Evidence map›Paper›PMID 39250886›Full record

ArticleNeuroepidemiology2025

Insights from ARCOS-V's Transition to Remote Data Collection during the COVID-19 Pandemic: A Descriptive Study.

Nathan Henry, Nathan I N Henry, Balakrishnan Nair, Anna Ranta, Rita Krishnamurthi, Anjali Bhatia, Valery Feigin

Abstract read
In one paragraph

Article in Neuroepidemiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Nathan Henry
Nathan I N HenryDepartment of Biostatistics and Epidemiology (DoBE), Auckland University of Technology (AUT), Auckland, New Zealand.
Balakrishnan NairNational Institute of Stroke and Applied Neurosciences (NISAN), Auckland University of Technology (AUT), Auckland, New Zealand.
Anna RantaDepartment of Medicine, University of Otago, Dunedin, New Zealand.
Rita KrishnamurthiNational Institute of Stroke and Applied Neurosciences (NISAN), Auckland University of Technology (AUT), Auckland, New Zealand.
Anjali BhatiaNational Institute of Stroke and Applied Neurosciences (NISAN), Auckland University of Technology (AUT), Auckland, New Zealand.
Valery FeiginNational Institute of Stroke and Applied Neurosciences (NISAN), Auckland University of Technology (AUT), Auckland, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe ARCOS-V study, an epidemiological study on stroke and transient ischaemic attack (TIA), faced the challenge of continuing data collection amidst the COVID-19 pandemic. This study aimed to describe the methodological changes and challenges encountered during the transition from paper-based methods to digital data collection for the ARCOS-V study and to provide insights into the potential of using digital tools to transform epidemiological research.

methodsThe study adapted to remote data collection using REDCap and Zoom, involving daily health record reviews, direct data entry by trained researchers, and remote follow-up assessments. The process was secured with encryption and role-based access controls. The transition period was analysed to evaluate the effectiveness and challenges of the new approach.

resultsThe digital transition allowed for uninterrupted monitoring of stroke and TIA cases during lockdowns. Using REDCap and Zoom improved data reach, accuracy, and security. However, it also revealed issues such as the potential for systematic data entry errors and the need for robust security measures to protect sensitive health information.

conclusionThe ARCOS-V study's digital transformation exemplifies the resilience of epidemiological research in the face of a global crisis. The successful adaptation to digital data collection methods highlights the potential benefits of such tools, particularly as we enter a new age of artificial intelligence (AI).

Indexed as

COVID-19Data CollectionIschemic Attack, TransientStrokeHumansPandemicsCOVID-19EpidemiologyPopulation-based studiesStrokeTransient ischaemic attack

Identifiers

PMID39250886
PMCPMC12500259

What OpenQuestion holds

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Registered trials

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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.