Evidence map›Paper›PMID 37380893›Full record

ArticleHerzschrittmachertherapie & Elektrophysiologie2023

Evaluation of a semiautomated App Store analysis for the identification of health apps for cardiac arrhythmias.

Dennis Lawin, Ute von Jan, Evgenii Pustozerov, Thorsten Lawrenz, Christoph Stellbrink, Urs-Vito Albrecht

Open access · hybridAbstract read
In one paragraph

Article in Herzschrittmachertherapie & Elektrophysiologie, 2023. 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
0.9field-weighted citation impact, top 23% of its field
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, 2 citations in OpenAlex.

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

6 authors at 2 institutions in 1 country.

Dennis LawinDepartment of Digital Medicine, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany. dennis.lawin@uni-bielefeld.de.
Ute von JanPeter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, Hannover Medical School, Hannover, Germany.
Evgenii PustozerovDepartment of Digital Medicine, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany.
Thorsten LawrenzDepartment of Cardiology and Intensive Care Medicine, University hospital OWL of Bielefeld University, Campus Klinikum Bielefeld, Bielefeld, Germany.
Christoph StellbrinkDepartment of Cardiology and Intensive Care Medicine, University hospital OWL of Bielefeld University, Campus Klinikum Bielefeld, Bielefeld, Germany.
Urs-Vito AlbrechtDepartment of Digital Medicine, Medical Faculty OWL, Bielefeld University, Bielefeld, Germany.
Bielefeld University · DEMedizinische Hochschule Hannover · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSmartphone apps are increasingly utilised by patients and physicians for medical purposes. Thus, numerous applications are provided on the App Store platforms.

objectivesThe aim of the study was to establish a novel, expanded approach of a semiautomated retrospective App Store analysis (SARASA) to identify and characterise health apps in the context of cardiac arrhythmias. MATERIALS AND

methodsAn automated total read-out of the "Medical" category of Apple's German App Store was performed in December 2022 by analysing the developer-provided descriptions and other metadata using a semiautomated multilevel approach. Search terms were defined, based on which the textual information of the total extraction results was automatically filtered.

resultsA total of 435 of 31,564 apps were identified in the context of cardiac arrhythmias. Of those, 81.4% were found to deal with education, decision support, or disease management, and 26.2% (additionally) provided the opportunity to derive information on heart rhythm. The apps were intended for healthcare professionals in 55.9%, students in 17.5%, and/or patients in 15.9%. In 31.5%, the target population was not specified in the description texts. In all, 108 apps (24.8%) provided a telemedicine treatment approach; 83.7% of the description texts did not reveal any information on medical product status; 8.3% of the apps indicated that they have and 8.0% that they do not have medical product status.

conclusionThrough the supplemented SARASA method, health apps in the context of cardiac arrhythmias could be identified and assigned to the target categories. Clinicians and patients have a wide choice of apps, although the app description texts do not provide sufficient information about the intended use and quality.

Indexed as

Mobile ApplicationsTelemedicineHumansRetrospective StudiesArrhythmiasCardiologyDigital MedicineHealth appsMobile health

Identifiers

PMID37380893
PMCPMC10462560
OpenAlexW4382360933

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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.