Evidence map›Paper›PMID 35657655›Full record

ArticleJMIR formative research2022

A Yoga Exercise App Designed for Patients With Axial Spondylarthritis: Development and User Experience Study.

Minh Tam Truong, Obioma Bertrand Nwosu, Maria Elena Gaytan Torres, Maria Paula Segura Vargas, Ann-Kristin Seifer, Marlies Nitschke, Alzhraa A Ibrahim, Johannes Knitza, Martin Krusche, Bjoern M Eskofier and 2 more

Open access · goldAbstract read
In one paragraph

Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.0field-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

6 citing papers in PubMed, 12 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

12 authors at 3 institutions in 2 countries.

Minh Tam TruongMachine Learning and Data Analytics Lab, Faculty of Engineering, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.ORCID https://orcid.org/0000-0002-7383-3121
Obioma Bertrand NwosuMachine Learning and Data Analytics Lab, Faculty of Engineering, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.ORCID https://orcid.org/0000-0002-0740-1646
Maria Elena Gaytan TorresMachine Learning and Data Analytics Lab, Faculty of Engineering, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.ORCID https://orcid.org/0000-0002-9712-5832
Maria Paula Segura VargasMachine Learning and Data Analytics Lab, Faculty of Engineering, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.ORCID https://orcid.org/0000-0002-0279-6829
Ann-Kristin SeiferMachine Learning and Data Analytics Lab, Faculty of Engineering, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.ORCID https://orcid.org/0000-0002-5891-218X
Marlies NitschkeMachine Learning and Data Analytics Lab, Faculty of Engineering, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.ORCID https://orcid.org/0000-0001-7318-9578
Alzhraa A IbrahimComputer Science Department, Faculty of Computers and Information, Assiut University, Assiut, Egypt.ORCID https://orcid.org/0000-0003-3308-061X
Johannes KnitzaDepartment of Internal Medicine 3, Rheumatology and Immunology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.ORCID https://orcid.org/0000-0001-9695-0657
Martin KruscheRheumatology and Clinical Immunology, Charité Universitätsmedizin Berlin, Berlin, Germany.ORCID https://orcid.org/0000-0002-0582-7790
Bjoern M EskofierMachine Learning and Data Analytics Lab, Faculty of Engineering, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.ORCID https://orcid.org/0000-0002-0417-0336
Georg SchettDepartment of Internal Medicine 3, Rheumatology and Immunology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.ORCID https://orcid.org/0000-0001-8740-9615
Harriet MorfDepartment of Internal Medicine 3, Rheumatology and Immunology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany.ORCID https://orcid.org/0000-0001-6688-2155
Friedrich-Alexander-Universität Erlangen-Nürnberg · DEAssiut University · EGCharité - Universitätsmedizin Berlin · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBesides anti-inflammatory medication, physical exercise represents a cornerstone of modern treatment for patients with axial spondyloarthritis (AS). Digital health apps (DHAs) such as the yoga app YogiTherapy could remotely empower patients to autonomously and correctly perform exercises.

objectiveThis study aimed to design and develop a smartphone-based app, YogiTherapy, for patients with AS. To gain additional insights into the usability of the graphical user interface (GUI) for further development of the app, this study focused exclusively on evaluating users' interaction with the GUI.

methodsThe development of the app and the user experience study took place between October 2020 and March 2021. The DHA was designed by engineering students, rheumatologists, and patients with AS. After the initial development process, a pilot version of the app was evaluated by 5 patients and 5 rheumatologists. The participants had to interact with the app's GUI and complete 5 navigation tasks within the app. Subsequently, the completion rate and experience questionnaire (attractiveness, perspicuity, efficiency, dependability, stimulation, and novelty) were completed by the patients.

resultsThe results of the posttest questionnaires showed that most patients were already familiar with digital apps (4/5, 80%). The task completion rates of the usability test were 100% (5/5) for the tasks T1 and T2, which included selecting and starting a yoga lesson and navigating to an information page. Rheumatologists indicated that they were even more experienced with digital devices (2/5, 40% experts; 3/5, 60% intermediates). In this case, they scored task completion rates of 100% (5/5) for all 5 usability tasks T1 to T5. The mean results from the User Experience Questionnaire range from -3 (most negative) to +3 (most positive). According to rheumatologists' evaluations, attractiveness (mean 2.267, SD 0.401) and stimulation (mean 2.250, SD 0.354) achieved the best mean results compared with dependability (mean 2.000, SD 0.395). Patients rated attractiveness at a mean of 2.167 (SD 0.565) and stimulation at a mean of 1.950 (SD 0.873). The lowest mean score was reported for perspicuity (mean 1.250, SD 1.425).

conclusionsThe newly developed and tested DHA YogiTherapy demonstrated moderate usability among rheumatologists and patients with rheumatic diseases. The app can be used by patients with AS as a complementary treatment. The initial evaluation of the GUI identified significant usability problems that need to be addressed before the start of a clinical evaluation. Prospective trials are also needed in the second step to prove the clinical benefits of the app.

Indexed as

digital health applicationhealth applicationsmHealthmobile healthmobile phonepatient empowermentphysical exercisesmartphonespondylarthritisusabilitywellnessyoga

Identifiers

PMID35657655
PMCPMC9206208
OpenAlexW4240056248

What OpenQuestion holds

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LicenceCC BY
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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.