Despite their potential to prevent musculoskeletal disorders (MSDs), occupational exoskeletons are often abandoned by users over time. This retrospective study investigates the conditions that foster sustained use of such devices in the workplace. We conducted 27 semi-structured interviews with exoskeleton users (24 men, 3 women). From these, a subset of 12 users—those who reported sustained and habitual use—was selected for in-depth analysis. A cluster analysis based on self-reported usage duration and habituation levels enabled the identification of this group. Thematic analysis of these 12 interviews revealed three key, interconnected themes associated with continued use: (1) identify one or more significant reasons for the usage; (2) benefit from engaging first interactions and (3) actively search for a satisfactory usage. These findings contribute to understanding how users construct meaningful and lasting engagement with occupational exoskeletons, and they offer practical insights to inform implementation and support strategies aimed at reducing device rejection and enhancing sustained adoption.

We compared automation monitoring evolution of static or adaptive automation for four different reliability levels over 90 minutes. Previous studies have demonstrated degraded human performance when monitoring automation and that it is possible to mitigate this monitoring performance drop by using adaptive automation. We used the Open Multi-Attribute Task Battery to manipulate two type of automation (static automation without manual take-over sessions and adaptive automation with planned take-over sessions) and four levels of reliability. Participants performed three simultaneous tasks, one of which was automated. Our results suggest that a perfectly reliable or a totally unreliable automation led to different strategies by the participants in terms of visual allocation policy. Under static automation, the time spent looking at the automated task in the 0% reliability level increased over the duration of the experiment; however, the opposite was observed for the 100% reliability level. Although similar, the magnitude of this pattern of results was largely diminished under adaptive automation. For static automation, the reported data also showed a direct link between trust in automation and visual scanning strategies. The more the trust increased, the less the automated task was looked at.

Previous research has suggested that information and action automation stages do not imply the same consequences for human performance in the supervision of automated systems. Still, only a few studies have simultaneously investigated these stages. When information and action automation are reliable, both can support performance. However, with unreliable aids, the literature has suggested that action automation tends to be more detrimental than information automation. This study aimed to assess the contributions of imperfect information and action automation on attentional allocation and to investigate a potential monitoring inefficiency in a multitasking environment. Participants (n = 96) completed three Multi-Attribute Task Battery (MATB) tasks. A monitoring task was automated with two types of automation (action or information) of four reliabilities each (0%; 56.25%; 87.5%; 100%). Ocular behaviors and performance were assessed. Results show that reliability of information automation influenced visual resource allocation. When information automation was the most reliable, participants spent the least amount of time sampling the monitoring task. Finally, the reliability of action automation triggered no effect on performance or cumulative dwell times. Our results suggest that in complex multitasking situations where information and action automation occurred simultaneously, participants allocated fewer visual resources to automated task with increased information automation reliability. Similarly, their performance was better only with increased information automation.

Automation regroups a variety of advanced tools meant to improve performance and decrease human workload. This article was designed to investigate how different automation solutions, engaging different human-machine cooperation modes, interact with expertise. Aircraft pilots (i.e., experts) and nonpilots (i.e., novices) were presented with a set of simplified flight piloting tasks monitored simultaneously using the Open MATB (Open Multiattribute Task Battery) in four different automation conditions (manual, assisted, cooperative, and supervisory control). Experts' performances at the Open MATB were higher than those of novices. Experts also exhibited a lower level of workload. Apart from function delegation, where no human performance is required, automation solutions benefit experts and novices similarly. Participants were then asked to choose repeatedly between the four automation conditions. Experts and novices exhibited different strategies. Novices spread their choices over the different automation conditions, whereas experts clearly favor cooperative control, the automation solution that enhanced their expertise.