Evidence map›Paper›PMID 30730031›Full record

ArticleInternational journal of computer assisted radiology and surgery2019

Wearable technology-based metrics for predicting operator performance during cardiac catheterisation.

Jonathan Currie, Raymond R Bond, Paul McCullagh, Pauline Black, Dewar D Finlay, Stephen Gallagher, Peter Kearney, Aaron Peace, Danail Stoyanov, Colin D Bicknell and 2 more

Registry-linked trialAbstract read
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT02928796 (Attentional Capacity and Clinician Performance), which is not on this map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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.

NCT02928796 unknown statusnot on this map

Attentional Capacity and Clinician Performance: Eye-Tracking Participants When Using a High-Fidelity Surgical Simulator That Emulates Cardiology Procedures

TypeobservationalSponsorUniversity College CorkRan2016Enrolled14ConditionsChange in Sustained AttentionArmsCognitive Stimulus
3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. European expert consensus on a structured approach to training robotic-assisted low anterior resection using performance metrics.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 2020
    Article
  4. 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

12 authors.

Jonathan CurrieSchool of Computing, Jordanstown Campus, Ulster University, Shore Road, Newtownabbey, BT37 0QB, Northern Ireland, UK.
Raymond R BondSchool of Computing, Jordanstown Campus, Ulster University, Shore Road, Newtownabbey, BT37 0QB, Northern Ireland, UK. rb.bond@ulster.ac.uk.ORCID http://orcid.org/0000-0002-1078-2232
Paul McCullaghSchool of Computing, Jordanstown Campus, Ulster University, Shore Road, Newtownabbey, BT37 0QB, Northern Ireland, UK.
Pauline BlackSchool of Nursing, Magee Campus, Ulster University, Londonderry, BT48 7JL, Northern Ireland, UK.
Dewar D FinlaySchool of Engineering, Jordanstown Campus, Ulster University, Londonderry, BT48 7JL, Northern Ireland, UK.
Stephen GallagherSchool of Psychology, Coleraine Campus, Ulster University, Cromore Road, Coleraine, BT52 1SA, Northern Ireland, UK.
Peter KearneyApplication of Science to Simulation Based Education and Research on Training (ASSERT) Centre, University College Cork, Cork, Ireland.
Aaron PeaceClinical Translational Research and Innovation Centre (C-TRIC), Londonderry, Northern Ireland, UK.
Danail StoyanovUniversity College London, London, UK.
Colin D BicknellImperial College London, London, UK.
Stephen LeslieNHS Highland, NHS Scotland, Edinburgh, UK.
Anthony G GallagherApplication of Science to Simulation Based Education and Research on Training (ASSERT) Centre, University College Cork, Cork, Ireland.

Funding

Engineering and Physical Sciences Research Council EPSRC Image Guided Therapies Network+ Proof of Concept GrantEuropean Commission INTERREG VA Programme, managed by the Special EU Programmes Body (SEUPB). Project: Centre for Personalised Medicine - Clinical Decision Making and Patient Safety.Northern Ireland Department of the Economy PhD Scholarship
6 · The paper itself

Abstract

introductionUnobtrusive metrics that can auto-assess performance during clinical procedures are of value. Three approaches to deriving wearable technology-based metrics are explored: (1) eye tracking, (2) psychophysiological measurements [e.g. electrodermal activity (EDA)] and (3) arm and hand movement via accelerometry. We also measure attentional capacity by tasking the operator with an additional task to track an unrelated object during the procedure.

methodsTwo aspects of performance are measured: (1) using eye gaze and psychophysiology metrics and (2) measuring attentional capacity via an additional unrelated task (to monitor a visual stimulus/playing cards). The aim was to identify metrics that can be used to automatically discriminate between levels of performance or at least between novices and experts. The study was conducted using two groups: (1) novice operators and (2) expert operators. Both groups made two attempts at a coronary angiography procedure using a full-physics virtual reality simulator. Participants wore eye tracking glasses and an E4 wearable wristband. Areas of interest were defined to track visual attention on display screens, including: (1) X-ray, (2) vital signs, (3) instruments and (4) the stimulus screen (for measuring attentional capacity).

resultsExperts provided greater dwell time (63% vs 42%, p = 0.03) and fixations (50% vs 34%, p = 0.04) on display screens. They also provided greater dwell time (11% vs 5%, p = 0.006) and fixations (9% vs 4%, p = 0.007) when selecting instruments. The experts' performance for tracking the unrelated object during the visual stimulus task negatively correlated with total errors (r = - 0.95, p = 0.0009). Experts also had a higher standard deviation of EDA (2.52 µS vs 0.89 µS, p = 0.04).

conclusionsEye tracking metrics may help discriminate between a novice and expert operator, by showing that experts maintain greater visual attention on the display screens. In addition, the visual stimulus study shows that an unrelated task can measure attentional capacity. Trial registration This work is registered through clinicaltrials.gov, a service of the U.S. National Health Institute, and is identified by the trial reference: NCT02928796.

Indexed as

Clinical CompetenceComputer SimulationWearable Electronic DevicesAttentionCardiac CatheterizationFemaleFixation, OcularHumansMaleAttentional capacityEye trackingSimulation-based trainingSurgical simulationWearable technology

Identifiers

PMID30730031
PMCPMC6420895

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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.