Evidence map›Paper›PMID 38109377›Full record

ArticleJMIR pediatrics and parenting2023

Usability and Perception of a Wearable-Integrated Digital Maternity Record App in Germany: User Study.

Michael Nissen, Carlos A Perez, Katharina M Jaeger, Hannah Bleher, Madeleine Flaucher, Hanna Huebner, Nina Danzberger, Adriana Titzmann, Constanza A Pontones, Peter A Fasching and 3 more

Open access · goldAbstract read
In one paragraph

Article in JMIR pediatrics and parenting, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.7field-weighted citation impact, top 13% 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

5 citing papers in PubMed, 4 citations in OpenAlex.

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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

13 authors at 2 institutions in 1 country.

Michael NissenMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID http://orcid.org/0000-0001-5472-132X
Carlos A PerezMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID http://orcid.org/0009-0003-9901-3396
Katharina M JaegerMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID http://orcid.org/0000-0002-2478-4079
Hannah BleherDepartment of Social Ethics, University of Bonn, Bonn, Germany.ORCID http://orcid.org/0000-0003-4386-4901
Madeleine FlaucherMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID http://orcid.org/0000-0002-8086-1112
Hanna HuebnerDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID http://orcid.org/0000-0001-6889-1493
Nina DanzbergerDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID http://orcid.org/0000-0003-0255-3760
Adriana TitzmannDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID http://orcid.org/0009-0001-2290-5965
Constanza A PontonesDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID http://orcid.org/0000-0002-3556-8523
Peter A FaschingDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID http://orcid.org/0000-0003-4885-8471
Matthias W BeckmannDepartment of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID http://orcid.org/0000-0002-8441-693X
Bjoern M EskofierMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID http://orcid.org/0000-0002-0417-0336
Heike LeutheuserMachine Learning and Data Analytics Lab, Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID http://orcid.org/0000-0001-9931-2271
Friedrich-Alexander-Universität Erlangen-Nürnberg · DEUniversity of Bonn · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Although digital maternity records (DMRs) have been evaluated in the past, no previous work investigated usability or acceptance through an observational usability study. Objective: The primary objective was to assess the usability and perception of a DMR smartphone app for pregnant women. The secondary objective was to assess personal preferences and habits related to online information searching, wearable data presentation and interpretation, at-home examination, and sharing data for research purposes during pregnancy. Methods: A DMR smartphone app was developed. Key features such as wearable device integration, study functionalities (eg, questionnaires), and common pregnancy app functionalities (eg, mood tracker) were included. Women who had previously given birth were invited to participate. Participants completed 10 tasks while asked to think aloud. Sessions were conducted via Zoom. Video, audio, and the shared screen were recorded for analysis. Task completion times, task success, errors, and self-reported (free text) feedback were evaluated. Usability was measured through the System Usability Scale (SUS) and User Experience Questionnaire (UEQ). Semistructured interviews were conducted to explore the secondary objective. Results: A total of 11 participants (mean age 34.6, SD 2.2 years) were included in the study. A mean SUS score of 79.09 (SD 18.38) was achieved. The app was rated "above average" in 4 of 6 UEQ categories. Sixteen unique features were requested. We found that 5 of 11 participants would only use wearables during pregnancy if requested to by their physician, while 10 of 11 stated they would share their data for research purposes. Conclusions: Pregnant women rely on their medical caregivers for advice, including on the use of mobile and ubiquitous health technology. Clear benefits must be communicated if issuing wearable devices to pregnant women. Participants that experienced pregnancy complications in the past were overall more open toward the use of wearable devices in pregnancy. Pregnant women have different opinions regarding access to, interpretation of, and reactions to alerts based on wearable data. Future work should investigate personalized concepts covering these aspects.

Indexed as

appapplicationapplicationsappsattitudeattitudesdata sharingdevelopdevelopmentdigitalelectronicelectronic maternity recordexperienceexperiencesfeaturefeatureshealth recordhealth recordsinformation behaviorinformation behaviourinformation seekingloglogbookmaternalmaternitymaternity logmaternity logbookmaternity recordmHealthmobile healthonline searchopinionopinionspaper hand-held recordperceptionperceptionspersonal health recordperspectiveperspectivesPHRpregnancypregnancy apppregnancy recordpregnantprivacysearchingsmartphonesmartphonesusabilitywearablewearables

Identifiers

PMID38109377
PMCPMC10750977
OpenAlexW4389889067

What OpenQuestion 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 OpenQuestion graph.