Background: The successful rehabilitation of musculoskeletal pain requires more than medical input alone. Conservative treatment, including physiotherapy and exercise therapy, can be an effective way of decreasing pain associated with musculoskeletal pain. However, face-to-face appointments are currently not feasible. New mobile technologies, such as mobile health technologies in the form of an app for smartphones, can be a solution to this problem. In many cases, these apps are not backed by scientific literature. Therefore, it is important that they are reviewed and quality assessed.

Objective: The aim is to evaluate and measure the quality of apps related to shoulder pain by using the Mobile App Rating Scale.

Methods: This study included 25 free and paid apps-8 from the Apple Store and 17 from the Google Play Store. A total of 5 reviewers were involved in the evaluation process. A descriptive analysis of the Mobile App Rating Scale results provided a general overview of the quality of the apps.

Results: Overall, app quality was generally low, with an average star rating of 1.97 out of 5. The best scores were in the "Functionality" and "Aesthetics" sections, and apps were scored poorer in the "Engagement" and "Information" sections. The apps were also rated poorly in the "Subjective Quality" section.

Conclusions: In general, the apps were well built technically and were aesthetically pleasing. However, the apps failed to provide quality information to users, which resulted in a lack of engagement. Most of the apps were not backed by scientific literature (24/25, 96%), and those that contained scientific references were vastly out-of-date. Future apps would need to address these concerns while taking simple measures to ensure quality control.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185331PMC
http://dx.doi.org/10.2196/34339DOI Listing

Publication Analysis

Top Keywords

mobile app
12
app rating
12
apps
9
apps shoulder
8
shoulder pain
8
rating scale
8
musculoskeletal pain
8
apps backed
8
backed scientific
8
scientific literature
8

Similar Publications

This global study analyzed data from the largest dataset ever studied in the Heart Rate Variability (HRV) biofeedback field, comprising 1.8 million user sessions collected from users of a mobile app during 2019 and 2020. We focused on HRV Coherence, which is linked to improved emotional stability and cognitive function.

View Article and Find Full Text PDF

Liquid-based encapsulation for implantable bioelectronics across broad pH environments.

Nat Commun

January 2025

Department of Biomedical Engineering and the Institute of Materials Science, University of Connecticut, Storrs, CT, 06269, USA.

Wearable and implantable bioelectronics that can interface for extended periods with highly mobile organs and tissues across a broad pH range would be useful for various applications in basic biomedical research and clinical medicine. The encapsulation of these systems, however, presents a major challenge, as such devices require superior barrier performance against water and ion penetration in challenging pH environments while also maintaining flexibility and stretchability to match the physical properties of the surrounding tissue. Current encapsulation materials are often limited to near-neutral pH conditions, restricting their application range.

View Article and Find Full Text PDF

Objective: To identify and synthesise existing literature about the use of mobile educational applications (apps) designed to enhance the learning experience of nurses and midwives.

Design: A narrative review using a systematic, structured and comprehensive search of the literature.

Data Sources: Medline Complete (EBSCO), CINAHL (EBSCO), ERIC (EBSCO) and Embase (OVID) electronic databases.

View Article and Find Full Text PDF

Enhanced brain tumor detection and segmentation using densely connected convolutional networks with stacking ensemble learning.

Comput Biol Med

January 2025

Emerging Technologies Research Lab (ETRL), College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia; Department of Computer Science, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia. Electronic address:

- Brain tumors (BT), both benign and malignant, pose a substantial impact on human health and need precise and early detection for successful treatment. Analysing magnetic resonance imaging (MRI) image is a common method for BT diagnosis and segmentation, yet misdiagnoses yield effective medical responses, impacting patient survival rates. Recent technological advancements have popularized deep learning-based medical image analysis, leveraging transfer learning to reuse pre-trained models for various applications.

View Article and Find Full Text PDF

Background: A mobile cognition scale for community screening in cognitive impairment with rigorous validation is in paucity. We aimed to develop a digital scale that overcame low education for community screening for mild cognitive impairment (MCI) due to Alzheimer's disease (AD) and AD.

Methods: A mobile cognitive self-assessment scale (CogSAS) was designed through the Delphi process, which is feasible for the older population with low education.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!