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

DHA Usability Yoga YogiTherapy ankylosing spondylitis axial spondylarthritis digital health application eHealth self-assessment

Journal

JMIR formative research
ISSN: 2561-326X
Titre abrégé: JMIR Form Res
Pays: Canada
ID NLM: 101726394

Informations de publication

Date de publication:
12 Dec 2023
Historique:
received: 20 03 2023
accepted: 19 10 2023
revised: 17 10 2023
medline: 12 12 2023
pubmed: 12 12 2023
entrez: 12 12 2023
Statut: epublish

Résumé

Mobile 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. This study aims to improve the design and technology of YogiTherapy and evaluate its usability and quality. We 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). In 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. The 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.

Sections du résumé

BACKGROUND BACKGROUND
Mobile 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.
OBJECTIVE OBJECTIVE
This study aims to improve the design and technology of YogiTherapy and evaluate its usability and quality.
METHODS METHODS
We 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).
RESULTS RESULTS
In 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.
CONCLUSIONS CONCLUSIONS
The 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.

Identifiants

pubmed: 38085558
pii: v7i1e47426
doi: 10.2196/47426
doi:

Types de publication

Journal Article

Langues

eng

Pagination

e47426

Informations de copyright

©Marlies Nitschke, Obioma Bertrand Nwosu, Lara Grube, Johannes Knitza, Ann-Kristin Seifer, Bjoern M Eskofier, Georg Schett, Harriet Morf. Originally published in JMIR Formative Research (https://formative.jmir.org), 12.12.2023.

Auteurs

Marlies Nitschke (M)

Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.

Obioma Bertrand Nwosu (OB)

Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.

Lara Grube (L)

Department of Internal Medicine 3- Rheumatology & Immunology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Deutsches Zentrum Immuntherapie, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

Johannes Knitza (J)

Department of Internal Medicine 3- Rheumatology & Immunology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Deutsches Zentrum Immuntherapie, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

Ann-Kristin Seifer (AK)

Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.

Bjoern M Eskofier (BM)

Machine Learning and Data Analytics Lab, Department Artificial Intelligence in Biomedical Engineering (AIBE), Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Translational Digital Health Group, Institute of AI for Health, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany.

Georg Schett (G)

Department of Internal Medicine 3- Rheumatology & Immunology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Deutsches Zentrum Immuntherapie, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

Harriet Morf (H)

Department of Internal Medicine 3- Rheumatology & Immunology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
Deutsches Zentrum Immuntherapie, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.

Classifications MeSH