six-Month Pilot Study

Background

Considering the growing population of older adults, addressing the influence of loneliness among this demographic group has become imperative, especially due to the link between social isolation and deterioration of mental and physical well-being. Technology has the potential to be used to create innovative solutions to increase socialization and potentially promote healthy aging.

Objective

This 6-month study examined the usability and acceptability of a technology-based socialization service and explored how stress and living situation affect older adults' and their ecosystem's perceptions of technology, investigating cross-sectional and longitudinal differences among and across user groups.

Methods

Participants were recruited in Tuscany and Apulia (Italy) through a network of social cooperatives and a research hospital, respectively. A total of 20 older adults were provided with the same technology installed on a tablet and on a smart television. The technology has three functionalities: video calling, playing games, and sharing news. Additionally, 20 informal caregivers (IC) and 13 formal caregivers (FC) connected to the older adults were included in the study. After both initial training in the use of the system (T0) and 6 months of using the system (T6), questionnaires on usability, acceptability, and technostress were filled in by older adults, IC, and FC. Nonparametric or parametric tests were conducted to investigate group differences at both time points and changes over time. Additional analyses on older adults were done to assess whether differences in usability and acceptability were related to living situation (ie, alone or with someone). Furthermore, correlation analyses were performed between usability, acceptability, and stress toward technology at T0 and T6.

Results

At both T0 and T6, older adults had lower usability scores than IC and FC and higher anxiety than IC. Over time, there was a significant decrease in older adults' attitudes toward technology score, depicting a negative attitude over time (T0 median 4.2, IQR 0.5; T6 median 3.7, IQR 0.8; Cohen d=0.7), while there was no change for IC and FC. At T0, those living alone had lower acceptability than those living with someone but this difference disappeared at T6. People or participants living with someone had a decline in anxiety, attitudes toward technology, enjoyment, and perceived usefulness. Stress toward technology affected usability and acceptability in the older adult group entering the study (ρ=-.85) but this was not observed after 6 months. In the IC group, stress affected trust at T0 (ρ=-.23) but not at T6.

Conclusions

At the start of the study, older adults judged the system to be less usable and more stressful than did the caregivers. Indeed, at first, technostress was correlated with usability and acceptability; however, with repeated use, technostress did not influence the perception of technology. Overall, getting accustomed to technology decreased anxiety and stress toward technology.

Pupillometry and autonomic nervous system responses to cognitive load and false feedback: an unsupervised machine learning approach

Objectives

Pupil dilation is controlled both by sympathetic and parasympathetic nervous system branches. We hypothesized that the dynamic of pupil size changes under cognitive load with additional false feedback can predict individual behavior along with heart rate variability (HRV) patterns and eye movements reflecting specific adaptability to cognitive stress. To test this, we employed an unsupervised machine learning approach to recognize groups of individuals distinguished by pupil dilation dynamics and then compared their autonomic nervous system (ANS) responses along with time, performance, and self-esteem indicators in cognitive tasks.

Methods

Cohort of 70 participants were exposed to tasks with increasing cognitive load and deception, with measurements of pupillary dynamics, HRV, eye movements, and cognitive performance and behavioral data. Utilizing machine learning k-means clustering algorithm, pupillometry data were segmented to distinct responses to increasing cognitive load and deceit. Further analysis compared clusters, focusing on how physiological (HRV, eye movements) and cognitive metrics (time, mistakes, self-esteem) varied across two clusters of different pupillary response patterns, investigating the relationship between pupil dynamics and autonomic reactions.

Results

Cluster analysis of pupillometry data identified two distinct groups with statistically significant varying physiological and behavioral responses. Cluster 0 showed elevated HRV, alongside larger initial pupil sizes. Cluster 1 participants presented lower HRV but demonstrated increased and pronounced oculomotor activity. Behavioral differences included reporting more errors and lower self-esteem in Cluster 0, and faster response times with more precise reactions to deception demonstrated by Cluster 1. Lifestyle variations such as smoking habits and differences in Epworth Sleepiness Scale scores were significant between the clusters.

Conclusion

The differentiation in pupillary dynamics and related metrics between the clusters underlines the complex interplay between autonomic regulation, cognitive load, and behavioral responses to cognitive load and deceptive feedback. These findings underscore the potential of pupillometry combined with machine learning in identifying individual differences in stress resilience and cognitive performance. Our research on pupillary dynamics and ANS patterns can lead to the development of remote diagnostic tools for real-time cognitive stress monitoring and performance optimization, applicable in clinical, educational, and occupational settings.

Investigating the impact of greenery elements in office environments on cognitive performance, visual attention and distraction: An eye-tracking pilot-study in virtual reality

The human-nature connection is one of the main aspects determining supportive and comfortable office environments. In this context, the application of eye-tracking-equipped Virtual Reality (VR) devices to support an evaluation on the effect of greenery elements indoors on individuals' efficiency and engagement is limited. A new approach to investigate visual attention, distraction, cognitive load and performance in this field is carried out via a pilot-study comparing three virtual office layouts (Indoor Green, Outdoor Green and Non-Biophilic). 63 participants completed cognitive tasks and surveys while measuring gaze behaviour. Sense of presence, immersivity and cybersickness results supported the ecological validity of VR. Visual attention was positively influenced by the proximity of users to the greenery element, while visual distraction from tasks was negatively influenced by the dimension of the greenery. In the presence of greenery elements, lower cognitive loads and more efficient information searching, resulting in improved performance, were also highlighted.

A review of machine learning in scanpath analysis for passive gaze-based interaction

The scanpath is an important concept in eye tracking. It refers to a person's eye movements over a period of time, commonly represented as a series of alternating fixations and saccades. Machine learning has been increasingly used for the automatic interpretation of scanpaths over the past few years, particularly in research on passive gaze-based interaction, i.e., interfaces that implicitly observe and interpret human eye movements, with the goal of improving the interaction. This literature review investigates research on machine learning applications in scanpath analysis for passive gaze-based interaction between 2012 and 2022, starting from 2,425 publications and focussing on 77 publications. We provide insights on research domains and common learning tasks in passive gaze-based interaction and present common machine learning practices from data collection and preparation to model selection and evaluation. We discuss commonly followed practices and identify gaps and challenges, especially concerning emerging machine learning topics, to guide future research in the field.

Exploring the Relationship between Behavioral and Neurological Impairments Due to Mild Cognitive Impairment: Correlation Study between Virtual Kiosk Test and EEG-SSVEP

Amnestic mild cognitive impairment (aMCI) is a transitional stage between normal aging and Alzheimer's disease, making early screening imperative for potential intervention and prevention of progression to Alzheimer's disease (AD). Therefore, there is a demand for research to identify effective and easy-to-use tools for aMCI screening. While behavioral tests in virtual reality environments have successfully captured behavioral features related to instrumental activities of daily living for aMCI screening, further investigations are necessary to establish connections between cognitive decline and neurological changes. Utilizing electroencephalography with steady-state visual evoked potentials, this study delved into the correlation between behavioral features recorded during virtual reality tests and neurological features obtained by measuring neural activity in the dorsal stream. As a result, this multimodal approach achieved an impressive screening accuracy of 98.38%.

Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study

BACKGROUND

Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach.

OBJECTIVE

We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers.

METHODS

The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls.

RESULTS

The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F1-score (93.3%).

CONCLUSIONS

The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.

Improving counseling effectiveness with virtual counselors through nonverbal compassion involving eye contact, facial mimicry, and head-nodding

An effective way to reduce emotional distress is by sharing negative emotions with others. This is why counseling with a virtual counselor is an emerging methodology, where the sharer can consult freely anytime and anywhere without having to fear being judged. To improve counseling effectiveness, most studies so far have focused on designing verbal compassion for virtual counselors. However, recent studies showed that virtual counselors' nonverbal compassion through eye contact, facial mimicry, and head-nodding also have significant impact on the overall counseling experience. To verify this, we designed the virtual counselor's nonverbal compassion and examined its effects on counseling effectiveness (i.e., reduce the intensity of anger and improve general affect). A total of 40 participants were recruited from the university community. Participants were then randomly assigned to one of two virtual counselor conditions: a neutral virtual counselor condition without nonverbal compassion and a compassionate virtual counselor condition with nonverbal compassion (i.e., eye contact, facial mimicry, and head-nodding). Participants shared their anger-inducing episodes with the virtual counselor for an average of 16.30 min. Note that the virtual counselor was operated by the Wizard-of-Oz method without actually being technically implemented. Results showed that counseling with a compassionate virtual counselor reduced the intensity of anger significantly more than counseling with a neutral virtual counselor (F(1, 37) = 30.822, p < 0.001, ηp2 = 0.454). In addition, participants who counseled with a compassionate virtual counselor responded that they experienced higher empathy than those who counseled with a neutral virtual counselor (p < 0.001). These findings suggest that nonverbal compassion through eye contact, facial mimicry, and head-nodding of the virtual counselor makes the participants feel more empathy, which contributes to improving the counseling effectiveness by reducing the intensity of anger.

Digital Marker for Early Screening of Mild Cognitive Impairment Through Hand and Eye Movement Analysis in Virtual Reality Using Machine Learning: First Validation Study

BACKGROUND

With the global rise in Alzheimer disease (AD), early screening for mild cognitive impairment (MCI), which is a preclinical stage of AD, is of paramount importance. Although biomarkers such as cerebrospinal fluid amyloid level and magnetic resonance imaging have been studied, they have limitations, such as high cost and invasiveness. Digital markers to assess cognitive impairment by analyzing behavioral data collected from digital devices in daily life can be a new alternative. In this context, we developed a "virtual kiosk test" for early screening of MCI by analyzing behavioral data collected when using a kiosk in a virtual environment.

OBJECTIVE

We aimed to investigate key behavioral features collected from a virtual kiosk test that could distinguish patients with MCI from healthy controls with high statistical significance. Also, we focused on developing a machine learning model capable of early screening of MCI based on these behavioral features.

METHODS

A total of 51 participants comprising 20 healthy controls and 31 patients with MCI were recruited by 2 neurologists from a university hospital. The participants performed a virtual kiosk test-developed by our group-where we recorded various behavioral data such as hand and eye movements. Based on these time series data, we computed the following 4 behavioral features: hand movement speed, proportion of fixation duration, time to completion, and the number of errors. To compare these behavioral features between healthy controls and patients with MCI, independent-samples 2-tailed t tests were used. Additionally, we used these behavioral features to train and validate a machine learning model for early screening of patients with MCI from healthy controls.

RESULTS

In the virtual kiosk test, all 4 behavioral features showed statistically significant differences between patients with MCI and healthy controls. Compared with healthy controls, patients with MCI had slower hand movement speed (t49=3.45; P=.004), lower proportion of fixation duration (t49=2.69; P=.04), longer time to completion (t49=-3.44; P=.004), and a greater number of errors (t49=-3.77; P=.001). All 4 features were then used to train a support vector machine to distinguish between healthy controls and patients with MCI. Our machine learning model achieved 93.3% accuracy, 100% sensitivity, 83.3% specificity, 90% precision, and 94.7% F1-score.

CONCLUSIONS

Our research preliminarily suggests that analyzing hand and eye movements in the virtual kiosk test holds potential as a digital marker for early screening of MCI. In contrast to conventional biomarkers, this digital marker in virtual reality is advantageous as it can collect ecologically valid data at an affordable cost and in a short period (5-15 minutes), making it a suitable means for early screening of MCI. We call for further studies to confirm the reliability and validity of this approach.

Mobile technology features and technostress in mandatory online teaching during the COVID-19 crisis

Technostress is defined as any unhealthy condition caused by dealing with modern technology in various harmful ways; examples of technostress include addiction and stress. Even though technostress has been extensively studied in the literature, little attention has been paid to investigating technostress among academics who work in exceptional circumstances, such as crises, and who might be experiencing different psychological states due to those circumstances. To fill the gap, this study aims to explore the factors influencing technostress among school teachers. The study examined technostress's level and factor graphics structure among 692 academics from different Arab countries during COVID-19. The technostress factors and their stories were explored and measured using sequential mixed methods and confirmatory and exploratory factor analysis. The study discusses various factors' direct and indirect effects on mobile technology integration in education and the theoretical and practical implications of managing technostress in online classes. A model of techno-stressors among Arab academics was found to include: schedule overload, complexity, uncertainty, uselessness, invasion, and compulsion. The direct effect of various factors on mobile technology integration in education is mainly positive, while indirect effects are more varied. The theoretical and practical implications of managing technostress in online classes include: considering the psychological and physiological impact of technostress on students' learning performance, decreasing overall satisfaction with the learning experience, and improving the overall quality of online courses. As a result of this study's findings, a new perspective is provided on how academics in particular circumstances (in this study, the occupation of Palestine) may behave and feel toward technology in teaching.