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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. 

We investigated the physiological (heart rate variability) and psychological (state of anxiety, pleasantness, and comfort) effects of ambient bergamot scent on the stress levels of office workers by exposing them to the scent while stressors persisted as the workers continued to work on the office tasks. Forty-eight young adults were randomly assigned to either a control or scent group. Our results show that the stress restoration effect of bergamot scent depends on gender. The change in heart rate variability revealed that bergamot scent increased stress among males but not for females. The reported pleasantness and comfort followed the same trend. Compared to the control groups, females in the scent group thought the office smelled pleasant and felt more comfortable, but males in the scent group reported the opposite. However, no gender effect was found in the level of state anxiety. Specifically, compared to the control groups, both males and females exposed to the bergamot scent self-reported decreasing stress levels. This inconsistency between self-reported stress and physiological measurements is not uncommon, especially among males who are socialized to downplay emotional experiences. Our data suggest that there is indeed a gender difference in the effectiveness of the bergamot scent for reducing stress in office workers. 

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. 

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. 

Abstract Noise is often considered a distractor; however recent studies suggest that sub-attentive individuals or individuals diagnosed with attention deficit hyperactivity disorder can benefit from white noise to enhance their cognitive performance. Research regarding the effect of white noise on neurotypical adults presents mixed results, thus the implications of white noise on the neurotypical population remain unclear. Thus, this study investigates the effect of 2 white noise conditions, white noise level at 45 dB and white noise level at 65 dB, on the cognitive performance, creativity, and stress levels of neurotypical young adults in a private office space. These conditions are compared to a baseline condition where participants are exposed to the office ambient noise. Our findings showed that the white noise level at 45 dB resulted in better cognitive performance in terms of sustained attention, accuracy, and speed of performance as well as enhanced creativity and lower stress levels. On the other hand, the 65 dB white noise condition led to improved working memory but higher stress levels, which leads to the conclusion that different tasks might require different noise levels for optimal performance. These results lay the foundation for the integration of white noise into office workspaces as a tool to enhance office workers’ performance. 

Abstract The outbreak of SARS-CoV-2 virus forced office workers to conduct their daily work activities from home over an extended period. Given this unique situation, an opportunity emerged to study the satisfaction of office workers with indoor environmental quality (IEQ) factors of their houses where work activities took place and associate these factors with mental and physical health. We designed and administered a questionnaire that was open for 45 days during the COVID-19 pandemic and received valid data from 988 respondents. The results show that low satisfaction with natural lighting, glare, and humidity predicted eye-related symptoms, while low satisfaction with noise was a strong predictor of fatigue or tiredness, headaches or migraines, anxiety, and depression or sadness. Nose- and throat-related symptoms and skin-related symptoms were only uniquely predicted by low satisfaction with humidity. Low satisfaction with glare uniquely predicted an increase in musculoskeletal discomfort. Symptoms related to mental stress, rumination, or worry were predicted by low satisfaction with air quality and noise. Finally, low satisfaction with noise and indoor temperature predicted the prevalence of symptoms related to trouble concentrating, maintaining attention, or focus. Workers with higher income were more satisfied with humidity, air quality, and indoor temperature and had better overall mental health. Older individuals had increased satisfaction with natural lighting, humidity, air quality, noise, and indoor temperature. Findings from this study can inform future design practices that focus on hybrid home-work environments by highlighting the impact of IEQ factors on occupant well-being. 

BACKGROUND: With the COVID-19 pandemic, organizations embraced Work From Home (WFH). An important component of transitioning to WFH is the effect on workers, particularly related to their productivity and work experience. OBJECTIVES: The objective of this study is to examine how worker-, workspace-, and work-related factors affected productivity and time spent at a workstation on a typical WFH day during the pandemic. METHODS: An online questionnaire was designed and administered to collect the necessary information. Data from 988 respondents were included in the analyses. RESULTS: Overall perception of productivity level among workers did not change relative to their in-office productivity before the pandemic. Female, older, and high-income workers were likely to report increased productivity. Productivity was positively influenced by better mental and physical health statuses, having a teenager, increased communication with coworkers and having a dedicated room for work. Number of hours spent at a workstation increased by approximately 1.5 hours during a typical WFH day. Longer hours were reported by individuals who had school age children, owned an office desk or an adjustable chair, and had adjusted their work hours. CONCLUSION: The findings highlight key factors for employers and employees to consider for improving the WFH experience.