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Abstract Accurately representing changes in mental states over time is crucial for understanding their complex dynamics. However, there is little methodological research on the validity and reliability of human-produced continuous-time annotation of these states. We present a psychometric perspective on valid and reliable construct assessment, examine the robustness of interval-scale (e.g., values between zero and one) continuous-time annotation, and identify three major threats to validity and reliability in current approaches. We then propose a novel ground truth generation pipeline that combines emerging techniques for improving validity and robustness. We demonstrate its effectiveness in a case study involving crowd-sourced annotation of perceived violence in movies, where our pipeline achieves a .95 Spearman correlation in summarized ratings compared to a .15 baseline. These results suggest that highly accurate ground truth signals can be produced from continuous annotations using additional comparative annotation (e.g., a versus b) to correct structured errors, highlighting the need for a paradigm shift in robust construct measurement over time. 

In modern workplaces, the well-being and productivity of employees are increasingly recognized as essential for organizational success. This study explores the impact of lighting interventions—specifically, Correlated Color Temperature (CCT) and illumination intensity—on stress and cognitive function restoration among office workers. A between-subjects experimental design involving 100 participants assessed the effects of various lighting conditions on stress responses and cognitive performance after exposure to stress-inducing and cognitive fatigue tasks. Five experimental conditions were studied: 100 lux & 7000 K, 100 lux & 3000 K, 1000 lux & 7000 K, 1000 lux & 3000 K, and a baseline condition of 500 lux at 3700 K. Results indicated that lighting conditions significantly influence both physiological stress markers (such as skin conductance level and heart rate) and cognitive performance. Specifically, warm, and dimmed lighting (3000 K, 100 lux) effectively reduced stress markers and perceived stress levels, aligning with theories suggesting the calming effects of warmer white light tones. Conversely, cooler lighting (7000 K) was associated with enhanced cognitive performance and reduced cognitive fatigue, potentially due to its similarity to natural daylight, stimulating an alert state conducive to mental tasks. These findings suggest that lighting interventions offer a non-intrusive strategy to improve well-being and productivity in office environments, particularly addressing acute symptoms. 

Stress experiences can have dire consequences for worker performance and well-being, and the social environment of the workplace is a key contributor to worker experience. This study investigated the relationship between hybrid workers’ self-ratings of productivity, mood, and stress with perceptions of positive (eustress) and negative (distress) stress states. We hypothesized that self-ratings would vary across combinations of eustress and distress experiences and that these differences would differ based on the social context. Ecological momentary assessments (EMA) were used to obtain ecologically valid data at four data points each workday across a 4-month study period in a cohort of seven office workers. Findings aligned with the Yerkes–Dodson law, such that higher states of arousal were associated with greater self-perceived productivity, and higher stress magnitudes were found when distress existed. Compared to other states, eustress was associated with higher productivity in work-related activities and better mood across all activity types. 

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.