Evidence map›Paper›PMID 38317902›Full record

ArticleHeliyon2024

Developing a novel energy-based approach for measuring mental workload.

Abdulaziz Alotaibi, John Gambatese, Chukwuma Nnaji

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Abdulaziz AlotaibiSchool of Civil and Construction Engineering, Oregon State University, Corvallis, OR, USA.
John GambateseSchool of Civil and Construction Engineering, Oregon State University, Corvallis, OR, USA.
Chukwuma NnajiDepartment of Construction Science, Texas A&M University, College Station, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Minimal research has been conducted to develop non-invasive processes for quantifying and evaluating worker mental workload - a critical concern - at the task level in the construction industry. One reason for this gap in research is the complex and dynamic nature of the construction process, which makes construction work more complicated to measure and predict compared to work in other industries. This paper presents a novel approach and corresponding conceptual model to quantify and evaluate construction worker perception of mental workload at the task level using the energy concept. A conceptual process for assessing mental workload (MWL), i.e., the feeling of stress, pressure, and being overwhelmed due to the task nature, factors, conditions, and resources that accompany the performance of the task, was developed from extant research and interviews. The Delphi method was utilized to characterize the energy-based model and provide initial verification. The results from the literature review, expert insight, and four rounds of the Delphi survey revealed 14 constituents, 51 components, and one metric for each component to measure the level of MWL felt by a worker. These constituents, components, and metrics were used to develop a model for measuring construction worker MWL. This study contributes to knowledge by developing a novel non-invasive method for assessing potential task-level MWL using an energy-based model. The energy-based assessment model contributes to practice by providing a tool that could be used to measure the potential impact of construction tasks on workers perceived mental workload.

Indexed as

Delphi techniqueMental energyMental workloadTask characteristicsWorker performance

Identifiers

PMID38317902
PMCPMC10838754

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.