Evidence map›Paper›PMID 39587534›Full record

ArticleBMC pregnancy and childbirth2024

Smartphone pregnancy apps: systematic analysis of features, scientific guidance, commercialization, and user perception.

Michael Nissen, Shih-Yuan Huang, Katharina M Jäger, Madeleine Flaucher, Adriana Titzmann, Hannah Bleher, Constanza A Pontones, Hanna Huebner, Nina Danzberger, Peter A Fasching and 2 more

Abstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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.

Michael NissenDepartment Artificial Intelligence in Biomedical Engineering, Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany. michael.nissen@fau.de.
Shih-Yuan HuangDepartment Artificial Intelligence in Biomedical Engineering, Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Katharina M JägerDepartment Artificial Intelligence in Biomedical Engineering, Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Madeleine FlaucherDepartment Artificial Intelligence in Biomedical Engineering, Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Adriana TitzmannDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander- Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Hannah BleherDepartment of Social Ethics, University of Bonn, Bonn, Germany.
Constanza A PontonesDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander- Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Hanna HuebnerDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander- Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Nina DanzbergerDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander- Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Peter A FaschingDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander- Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Bjoern M EskofierDepartment Artificial Intelligence in Biomedical Engineering, Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Heike LeutheuserDepartment Artificial Intelligence in Biomedical Engineering, Machine Learning and Data Analytics Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.

Funding

Bundesministerium für Gesundheit 2519DAT400Deutsche Forschungsgemeinschaft ES 434/8-1
6 · The paper itself

Abstract

backgroundOver 50% of pregnant women use pregnancy applications (apps). Some app s lack credibility, information accuracy, and evidence-based clinical advice, containing potentially harmful functionality. Previous studies have only conducted a limited analysis of pregnancy app functionalities, expert involvement/evidence-based content, used commercialization techniques, and user perception.

methodsWe used the keyword "pregnancy" to scrape (automatically extract) apps and app information from Apple App Store and Google Play. Unique functionalities were derived from app descriptions and user reviews. App descriptions were screened for evidence-based content and expert involvement, and apps were subsequently analyzed in detail. Apps were opened and searched for used commercialization techniques, such as advertisements or affiliate marketing. Automated text analysis (natural language processing) was used on app reviews to assess users' perception of evidence-based content/expert involvement and commercialization techniques.

resultsIn total, 495 apps were scraped. 226 remained after applying exclusion criteria. Out of these, 36 represented 97%/88% of the total market share (Apple App Store/Google Play), and were thus considered for review. Overall, 49 distinct functionalities were identified, out of which 6 were previously unreported. Functionalities for fetal kick movement counting were found. All apps are commercial. Only 15 apps mention the involvement of medical experts. 10.3% of two-stars user reviews include commercial topics, and 0.6% of one-/two-/three-/five stars user reviews include references to scientific content accuracy.

conclusionProblematic features and inadequate advice continue to be present in pregnancy apps. App developers should adopt an evidence-based development approach and avoid implementing as many features as possible, potentially at the expense of their quality or over-complication ("feature creep"). Financial incentives, such as grant programs, could support adequate content quality. Caregivers play a key role in pregnant individuals' decision-making, should be aware of potential dangers, and could guide them to trustworthy apps.

Indexed as

Mobile ApplicationsSmartphoneFemaleHumansPerceptionPregnancyMHealthMobile healthMobile health appMobile phone appParenting appsPregnancy applicationsPregnantSmartphone applications

Identifiers

PMID39587534
PMCPMC11587608

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.