Workplace stress has been identified as the health epidemic of the 21st century. Among office workers, stress is one of the most common reasons for missing work (absenteeism) and a leading cause of underperformance while at work (presenteeism). Moreover, in the U.S., there are 81 million office workers who spend 75% or more of their day working at a desk. Unfortunately, increased daily sitting time is linked to significant health conditions, including cardiovascular diseases and diabetes. Multiple other health-related issues arise in these workers due to poor ergonomic habits. Health conditions are further exacerbated by building-level control of environmental conditions, most specifically lighting and temperature.
Advancing sensing technologies and computational techniques, including machine learning and artificial intelligence, provide opportunities for us to evaluate individualized mechanisms of these health and well-being concerns, monitor changes and impacts over time, and provide precise just-in-time notifications or automated adjustments. Our transdisciplinary team which includes occupational scientists, psychologists, civil and environmental engineers, electrical engineers, and computer scientists is working on various projects focused on leveraging these technologies to promote positive worker health, well-being, and performance within the workplace.
Mapping Positive and Negtive Workplace Stress
While stress is almost always treated as unfavorable, stress can be positive (eustress). Opportunities exist to better understand how to promote eustress that is energizing and essential for productivity and minimize distress that leads to negative emotions, disturbed bodily states, strain, and burnout. Through our work, we aim to generate new analytic models to uncover and map the patterns and pathways that influence work-related stress to understand the primary contributing factors to stress across space and time. The project will develop methods for integrating different data types from the physical and social environment (e.g., temperature, lighting, conversational tones), physiology (e.g., heart rate data, electrodermal activity, movement), and personal experiences (e.g., ecologic momentary assessment) to identify patterns that inform personalized solutions for improving self-awareness and managing work-related health and well-being. We will develop individually contextualized understandings of stress among office workers using machine learning methods that incorporate heterogeneous and noisy multimodal data streams at multiple temporal resolutions while enabling the unsupervised discovery of behavioral routines.
Intelligent Workstation
One method of enhancing office worker health is to leverage advances in artificial intelligence to develop an intelligent workstation that can support healthy environmental and behavioral changes either automatically or via an interactive interface. Our transdisciplinary research team envisions the emergence of a genuine partnership and coevolution of workers and their workstations. Specifically, we are working to design an intelligent workstation that learns worker preferences and patterns. We are exploring the ways in which an intelligent workstation and worker can coevolve through an ongoing, bidirectional process of sensing, feedback and learning to adjust postural, thermal and visual conditions at the workstation and moderate poor behaviors.
Several important research challenges will be addressed by our work:
Machine learning of user preferences, habits and patterns
Identification of shared objectives and supportive actions
Establishing machine-human symbiosis to take actions that progress toward best practices (e.g., healthiest)
Watch our video below to learn more about this exciting project:
Funding
Detecting and Mapping Stress Patterns Across Space and Time: Multimodal Modeling of Individuals in Real-world Physical and Social Work Environments
Funding Agency: NSF - Division of Information and Intelligent Systems (IIS)
Smart and Connected Health Program (SCH)
PI: Roll; Co-PIs: Becerik, Lucas, Narayanan
Award Number: 2204942
Total Funding: $1,099,995
Funding Period: 9/1/22 - 8/30/26
Mitigating Risk and Promoting Occupational Safety and Health When Developing and Integrating AI in the Workplace
Funding Agency: CDC - National Institute for Occupational Safety and Health (NIOSH)
IPA: Roll
Award Number: 22IPA2216235
Total Funding: $30,815
Funding Period: 9/1/22 - 8/31/23
Automated Detection of Stress in Offices Using Machine Learning
Funding Agency: NIOSH - Southern California Education & Research Center
PI: Awada; Co-Is: Becerik, Lucas, Roll
Total Funding: $6,585
Funding Period: 8/1/21 - 7/31/22
Coadaptation of Intelligent Office Desks and Human Users to Promote Worker Productivity, Health and Wellness
Funding Agency: NSF - Division of Civil, Mechanical, and Manufacturing Innovation (CMMI)
Co-PIs: Becerik, Roll, Lucas
Award Number: 1763134
Total Funding: $667,716
Funding Period: 8/1/18 - 12/31/22
Publications
Awada, M., Becerik Gerber, B., Lucas, G. M., & Roll, S. C. (2024). Stress appraisal in the workplace and its associations with productivity and mood: Insights from a multimodal machine learning analysis. PLoS ONE, 19(1), e0296468. https://doi.org/10.1371/journal.pone.0296468Show abstract
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.
Workload experienced over the whole day, not just work periods, may impact worker cognitive performance. We hypothesized that experiencing greater than typical whole day workload would be associated with lower visual processing speed and lower sustained attention ability, on the next day. To test this, we used dynamic structural equation modeling to analyze data from 56 workers with type 1 diabetes. For a two-week period, on smartphones they answered questions about whole day workload at the end of each day, and completed cognitive tests 5 or 6 times throughout each day. Repeated smartphone cognitive tests were used, instead of traditional one- time cognitive assessment in the laboratory, to increase the ecological validity of the cognitive tests. Examples of reported occupations in our sample included housekeeper, teacher, physician, and cashier. On workdays, the mean number of work hours reported was 6.58 (SD 3.5). At the within-person level, greater whole day workload predicted decreased mean processing speed the next day (standardized estimate=-0.10, 95% CI -0.18 to -0.01) using a random intercept model; the relationship was not significant and only demonstrated a tendency toward the expected effect (standardized estimate= -0.07, 95% CI -0.15 to 0.01) in a model with a random intercept and a random regression slope. Whole day workload was not found to be associated with next-day mean sustained attention ability. Study results suggested that just one day of greater than average workload could impact next day processing speed, but future studies with larger sample sizes are needed to corroborate this finding.
Keywords. Whole day workload; Sustained attention; Processing speed; Cognitive performance; Type 1 diabetes
Associations between various forms of activity engagement (e.g. work, leisure) and the experience of stress in workers have been widely documented. The mechanisms underlying these effects, however, are not fully understood. Our goal was to investigate if perceived whole day workload accounted for the relationships between daily frequencies of activities (i.e. work hours and leisure/rest) and daily stress. We analysed data from 56 workers with type 1 diabetes (T1D) who completed approximately two weeks of intensive longitudinal assessments. Daily whole day workload was measured with an adapted version of the National Aeronautics and Space Administration Task Load Index (NASA-TLX). A variety of occupations were reported, including lawyer, housekeeper and teacher. In multilevel path analyses, day-to-day changes in whole day workload mediated 67% (p < .001), 61% (p < .001), 38% (p < .001), and 55% (p < .001) of the within-person relationships between stress and work hours, rest frequency, active leisure frequency, and day of week, respectively. Our results provided evidence that whole day workload perception may contribute to the processes linking daily activities with daily stress in workers with T1D. Perceived whole day workload may deserve greater attention as a possible stress intervention target, ones that perhaps ergonomists would be especially suited to address.
Keywords. Workload, stress, rest, work hours, type 1 diabetes
Awada, M., Becerik-Gerber, B., Lucas, G., & Roll, S. (2023). Predicting office workers’ productivity: A machine learning approach integrating physiological, behavioral, and psychological indicators. Sensors, 23(21), 8694. https://doi.org/10.3390/s23218694Show abstract
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.
Liu, R., Awada, M., Becerik-Gerber, B., Lucas, G. M., & Roll, S. C. (2023). Gender moderates the effects of ambient bergamot scent on stress restoration in offices. Journal of Environmental Psychology, 91, 102135. https://doi.org/10.1016/j.jenvp.2023.102135Show abstract
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.
Awada, M., Becerik-Gerber, B., Lucas, G., Roll, S., & Liu, R. (2023). A new perspective on stress detection: An automated approach for detecting eustress and distress. IEEE Transactions on Affective Computing. Advance online publication. https://doi.org/10.1109/TAFFC.2023.3324910Show abstract
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% F1-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 F1-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.
Seyedrezaei, M., Awada, M., Becerik-Gerber, B., Lucas, G., & Roll, S. (2023). Interaction effects of indoor environmental quality factors on cognitive performance and perceived comfort of young adults in open plan offices in North American Mediterranean climate. Building and Environment, 244, 110743. https://doi.org/10.1016/j.buildenv.2023.110743Show abstract
While Indoor Environmental Quality (IEQ) factors in an environment co-exist, the interaction effects of these factors and their impacts on cognitive functioning and perceived comfort have not been comprehensively examined. In this study, the interaction effects between temperature, lighting Correlated Color Temperature (CCT), and noise levels on selective attention, sustained attention, creativity, acoustics, thermal, visual, and overall IEQ comfort of young adults in open-plan offices in North American Mediterranean climate were presented. In a mixed-design controlled experimental setting, 52 young adults were recruited, and their objective cognitive performance and subjective comfort were assessed through statistical analysis. The experimental set points included [20 °C, 25 °C], [2700 K, 6500 K], and [50 dB, 65 dB] for temperature, lighting color, and noise, respectively. Additionally, the work took into consideration the gender and Body Mass Index (BMI) of participants. The results show that temperature moderated the effect of noise level and lighting CCT on selective attention, while no effect of IEQ factors on sustained attention was found. Creativity was influenced by gender and its interaction with the noise level. Concerning perceived comfort, acoustic comfort varied significantly with temperature. Thermal comfort was influenced by the combined moderating effect of lighting CCT and BMI on temperature, while visual comfort was driven by the moderation effect of gender on lighting CCT. Overall comfort was affected by the noise level and temperature. Finally, cognitive performance indicators were correlated with perceived IEQ comfort votes. Based on the findings of this study, considerations of interactions between noise, lighting CCT, temperature, gender, and BMI can shape occupant-centric priorities for enhanced cognitive functioning and comfort.
Awada, M., Becerik-Gerber, B., Liu, R., Seyedrezaei, M., Lu, Z., Xenakis, M., Lucas, G., Roll, S. C., & Narayanan, S. (2023). Ten questions concerning the impact of environmental stress on office workers. Building and Environment, 229, 109964. https://doi.org/10.1016/j.buildenv.2022.109964Show abstract
We regularly face stress during our everyday activities, to the extent that stress is recognized by the World Health Organization as the epidemic of the 21st century. Stress is how humans respond physically and psychologically to adjustments, experiences, conditions, and circumstances in their lives. While there are many reasons for stress, work and job pressure remain the main cause. Thus, companies are increasingly interested in creating healthier, more comfortable, and stress-free offices for their workers. The indoor environment can induce environmental stress when it cannot satisfy the individual needs for health and comfort. In fact, office environmental conditions (e.g., thermal, and indoor air conditions, lighting, and noise) and interior design parameters (e.g., office layout, colors, furniture, access to views, distance to window, personal control and biophilic design) have been found to affect office workers' stress levels. A line of research based on the stress recovery theory offers new insights for establishing offices that limit environmental stress and help with work stress recovery. To that end, this paper answers ten questions that explore the relation between the indoor office-built environment and stress levels among workers. The answers to the ten questions are based on an extensive literature review to draw conclusions from what has been achieved to date. Thus, this study presents a foundation for future environmental stress related research in offices.
Becerik-Gerber, B., Lucas, G., Aryal, A., Awada, M., Bergés, M., Billington, S., Boric-Lubecke, O., Ghahramani, A., Heydarian, A., Höelscher, C., Jazizadeh, F., Khan, A., Langevin, J., Liu, R., Marks, F., Mauriello, M. L., Murnane, E., Noh, H., Pritoni, M., Roll, S., Schaumann, D., Seyedrezaei, M., Taylor, J. E., Zhao, J., & Zhu, R. (2022). The field of human building interaction for convergent research and innovation for intelligent built environments. Scientific Reports, 12, 22092. https://doi.org/10.1038/s41598-022-25047-yShow abstract
Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops. Three primary areas contribute to and require attention in HBI research: humans (human experiences, performance, and well-being), buildings (building design and operations), and technologies (sensing, inference, and awareness). Three critical interdisciplinary research domains intersect these areas: control systems and decision making, trust and collaboration, and modeling and simulation. Finally, at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability. Compelling research questions are posed for each primary area, research domain, and core principle. State-of-the-art methods used in HBI studies are discussed, and examples of original research are offered to illustrate opportunities for the advancement of HBI research.
Keywords. Civil engineering; Environmental impact; Mechanical engineering; Occupational health; Quality of life
Becerik-Gerber, B., Lucas, G., Aryal, A., Awada, M., Bergés, M., Billington, S. L., Boric-Lubecke, O., Ghahramani, A., Heydarian, A., Jazizadeh, F., Liu, R., Zhu, R., Marks, F., Roll, S., Seyedrezaei, M., Taylor, J. E., Höelscher, C., Khan, A., Langevin, J., Mauriello, M. L., Murnane, E., Noh, H., Pritoni, M., Schaumann, D., & Zhao, J. (2022). Ten questions concerning human-building interaction research for improving the quality of life. Building and Environment, 226, 109681. https://doi.org/10.1016/j.buildenv.2022.109681Show abstract
This paper seeks to address ten questions that explore the burgeoning field of Human-Building Interaction (HBI), an interdisciplinary field that represents the next frontier in convergent research and innovation to enable the dynamic interplay of human and building interactional intelligence. The field of HBI builds on several existing efforts in historically separate research fields/communities and aims to understand how buildings affect human outcomes and experiences, as well as how humans interact with, adapt to, and affect the built environment and its systems, to support buildings that can learn, enable adaptation, and evolve at different scales to improve the quality-of-life of its users while optimizing resource usage and service availability. Questions were developed by a diverse group of researchers with backgrounds in design, engineering, computer science, social science, and health science. Answers to these questions draw conclusions from what has been achieved to date as reported in the available literature and establish a foundation for future HBI research. This paper aims to encourage interdisciplinary collaborations in HBI research to change the way people interact with and perceive technology within the context of buildings and inform the design, construction, and operation of next-generation, intelligent built environments. In doing so, HBI research can realize a myriad of benefits for human users, including improved productivity, health, cognition, convenience, and comfort, all of which are essential to societal well-being.
Keywords. Building lifecycle; Human-centered; Occupants; Built environment; Well-being; Interaction; Quality of life
Awada, M., Becerik-Gerber, B., Lucas, G., & Roll, S. (2022). Cognitive performance, creativity and stress levels of neurotypical young adults under different white noise levels. Scientific Reports, 12, 14566. https://doi.org/10.1038/s41598-022-18862-wShow 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.
Sedentary activity and static postures are associated with work-related musculoskeletal disorders (WMSDs) and worker discomfort. Ergonomic evaluation for office workers is commonly performed by experts using tools such as the Rapid Upper Limb Assessment (RULA), but there is limited evidence suggesting sustained compliance with expert’s recommendations. Assessing postural shifts across a day and identifying poor postures would benefit from automation by means of real-time, continuous feedback. Automated postural assessment methods exist; however, they are usually based on ideal conditions that may restrict users’ postures, clothing, and hair styles, or may require unobstructed views of the participants. Using a Microsoft Kinect camera and open-source computer vision algorithms, we propose an automated ergonomic assessment algorithm to monitor office worker postures, the 3D Automated Joint Angle Assessment, 3D-AJA. The validity of the 3D-AJA was tested by comparing algorithm-calculated joint angles to the angles obtained from manual goniometry and the Kinect Software Development Kit (SDK) for 20 participants in an office space. The results of the assessment show that the 3D-AJA has mean absolute errors ranging from 5.6° ± 5.1° to 8.5° ± 8.1° for shoulder flexion, shoulder abduction, and elbow flexion relative to joint angle measurements from goniometry. Additionally, the 3D-AJA showed relatively good performance on the classification of RULA score A using a Random Forest model (micro averages F1-score = 0.759, G-mean = 0.811), even at high levels of occlusion on the subjects’ lower limbs. The results of the study provide a basis for the development of a full-body ergonomic assessment for office workers, which can support personalized behavior change and help office workers to adjust their postures, thus reducing their risks of WMSDs.
Awada, M., Becerik-Gerber, B., Lucas, G., & Roll, S. (2021). Associations among home indoor environmental quality factors and worker health while working from home during COVID-19 pandemic. Journal of Engineering for Sustainable Buildings and Cities, 2(4), 041001. https://doi.org/10.1115/1.4052822Show 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.
Stay-at-home mandates following the COVID-19 pandemic increased work from home (WFH). While WFH offers many benefits, navigating work in nontraditional contexts can be a challenge. The objective of this study was to explore the benefits and challenges of WFH during COVID-19 to identify supports and resources necessary. Comments from two free-response questions on a survey regarding experiences of WFH (N = 648, N = 366) were analyzed using inductive qualitative content analysis. Four themes emerged: time use, considerations of working in the home space, intersections between work-life and home-life, and temporality of WFH as situated within a pandemic. Across all themes were concerns related to participation in both work and home roles, work performance, and well-being. Findings highlight the importance of support during times of disruption of occupational patterns, roles, and routines. Despite challenges, many individuals hoped to continue WFH. Organizations should consider the complex intersections of work-life and home-life to develop supportive policies and resources.
Roll, S. C., Lucas, G. M., & Becerik-Gerber, B. (2021). Authors’ response to “Work from home (WFH) during COVID-19: Is virtual reality (VR) a new solution to new problems?”. Journal of Occupational and Environmental Medicine, 63(10), e757-e758. https://doi.org/10.1097/JOM.0000000000002340Show abstract
Awada, M., Lucas, G., Becerik-Gerber, B., & Roll, S. (2021). Working from home during the COVID-19 pandemic: Impact on office worker productivity and work experience. Work, 69(4), 1171-1189. https://doi.org/10.3233/WOR-210301Show abstract
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.
Accepted for AOTA INSPIRE 2021 but unable to be presented due to online event limitations.
This study explored office workers' perspectives on including artificial intelligence (AI) in their office workspace. Following an iterative analysis of six focus-group interviews with a total of 45 participants, three constructs emerged. Rich discussions demonstrated how acceptability of an AI workstation is complex and affected by the person, context, and their occupations.
Aryal, A., Becerik-Gerber, B., Lucas, G. M., & Roll, S. C. (2021). Intelligent agents to improve thermal satisfaction by controlling personal comfort systems under different levels of automation. IEEE Internet of Things Journal, 8(8), 7089-7100. https://doi.org/10.1109/JIOT.2020.3038378Show abstract
Heating, ventilation, and air conditioning (HVAC) systems account for 43% of building energy consumption, yet only 38% of commercial building occupants are satisfied with the thermal environment. The primary reasons for low occupant satisfaction are that HVAC operations do not integrate occupant comfort requirements nor control the thermal environment at the individual level. Personal comfort systems (PCS) enable local control of the thermal environment around each occupant. However, full manual control of PCS can be inefficient, and fully automated PCS reduces an occupant’s perceived control over the environment, which can then lead to lower satisfaction. A better solution might lie somewhere between fully manual and fully automated environmental control. In this paper, we describe the development and implementation of an internet of things (IoT) based intelligent agent that learns individual occupant comfort requirements and controls the thermal environment using PCS (i.e., a local fan and a heater). We tested different levels of automation where control is shared between an intelligent agent and the end user. Our results show that PCS use improves occupant satisfaction and including some level of automation can improve occupant satisfaction further than what is possible with manually operated PCS. Among the levels of automation investigated, Inquisitive Automation, where the user approves/declines the control actions of the intelligent agent before execution, led to highest occupant satisfaction with the thermal environment.
Xiao, Y., Becerik-Gerber, B., Lucas, G., & Roll, S. C. (2021). Impacts of working from home during COVID-19 pandemic on physical and mental well-being of office workstation users. Journal of Occupational and Environmental Medicine, 63(3), 181-190. https://doi.org/10.1097/JOM.0000000000002097Show abstract
Objective. To understand impacts of social, behavioral and physical factors on well-being of office workstation users during COVID-19 work from home (WFH).
Methods. A questionnaire was deployed from April 24 to June 11, 2020 and 988 responses were valid. Linear regression, multinomial logistic regression and chi-square tests were used to understand factors associated with overall physical and mental health statuses and number of new physical and mental health issues.
Results. Decreased overall physical and mental well-being after WFH were associated with physical exercise, food intake, communication with coworkers, children at home, distractions while working, adjusted work hours, workstation set-up and satisfaction with workspace indoor environmental factors.
Conclusion. This study highlights factors that impact workers’ physical and mental health well-being while WFH and provides a foundation for considering how to best support a positive WFH experience.
Keywords. COVID-19 pandemic, home office, mental well-being, physical well-being, work from home
Workplace environments have a significant impact on worker performance, health, and well-being. With machine learning capabilities, artificial intelligence (AI) can be developed to automate individualized adjustments to work environments (e.g., lighting, temperature) and to facilitate healthier worker behaviors (e.g., posture). Worker perspectives on incorporating AI into office workspaces are largely unexplored. Thus, the purpose of this study was to explore office workers’ views on including AI in their office workspace. Six focus group interviews with a total of 45 participants were conducted. Interview questions were designed to generate discussion on benefits, challenges, and pragmatic considerations for incorporating AI into office settings. Sessions were audio-recorded, transcribed, and analyzed using an iterative approach. Two primary constructs emerged. First, participants shared perspectives related to preferences and concerns regarding communication and interactions with the technology. Second, numerous conversations highlighted the dualistic nature of a system that collects large amounts of data; that is, the potential benefits for behavior change to improve health and the pitfalls of trust and privacy. Across both constructs, there was an overarching discussion related to the intersections of AI with the complexity of work performance. Numerous thoughts were shared relative to future AI solutions that could enhance the office workplace. This study’s findings indicate that the acceptability of AI in the workplace is complex and dependent upon the benefits outweighing the potential detriments. Office worker needs are complex and diverse, and AI systems should aim to accommodate individual needs.
Aryal, A., Becerik-Gerber, B., Anselmo, F., Roll, S. C., & Lucas, G. (2019). Smart desks to promote comfort, health and productivity in offices: A vision for future workplaces. Frontiers in Built Environment, 5, 76. https://doi.org/10.3389/fbuil.2019.00076Show abstract
People spend most of their day in buildings, and a large portion of the energy in buildings is used to control the indoor environment for creating acceptable conditions for occupants. However, majority of the building systems are controlled based on a ‘one size fits all’ scheme which cannot account for individual occupant preferences. This leads to discomfort, low satisfaction and negative impacts on occupants’ productivity, health and well-being. In this paper, we describe our vision of how recent advances in Internet of Things (IoT) and machine learning can be used to add intelligence to an office desk to personalize the environment around the user. The smart desk can learn individual user preferences for the indoor environment, personalize the environment based on user preferences, and act as an intelligent support system for improving user comfort, health and productivity. We briefly describe the recent advances made in different domains that can be leveraged to enhance occupant experience in buildings and describe the overall framework for the smart desk. We conclude the paper with a discussion of possible avenues for further research.
Fukumura, Y. E., Becerik-Gerber, B., Lucas, G. M., & Roll, S. C. (2020). Understanding office worker behavior to Inform an artificial intelligence workstation. Poster presentation at NIH Health in Buildings Roundtable Conference, Virtual Presentation.
Aryal, A., Becerik-Gerber, B., Anselmo, F., Roll, S. C., & Lucas, G. (2018). Smart desk to promote health and productivity. Poster presentation at NIH/NSF/CDC/GSA Health in Buildings for Today and Tomorrow: An Interdisciplinary Conference on Health and the Sustainable Built Environment, Bethesda, MD.
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