A Systematic Review of Empirical Measures of Workload Capacity

Thorpe, Alexander; Nesbitt, Keith; Eidels, Ami

doi:10.1145/3422869

Cited by 9 publications

(6 citation statements)

References 20 publications

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“…High mental workload has been associated with accident risk in various work domains (e.g., Chen, Song, & Lin, 2016;Habib, Shalkamy, & El-Basyouny, 2019). There are a variety of subjective and objective measures of workload (Charles & Nixon, 2019;Matthews, Reinerman-Jones, Barber, & Abich, 2015;Thorpe, Nesbitt, & Eidels, 2020). In general, they are all based on the assumption that workload will vary as a function of task demand and the limited human information-processing resources available to meet those demands (Gopher, & Donchin, 1986).…”

Section: Introductionmentioning

confidence: 99%

Using Past and Present Indicators of Human Workload to Explain Variance in Human Performance

et al. 2021

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Cognitive workload is assumed to influence performance due to resource competition. However, there is a lack of evidence for a direct relationship between changes in workload within an individual over time and changes in that individual's performance. We collected performance data using a multiple object-tracking task in which we measured workload objectively in real-time using a modified detection response task. Using a multi-level Bayesian model controlling for task difficulty and past performance, we found strong evidence that workload both during and preceding a tracking trial was predictive of performance, such that higher workload led to poorer performance. These negative workload-performance relationships were remarkably consistent across individuals. Importantly, we demonstrate that fluctuations in workload independent from the task demands accounted for significant performance variation. The outcomes have implications for designing real-time adaptive systems to proactively mitigate human performance decrements, but also highlight the pervasive influence of cognitive workload more generally.

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Section: Introductionmentioning

confidence: 99%

Using Past and Present Indicators of Human Workload to Explain Variance in Human Performance

et al. 2021

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“…Consistent with previous reviews, we identified the application of subjective measurement methods to be the most frequently used approach for the assessment of the MWL. [ 66 ]. Although we were able to identify a most frequently applied method, one of the main findings of this review was the heterogeneity of applied assessments, which is also in line with previous analyses [ 20 , 66 ].…”

Section: Discussionmentioning

confidence: 99%

“…However, none of the studies mentioned in this section explicitly addresses the relationship between MWL and HIS/DHT. Many studies also consider MWL as a starting point for further consequences on the performance of the HCPs, for example, a hazard to patient safety or job satisfaction (66), rather than the factors contributing to a high MWL.…”

Section: Comparison With Prior Workmentioning

confidence: 99%

Examining Mental Workload Relating to Digital Health Technologies in Health Care: Systematic Review

Kremer¹,

Lipprandt²,

Röhrig³

et al. 2022

J Med Internet Res

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Background The workload in health care is increasing and hence, mental health issues are on the rise among health care professionals (HCPs). The digitization of patient care could be related to the increase in stress levels. It remains unclear whether the health information system or systems and digital health technologies (DHTs) being used in health care relieve the professionals or whether they represent a further burden. The mental construct that best describes this burden of technologies is mental workload (MWL). The measurement methods of MWL are particularly relevant in this sensitive setting. Objective This review aimed to address 2 different but related objectives: identifying the factors that contribute to the MWL of HCPs when using DHT and examining and exploring the applied assessments for the measurement of MWL with a special focus on eye tracking. Methods Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement, we conducted a systematic review and processed a literature search in the following databases: MEDLINE (PubMed), Web of Science, Academic Search Premier and CINAHL (EBSCO), and PsycINFO. Studies were eligible if they assessed the MWL of HCPs related to DHT. The review was conducted as per the following steps: literature search, article selection, data extraction, quality assessment (using the Standard Quality Assessment Criteria for Evaluation Primary Research Papers From a Variety of Fields [QualSyst]), data analysis, and data synthesis (narrative and tabular). The process was performed by 2 reviewers (in cases of disagreement, a third reviewer was involved). Results The literature search process resulted in 25 studies that fit the inclusion criteria and examined the MWL of health care workers resulting from the use of DHT in health care settings. Most studies had sample sizes of 10-50 participants, were conducted in the laboratory, and had quasi-experimental or cross-sectional designs. The main results can be grouped into two categories: assessment methods and factors related to DHT that contribute to MWL. Most studies applied subjective methods for the assessment of MWL. Eye tracking did not play a major role in the selected studies. The factors contributing to a higher MWL were clustered into organizational and systemic factors. Conclusions Our review of 25 papers shows a diverse assessment approach toward the MWL of HCPs related to DHT as well as 2 groups of relevant contributing factors to MWL. Our results are limited in terms of interpretability and causality due to methodological weaknesses of the included studies and may be limited by some shortcomings in the search process. Future research should concentrate on adequate assessments of the MWL of HCPs dependent on the setting, the evaluation of quality criteria, and further assessment of the contributing factors to MWL. Trial Registration PROSPERO (International Prospective Register of Systematic Reviews) CRD42021233271; https://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42021233271

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“…Cognitive load has many descriptions, generally, it is the application of mental resources to deal with the strain caused by the workload of a task and to reach the task's completion. (Thorpe, Nesbitt, & Eidels, 2020;Young, Brookhuis, Wickens, & Hancock, 2015). There is a well-established effect of cognitive load on performance outcomes.…”

Section: Cognitive Load and Performance Outcomesmentioning

confidence: 99%

The Relationship Between Teaming Behaviours and Joint Capacity of Hybrid Human-Machine Teams

Hedley¹,

Bennett²,

Love³

et al. 2022

Preprint

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In the modern world, there are important tasks that have become too complex for a single unaided individual to manage. Some safety-critical tasks are conducted by teams to improve task performance and minimize risk of error. These teams have traditionally consisted of human operators, yet nowadays AI and machine systems are incorporated into team environments to improve performance and capacity. We used a computerized task, modeled after a classic arcade game, to investigate human behavioural patterns when working dynamically within human-machine and human-human teams. We manipulated the relationship between team members; sometimes they were incentivised to collaborate, sometimes compete, and sometimes to work independently. We evaluated motion patterns and task performance in the main task. We compared behavioural strategies and game performance between different team compositions (human-human vs. human-machine) and different intra-team types (competitive, collaborate, independent). Joint analysis of operators' performance and behavior patterns allowed better understanding of how human team members can collaborate with other human and non-human players. Such analysis can further inform theories of how human players interpret the actions of other team members - humans and bots - and incorporate this knowledge to drive their own actions.

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A Systematic Review of Empirical Measures of Workload Capacity

Cited by 9 publications

References 20 publications

Using Past and Present Indicators of Human Workload to Explain Variance in Human Performance

Using Past and Present Indicators of Human Workload to Explain Variance in Human Performance

Examining Mental Workload Relating to Digital Health Technologies in Health Care: Systematic Review

The Relationship Between Teaming Behaviours and Joint Capacity of Hybrid Human-Machine Teams

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