Evidence map›Paper›PMID 38353872›Full record

ArticleJournal of medical systems2024

An mHealth Application in German Health Care System: Importance of User Participation in the Development Process.

Peter Bickmann, Ingo Froböse, Christopher Grieben

Abstract read
In one paragraph

Article in Journal of medical systems, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

3 authors.

Peter BickmannInstitute of Movement Therapy, German Sport University Cologne, Cologne, Germany. peter.bickmann@gmx.de.
Ingo FroböseInstitute of Movement Therapy, German Sport University Cologne, Cologne, Germany.
Christopher GriebenUniversity of Applied Management, Ismaning, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper addresses the challenges and solutions in developing a holistic prevention mobile health application (mHealth app) for Germany's healthcare sector. Despite Germany's lag in healthcare digitalization, the app aims to enhance primary prevention in physical activity, nutrition, and stress management. A significant focus is on user participation and usability to counter the prevalent issue of user attrition in mHealth applications, as described by Eysenbach's 'law of attrition'. The development process, conducted in a scientific and university context, faces constraints like limited budgets and external service providers. The study firstly presents the structure and functionality of the app for people with statutory health insurance in Germany and secondly the implementation of user participation through a usability study. User participation is executed via usability tests, particularly the think-aloud method, where users verbalize their thoughts while using the app. This approach has proven effective in identifying and resolving usability issues, although some user feedback could not be implemented due to cost-benefit considerations. The implementation of this study into the development process was able to show that user participation, facilitated by methods like think-aloud, is vital for developing mHealth apps. Especially in health prevention, where long-term engagement is a challenge. The findings highlight the importance of allocating time and resources for user participation in the development of mHealth applications.

Indexed as

Mobile ApplicationsTelemedicineExerciseGermanyHealth Care SectorHumansGerman health careHealth preventionmHealthUsabilityUser participation

Identifiers

PMID38353872
PMCPMC10866790

What OpenQuestion holds

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LicenceCC BY
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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.