Yoko Ellie Fukumura

The COVID-19 pandemic led to stay-at-home mandates and lockdowns around the globe. During this time of occupational disruption and social distancing measures, many engaged through online environments. Social media are ever-increasing hosts of occupation and participation, rich with research opportunities. In this study, we explored COVID-19 experiences by analyzing parody videos posted on YouTube by various content creators. We analyzed the lyrics of 27 viral videos (accrued 1 million or more views) by 20 content creators. Using a transactional framework, we identified five themes related to occupational disruption in the lyrics and explored the polarity of the concepts within each theme: old norms vs. new normal, time expanded vs. time condensed, control seeking vs. lack of control, social isolation vs. excess socialization, and cynicism vs. hope. Our findings demonstrated the complex transactions between previous habits and routines, changing spaces of occupation, and meaning of daily occupations within evolving social, economic, and physical contexts. While this study showcased YouTube videos as one untapped source of contextualized knowledge for occupational science, there is a need for further exploration of methodological and ethical challenges of studying digital content.

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.

Keywords. Ergonomic assessment; RULA; Engineering office environments; Depth camera; Computer vision; Machine learning

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.

Keywords. work; survey; context; health; occupational balance

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

Background. The authors’ objective in this systematic review was to describe the evidence for preventive and rehabilitative interventions for musculoskeletal disorders in oral health care.

Types of Studies Reviewed. The authors conducted systematic search, screening, and eligibility processes to identify experimental, quasiexperimental, observational, and survey research studies in which the investigators either directly evaluated or predicted the effects of preventive or rehabilitative interventions on the reduction of musculoskeletal symptoms in oral health care professionals.

Results. The authors identified and screened 3,571 unique abstracts, assessed 256 full-text articles for eligibility, and included 34 articles in the review. Investigators in 17 experimental studies described the results of preventive or rehabilitation interventions and in 17 survey research studies predicted or correlated preventive or protective techniques to a reduction in musculoskeletal symptoms. The primary techniques evaluated in the studies included equipment modification, ergonomic training, and physical exercise.

Conclusions and Practical Implications. The evidence suggests that magnification loupes and indirect-vision techniques have a positive effect on the reduction of musculoskeletal symptoms. In terms of evaluating intervention efficacy, other techniques have mixed evidence or are limited by low-level study design.

Keywords. Ergonomics; injury prevention; musculoskeletal disorders; dentists; dental hygienists

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.

Introduction. The Rapid Upper Limb Assessment (RULA) is an ergonomic assessment tool used to screen for risk of musculoskeletal injury due to working posture. The RULA is traditionally applied once during a work task to approximate overall risk. No method exists for estimating a RULA score for work requiring frequent shifts in posture across an extended period of time.

Purpose. The goal of this study was to identify an optimal sampling method for applying the RULA across a long time-period that accurately represents overall risk.

Methods. Four right-handed female dental hygiene students were video recorded from three angles while performing hand scaling during patient clinic visits (88.97 minutes on average). RULA was continuously scored across the entire session, updating the score when a significant postural shift lasting for more than 15 seconds occurred. A time-weighted average (TWA) RULA score was calculated. Three sampling methods were evaluated: equivalent interval samples, random samples, and random samples selection weighted within “clock positions.” Each method was compared to the TWA using a paired samples t-test and percent difference.

Results. TWA RULA across the four students ranged from 3.4 to 4.3. Preliminary sampling averages using 10 samples were all within 0.2 of the TWA. Further iterations evaluating various sample sizes is ongoing.

Discussion. Preliminary results suggest that all three sampling methods provide a reasonably accurate approximation of the TWA score at the sampling rate tested. Future iterations of this analysis will be continued to identify the minimum required sampling rate to meet our TWA criterion.