Humans’ performance varies due to the mental resources that are available to successfully pursue a task. To monitor users’ current cognitive resources in naturalistic scenarios, it is essential to not only measure demands induced by the task itself but also consider situational and environmental influences. We conducted a multimodal study with 18 participants (nine female, M = 25.9 with SD = 3.8 years). In this study, we recorded respiratory, ocular, cardiac, and brain activity using functional near-infrared spectroscopy (fNIRS) while participants performed an adapted version of the warship commander task with concurrent emotional speech distraction. We tested the feasibility of decoding the experienced mental effort with a multimodal machine learning architecture. The architecture comprised feature engineering, model optimisation, and model selection to combine multimodal measurements in a cross-subject classification. Our approach reduces possible overfitting and reliably distinguishes two different levels of mental effort. These findings contribute to the prediction of different states of mental effort and pave the way toward generalised state monitoring across individuals in realistic applications.

Decoding Mental Effort in a Quasi-Realistic Scenario: A Feasibility Study on Multimodal Data Fusion and Classification

Stress is various mental health disorders including depression and anxiety among college students. Early stress diagnosis and intervention may lower the risk of developing mental illnesses. We examined a machine learning-based method for identification of stress using data collected in a naturalistic study utilizing self-reported stress as ground truth as well as physiological data such as heart rate and hand acceleration. The study involved 54 college students from a large campus who used wearable wrist-worn sensors and a mobile health (mHealth) application continuously for 40 days. The app gathered physiological data including heart rate and hand acceleration at one hertz frequency. The application also enabled users to self-report stress by tapping on the watch face, resulting in a time-stamped record of the self-reported stress. We created, evaluated, and analyzed machine learning algorithms for identifying stress episodes among college students using heart rate and accelerometer data. The XGBoost method was the most reliable model with an AUC of 0.64 and an accuracy of 84.5%. The standard deviation of hand acceleration, standard deviation of heart rate, and the minimum heart rate were the most important features for stress detection. This evidence may support the efficacy of identifying patterns in physiological reaction to stress using smartwatch sensors and may inform the design of future tools for real-time detection of stress. 

Evaluating Mental Stress Among College Students Using Heart Rate and Hand Acceleration Data Collected from Wearable Sensors

Emerging evidence suggested that people with severe mental disorders were more vulnerable to the negative effects of the COVID-19 pandemic. However, few researches investigated the influence of global pandemics on people at clinical high risk (CHR) for psychosis. This study aimed to investigate the impact of the COVID-19 pandemic on clinical symptoms, psychological distress, and eye-tracking characteristics in CHR individuals and healthy participants. Forty-nine CHR individuals and 50 healthy controls (HC) were assessed by PTSD Checklist for DSM-5 (PCL-5), Perceived Stress Scale, 10-item version (PSS-10), and Coronavirus Impact Scale (CIS). Eye movement performances were measured by the tests of fixation stability, free-viewing, and anti-saccade. According to the mean score of CIS, participants were stratified into high-impact (n = 35) and low-impact (n = 64) subgroups. Compared with the HC group, CHR participants reported significantly higher levels of post-traumatic symptoms caused by the COVID-19 pandemic and showed abnormalities in most of the eye movement indexes. Among the altered indexes, the saccade amplitude of fixation stability test (far distractor), the scan path length of free-viewing test, and the accuracy of anti-saccade test were negatively affected by the severity of impact level in the CHR group. Moreover, the altered eye movement indexes were significantly associated with the total scores of CIS, PCL-5, and subscales of the Scale of Prodromal Syndromes (SOPS) among CHR individuals. Overall, our findings suggested the negative impact of the COVID-19 pandemic on the eye movement characteristics of CHR individuals. The present study provides valuable information on physiological distress related to the COVID-19 pandemic and sensitive neuropsychological biomarkers that interacted with social and environment stress in the CHR population. 

https://doi.org/10.1016/j.pnpbp.2022.110578

The impact of COVID-19 pandemic on individuals at clinical high-risk for psychosis: Evidence from eye-tracking measures

Advancements in wearable sensor technologies have revolutionized healthcare, particularly in nurses stress level detection using accelerometer-based data. Accurate detection of nurse stress throughout a day holds significant potential to enhance healthcare outcomes and optimize nursing workflows. However, existing research in this domain has been limited by small data-sets and short sampling duration’s, leading to constrained generalizability. To address these limitations comprehensively, this paper presents a data on nurses’ mental health in a practical setting are gathered using a wearable accelerometer sensor. To record the physiological and behavioral traits of the nurses, a single triaxial accelerometer is mounted on their heads. Multiple machine learning methods, such as Nearest Neighbor Classifiers (k-NN), Neural Networks (NN), Support Vector Machines (SVM), Naive Bayes (NB), Discriminant Analysis (DA), Decision Trees (DT), and Ensemble Classifiers, were used to assess and compare the accuracy of nurses’ stress levels. The Medium Gaussian-SVM study showed the best average performance of 80.4% and mean efficacy F-measure of 80.2% across all cases. whereas the Coarse Gaussian-SVM evaluation exhibited the lowest mean performance of 26.5%, accompanied by a mean F-measure of 26.1%. On a scale of 1–7, a questionnaire evaluated privacy issues. The results show our method’s potential for accurately determining nurses’ levels of stress, making it helpful for implementation in the future.

Automated Stress Level Detection for Hospital Nurses: A Single Triaxial Wearable Accelerometer Sensor System Approach

OBJECTIVE
Present a collection of papers focusing on improving healthcare practice through the implementation of human factors and ergonomics principles that were presented at the International Ergonomics Association (IEA) 2021 international conference.


BACKGROUND
The mission of the IEA is to elaborate and advance ergonomics science and practice and to expand its scope of application.


METHOD
We reviewed papers that were submitted for presentation at the IEA 2021 international conference and focused on improving healthcare practice through the implementation of human factors and ergonomics principles.


RESULTS
The eight papers that are included in this special issue cover varied aspects of human factors application and implementation.


CONCLUSION
This special issue provides clear evidence that the science of human factors is relevant and is continuing to grow and so is its implementation in healthcare.


APPLICATION
This special issue offers a selection of applied works, providing a wide scope of human factors guidelines, methods, and theories in healthcare work environments.

Improving Healthcare Practice Through the Implementation of Human Factors and Ergonomics Principles.

OBJECTIVE
(1) To assess mental workloads of intensive care unit (ICU) nurses in 12-hour working shifts (days and nights) using eye movement data; (2) to explore the impact of stress on the ocular metrics of nurses performing patient care in the ICU.


BACKGROUND
Prior studies have employed workload scoring systems or accelerometer data to assess ICU nurses' workload. This is the first naturalistic attempt to explore nurses' mental workload using eye movement data.


METHODS
Tobii Pro Glasses 2 eye-tracking and Empatica E4 devices were used to collect eye movement and physiological data from 15 nurses during 12-hour shifts (252 observation hours). We used mixed-effect models and an ordinal regression model with a random effect to analyze the changes in eye movement metrics during high stress episodes.


RESULTS
While the cadence and characteristics of nurse workload can vary between day shift and night shift, no significant difference in eye movement values was detected. However, eye movement metrics showed that the initial handoff period of nursing shifts has a higher mental workload compared with other times. Analysis of ocular metrics showed that stress is positively associated with an increase in number of eye fixations and gaze entropy, but negatively correlated with the duration of saccades and pupil diameter.


CONCLUSION
Eye-tracking technology can be used to assess the temporal variation of stress and associated changes with mental workload in the ICU environment. A real-time system could be developed for monitoring stress and workload for intervention development.

Quantifying Workload and Stress in Intensive Care Unit Nurses: Preliminary Evaluation Using Continuous Eye-Tracking.

The aim of this study is to explore real-time stress monitoring models (based on physiological features) for intensive care unit (ICU) nurses. The quantification of stress in ICU nurses has been limited to subjective ratings, with a general gap in continuous measurement; real-time stress monitoring based on continuous physiological measurement is needed to assess the negative outcome of stress. Electrodermal activity, eye tracking, accelerometer data, and skin temperatures were recorded continuously through 12-hour shifts for ICU nurses (23 participants). A machine learning algorithm was applied to identify stress over time based on physiological features. eXtreme Gradient Boosting (XGBoost) was performed with an accuracy of 0.88. Skin temperature contributed the most to real-time stress identification for monitoring. Future work should investigate the efficacy of using skin temperature for stress identification in real-time for ICU nurses.

Real-time Stress Monitoring for Intensive Care Unit (ICU) Nurses

The aim of this study is to explore nurse activities leading to high cognitive stress for intensive care unit (ICU) nurses. The quantification of stress-related nurse activities has been acquired before and after the working shift, with a general gap in analyzing the whole shift. To address this gap, the task analysis of stress-related nurse activities during whole shift was conducted for two participants. The administration tasks (processing clinical data, exchanging professional information and ordering examination), and communicating with patients were most performed when under high cognitive stress. Future work should investigate the contribution of administration and patient-related tasks to high cognitive stress for ICU nurses.

Exploring cognitive stress-related tasks of Intensive Care Unit nurses –A comparison study

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Papers

Quantifying Workload and Stress in Intensive Care Unit Nurses: Preliminary Evaluation Using Continuous Eye-Tracking.

Real-time Stress Monitoring for Intensive Care Unit (ICU) Nurses

Exploring cognitive stress-related tasks of Intensive Care Unit nurses –A comparison study