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1. Stress appraisal in the workplace and its associations with productivity and mood: Insights from a multimodal machine learning analysis

   https://doi.org/10.1371/journal.pone.0296468  

   Awada, Mohamad; Becerik_Gerber, Burcin; Lucas, Gale M; Roll, Shawn C (January 2024, PLOS ONE)

   Khan, Iftikhar Ahmed (Ed.)

   Previous studies have primarily focused on predicting stress arousal, encompassing physiological, behavioral, and psychological responses to stressors, while neglecting the examination of stress appraisal. Stress appraisal involves the cognitive evaluation of a situation as stressful or non-stressful, and as a threat/pressure or a challenge/opportunity. In this study, we investigated several research questions related to the association between states of stress appraisal (i.e., boredom, eustress, coexisting eustress-distress, distress) and various factors such as stress levels, mood, productivity, physiological and behavioral responses, as well as the most effective ML algorithms and data signals for predicting stress appraisal. The results support the Yerkes-Dodson law, showing that a moderate stress level is associated with increased productivity and positive mood, while low and high levels of stress are related to decreased productivity and negative mood, with distress overpowering eustress when they coexist. Changes in stress appraisal relative to physiological and behavioral features were examined through the lenses of stress arousal, activity engagement, and performance. An XGBOOST model achieved the best prediction accuracies of stress appraisal, reaching 82.78% when combining physiological and behavioral features and 79.55% using only the physiological dataset. The small accuracy difference of 3% indicates that physiological data alone may be adequate to accurately predict stress appraisal, and the feature importance results identified electrodermal activity, skin temperature, and blood volume pulse as the most useful physiologic features. Implementing these models within work environments can serve as a foundation for designing workplace policies, practices, and stress management strategies that prioritize the promotion of eustress while reducing distress and boredom. Such efforts can foster a supportive work environment to enhance employee well-being and productivity. 

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2. Predicting Office Workers’ Productivity: A Machine Learning Approach Integrating Physiological, Behavioral, and Psychological Indicators

   https://doi.org/10.3390/s23218694  

   Awada, Mohamad; Becerik-Gerber, Burcin; Lucas, Gale; Roll, Shawn C (November 2023, Sensors)

   This research pioneers the application of a machine learning framework to predict the perceived productivity of office workers using physiological, behavioral, and psychological features. Two approaches were compared: the baseline model, predicting productivity based on physiological and behavioral characteristics, and the extended model, incorporating predictions of psychological states such as stress, eustress, distress, and mood. Various machine learning models were utilized and compared to assess their predictive accuracy for psychological states and productivity, with XGBoost emerging as the top performer. The extended model outperformed the baseline model, achieving an R2 of 0.60 and a lower MAE of 10.52, compared to the baseline model’s R2 of 0.48 and MAE of 16.62. The extended model’s feature importance analysis revealed valuable insights into the key predictors of productivity, shedding light on the role of psychological states in the prediction process. Notably, mood and eustress emerged as significant predictors of productivity. Physiological and behavioral features, including skin temperature, electrodermal activity, facial movements, and wrist acceleration, were also identified. Lastly, a comparative analysis revealed that wearable devices (Empatica E4 and H10 Polar) outperformed workstation addons (Kinect camera and computer-usage monitoring application) in predicting productivity, emphasizing the potential utility of wearable devices as an independent tool for assessment of productivity. Implementing the model within smart workstations allows for adaptable environments that boost productivity and overall well-being among office workers. 

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3. A New Perspective on Stress Detection: An Automated Approach for Detecting Eustress and Distress

   https://doi.org/10.1109/TAFFC.2023.3324910  

   Awada, Mohamad; Becerik-Gerber, Burcin; Lucas, Gale; Roll, Shawn; Liu, Ruying (October 2023, IEEE Transactions on Affective Computing)

   Previous studies have solely focused on establishing Machine Learning (ML) models for automated detection of stress arousal. However, these studies do not recognize stress appraisal and presume stress is a negative mental state. Yet, stress can be classified according to its influence on individuals; the way people perceive a stressor determines whether the stress reaction is considered as eustress (positive stress) or distress (negative stress). Thus, this study aims to assess the potential of using an ML approach to determine stress appraisal and identify eustress and distress instances using physiological and behavioral features. The results indicate that distress leads to higher perceived stress arousal compared to eustress. An XGBoost model that combined physiological and behavioral features using a 30 second time window had 83.38% and 78.79% F 1 -scores for predicting eustress and distress, respectively. Gender-based models resulted in an average increase of 2-4% in eustress and distress prediction accuracy. Finally, a model to predict the simultaneous assessment of eustress and distress, distinguishing between pure eustress, pure distress, eustress-distress coexistence, and the absence of stress achieved a moderate F 1 -score of 65.12%. The results of this study lay the foundation for work management interventions to maximize eustress and minimize distress in the workplace. 

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4. A self-aware and active-guiding training & assistant system for worker-centered intelligent manufacturing

   Tao, W.; Lai, Z.H.; Leu, M.C.; Yin, Z.; Qin, R. (August 2019, Manufacturing Letters)

   Training and on-site assistance is critical to help workers master required skills, improve worker productivity, and guarantee the product quality. Traditional training methods lack worker-centered considerations that are particularly in need when workers are facing ever changing demands. In this study, we propose a worker-centered training & assistant system for intelligent manufacturing, which is featured with self-awareness and active-guidance. Multi-modal sensing techniques are applied to perceive each individual worker and a deep learning approach is developed to understand the worker’s behavior and intention. Moreover, an object detection algorithm is implemented to identify the parts/tools the worker is interacting with. Then the worker’s current state is inferred and used for quantifying and assessing the worker performance, from which the worker’s potential guidance demands are analyzed. Furthermore, onsite guidance with multi-modal augmented reality is provided actively and continuously during the operational process. Two case studies are used to demonstrate the feasibility and great potential of our proposed approach and system for applying to the manufacturing industry for frontline workers. 

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5. Crisis response among essential workers and their children during the COVID-19 pandemic

   https://doi.org/10.1016/j.ypmed.2021.106852  

   Sugg, M.M. (February 2022, Preventive medicine)

   Limited research has been conducted on the mental health concerns of frontline and essential workers and their children during the COVID-19 pandemic in the United States (U.S.). This study examined the association between working on the frontlines in the U.S. during the COVID-19 pandemic (March to July 2020) and personal crisis text concerns (e.g., self-harm, suicidal thoughts, anxiety/stress, and substance abuse) for frontline essential workers and the children of frontline workers. We used a novel data set from a crisis texting service, Crisis Text Line (CTL), that is widely used throughout the U.S. Generalized Estimating Equations examined the individual association between eight specific crisis types (Depression, Stress/Anxiety, Self-Harm, Suicidal Thoughts, Substance Abuse, Isolation, Relationship Issues, and Abuse) and being in frontline work or being a child of a frontline worker during the early phase of the pandemic. Using CTL concerns as a proxy for the prevalence of mental health issues, we found that children of workers, specifically the youngest demographic (13 years and under), females, and non-conforming youth had a higher risk of specific crisis events during the COVID-19 pandemic. Additionally, Hispanic children of workers reported higher rates of stress/anxiety, whereas African American children of workers had higher rates of abuse and depression. Frontline workers had a higher risk of suicidal thoughts, and the risk of crisis events was generally highest for non-binary, transgender, and male users. Increases in CTL usage among frontline workers were noted across 7–28 days after spikes in local COVID-19 cases. The research to date has focused on the mental health of frontline essential workers, but our study highlights troubling trends in psychological stress among children of these workers. Supportive interventions and mental health resources are needed not only for frontline essential workers but for their children too. 

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