Evidence map›Paper›PMID 38085558›Full record

ArticleJMIR formative research2023

Refinement and Usability Analysis of an eHealth App for Ankylosing Spondylitis as a Complementary Treatment to Physical Therapy: Development and Usability Study.

Marlies Nitschke, Obioma Bertrand Nwosu, Lara Grube, Johannes Knitza, Ann-Kristin Seifer, Bjoern M Eskofier, Georg Schett, Harriet Morf

Open access · goldAbstract read
In one paragraph

Article in JMIR formative research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.6field-weighted citation impact, top 10% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
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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

8 authors at 1 institution in 1 country.

Marlies NitschkeMachine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.ORCID https://orcid.org/0000-0001-7318-9578
Obioma Bertrand NwosuMachine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.ORCID https://orcid.org/0000-0002-0740-1646
Lara GrubeDepartment of Internal Medicine 3- Rheumatology & Immunology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID https://orcid.org/0009-0005-2025-7190
Johannes KnitzaDepartment of Internal Medicine 3- Rheumatology & Immunology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID https://orcid.org/0000-0001-9695-0657
Ann-Kristin SeiferMachine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.ORCID https://orcid.org/0000-0002-5891-218X
Bjoern M EskofierMachine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.ORCID https://orcid.org/0000-0002-0417-0336
Georg SchettDepartment of Internal Medicine 3- Rheumatology & Immunology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID https://orcid.org/0000-0001-8740-9615
Harriet MorfDepartment of Internal Medicine 3- Rheumatology & Immunology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.ORCID https://orcid.org/0000-0001-6688-2155
Friedrich-Alexander-Universität Erlangen-Nürnberg · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMobile eHealth apps have been used as a complementary treatment to increase the quality of life of patients and provide new opportunities for the management of rheumatic diseases. Telemedicine, particularly in the areas of prevention, diagnostics, and therapy, has become an essential cornerstone in the care of patients with rheumatic diseases.

objectiveThis study aims to improve the design and technology of YogiTherapy and evaluate its usability and quality.

methodsWe newly implemented the mobile eHealth app YogiTherapy with a modern design, the option to change language, and easy navigation to improve the app's usability and quality for patients. After refinement, we evaluated the app by conducting a study with 16 patients with AS (4 female and 12 male; mean age 48.1, SD 16.8 y). We assessed the usability of YogiTherapy with a task performance test (TPT) with a think-aloud protocol and the quality with the German version of the Mobile App Rating Scale (MARS).

resultsIn the TPT, the participants had to solve 6 tasks that should be performed on the app. The overall task completion rate in the TPT was high (84/96, 88% completed tasks). Filtering for videos and navigating to perform an assessment test caused the largest issues during the TPT, while registering in the app and watching a yoga video were highly intuitive. Additionally, 12 (75%) of the 16 participants completed the German version of MARS. The quality of YogiTherapy was rated with an average MARS score of 3.79 (SD 0.51) from a maximum score of 5. Furthermore, results from the MARS questionnaire demonstrated a positive evaluation regarding functionality and aesthetics.

conclusionsThe refined and tested YogiTherapy app showed promising results among most participants. In the future, the app could serve its function as a complementary treatment for patients with AS. For this purpose, surveys with a larger number of patients should still be conducted. As a substantial advancement, we made the app free and openly available on the iOS App and Google Play stores.

Indexed as

ankylosing spondylitisaxial spondylarthritisDHAdigital health applicationeHealthself-assessmentUsabilityYogaYogiTherapy

Identifiers

PMID38085558
PMCPMC10751630
OpenAlexW4387766337

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.