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

Kim, Se Young; Park, Hahyeon; Kim, Hongbum; Kim, Joon; Seo, Kyoungwon

doi:10.1016/j.ipm.2022.103093

Cited by 24 publications

(17 citation statements)

References 71 publications

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“…Moreover, they might lack the requisite training and support to utilize technology effectively [ 64 ]. Kim and colleagues [ 65 ] reported that technostress often triggers cognitive overload and significant stress in teachers, which may impact their performance.…”

Section: Discussionmentioning

confidence: 99%

“…As stated by Ref. [ 65 ] Bozkurt & Sharma (2021), the technical demands of teaching online, coupled with the absence of physical interaction, can significantly elevate technostress levels. Further studies should focus on developing strategies to minimize technostress in such situations and ensure adequate support is provided to teachers and students.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

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

Khlaif,

Sanmugam,

Hattab

et al. 2023

Heliyon

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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

Khlaif,

Sanmugam,

Hattab

et al. 2023

Heliyon

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“…This study used the virtual kiosk test, previously developed in our research [ 26 ], as a source of VR-derived biomarkers. VR-derived biomarkers consist of features used to assess cognitive impairment by analyzing behavioral data, including hand and eye movements, collected from VR environments [ 17 ].…”

Section: Methodsmentioning

confidence: 99%

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

Park,

Kim,

Park

et al. 2024

J Med Internet Res

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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.

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“…Among various IADL tasks, ordering menu items from a kiosk was found to be the assignment where patients with MCI showed the lowest performance [ 28 , 30 ]. The reason why kiosk-related IADL tasks are significantly impaired in patients with MCI is that the kiosk use scenario requires comprehensive and complex cognitive functions, including memory to recall multiple menu items, attention to focus on the correct menu item among the various menu items, and executive function to carry out an order consecutively [ 31 , 32 ]. This is why, in this study, an IADL task of ordering menu items from a kiosk was used for early screening of patients with MCI.…”

Section: Introductionmentioning

confidence: 99%

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

Kim,

Park,

Choi

et al. 2023

J Med Internet Res

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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.

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

Cited by 24 publications

References 71 publications

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

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

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

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

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