Theor. Appl. Ergon., Volume 2, Issue 3 (September 2026) – 9 articles

The amount of time office workers spend seated poses multiple health risks and has been associated with increased musculoskeletal pain. To evaluate the potential association of discomfort and computer utilization with workstation type, we conducted a retrospective analysis of data collected at a large US company as part of their corporate wellness initiative. Workers had either an electric height-adjustable or a traditional desk for more than a year prior to the data collection phase. Data collected included participants’ discomfort levels and multiple objective measures of computer utilization as captured by the office ergonomic software package, RSI Guard. Data were collected over a year-long period and reported for each participant (n ≈ 10,145). After a year, the percentage of participants reporting no discomfort increased by 2.4%. Active computing time was reviewed for the two categories, with traditional users having 2.95 h per day while the height-adjustable desk users had 3.66 h per day (p < 0.001). This difference in active computing time equates to 43 additional active minutes per day for individuals with access to height-adjustable workstations. Findings suggest that individuals with access to height-adjustable workstations may be associated with increased computer use and negligible changes in discomfort over a one-year period. Full article

We investigated the influence of age on the debilitating effect of glare for glare sources applied to the task or to the eye. Low-contrast logMAR letters were presented on a computer monitor, and the visual performance of 66 adults (23 to 66 yrs.) was measured under three conditions: no glare, ocular disability glare and task disability glare. A significant decline in visual performance with age was found in each condition. Factoring out the decline due to age alone (the ‘no-glare’ condition), a large, significant (p < 0.001) effect of age on ocular disability glare (a decline of 0.089 logMAR units per decade) remained, as did a very small effect on task disability glare (a decline of 0.026 logMAR units per decade, p = 0.051). These results show that different mechanisms are responsible for the impact of the two types of disability glare, indicating the need to differentiate between them in their classification. Full article

The treatment planning stage accounts for most reported patient safety events in radiation therapy, and automated quality assurance (QA) checklists are a common countermeasure; few of these tools, however, undergo formal human factors evaluation before they are released into clinical use. Building on our prior work demonstrating suboptimal usability of an institutional dosimetry QA checklist (DQC) and its participatory, theory-driven redesign, this study evaluated an enhanced DQC using Borycki and Kushniruk’s multi-phase, multi-method usability evaluation framework, which integrates cognitive and socio-technical perspectives to create a “safety net” against usability problems and technology-induced errors. Three phases were carried out in sequence at one academic medical center: (1) rapid think-aloud usability testing with two cohorts of dosimetrists and physicists (n = 10) separated by an improvement cycle; (2) remote simulation-based testing in which dosimetrists (n = 7) worked through ten high-fidelity synthetic treatment plans containing embedded errors; and (3) six weeks of near-live testing with weekly iterative refinement, involving 15 interview participants and 21 users who provided in-tool feedback (dosimetrists, physicists, and trainees). Reported usability, usefulness, and safety issues decreased by 49% between think-aloud cohorts (59 to 30), and perceived usability met recommended standards (System Usability Scale > 80). Simulation-based testing was feasible; perceived usability, completion time, embedded-error performance, and perceived realism all significantly favored easy over hard plans (participant-level exact Wilcoxon signed-rank tests, p ≤ 0.031; rank-biserial correlation 1.00). Near-live testing surfaced predominantly deeper usefulness and safety issues (77% of 142 coded comments), and the new checklist accounted for 43.6–67.5% of all checklist runs across user groups (relative use frequency). The framework provided complementary, progressively deeper insights and readied the enhanced DQC for clinical implementation. Full article

The aim of the present study was to develop an instrument that enables evaluation of AI-based mental health apps, which are promising digital interventions for promoting psychological well-being. The instrument was used to conduct an initial evaluation of an early pilot stage of the well-being app MALU. As part of a non-representative hypothesis-testing longitudinal study, N = 11 participants aged 18 to 34 used the app over a period of two weeks. The participants were surveyed at three points regarding perceived stress (Perceived Stress Scale), sleep problems (short version of the Insomnia Severity Index), and chatbot usability (Chatbot Usability Scale). The results showed a significant decrease in perceived stress between the first and third measurement points (Z = −2.31, p = 0.01), as well as for perceived sleep problems between the second and third measurement points (Z = −1.86, p = 0.03). Perceived chatbot usability increased significantly over the course of the study (Z = 2.37, p = 0.01). The results suggest potential effectiveness of the app in reducing stress and sleep problems as well as an improvement in the user experience regarding the chatbot interaction over time. The evaluation instrument proved suitable for use in early development phases. Full article

The objective of this study was to assess the performance of posture recognition systems based on artificial intelligence (AI) using deep learning (DL) and machine learning (ML) approaches for the prevention of work-related musculoskeletal disorders (WMSDs) in manufacturing. The study was conducted as a systematic review and meta-analysis following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Four open-access databases were screened in May 2026 without date restrictions: PubMed/MedLine, Google Scholar, ScienceDirect, and IEEE Xplore. The selected studies had to be original, peer-reviewed studies written in English. The study had to evaluate the performance of an AI posture recognition system (ML or DL) for the prevention of WMSDs in manufacturing using at least one of the following parameters: accuracy, specificity, sensitivity, precision, or F1 score. The risk of bias for each included study was assessed using PROBAST (Prediction Model Study Risk of Bias Assessment Tool). A meta-analysis was conducted to pool the values of the five performance metrics separately. Logit and Freeman-Tukey transformations were applied prior to pooling, and the results were compared after back-transformation. Cochran’s Q test, the I² statistic, and inter-study variability (τ²), computed using the generalized inverse variance method with the restricted maximum likelihood model, were applied to assess heterogeneity. Forest plots including pooled values with 95% confidence intervals were used to present the results. Subgroup analyses and meta-regressions were performed to test the effect of AI methods and ergonomic tools on performance and to explore potential causes of heterogeneity. Finally, publication bias (Egger’s test) and certainty of evidence (GRADE method—Grading of Recommendations Assessment, Development, and Evaluation) were assessed to ensure the generalizability of the results. Ten studies were included: Among the 200 studies identified through database searches and the snowball method, 12 met the inclusion criteria and were selected. Two studies were excluded due to an insufficient number of participants, bringing the final number of studies considered to 10. The logit transformation yielded the best overall fit for the normality of the data distribution for the use of a random-effects model. High posture detection performance was observed, with pooled values ranging from 84.78% (95% CI: 80.23–88.19%, specificity with Freeman-Tukey) to 93.40% (95% CI: 89.57–95.89%, precision with logit). The values obtained with logit were higher than those obtained with Freeman-Tukey, with differences ranging from 2.75% to 4.75% across all performance metrics. Meta-regression showed that DL outperformed ML for all metrics, with differences ranging from 5% to 17%. RULA and REBA achieved better performance than other ergonomic tools. However, high heterogeneity (I² > 90%) and substantial inter-study variability were observed in all analyses, and a very low level of evidence was evidenced for all performance parameters. Consequently, the results should be interpreted with caution, particularly regarding the deployment of the systems in real-world settings. Future work could strengthen training and testing procedures on datasets, as well as external validation. These aspects are essential for effective use in manufacturing environments to ensure operator safety by reducing their exposure to WMSD risks associated with work postures. Full article

Building design studies in ergonomics, we take up the construct of curriculum ergonomics to conceptualize how mathematics teachers engage with curriculum resources and how prior experiences inform their use. Research on the relationship between teaching and curriculum resources emphasizes how teachers understand and take up curriculum resources and how doing so leads them to iteratively transform those resources, with less focus on how the design of curriculum resources influences what teachers do. We explore (1) how features in teachers’ primary curriculum influenced how they designed lessons with both their primary versus unfamiliar curriculum resources and (2) how features in unfamiliar curriculum resources influenced how teachers designed lessons. To do so, we selected teachers with varied curriculum backgrounds and documented how they designed lessons using three sets of curriculum resources. Our analysis provides insight into how teachers’ prior curriculum experiences shape their engagement with unfamiliar curriculum resources and informs the development of design principles for curriculum resources to better support teachers in designing and enacting ambitious mathematics teaching. Full article

Advances in AI are increasing the scope for “reasonable disagreements” between humans and artificial systems. Intelligent robots and virtual agents are increasingly tasked with complex, potentially open-ended assignments that require the AI to function autonomously. AI judgments and decisions can lack transparency and explainability, leading to conflict with the human operator or user, requiring compromise to resolve the conflict. This article presents a new theory of human-AI conflict and compromise, drawing on research on decision-making, conflict in human teams, trust in human-robot teaming, and the challenges for humans of interacting with AI. It is proposed that the nature of conflict depends on the human’s mental model of AI functioning. If the human sees the AI as an advanced tool, conflict arises from differences in choice and implementation of algorithms for joint decision-making. Research on multi-cue judgment within the framework of the Brunswik Lens Model illustrates this type of conflict and its resolution. By contrast, if the mental model attributes humanlike characteristics to the AI, including a Theory of Mind, conflict can arise from different person-centered narratives for framing the task or team functioning. When narratives clash, the robot may be perceived as unsupportive or in violation of team role expectancies. In this case, system design may require using AI natural language capabilities and dialogue can facilitate compromise through context-sensitive matching of the human’s mental model to robot functionality. Full article

Manual lifting is a common occupational activity associated with an increased risk of low back disorders. In this study, 22 participants from UPMC Hamot, organized into 11 pairs, were recruited. A combination of motion capture techniques and an injury assessment tool was used to investigate the relationships among body anthropometrics, three-dimensional trunk and lower-limb kinematics, and lumbar spinal loading. Potential differences in lifting mechanics were observed between male and female participants. Males exhibited greater trunk flexion and higher compressive loading, while females demonstrated greater hip and knee flexion with reduced trunk motion. These findings indicate that spinal loading during lifting is influenced by an interaction between anthropometric characteristics and movement coordination patterns, with variable behavioral trends affecting load distribution across the trunk and lower extremities. The results provide biomechanical insight that may inform the development of bio-ergonomic training techniques aimed at reducing lumbar spine loading and minimizing injury risk in occupational lifting tasks. Full article