Active Research
Worker Health and Well-being in Emerging Work Contexts

While musician health literature has grown significantly in the past three decades, a holistic understanding of musician well-being remains lacking. This mapping review aimed to create a topographical review of existing literature on musician well-being to identify key knowledge gaps. This review sought to identify the aspects of musician well-being that have recently been studied, the musician populations that have been studied, and the study designs that have been used to assess musician well-being. This mapping review design was informed by the Focused Mapping Review and Synthesis (FMRS) approach. Studies were categorized and visualized based on study design, type of musician sampled (i.e., profession, instrument, musical genre), and well-being determinants, using the Ecology of Musical Performance (EMP) Model as a foundational framework. This review found that the majority of well-being studies identified focused on musculoskeletal health. Additionally, few intervention studies were identified, and all involved samples of music students in intervention studies were in K-12 or higher education programs. While the EMP model proposes a holistic approach to understanding musician well-being, many crucial well-being determinants highlighted by the model have not been recently studied within the musician health research. Addressing these gaps will provide a more comprehensive foundation for improving the health and well-being of all musicians.

Keywords. Literature review, mapping review, musician health, well-being

The noise produced in demolition sites can mask safety-critical sounds that inform operators about task conditions and hazards. These problems are exacerbated in teleoperated demolition, where the separation between operator and site compromises operators' situation awareness and cognitive loads. This paper assessed the effects of environmental sounds with and without attenuation on the operators' performance and response (e.g., stress, attention, task engagement) during teleoperated demolition. Eighty participants completed three virtual demolition tasks under different environmental sound conditions, i.e., no sound (NS), unfiltered sound (US), and filtered sound (FS) with 20-dB attenuation of background noise and robot's sounds to allow focus on safety and task conditions. The results show that US induced more stress than NS and FS. Also, FS resulted in fewer collisions, faster reaction times, and greater attention and task engagement than US. These results can support the design of sound feedback interfaces for teleoperation in construction.

Keywords. Demolition, Electrodermal activity, Environmental sound, Heart rate variability, Performance, Robot, Safety, Stress, Teleoperation

In people, the experiencing, remembering, and believing selves are distinct modes of being that co-exist, though at any particular moment one mode can be dominant. While qualitative methods are effective for querying the remembering and believing selves, Ecological Momentary Assessment (EMA) methods may be better suited to capture the perspective of the experiencing self. Using EMA to investigate occupation allows participants to engage in their regular occupations in their natural environments, pausing to record information about experiences that occurred seconds or minutes prior. To demonstrate the use of EMA to capture the experiencing self (the ‘I’ in the present moment not occupied with memories or beliefs), we examined associations between working, recovering, and various aspects of being (e.g., mood) in secondary analyses using EMA data from workers with type one diabetes (T1D, n = 92). Densely repeated sampling of workers’ experiences through EMA allowed for a correspondingly comprehensive representation of how both working and recovering were associated with various aspects of being, yielding insights relevant to the clinical needs of workers with T1D and to understanding their experiencing self’s view of work and recovery generally. The potential benefits of EMA for occupational science include allowing a more comprehensive understanding of the immediate experience of occupation, enabling investigation of the experiencing self at different timescales (e.g., experience of work in the moment, versus average experience of work over a day), and providing additional means through which to inform occupation centered interventions in populations with chronic conditions.

Keywords. Occupational science, ecological momentary assessment, experience sampling method, work, recovery from work, experiencing self, diabetes

With the exponential rise in the prevalence of automation, trust in such technology has become more critical than ever before. Trust is confidence in a particular entity, especially in regard to the consequences they can have for the trustor, and calibrated trust is the extent to which the judgments of trust are accurate. The focus of this paper is to reevaluate the general understanding of calibrating trust in automation, update this understanding, and apply it to worker’s trust in automation in the workplace. Seminal models of trust in automation were designed for automation that was already common in workforces, where the machine’s “intelligence” (i.e., capacity for decision making, cognition, and/or understanding) was limited. Now, burgeoning automation with more human-like intelligence is intended to be more interactive with workers, serving in roles such as decision aid, assistant, or collaborative coworker. Thus, we revise “calibrated trust in automation” to include more intelligent automated systems.

Keywords. trust, automation, calibrated trust, workers

Teleoperated robots are increasingly being used in construction sites, but there is still a need for better human-robot interfaces. Auditory feedback using sonification has commonly been used to complement visual feedback in teleoperation applications. However, it is not yet well understood how operational and environmental sounds can support operators in complex tasks such as construction machine operations. In this paper, we describe the development of a virtual environment to assess the impacts of operational and environmental sounds on operators’ performances and cognitive loads during teleoperated demolition. Our goal is to examine whether different levels of operational and environmental sounds can increase the workers’ situation awareness, operational efficiency, and safety performance. Alternatively, we anticipate that some environmental noises may negatively affect the operators’ performances by increasing their mental workload and stress levels. Consequently, we plan to evaluate the effectiveness of including audio filters that minimize unwanted sounds while prioritizing desired sounds.

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.

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.

Keywords. productivity; stress; mood; eustress; distress; psychological state; physiological features; behavioral features

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

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

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.

Keywords. workspace; office work; computer workstations; artificial intelligence

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.

Keywords: Personalized environments, Smart desks, IoT (internet of things), Smart buildings, Indoor environment quality (IEQ)